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Gangguan sistem muskuloskeletal adalah gangguan yang terjadi pada sistem tubuh sehingga mengakibatkan rasa sakit pada struktur tubuh tertentu. Minimnya pengetahuan tentang gangguan sistem muskuloskeletal adalah salah satu faktor yang menyebabkan gangguan sistem muskuloskeletal terlebih khusus osteoporosis dan osteoarthritis. Dengan menggunakanmetode penalaran certainty factor untuk menghitung nilai kepercayaan dari hasil diagnosis sistem, diharapkan bisa mendapatkan hasil yang baik. Selanjutnya, untuk pengembangansistem pakar ini juga menggunakan Expert System Development Life Cycle (ESDLC) dan kakas pemodelan Unified Modeling Language (UML) untuk menggambarkan prosesnya secaraterperinci, serta didukung oleh bahasa pemrograman yang java. Pengujian yang dilakukan, memberikan hasil bahwa sistem dapat menyediakan diagnosis nama penyakit, persentase dan solusi dari penyakit. Dari contoh 10 kasus menggunakan data acak, sistem memiliki tingkat akurasi 70%.

When searching for information with an information retrieval (IR) system, sometimes the results of the search documents provided by the system do not match the information needs of the user. Pseudo Relevance Feedback (PRF) based Query expansion (QE) tries to overcome these problems by adding words that are expected to improve retrieval results from top N ranked documents retrieved. The use of firefly algorithm (FA) as one of the optimization methods has been proven by the previous study to improve the performance of the IR system. However, in that study the weighting of words was done using the rocchio function of the Pseudo Relevant Document (PRD), so it is feared that the performance of IR system will be reduced if the number of relevant documents in PRD is little or none at all. Therefore, scoring by term relationship between query and PRD is used in this study combined with rocchio algorithm. The results of the study showed that usage of term relationship word co-occurrence or word similarity can improve the performance of the IRS that was previously built. In addition, word co-occurrence with jaccard have the best performance compared to the previous study and other combinations. FA itself was able to choose the optimal terms, even though the number of top N ranked documents increased. Furthermore, the combination of term relationship and rocchio algorithm can increase the convergence rate than the ones without rocchio algorithm.

Pseudo relevance feedback (PRF) enhances the retrieval performance of the relevance feedback. Pseudo relevance feedback assumes that the k highest-ranking documents in the first retrieval are relevant and extract query expansion from them. Rocchio algorithm is a classical algorithm for implementing relevance feedback into vector space models. The Rocchio algorithm forms a new query moves toward the centroid of the relevant documents and keeps away from centroid of the irrelevant documents. However, in the relevance feedback method, irrelevant documents are ignored. In this paper, we conduct a method for pseudo irrelevance feedback (PIRF) documents components that effectively applied to the Rocchio algorithm. Documents with a high ranking outside of k relevant documents and those documents dissimilar to any k relevant documents can extract good query expansion if the documents are applied as irrelevant documents. The Rocchio algorithm uses PRF as a component of relevant documents and this research method for irrelevant documents as a component of irrelevant documents denoted by Roc PRF PIRF (filter). Experiment on CISI dataset show that Roc PRF PIRF (filter) improved performance by testing several variations the number of irrelevant documents compared to the standard Rocchio algorithm and Rocchio algorithm with irrelevant documents but without proposed method).

This research aims to help the user monitor their heart’s condition and notify other people in case of an emergency due to an abnormal heartbeat (normal heartbeat around 60-100 beats per minute). From the heart rate graph, the user could do a consultation with an expert in the nearest hospital using a map feature that will show the nearest hospital with a 1 KM radius from the user’s current location. The heart is undeniably an essential part of the human body system. The heart's missions are to pump blood to the lungs to expel the waste carbon dioxide that resulted from the respiration and pump blood that will deliver oxygen throughout the human body's circulation system. While the blood that carries oxygen being pumped throughout the human body, the heart will provide the oxygen to all cells. Body cells need to use oxygen to get the energy from food through a process called cellular respiration. Monitoring our heartbeat and pulse rate can increase people’s awareness about their heart’s health, motivating, and encouraging people to maintain a healthy eating plan and exercise. This research will be implemented ECG Sensor. The sensor will then send the data to the android application via HC-06 Bluetooth module. Monitoring the heart rate and analyzing the user’s heartbeat will be done in the android with a microcontroller device's help.

This paper is carried out to critically examine and evaluate the implementation of offline data transfer, most commonly known as sneakernet. This work reviews the details of the available information of the current and proposes possible future deployment of sneakernet. The idea of the concept theory is based on the available and future advancement of the technology. The expensive data transfer for delay-tolerant applications is solved in this work by employing massive sneakernet in various range utilization. This concept is able to reduce the burden of backbone networks and still be able to bring the information from source to destination securely. Another advantages of the proposed concept are the introduction of the security layer during information in transit. The viability of the concept is assessed based on the use-cases and the standard sneakernet procedure.

Content management has continuously been among the most challenging problems, especially on the Internet of Things (IoT), where devices are set to be "hungry" for content. In this context, Information-Centric Networking (ICN), a promising Future Internet Architecture, can facilitate the IoT requirements of continuous and long-lasting connectivity, which has burdened the IP-based network system. In ICN, a content naming structure is composed to direct content requests to the nearest content provider with the built-in in-network caching function, storing the content. For a successful implementation of ICN-based IoT, an intelligent algorithm approach for IoT-based ICN implementation aiming to improve content management is proposed in this study. We establish a hybrid ICN with the most suitable machine learning algorithms satisfying the requirements to realize a feasible IoT technology. The selected algorithms from Supervised Learning, Unsupervised Learning, and Reinforcement Learning are evaluated before being chosen as the content forwarding process. The numerical findings show the superiority of the Extended Learning Classifier System under Reinforcement Learning’s scheme compared to the other algorithms.

Sulitnya menentukan klasifikasi judul skrpsi berdasarkan peminatan yang diambil oleh mahasiswa informatika unjani merupakan salah satu permasalahan penting yang dihadapi oleh pihak Jurusan. Tujuan dari penelitian ini yaitu memberikan sebuah penunjang keputusan bagi pihak Jurusan agar setiap judul skripsi yang diajukan oleh mahasiswa sesuai dengan peminatan. Berdasarkan hasil penelitian yang telah dilakukan, model yang dibangun menggunakan algoritma KNN menghasilkan tingkat akurasi yang lebih tinggi jika dibandingkan dengan model yang dibangun menggunakan algoritma VSM. Nilai akurasi tertinggi berdasarkan hasil pengujian pada penelitian ini adalah sebasar 96,85%.

In this research, the case of intent classification for Customer Relation Management (CRM) how to handle complaints as a domain to be followed up, where datasets are extracted from the conversation on Twitter. The research objectives support three key findings to comparing the CNNs and BRNNs model to intent recognition by vectorization text: (1) Which architecture performs better (accuracy) depends on how important it is to semantically understand the whole sequence and (2) Learning rate changes performance relatively smoothly, while the optimal result iterated by change hidden size and batch size result in large fluctuations. (3) Last, how word vectorization is able to define sub-domain of the complaints by word vector classification.

Almost all universities divide their IT freshman into classes randomly or based on students score, either their score during the selection test held by the university or National Examination score. Universities often find case that a class consists of all ‘smart’ students and a class consists of all ‘lazy’ students. This thesis intends to create an application to help universities divides their Information Technology freshman into classes based on freshman competency and experience about Information Technology (IT) on the senior high school. The experiment is conducted by collecting data IT students who are not in the first semester. The data consists of their experience about IT as well as other knowledge fields and their current GPA. The results of the experiment show that from 50 data samples collected, the application correctly predicts 34 students GPA range based on respondents competency with IT and other knowledge fields during their study in senior high school.

Kolintang or Indonesian's xylophones are a traditional musical instruments originating from Minahasa (Sulawesi-Utara) which belong to the group of pitched percussion instruments, as an idiophonic musical instrument the source of kolintang sound comes from its vibrating bar when hit. Unfortunately, nowadays this traditional musical instrument is quite difficult to obtain, with a large and heavy size, as well as requires extensive storage space and should not be damp. In addition, the lack of knowledge and interests of the younger generation. In the event of allowed to continue, will be forgotten by the younger generation, even though it is a cultural heritage that must be preserved. Our purpose in this research, to build an application for kolintang musical instruments using a webcam as a PC motion detection sensor, thus providing a new atmosphere in playing the kolintang musical instruments on portable computer. This application has been successfully built, where the actual results obtained are in accordance with the expected results, that is kolintang musical instruments can use motion detection sensors: webcam, and other interactive media such as keyboard, mouse, and webcam in portable computer. Besides that, this application also can be run on smartphones.

Harga minyak kelapa sawit bisa mengalami kenaikan, penurunan maupun tetap setiap hari karena faktor yang mempengaruhi harga minyak kelapa sawit seperti harga minyak nabati lain (minyak kedelai dan minyak canola), harga minyak mentah dunia, maupun nilai tukar riil antara kurs dolar terhadap mata uang negara produsen (rupiah, ringgit, dan canada) atau mata uang negara konsumen (rupee). Untuk itu dibutuhkan prediksi harga minyak kelapa sawit yang cukup akurat agar para investor bisa mendapatkan keuntungan sesuai perencanaan yang dibuat. tujuan dari penelitian ini yaitu untuk mengetahui perbandingan accuracy, precision, dan recall yang dihasilkan oleh algoritma Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam menyelesaikan masalah prediksi harga minyak kelapa sawit dalam investasi. Berdasarkan hasil pengujian dalam penelitian yang telah dilakukan, algoritma Support Vector Machine memiliki accuracy, precision, dan recall dengan jumlah paling tinggi dibandingkan dengan algoritma Naïve Bayes dan algoritma K-Nearest Neighbor. Nilai accuracy tertinggi pada penelitian ini yaitu 82,46% dengan precision tertinggi yaitu 86% dan recall tertinggi yaitu 89,06%.

To improve the performance of phoneme based Automatic Speech Recognition (ASR) in noisy environment; we developed a new technique that could add robustness to clean phonemes features. These robust features are obtained from Complex Wavelet Packet Transform (CWPT) coefficients. Since the CWPT coefficients represent all different frequency bands of the input signal, decomposing the input signal into complete CWPT tree would also cover all frequencies involved in recognition process. For time overlapping signals with different frequency contents, e. g. phoneme signal with noises, its CWPT coefficients are the combination of CWPT coefficients of phoneme signal and CWPT coefficients of noises. The CWPT coefficients of phonemes signal would be changed according to frequency components contained in noises. Since the numbers of phonemes in every language are relatively small (limited) and already well known, one could easily derive principal component vectors from clean training dataset using Principal Component Analysis (PCA). These principal component vectors could be used then to add robustness and minimize noises effects in testing phase. Simulation results, using Alpha Numeric 4 (AN4) from Carnegie Mellon University and NOISEX-92 examples from Rice University, showed that this new technique could be used as features extractor that improves the robustness of phoneme based ASR systems in various adverse noisy conditions and still preserves the performance in clean environments.

Information from computer programs can be extracted from its source code, external documentation, and compiled code. Although compiled code is an assured information source which is always exists in published computer programs, it is seldom used by the existing search engines since some reverse engineering tasks are needed. In this research, a search engine for Java archives that uses byte code (compiled code for Java Archive) as its information source is developed. It enables user to search within a collection of Java Archives without relying with source code and external documentation. Compared with Penta and FindJar [2] [7], A novel term extraction process beyond the file and class name is proposed, which includes field name, method name, string literal used in program, program flow weighting, and method expansion. Exclusive tokenization, stopping, and stemming are also implemented to improve effectiveness. Based on evaluation, it has a fairly good effectiveness although it may vary based on terms stored on index. Its effectiveness is higher than FindJar main features reimplementation which indicates that detailed compiled code has positive influences in computer programs search engine. Efficiency depends on how many terms stored on index and how many process used at certain step.

WordNet, a hand-made, general-purpose, and machine-readable thesaurus, has been used in information retrieval research by many researchers, but failed to improve the performance of their retrieval system. Thereby in this paper we investigate why the use of WordNet has not been successful. Based on this analysis we propose a method of making WordNet more useful in information retrieval applications by combining it with other knowledge resources. A simple word sense disambiguation is performed to avoid misleading expansion terms. Experiments using several standard information retrieval test collections show that our method results in a significant improvement of information retrieval performance. Failure analysis were done on the cases in which the proposed method fail to improve the retrieval effectiveness. We found that queries containing negative statements and multiple aspects might cause problems in the proposed method and we also investigated the solution to these problems.

This paper proposes a method to improve the performance of information retrieval systems by expanding queries using heterogeneous thesauri. The expansion terms are taken from a hand-crafted thesaurus, co-occurrence-based automatically constructed thesaurus, and predicate-argument-based automatically constructed thesaurus. To avoid the effects of wrong expansion terms, a weighting method is devised such that the weight of expansion terms depend not only on the weight of all terms in query, but also the weight of those terms in each thesaurus. Experiments show that using heterogeneous thesauri with an appropriate weighting method results in better retrieval performance than using only one type of thesaurus.

This paper proposes the use of multiple thesaurus types for query expansion in information retrieval. Hand-crafted thesaurus, corpus-based co-occurrence-based thesaurus and syntactic-relation-based thesaurus are combined and used as a tool for query expansion. A simple word sense disambiguation is performed to avoid misleading expansion terms. Experiments using TREC-7 collection proved that this method could improve the information retrieval performance significantly. Failure analysis was done on the cases in which the proposed method fail to improve the retrieval effectiveness. We found that queries containing negative statements and multiple aspects might cause problems in the proposed method.

Bio-inspired computing is the idea to emulate and to get inspirations from biological systems which is then used to solve complex problems. Artificial immune systems, as one of the bio-inspired computing has contributed in solving complex problems. From one generation to another generation, AIS's practitioners realize that to get better algorithms we need the inspiration with the better understanding about the behavior of the immune system. Modeling is the key to get better understanding and inspiration from immune system. Agent-based modeling can be used because of the components of the immune system can be described as agents that interact with each other and set up a system behavior. There are two approaches to agent-based modeling, i.e. modeling of the immune system itself and model the artificial immune system. In this paper we design a model for immune system using agent-based approach. This model specifically based on clonal selection and its behavior. The design of model are using UML diagram to describe the behavior and the interaction of agents. This design can be used for simulating existing artificial immune system models, developing new artificial immune system models, or evaluating artificial immune system models that have been already adapted to technical problems.

Researchers conducted experiments on parallel algorithms, which are inspired by the clonal selection, called Clonal Selection Algorithm (CSA). This algorithm is a population-based heuristic solution. Course-grained parallelism model applied to improve the execution time. Inter-process communication overhead is addressed by adjusting the communication frequencies and size of data communicated. In this research, conducted experiments on six parallel computing models represent all possible partitions and communications. Experiments conducted using data of NP-Problem, Traveling Salesman Problem (TSP). The algorithm is implemented using the model of message passing libraries using MPJExpress. Experiments conducted in a cluster computation environment. Result shows the best parallelism model is achieved by partitioning the initial population data at the beginning of communication and the end of generation. Communication frequency can be up to per 1% of the population size generated. Using four dataset from TSPLib, this reseache shows effect of the communication frequency that increased the best cost, from 44.16% to 87.01% for berlin52.tsp; from 9.61% to 53.43% for kroA100.tsp, and from 12.22% to 17.18% for tsp225.tsp. With eight processors, using communication frequency will be reduced the execution time e.g 93.07%, 91.60%, 89.60%, 74.74% for burma14.tsp, berlin52.tsp, kroA100.tsp, and tsp225.tsp respectively. We conclude that frequency of communication greatly affects the execution time, and also the best cost. It improved execution time and best cost.

This paper proposes a method to overcome the drawbacks of WordNet when applied to information retrieval by complementing it with Roget's thesaurus and corpus-derived thesauri. Words and relations which are not included in WordNet can be found in the corpus-derived thesauri. Effects of polysemy can be minimized with weighting method considering all query terms and all of the thesauri. Experimental results show that our method enhances information retrieval performance significantly.