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The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) are well-known nature-inspired algorithms. ACO is a metaheuristic search algorithm that takes inspiration from the behavior of real ants. In contrast, GWO is a grey wolf population-based heuristic algorithm. The important procedure in optimization is exploration and exploitation. ACO has excellent global and local search capabilities, and the exploration process is performed better than the exploitation process. In the case of regular, GWO is a greatly competitive algorithm compared to other common meta-heuristic algorithms, as it has super performance in the exploitation phase. This study proposed hybrid ACO and GWO algorithms. This hybridization is to acquire the balance between exploitation and exploration in optimization Swarm Intelligence algorithm—comprehensive examination using CEC 2014 benchmark functions. Detail investigations indicate that ACO-GWO could find solutions to unimodal, multi-modal, and hybrid problems in evaluation functions. The results show that the ACO-GWO algorithm outperforms its predecessors in several benchmark function cases. In addition, the proposed ACO-GWO algorithm could achieve an exploitation-exploration balance. Even though ACO-GWO has one disadvantage: since ACO-GWO directly combines two algorithms (ACO and GWO) with two different agents, it has superior demands on computational complexity. Doi: 10.28991/ESJ-2024-08-04-023 Full Text: PDF

This study aims to predict the weight of chicken on a close house farm using the Fuzzy Logic method by implementing the LUKASEWICZ method. The data used in this study are the factors that affect the weight of the chicken including the number of chickens entering, the initial weight of the chicken, the temperature of the cage, the humidity of the cage, the quantity of water, the quantity of feed, and air circulation (wind speed) in the cage. The results of the calculation of Fuzzy with the łukasiEwicz method of these factors can be used to predict the chicken boboy during the harvest period and according to the weight set during the harvest period. The accuracy of the prediction value with the Absolute Percentage Error (MAPE) mean test shows the value of 5,3981%. It was concluded that the calculation of fuzzy with the łukasiewicz method can be used to predict the weight of chicken during the harvest period.

A collaborative activity used to accomplish shared objectives is teamwork. It is essential to know how unequal contributions can inhibit team members' chances to give their all in achieving these objectives. It will be necessary to manage resources in this joint approach. Monitoring each team member’s performance in one technique to do this. In previous research, performance measurement was designed using Prometer with several parameters, utilizing the crisp set at each stage. This study developed the method by adding variables and utilizing fuzzy logic, which can consider the membership value for each value involved. The membership value considered for each variable is expected to provide a significant assessment of each team working on developing software projects using the GitHub platform. The results will be monitored based on the involvement of each collaborator in project work through the data recorded in the pull requests, issues, commits, additions code, and deletion code variables. The results obtained by utilizing the variables and several rules that have been designed with the Mamdani implication function are then compared with the observations obtained by the Project Manager so that an accuracy value of 86.67% is accepted for the use of inclusive and exclusive rules (operand AND).

Horticultural crops are the most produced by farmers in the Napugera Village area because they are more profitable to cultivate because they are short in life and from an economic point of view they are obtained faster. However, has often experienced obstacles, one of which is determining the suitability of agricultural land for horticultural crops. Determining the selection of horticultural crops for the types of red onion, red red chili, and tomato crops on a field based on land conditions is very necessary for farmers in the Napugera Village area as a support for decision-making. There are 8 criteria assessed including temperature, rainfall, air humidity, soil type, soil texture, soil ph, land slope and topography. In this study, a profile-matching interpolation method was developed for a decision-making system that can be used as a tool in providing decisions on plants that are suitable for planting in a field easily, quickly, and accurately. The profile-matching interpolation method is one of the methods of scoring data. The results showed that the type of onion horticultural crop with a final value of 3,764 is suitable for cultivation in the Napugera Village area.

Indonesia has 106.9 million active TikTok users aged 18 and above. TikTok is designed for engagement in many ways, as it actively encourages two-way communication and eye-catching content. Uploaded content must have its uniqueness variable. In increasing the engagement of a TikTok account, criteria are chosen based on the COBRA concept (consuming, contributing, and creating) and alternatives based on social media content trends in Indonesia (tutorial, educational, a day in my life, behind the scene dan tips and trick). This research was conducted by implementing the Analytic Hierarchy Process (AHP) method to select the content that must be prioritized to get engagement from the wider community. From the data processing results obtained, tutorial content is the best content in increasing engagement results, especially TikTok. Content that has the lowest engagement is behind the scene content. Further research can be carried out through a group decision support system with various related experts. It can also be combined with the BORDA, TOPSIS, and Profile Matching methods to optimize ranking results.

The convolution process in the Capsule Network method can result in a loss of spatial data from the Electroencephalogram signal, despite its ability to characterize spatial information from Electroencephalogram signals. Therefore, this study applied the Continuous Capsule Network method to overcome problems associated with emotion recognition based on Electroencephalogram signals using the optimal architecture of the (1) 1st, 2nd, 3rd, and 4th Continuous Convolution layers with values of 64, 128, 256, and 64, respectively, and (2) kernel sizes of 2×2×4, 2×2×64, and 2×2×128 for the 1st, 2nd, and 3rd Continuous Convolution layers, and 1×1×256 for the 4th. Several methods were also used to support the Continuous Capsule Network process, such as the Differential Entropy and 3D Cube methods for the feature extraction and representation processes. These methods were chosen based on their ability to characterize spatial and low-frequency information from Electroencephalogram signals. By testing the DEAP dataset, these proposed methods achieved accuracies of 91.35, 93.67, and 92.82% for the four categories of emotions, two categories of arousal, and valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 94.23, 96.66, and 96.05% for the four categories of emotions, the two categories of arousal, and valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 96.20, 97.96, and 97.32% for the four categories of emotions, the two categories of arousal, and valence, respectively. Doi: 10.28991/ESJ-2023-07-01-09 Full Text: PDF

Requirements engineering (RE) is the process of defining user requirements that are used as the main reference in the system development process. The quality of the RE results is measured based on the consistency and completeness of the requirements. The collection of requirements from multiple stakeholders can cause requirements conflict and have an impact on the inconsistency and incompleteness of the resulting requirements model. In this study, a method for automatic conflict detection and resolution in the Automatic Requirements Engineering Model (AREM) was developed. AREM is a model that automates the process of elicitation, analysis, validation, and requirements specification. The requirement conflict detection method was developed using an intelligent agent approach combined with a Weighted Product approach. Meanwhile, Conflict resolution is made automatically using a rule-based model and clustering method. Testing the ability of the method to detect and resolve conflicting requirements was carried out through five data sets of requirements from five system development projects. Based on the test results, it is known that the system is able to produce a set of objects that have conflicts in the data requirements. For conflict resolution, experiments were conducted with five conflict resolution scenarios. The experimental results show that the method is able to resolve conflicts by producing the highest completeness value, but the results of conflict resolution also produce a number of soft goals. The success of the method in detecting and resolving conflicts in the model is able to overcome the problem of inconsistencies and incompleteness in the requirements model.

The composition of air concentration is not constant. It constantly changes with minor changes at any time, so more than one measurement is needed to represent the air concentration level for a full day. The fuzzy non-stationary method can overcome uncertainty in an environment that is not constant or caused by minor temporal changes based on time variables. This study uses a non-stationary fuzzy method to determine the level of O3 concentration based on the input variables of temperature, humidity, and wind speed. The tests were conducted in September, October, and November using four types of implication process interpretation, namely interpretation 1 (classical logic), interpretation 2 (classical logic), interpretation 3 (algebraic), and interpretation 3 (standard). The test results in September showed a tendency for error percentage using the MAPE amount of 19, October's amount of 25, and November's amount of 18.

Participants' emotional reactions are strongly influenced by several factors such as personality traits, intellectual abilities, and gender. Several studies have examined the baseline reduction approach for emotion recognition using electroencephalogram signal patterns containing external and internal interferences, which prevented it from representing participants’ neutral state. Therefore, this study proposes two solutions to overcome this problem. Firstly, it offers a modified weighted mean filter method to eliminate the interference of the electroencephalogram baseline signal. Secondly, it determines an appropriate baseline reduction method to characterize emotional reactions after the smoothing process. Data collected from four scenarios conducted on three datasets was used to reduce the interference and amplitude of the electroencephalogram signals. The result showed that the smoothing process can eliminate interference and lower the signal's amplitude. Based on the three baseline reduction methods, the Relative Difference method is appropriate for characterizing emotional reactions in different electroencephalogram signal patterns and has higher accuracy. Based on testing on the DEAP dataset, these proposed methods achieved accuracies of 97.14, 99.70, and 96.70% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Furthermore, on the DREAMER dataset, these proposed methods achieved accuracies of 89.71, 97.63, and 96.58% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Finally, on the AMIGOS dataset, these proposed methods achieved accuracies of 99.59, 98.20, and 99.96% for the four categories of emotions, the two categories of arousal, and the two categories of valence, respectively. Doi: 10.28991/ESJ-2022-06-06-03 Full Text: PDF

Several studies related to emotion recognition based on Electroencephalogram signals have been carried out in feature extraction, feature representation, and classification. However, emotion recognition is strongly influenced by the distribution or balance of Electroencephalogram data. On the other hand, the limited data obtained significantly affects the imbalance condition of the resulting Electroencephalogram signal data. It has an impact on the low accuracy of emotion recognition. Therefore, based on these problems, the contribution of this research is to propose the Radius SMOTE method to overcome the imbalance of the DEAP dataset in the emotion recognition process. In addition to the EEG data oversampling process, there are several vital processes in emotion recognition based on EEG signals, including the feature extraction process and the emotion classification process. This study uses the Differential Entropy (DE) method in the EEG feature extraction process. The classification process in this study compares two classification methods, namely the Decision Tree method and the Convolutional Neural Network method. Based on the classification process using the Decision Tree method, the application of oversampling with the Radius SMOTE method resulted in the accuracy of recognizing arousal and valence emotions of 78.78% and 75.14%, respectively. Meanwhile, the Convolutional Neural Network method can accurately identify the arousal and valence emotions of 82.10% and 78.99%, respectively. Doi: 10.28991/ESJ-2022-06-02-013 Full Text: PDF

Ventilator shortages is a common problem faced by hospitals during the COVID-19 pandemic era. Healthcare workers are forced to make choices because of how big the difference between resources and lives needing it. This issue rarely comes, because normally every patient has the same rights to receive treatment and resources, but it becomes a clear problem when there are barely enough resources. Therefore, a prioritization mechanism that can objectively decide the allocation must be made to achieve the best outcome. A decision support system is a system that can support humans using data as decision makers to help them decide semi-structured/unstructured problems. The goal of this research is to create a DSS to prioritize patients who need a ventilator by incorporating two different methods, which are AHP, Interpolation, and SAW. It is hoped that the result of the research can be used to rank patients based on predetermined criterias and policy.

Mechanical keyboards are designed with various shapes, variations, and specifications that are different from other types of keyboards. The mechanical keyboard itself has an aesthetic function that allows users to customize it. There are various specifications on mechanical keyboards, causing various considerations, which can make it difficult for users to choose a mechanical keyboard that fits the desired criteria. Supported by observations in the Indonesia Mechanical Keyboard Group (IMKG), some users are still limited in their knowledge of mechanical keyboard products available in Indonesia, also, currently there is no solution that can handle this problem. Based on these problems, in this research, an DSS is built that can help overcome these problems, by providing recommendations for a mechanical keyboard according to the wishes of the user. DSS is implemented in web form using the AHP method for the weighting process and Profile Matching for the scoring process. The criteria used are determined by conducting a survey regarding the specifications that are the priority considerations in choosing a mechanical keyboard. At the end of the study, the DSS that was successfully built was able to provide mechanical keyboard priority recommendations according to user preferences and get an average evaluation result of 36.17 out of a total maximum value of 40.

Laptop is a desktop personal computer (PC) whose dimensions are reduced to increase flexibility in its use. However, the large number of products will make it difficult for consumers to choose a laptop that suits the needs of consumers who want to buy it. The purpose of this research is to help buyers who want to buy laptop products according to their needs by making a Decision Support System (DSS). There are 12 criteria considered in this research, price, processor, RAM capacity, hard disk capacity, SSD capacity, V-RAM capacity, maximum RAM upgrade capacity, laptop weight, screen size, screen type, screen refresh rate, and screen resolution. Choosing a laptop product there is a criterion value of a laptop product and a value of preference criteria from the buyer as a decision maker. Also the criteria values on laptop products have different contributions to the overall value of the laptop product. Thus, the methods used are Analytical Hierarchy Process (AHP), Profile Matching (PM) with linear interpolation, and Simple Addictive Weighting (SAW) to determine the recommended options. Lastly, SPK that has been made will be able to provide recommendations best alternative choices and best suit the needs of buyers for selecting laptop products.

The Goal-Oriented Requirements Engineering (GORE) is one approach that is widely used for the early stages of software development. This method continues to develop in the last three decades. In this paper, a literature study is conducted to determine the GORE state of the art. The study begins with a Systematic Literature Review (SLR) was conducted to determine the research trend in the last five years. This study reviewed 126 papers published from 2016 to 2020. The research continues with the author's search for scientific articles about GORE. There are 26 authors who actively publish GORE research results. Twenty-six authors were grouped into seven groups based on their relation or co-authoring scientific articles. An in-depth study of each group resulted in a holistic mapping of GORE research. Based on the analysis, it is known that most research focuses on improving GORE for an automated and reliable RE process, developing new models/frameworks/methods originating from GORE, and implementing GORE for the RE process. This paper contributes to a holistic mapping of the GORE approach. Through this study, it is known the various studies that are being carried out and research opportunities to increase automation in the entire RE process.

The availability of drugs is one of the things that must be considered because if there is a deficiency or excess it can cause loss or disruption in patient care. The process to procure drugs that are still being carried out with uncertain considerations will create scheduling irregularities, this will have an impact on inventory costs due to accumulated inventory in warehouses or the absence of these drugs. This study aims to produce a decision support system for drug procurement using a combination of ABC methods, VEN analysis, ROP and EOQ. The test results show that the system can provide 3 recommendations for decision makers with consideration of the results of the ABC and VEN matrices and procurement calculations based on EOQ and ROP. The result of calculating the total Inventory Cost in the case example of the orodine drug based on the pharmacy calculation is IDR 708,500 while the calculation using the Economic Order Quantity method is IDR 689,381 from the calculation results obtained a savings of IDR 19,119.

Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution in each class. Furthermore, the synthetic oversampling method (SMOTE) is a preprocessing technique widely used to synthesize new data and balance the different numbers of samples in each class. One of the SMOTE method’s expansions is based on the initial selection approach, which determines the best candidates to be oversampled in the data before the process of synthetic example generation starts. However, SMOTE and most of the existing oversampling methods based on initial selection still found overlapping data on the final result. This issue makes it difficult for any classifiers to determine the decision boundary of each class. Therefore, this research proposes a new oversampling technique called Radius-SMOTE, which emphasizes the initial selection approach by creating synthetic data based on a safe radius distance. Furthermore, new synthetic data are prevented from overlapping in the opposite class with the safe radius distance. The Radius-SMOTE was evaluated extensively with thirteen artificial imbalanced datasets from the KEEL repository. The experimental results show that the proposed method is able to achieve the best results on 5 datasets, namely yeast-1-4-5-8_vs_7, ecoli-0-1-3-7_vs_2-6, Umbilical cord, Pima, and Haberman dataset in term of various assessment metrics. Besides that, the computational cost for our proposed method is also relatively low, with an average time of 0.5 to 1 second on the 13 tested datasets.

There are a few issues related to the existing symmetric encryption models for color image data, such as the key generation and distribution problems. In this paper, we propose a compression-encryption model to solve these problems. This model consists of three processes. The first process is the dynamic symmetric key generation. The second one is the compression process, which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map. The last process is the symmetric key distribution. The symmetric key is encrypted twice using Rivest-Shamir-Adleman (RSA) to provide both authentication and confidentiality. Then, it is inserted into the cipher image using the End of File (EoF) method. The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key. Hence, the image data is safe from ciphertext-only attacks. This model is fast and able to withstand entropy attacks, statistical attacks, differential attacks, and brute-force attacks.

The phenomenon of the rapid transfer of learning to online systems, such as e-Learning, has occurred massively. Institutions must ensure that student assessments run well. The characteristics of learning in e-Learning require an appropriate assessment method. The fuzzy logic method can be an option. Research shows that fuzzy logic is capable of providing flexible and objective performance evaluation. Fuzzy logic is a method that can overcome the uncertainty of transparency and objectivity of student assessments. In general, fuzzy logic applications are carried out by standards. Modification is an attempt to reveal the flexibility and to optimize the use of fuzzy logic. This study presents an analysis of fuzzy logic modification for the assessment of Algorithm and Data Structures courses held in e-Learning. These modifications include (i) modification of the parameter score with score compatibility, (ii) consequent modification of the fuzzy rules and (iii) modification of the implication process. The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.

Proper nouns (often abbreviated PN or NNP) are a class of words important in labelling and subsequent text processing, especially in natural language processing (NLP). Name entity recognition (NER) is one study that requires PN. The lack of labeled data for Indonesian text, especially the PN label, may be attributed for the lack of NER research in the Indonesian language. This study aims to detect PN in Indonesian-language translations of the Quran guided by deriving location information from the Quran as its source text. In the Indonesian language, PN are written using initial capital letters, which are used to determine and guide PN location. This article proposes that PN in Indonesian-language translations of the Quran can be determined based on PoS Information of Quranic text by developing a certain production rule. The results of this research showed that the proposed method has promising results for further research.

Acute Myeloid Leukimia (AML) is a type of cancer which attacks white blood cells from myeloid. AML subtypes M1, M2, and M3 are affected by the same type of cells called myeloblasts, so it needs more detailed analysis to classify.Momentum Backpropagation is used to classified. In its application, optimal selection of architecture, learning rate, and momentum is still done by random trial. This is one of the disadvantage of Momentum Backpropagation. This study uses a genetic algorithm (GA) as an optimization method to get the best architecture, learning rate, and momentum of artificial neural network. Genetic algorithms are one of the optimization techniques that emulate the process of biological evolution. The dataset used in this study is numerical feature data resulting from the segmentation of white blood cell images taken from previous studies which has been done by Nurcahya Pradana Taufik Prakisya. Based on these data, an evaluation of the Momentum Backpropagation process was conducted the selection parameter in a random trial with the genetic algorithm. Furthermore, the comparison of accuracy values was carried out as an alternative to the ANN learning method that was able to provide more accurate values with the data used in this study. The results showed that training and testing with genetic algorithm optimization of ANN parameters resulted in an average memorization accuracy of 83.38% and validation accuracy of 94.3%. Whereas in other ways, training and testing with momentum backpropagation random trial resulted in an average memorization accuracy of 76.09% and validation accuracy of 88.22%.

Wrist detection is a crucial element in the hand-pose estimation and hand-gesture recognition processes in Human-Computer Interaction applications. Most methods use horizontal parallel lines to scan for the location of a wrist line. The challenging problems in wrist detection are determining the orientation and localising the horizontal parallel lines that scan for various hand poses. The proposed method automatically detects a wrist, based on a minimum bounding box and geometric features. It also determines the start and stop points to localise the scanning. The evaluation used a set of 1240 hand images with ground-truth data taken from three sets of data. The hand images contained several gestures and individuals to prove that the method is robust against various gestures. The evaluation shows that the method successfully detects the image orientation and the wrist points with high accuracy. Keywords: Wrist detection, Minimum bounding box, Hand gesture

Coronavirus disease 2019 (COVID-19) was declared as a global pandemic by the World Health Organization (WHO) on 12 March 2020. Indonesia is reported to have the highest number of cases in Southeast Asia. Accurate prediction of the number of COVID-19 cases in the upcoming few days is required as one of the considerations in making decisions to provide appropriate recommendations in the process of mitigating global pandemic infectious diseases. In this research, a metaheuristics optimization algorithm, the flower pollination algorithm, is used to forecast the cumulative confirmed COVID-19 cases in Indonesia. The flower pollination algorithm is a robust and adaptive method to perform optimization for curve fitting of COVID-19 cases. The performance of the flower pollination algorithm was evaluated and compared with a machine learning method which is popular for forecasting, the recurrent neural network. A comprehensive experiment was carried out to determine the optimal hyperparameters for the flower pollination algorithm and recurrent neural network. There were 24 and 72 combinations of hyperparameters for the flower pollination algorithm and recurrent neural network, respectively. The best hyperparameters were used to develop the COVID-19 forecasting model. Experimental results showed that the flower pollination algorithm performed better than the recurrent neural network in long-term (two weeks) and short-term (one week) forecasting of COVID-19 cases. The mean absolute percentage error (MAPE) for the flower pollination algorithm model (0.38%) was much lower than that of the recurrent neural network model (5.31%) in the last iteration for long-term forecasting. Meanwhile, the MAPE for the flower pollination algorithm model (0.74%) is also lower than the recurrent neural network model (4.8%) in the last iteration for short-term forecasting of the cumulative COVID-19 cases in Indonesia. This research provides state-of-the-art results to help the process of mitigating the global pandemic of COVID-19 in Indonesia.

The cost of hospitalization from a patient can be estimated by performing a cluster of patient. One of the algorithms that is widely used for clustering is K-means. K-means algorithm, based on distance still has weaknesses in terms of measuring the proximity of meaning or semantics between data. To overcome this problem, semantic similarity can be used to measure the similarity between objects in clustering, so that, semantic proximity can be calculated. This study aims to conduct clustering of patient data by paying attention to the similarity of the patient’s disease. ICD code is used as a guide in determining a patient’s disease. The K-means method is combined with semantic similarity to measure the proximity of the patient’s ICD code. The method used to measure the semantic similarity between data, in this study, is the semantic similarity of Girardi, Leacock & Chodorow, Rada, and Jaccard Similarity. Cluster quality measurement uses the silhouette coefficient method. Based on the experimental results, the method of measuring semantic similarity data is capable to produce better quality clustering results than without semantic similarity. The best accuracy is 91.78% for the three semantic similarity methods, whereas without semantic similarity the best accuracy is 84.93%.

The amount of land for the current location of housing development has resulted in developers choosing the location of housing development regardless of the condition of the land, infrastructure, socio-economic. To overcome this problem a computer system is needed in the form of a GDSS that can assist in the selection of Housing Development Locations. This study aims to implement a GDSS with ANP and Borda methods to determine the selection of the right and fast housing development location. GDSS is needed because there are 3 Individual Decision Makers, DM-1 assessing based on Land Conditions, DM-2 assessing Infrastructure-based, DM-3 assess the Socio-Economic and Decision Maker based groups to make the final decision. The ANP method is used to weight the criteria from each alternative location, to the alternative ranking of housing construction locations for each individual Decision Maker. The Borda method is used to combine the results of ranking carried out by the Group Decision Maker so that it gets the final ranking as a determinant of the Location of Housing Development. The final result of this research is a decision support system that can help developers to get a priority recommendation according to the needs of the developer.

Based on BPS data, the growth of plantation crop production in NTB Province in 2011 to 2016 was recorded to have decreased by an average of 3.3 thousand tons annually. Coffee plants in particular are 0.1 thousand tons on average, the lack of public interest in planting coffee properly on land owned so that it impacts on land use that is not in accordance with its potential which will result in decreased productivity and erosion of land quality [1]. The first study of land suitability analysis for coffee plantations used a matching method in robusta coffee with a matching method producing a class (S1) of 0,46% [2] the second using a matching method on robusta coffee producing a class (S1) of 0,015% [3] These results indicate the ability of each land is different so that the results of the analysis vary. This study applies the ANP method and modified matching profile where the level of recommendations of coffee plants on the ability of land in East Lombok Regency through validation based on coffee production data from the East Lombok District Agricultural Service produces a match in rank 1 of 87,5% and 75% with non-modified profile matching.

Seleksi asisten dosen di Fakultas Teknologi Informasi Universitas Kristen Satya Wacana (FTI UKSW) dilakukan setiap awal semester. Proses seleksi tersebut mengalami beberapa kendala, antara lain mahasiswa harus datang langsung ke kantor Tata Usaha untuk melakukan pendaftaran yang ditulis di form pendaftaran. Form pendaftaran juga pernah hilang sehingga harus dilakukan pendaftaran ulang. Proses seleksi dilakukan oleh koordinator dosen matakuliah dengan mengadakan tes atau wawancara untuk mencari beberapa informasi untuk dijadikan pertimbangan dalam memilih asisten dosen. Proses seleksi memakan waktu yang lama, sehingga menyebabkan kegiatan praktikum menjadi terlambat untuk dimulai. Pada penelitian ini dibuat pemodelan Sistem Pendukung Keputusan (SPK) menggunakan metode TOPSIS dan Profile Matching berbasis teknologi Web Service untuk mengatasi masalah dalam proses pendaftaran dan seleksi asisten dosen di FTI UKSW. Hasil akhir perhitungan menggunakan metode TOPSIS dan Profile Matching ini berbentuk ranking yang dapat digunakan untuk membantu dosen koordinator matakuliah dalam menentukan calon asisten dosen yang akan diterima.

Graduation is important matter in college. Length of study can be used to evaluate curriculum. It affect accreditation score of the sutdy program. Based on Akreditasi Program Studi Magister Buku V Pedoman Penilaian Instrumen Akreditasi 3rd standard there is rule about students and graduation, such as profile of the graduates including average length of study time and gpa (grade point average) of graduates. In this study, system built to predict Gadjah Mada University Master of Computer Science student applicant’s length of study. It used new case with 13 features from applicant that will be predict as new case, then calculate local similarity using euclidean distance and hamming distance while global similarity using nearest neighbor. Maximum value of global similarity taken as solution while revised will be done if it’s value below threshold. Result of this study show that system can help study program to manage educational process. It show 76% accuracy of 50 data.

In treating insomnia patients, acupuncturists who are not always in their clinics trust their patients to their assistants but because of their assistants limited knowledge, their assistants can not determine the right acupoints. Therefore, an application that able to store their knowledge about insomnia disease treatment is needed so that their assistants can handle the patients like they do. In this research, an expert system application using certainty factor method to determine the acupoint in dealing with insomnia disease was built. This research used certainty factor to accommodate uncertainty about symptoms and rules. The mechanism of certainty factor on symptoms used a measure of increased belief (MB) and a measure of increased disbelief (MD). The built expert system resulted acupoints based on symptoms experienced by insomnia patients. Accuracy value produced by the system that used certainty factor for determining acupoint dealing with insomnia is 0.933. It showed that the acupoint produced by the system is 93.3% relevant according acupuncturist expertise in treating insomnia patients.

Goat farm businessman should considered environmental and financial criteria in breeding their commodities. The environmental factors are temperature, humidity, rain intensity, and altitude. For financial criteria, used several sub criteria i.e NPV (Net Present Value), ROI (Return On Investment), BCR (Benefit Cost Ratio), PBP (Payback Period), and BEP (Break Event Point) to determine financial feasibility. This research aims to develop a decision support system for selecting type of goat to breed by combining AHP, Profile Matching, and TOPSIS. AHP method was used for calculating the weight, Profile Matching for environment suitability evaluation, and TOPSIS for producing a valid decision that represents the goat expert's decision. The result showed that three methods can be integrated, and an experimental results which was validated by expert show that Bligon goat had the highest preference value (0.8835847). This can be concluded that DSS decision was valid and it successfully represented expert’s consideration.

Determining the best employee at Lombok Garden inteded to stimulate the performance of the hotel employees Lombok Garden. Improved performance of employees it will have a direct effect on the quality of hotel services. Employee performance appraisement are conducted by six assessors, namely the head of each department and consists of several criteria. Assessments will be difficult if done manually considering each appraiser has its own preferences in assessment. To solve that problem, we need a computer system that helps decision-making is a group decision support system (GDSS) determination of the best employees in the hotel Lombok Garden.Group decision support system developed in this study using TOPSIS (Technique For Order Preference By Similiarity To Ideal Solution) and Borda to assist decision-making group. TOPSIS method is used for decision-making in each appraiser, while the Borda method used to combine the results of each assessor's decision so as to obtain the final result of the best employees in Lombok Garden.Based on the final result of the system of determination of the best employees in the form of a ranking of the final value of each employee. The highest value will be used as a recommendation as the best employee at Lombok Garden.

Through the State Civil Apparatus Law, the Government attempt to reduce nepotism by creating an open competition system among civil servants in the process of filling positions. Regional Civil Service Agency (BKD) Tarakan already has personnel database and decision support system that can combine the existing database with the scoring model to get the candicate profile who fit with vacant positions is needed to support more objective performance. The Application of profile matching method in this decision support system is expected to help the candidate selection process on structural officer in the Government of Tarakan comply with the ability of a required field in a position. From the research we concluded that the change in the value of the candicate profile, and the number of subcriteria that used to categorize the positions can affect the closeness of candidates with vacant position and the use of profile matching method for case that the highest value is the best value requires that the ideal value used is the value the maximum in order to avoid exceeding the expectations of ideal.

Pertumbuhan jumlah data rekam medik yang pesat, menjadi masalah tersediri yang harus diantisipasi. Untuk menangani fenomena information overload dalam informasi rekam medis, perlu studi yang mendalam untuk dapat mengembangkan model filtering informasi rekam medik yang secara efektif mendukung peningkatan kualitas rekomendasi proses pencarian informasi. Berbagai penelitian terkait pencarian informasi medis telah banyak dilakukan, diantaranya mengembangkan penelitian dengan konsentrasi pada kebaruan dan keberagaman, menggunakan fuzzy ontology, berbasis factor tensor, memepertimbangkan niatan/intention pengguna ketika melakukan pencarian serta pendekatan dengan menggabungkan pencarian berbasis frasa dengan alat pemetaan konsep yang ada menggunakan MetaMap dan sumber data ULMS Metathesaurus.

Rainfall prediction can be used for various purposes and the accuracy in predicting is important in many ways. In this research, data of rainfall prediction use daily rainfall data from 2013-2014 years at rainfall station in Putussibau, West Kalimantan. Rainfall prediction using four parameters: mean temperature, average humidity, wind speed and mean sea level pressure. This research to determine how performance Neural Fuzzy Inference System with Levenberg-Marquardt training algorithm for rainfall prediction. Fuzzy logic can be used to resolve the linguistic variables used in rule of rainfall. While neural networks have ability to adapt and learning process, due to recognize patterns of data from input need training to prediction. And Levenberg-Marquardt algorithm is used for training because of effectiveness and convergence acceleration. The results showed five models NFIS-LM developed using a variety of membership functions as input obtained that model NFIS-LM with twelve of membership functions and use four inputs, such as mean temperature, average humidity, wind speed and mean sea level pressure gives best results to predict rainfall with values Mean Square Error (MSE) of 0.0262050. When compared with model NN-Backpropagation, NFIS-LM models showed lower accuracy. It is shown from MSE generated where model NN-Backpropagation generate MSE of 0.0167990.

RETRACTED Following a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Comparison of running time between C4.5 and k-nearest neighbor (k-NN) algorithm on deciding mainstay area clustering” Vol 2, No 1, pp. 1-6, March 2016, DOI: https://doi.org/10.26555/ijain.v2i1.49. This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted. The article contained redundant material, the editor investigated and found that the paper published in IOSR Journal of Computer Engineering (IOSR - JCE), Vol. 18, No. 2 (Mar-April 2016), pp. 86-92, 2016, DOI: http://doi.org/10.9790/0661-1802048692, URL: http://www.iosrjournals.org/iosr-jce/papers/Vol18-issue2/Version-4/K1802048692.pdf entitled "AnalysisofC4.5 andK-Nearest Neighbor(KNN)MethodonAlgorithmofClusteringForDeciding Mainstay Area". The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article.

The fast increasing volume of information databases made some difficulties for a user to find the information that they need. Its important for researchers to find the best method for challenging this problem. user intention detection can be used to increase the relevancies of information delivered from the information retrieval system. This research used a systematic mapping process to identify what area, approaches, and models that mostly used to detect user intention in information retrieval in four years later. the result of this research identified that item-based approach is still the most approach researched by researchers to identify intent diversity in information retrieval. The used of item-based approach still increasing from 2015 until 2017. 34% paper used topic models in their research. It means that Topic models still the necessary models explored by the researchers in this study.

Our review paper research categorize the methods in the method of Fuzzy Multi-Criteria Decision Making (FMCDM) to find the method is widely used in the case of land clearing for plantation. Model FMCDM is used to assess the parameter in multi-criteria-based decision making. The dominant percentage of the result was obtained using Fuzzy Analytic Hierarchy Process (FAHP) method. While the application of other methods for the same problem are Fuzzy Ordered Weighted Averaging (FOWA), Fuzzy Elimination Et Choix Traduisant la Realite or Elimination and Choice Translating Reality (FELECTRE), Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), Fuzzy, Artificial Neural Networks (FANNs) has less. Some the research result also implemented hybrid in FMCDM Method to give some weight in the assessment of decision making. There was also a paper which integrates FMCDM to the GIS method on the land clearing. Therefore, it is concluded that the issue on the land clearing can be done through collaboration of several models of FMCDM, so that it can be developed by involving the decision model using multi-stakeholder model

Abstrak Kelemahan dari jaringan syaraf tiruan backpropagation adalah sangat lama untuk konvergen dan permasalahan lokal mininum yang membuat jaringan syaraf tiruan (JST) sering terjebak pada lokal minimum. Kombinasi parameter arsiktektur, bobot awal dan bias awal yang baik sangat menentukan kemampuan belajar dari JST untuk mengatasi kelemahan dari JST backpropagation. Pada penelitian Ini dikembangkan sebuah metode untuk menentukan kombinasi parameter arsitektur, bobot awal dan bias awal. Selama ini kombinasi ini dilakukan dengan mencoba kemungkinan satu per satu, baik kombinasi hidden layer pada architecture maupun bobot awal, dan bias awal. Bobot awal dan bias awal digunakan sebagai parameter dalam perhitungan nilai fitness. Ukuran setiap individu terbaik dilihat dari besarnya jumlah kuadrat galat (sum of squared error = SSE) masing – masing individu, individu dengan SSE terkecil merupakan individu terbaik. Kombinasi parameter arsiktektur, bobot awal dan bias awal yang terbaik akan digunakan sebagai parameter dalam pelatihan JST backpropagation. Hasil dari penelitian ini adalah sebuah solusi alternatif untuk menyelesaikan permasalahan pada pembelajaran backpropagation yang sering mengalami masalah dalam penentuan parameter pembelajaran. Hasil penelitian ini menunjukan bahwa metode algoritma genetika dapat memberikan solusi bagi pembelajaran backpropagation dan memberikan tingkat akurasi yang lebih baik, serta menurunkan lama pembelajaran jika dibandingkan dengan penentuan parameter yang dilakukan secara manual. Kata kunci Jaringan syaraf tiruan, algoritma genetika, backpropagation, SSE, lokal minimum Abstract The weakness of back propagation neural network is very slow to converge and local minima issues that makes artificial neural networks (ANN) are often being trapped in a local minima. A good combination between architecture, intial weight and bias are so important to overcome the weakness of backpropagation neural network. This study developed a method to determine the combination parameter of architectur, initial weight and bias. So far, trial and error is commonly used to select the combination of hidden layer, intial weight and bias. Initial weight and bias is used as a parameter in order to evaluate fitness value. Sum of squared error(SSE) is used to determine best individual. individual with the smallest SSE is the best individual. Best combination parameter of architecture, initial weight and bias will be used as a paramater in the backpropagation neural network learning. The results of this study is an alternative solution to solve the problems on the backpropagation learning that often have problems in determining the parameters of the learning. The result shows genetic algorithm method can provide a solution for backpropagation learning and can improve the accuracy, also reduce long learning when it compared with the parameters were determined manually. Keywords: Artificial neural network, genetic algorithm, backpropagation, SSE, local minima.

Abstrak Modifikasi Algoritma Genetika pada penelitian ini dilakukan berdasarkan temuan-temuan para peneliti sebelumnya tentang kelemahan Algoritma Genetika. Temuan-temuan yang dimakasud terkait proses crossover sebagai salah satu tahapan terpenting dalam Algoritma Genetika dinilai tidak menjamin solusi yang lebih baik oleh beberapa peneliti. Berdasarkan temuan-temuan oleh beberapa peneliti sebelumnya, maka penelitian ini akan mencoba memodifikasi Algoritma Genetika dengan mengeliminasi proses crossover yang menjadi inti permasalahan dari beberapa peneliti tersebut. Eliminasi proses crossover ini diharapkan melahirkan algoritma yang lebih efektif sebagai alternative untuk penyelesaian permasalahan khususnya penjadwalan pelajaran sekolah. Tujuan dari penelitian ini adalah Memodifikasi Algoritma Genetika menjadi algoritma alternatif untuk menyelesaikan permasalahan penjadwalan sekolah, sehingga diharapkan terciptanya algoritma alternatif ini bisa menjadi tambahan referensi bagi para peneliti untuk menyelesaikan permasalahan penjadwalan lainnya. Algoritma hasil modifikasi yang mengeliminasi tahapan crossover pada algoritma genetika ini mampu memberikan performa 3,06% lebih baik dibandingkan algoritma genetika sederhana dalam menyelesaikan permasalahan penjadwalan sekolah. Kata kunci—algoritma genetika, penjadwalan sekolah, eliminasi crossover Abstract Modified Genetic Algorithm in this study was based on the findings of previous researchers about the weakness of Genetic Algorithms. crossover as one of the most important stages in the Genetic Algorithms considered not guarantee a better solution by several researchers. Based on the findings by previous researchers, this research will try to modify the genetic algorithm by eliminating crossover2 which is the core problem of several researchers. Elimination crossover is expected to create a more effective algorithm as an alternative to the settlement issue in particular scheduling school. This study is intended to modify the genetic algorithm into an algorithm that is more effective as an alternative to solve the problems of school scheduling. So expect the creation of this alternative algorithm could be an additional resource for researchers to solve other scheduling problems. Modified algorithm that eliminates the crossover phase of the genetic algorithm is able to provide 2,30% better performance than standard genetic algorithm in solving scheduling problems school. Keywords—Genetic Algorithm, timetabling school, eliminate crossover

Abstrak Pengambilan keputusan dalam penentuan pemenang tender pekerjaan konstruksi (tidak kompleks) pada pembangunan gedung kuliah Fakultas Ekonomi Univesitas Udayana (UNUD) oleh panitia tender di Bagian Perlengkapan Rektorat UNUD masih dilakukan secara manual (dengan software Microsoft Excel dan Word), sehingga untuk membantu dan mempercepat pengambilan keputusan tersebut (dalam situasi beberapa/banyak peserta tender memenuhi semua evaluasi kriteria dan memiliki harga penawaran terkoreksi terendah yang sama di bawah HPS) maka penelitian ini bertujuan untuk membangun SPK (Sistem Pendukung Keputusan) dengan metode Fuzzy AHP. Versi Fuzzy AHP yang dipakai adalah model Chang (1992) karena memiliki langkah-langkah sederhana dan mudah diaplikasikan pada penelitian ini. Hasil penelitian menunjukkan bahwa SPK yang dibangun menghasilkan perangkingan peserta 1, 2, dan 3 yang sama dengan sistem manual yang ada di Bagian Perlengkapan Rektorat UNUD, meskipun perangkingan 4, 5, 6 yang juga dihasilkan SPK ini tidak ada di sistem manual karena perangkingan 4, 5, 6 tidak memenuhi evaluasi kriteria kualifikasi (syarat untuk lulus tender adalah memenuhi semua evaluasi kriteria). Maka disimpulkan bahwa SPK yang dibangun menghasillkan informasi yang valid. Kata kunci— sistem pendukung keputusan, fuzzy AHP, tender, pekerjaan konstruksi Abstract Decision-making to determine the winner of project tender (not complex one) on the construction of college buildings for Economics Faculty of Udayana University by tender committee at the Rectorate Equipment Section of Udayana University, still is carried out manually (applying Microsoft Excel and Word), so to assist and accelerate the decision (in this situation a few/many bidders met all evaluation criteria and have the same lowest bidding price corrected under HPS), this study aims to build a DSS (Decision Supporting System) with Fuzzy AHP method. The applied Fuzzy AHP version is Chang model (1992) because it has simple steps and easy to apply in this study. The results showed that SPK produced ranking method of 1, 2, and 3 that are similar to the existing manual system in Equipment Section of the Rectorate, though the ranking method of 4, 5, 6, which also produced by SPK, is not contained in the manual system because ranking method of 4, 5, 6 did not meet the qualifying criteria evaluation (a requirement for graduation is to fulfill all tender evaluation criteria). It, therefore, comes to conclude that the DSS produce valid information. Keywords— decision supporting system, fuzzy AHP, tender, construction project

Abstrak Pada pelaksanaan acara perkuliahan atau saat proses balajar mengajar, dosen sering terkendala dengan kemampuan mahasiswa pada suatu matakuliah di satu kelas yang tidak merata. Oleh karena itu dosen terlebih dulu mengetahui kemampuan setiap mahasiswanya dengan salah satu caranya adalah dengan melihat karakteristik setiap mahasiswa dan kemudian dibandingkan dengan mahasiswa-mahasiswa sebelumnya dalam menyelesaian suatu mata kuliah. Pada penelitian ini, akan dilakukan perbandingan tingkat akurasi antara tiga algoritma, yaitu : Nearest Neighbour, C45 dan LVQ, pada kasus klasifikasi kemampuan mahasiswa untuk keperluan penentuan pembagian kelas mahasiswa baru. Selain itu, juga akan diperbandingkan tingkat kecepatan setiap algoritma dalam mendapatkan kelas kasus lama yang paling mirip dengan kasus baru yang dimasukkan. Kesimpulan yang didapatkan setelah sistem dibangun dan kemudian membandingkan ketiga algoritma tersebut adalah algoritma nearest neighbour dapat menghasilkan akurasi tertinggi. Kata kunci— nearest neighbour, c45, jaringan syaraf tiruan, lvq, mahasiswa Abstract During the lecture or in teaching-learning process, a lecturer sometimes finds that heterogeneous classroom as the obstacles due to the differences in students’ performances. Thus, the lecturer should be aware of this phenomenon and one way to overcome this is to find out the characteristics of each student in order to compare them with the previous students while completing their study. In this research, the three algorithms are compared, they are namely; Nearest Neighbor, C45, and LVQ, this is done to classify the students’ ability and to decide the class for the new students. In addition, the speed level of each algorithm is compared by means of getting the nearest previous case study to the new class. Finally, the ideas coming up as the conclusion for this research is that after the system is developed and those three algorithms are being compared, the result shows that there is nearest neighbour algorithm can produce the highest accuracy. Keywords—nearest neighbour, c45, artificial neural network, LVQ, students’ performances.

Abstrak Sejak tahun 2005 di Indonesia sudah dilaksanakan pemilihan kepala daerah (pilkada) secara langsung mulai dari pemilihan kepala desa, bupati, walikota sampai dengan gubernur. Majalah SwaSembada menyebutkan ada pemilihan langsung untuk 500 anggota DPR; 33 gubernur, serta sekitar 460 pemilihan untuk jabatan bupati dan wali kota. Dalam penelitian ini dibuat sebuah aplikasi yang mampu mensimulasikan suatu bentuk aplikasi pengambilan keputusan kasus Penentuan Pilihan Calon Kepala Daerah di Indonesia menggunakan metode Fuzzy MADM yang dikembangkan oleh Moon Hyun Joo dan Chang Sun Kang. Sistem ini mempunyai kemampuan menampung input kriteria yang diinginkan dari pengguna, alternatif pasangan calon, dan pada akhirnya mampu memberikan tampilan visual berupa himpunan solusi terbaik dari beberapa alternatif yang diberikan menggunakan metode perangkingan Nilai Total integral. Dari dua kasus pemilihan bupati yang diujikan ternyata hasil perangkingan tidak selalu sama tergantung dari tingkat keoptimisan (a) yang dipakai. Kata kunci— fuzzy, fmadm, pemilihan kepala daerah Abstract Since 2005, in Indonesia the election of regional leader (pilkada) has been held directly start from election of countryside head, regent, mayor up to governor. SwaSembada Magazine mentioned that there are direct elections to elect 500 members of DPR; 33 governors, and also about 460 elections for the occupation of lord mayor and regent. The aim of this research is to create an application which is capable to simulate a Decision Making System in the case of Determination the Choice in the Election of Regional Leader in Indonesia using the Fuzzy MADM method developed by Moon Hyun Joo and Chang Sun Kang. This system is able to accomodate the criterion input as the consumer want, alternative of candidate couple, and finally can give the visual appearance in the form of best solution gathering from some given alternatives using the Nilai Total integral sort method. This system has been implemented in two elections of Regional Leader, and from the test can be concluded that the result of the sort is not always the same. It depends on the optimism storey level (a) that used in the system. Keywords— fuzzy, fmadm, election of regional leader

Abstrak Pada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product. Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu Abstract It is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average. The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu

E-learning content being a barrier for e-learning is no longer true on today’s Internet. The current concerns are how to effectively annotate and organize the available content (both textual and non-textual) to facilitate effective sharing, reusability and customization. In this paper, we explain a component-oriented approach to organize content in an ontology. We also illustrate our 3-tier e-learning content management architecture and relevant interfaces. We use a simple yet intuitive example to successfully demonstrate the current working prototype which is capable of compiling personalized course materials. The e-learning system explained here uses the said ontology.

The number of sources of information brings several problems, among them about how to combine the distributed data storage and different. Information on an organization or company is usually stored in separate locations and different formats. When there is an increase in storage capacity and the amount of information search costs, the company faced with an abundance amount of data. System development methodology used is object oriented design with a development framework

Availability of information is increasing rapidly, the method for encoding and storage of information also increased. The number of sources of information brings several problems, among them about how to combine the distributed data storage and different. Information on an organization or company is usually stored in separate locations and different formats. When there is an increase in storage capacity and the amount of information search costs, the company faced with an abundance amount of data. System development methodology used is Rapid Application Development (RAD) with a development framework Guidlines for Rapid Application Engineering (GRAPPLE). The software used in building this application is Borland Jbuilder 9. Rational Rose 2000 is used for the analysis and design using Unified Modeling Language (UML). Mysql server is a type of database to be accessed on each operating system. Operating system used in this study is Windows XP and Linux Fedora Core. This application was developed with aglets, which is one of software for building mobile agent applications.

Personalization of Ontology Based E-learning System. Today, a form of technology known as Web 2.0 thatthoroughly supports web-to-web interactions is present. Interactions, such as information sharing in the forms ofdocument sharing (slideshare), picture sharing (flickr), video sharing (youtube), Wikis, and online networking (weblogand web-forum) are principally accomodating community empowerment services. These factors cause the appearanceof social interaction through Internet as well as learning interaction and anywhere-anytime training which is recentlycalled e-Learning. Basically, e-Learning needs a self-employed learning method and learning habits that emphasize onthe learner as the most important role. However, e-learning system which is expected to boost the intensity of selfemployedlearning is uncapable to represent the importance. This is proven with the current e-Learning system inIndonesia that only accomodates the delivery of learning materials identical to all active learners, ignores the cognitiveaspects and does not offer any approach or experience of interactive self-learning and disregards the aspect of users’ability to adapt. The proposed e-learning system which is Web 2.0-based utilizes ontology as the representation ofmeaning of knowledge formed by the learner.

In this paper are presented the development of a model of intelligent software agents to support web-based computing of project performance information that being executed by the company. The project performance is calculated by using earned value management method. We have developed a prototype of the intelligent agent software based on Jadex agent framework. Based on some simple data simulations of enterprise projects, our agents have showed capabilities in calculating the performance value of projects and displaying into barchart or S-graph.

Image classification can be done by using attribute of text that come along with the image, such as file name, size, or creator. Image classification also can be done base on visual content of the image. In this research, we implement a image classification model base on image visual content. The image classification is based on decision tree method that adapt C4.5 algorithm. The decision variable used in the decision tree generation process is image visual features, i.e. color moment order-1, color moment order-2, color moment order-3, entropy, energy, contrast, and homogeneity. The result of this research is an application that can classified image base on the knowledge of the previous classification cases. Keywords: image classification, decision tree, C4.5 algorithm

This paper presents the utilization of knowledge management system for information and knowledge model development of enterprise projects. This information and knowledge management model is based on ontology semantic data model. The ontology data model is new technique for representing information and knowledge base on more semantically conception of meanings of objects, their properties, and relations between them that may arise within certain domain knowledge. The concern of the knowledge management model is to ensure that the model allows the process of creation, access, and utilization of data in a semantically manner (for querying process) and information or knowledge of enterprise projects. The experimentation shows that project ontology model has satisfied all consistent, valid, complete, and correct ontology model criteria and can be used for semantic reasoning computation. A prototype of the proposed model can access information and knowledge from the knowledge ontology model. Kata Kunci: knowledge management system, semantic data model, ontology model, semantiq query, enterprise projects

East Sumba district has big tourism potency. However this potency has been ignored by tourist, because the lack of information. Currently information provided is only in static character. This research aims to determine tourism potency in East Sumba District with WebGIS implementation using Mapserver Opensource, PHP/Mapscript, and Arcview. This research found that: 1) a simple data structure in MS4W was made to solve loading time problem; 2) in order to give detail information for tourist, data structure in Arcview covered village view or position; 3) an attractive facility in Tourism WebGIS of East Sumba District is searching facility (search). This facility could assist the consumer in order to get detail information about the tourism WebGIS database location, 4) no resistance found when Tourism WebGIS of East Sumba District tested in various browsers. Abstract in Bahasa Indonesia: Sumba Timur merupakan salah satu wilayah yang memiliki potensi pariwisata yang besar. Namun potensi pariwisata ini tidak dapat diketahui oleh wisatawan, dikarenakan kurangnya informasi tentang pariwisata Kabupaten Sumba Timur. Disamping itu, Informasi yang disediakan selama ini hanya bersifat statis. Mengacu pada pemahaman tersebut maka penelitian ini mengkaji Potensi Pariwisata Kabupaten Sumba Timur memanfaatkan WebGIS dengan menggunakan MapServer OpenSource, PHP, MapScript, dan ArcView. Berdasarkan pemahaman diatas, maka temuan dalam penelitian ini adalah 1) Struktur penyimpanan data dalam MS4W dibuat sesederhana mungkin; 2) Struktur tabel dalam arcview, perlu dibangun sampai tingkat desa untuk menberikan informasi posisi daerah wisata secara lebih detail; 3) Salah satu daya tarik dalam WebGIS Pariwisata Kabupaten Sumba Timur adalah fasilitas search (pencarian). Kehadiran fasilitas search pada dasarnya untuk membantu pengguna dalam mendapatkan informasi dalam lokasi database WebGIS pariwisata secara detail; 4) Berdasarkan pengujian pada berbagai browser, maka WebGIS Pariwisata Kabupaten Sumba Timur pada prinsipnya tidak memiliki kendala yang berarti. Kata kunci: WebGIS, Pariwisata, Sumba Timur