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Di era serba digital saat ini, data menjadi sebuah aset yang sangat berharga. Berbagai macam teknik digunakan untuk mencuri data pribadi yang berpotensi disalahgunakan oleh pihak yang tidak bertanggung jawab. Objek yang digunakan pada penelitian ini adalah AQUVAPRN.exe yang memiliki jenis malware RAT (Remote Access Trojan) yang saat malware ini berjalan pembuat malware tersebut dapat mengambil data pribadi pengguna yang sistem operasinya terinfeksi. Cara kerja dari malware AQUVAPRN.exe dengan berjalan pada latar belakang saat aplikasi dijalankan atau dieksekusi lalu membuat beberapa proses seperti mengubah file registry, membuat file, membaca file, dan melakukan koneksi internet dengan IP Address tertentu secara terus menerus tanpa diketahui oleh pengguna dari komputer itu sendiri. Hasil yang diperoleh terhadap malware AQUVAPRN.exe berupa alamat IP Address 109.51.76.80, memiliki domain Kota Lisbon Negara Portugal, memiliki nilai hash MD5 55c2c12970cda52f58bfad7b8c7d37d5. Diketahui pula, malware AQUVAPRN.exe menggunakan teknik anti reverse engineering tepatnya obfuscation yang menghambat atau menghalangi malware untuk dibedah atau di reverse engineering agar mengetahui code penyusun dari malware. Didapatkan pula PID dari proses AQUVAPRN.exe adalah 8332 dengan alat virtual (Virtual Address) 0x8e0f57042080.

Perkembangan teknologi memaksa pelaku bisnis untuk memberikan layanan terbaik dengan menjadikan sistem rekomendasi sebagai salah satu solusi untuk menjaga loyalitas konsumen. Sudah banyak dilakukan penelitian terkait dengan sistem rekomendasi untuk mengatasi permasalahan Cold-Start ataupun Serendipitous Problem. Penelitian ini melakukan Hybrid Collaborative Filtering dan Content Based filtering dengan menggunakan Switching method sebagai media untuk memilih data dan atribut yang tepat. Selanjutnya data diproses menggunakan algoritma TF-IDF dan KNN. Penelitian ini melakukan beberapa pengujian dengan menggunakan berbagai macam nilai K serta komposisi data training dan testing. Hasil pengujian menunjukkan bahwa akurasi tertinggi yang dihasilkan oleh model yang telah dikembangkan adalah 83.62% untuk metode switching dengan atribut product category sebagai variable label, dan 74.9% untuk metode switching dengan atribut rating sebagai variable label. Rasio data training dan testing yang digunakan dalam penelitian ini adalah 70:30 dengan nilai K=3. Hasil penelitian juga menemukan bahwa ada korelasi signifikan antara nilai K dengan nilai akurasi dimana nilai K yang tinggi akan menghasilkan akurasi yang tinggi juga.

This massive development of information technology makes it easier for people's lives in various fields, one of them is social media, social media that people use a lot to get information about news or events that are happening in Indonesia, one of which is social media Twitter which provides a lot of information for the people of Indonesia, one of which is information about Covid-19 which is currently rife in the territory of Indonesia Sentiment analysis is a branch of Natural Language Processing (NLP) which can help determine the sentiments that occur in society. This study uses data in the form of tweets to carry out sentiment analysis obtained on Twitter social media.This research utilizes one of the Supervised Learning algorithms, namely Support Vector Machine. In this study, three (3) kernels are used for the Support Vector Machine, each of which is Linear, Radial basis function and Polynomial, to find which kernel produces the highest accuracy value. From the experiments carried out using data sharing for training as much as 70% and for testing data as much as 30% of the total data of 6000 data, the resulting accuracy value for the Support Vector Machine method on the Linear kernel produces an accuracy value of 89% and for the Radial kernel base function accuracy by 90% and for the Polynomial kernel it produces an accuracy of 88%. So it is concluded for the three (3) kernels for testing the Support Vector Machine method on the Radial basis function kernel to produce the best accuracy value

Anyelir Waste Bank is one of the waste banks assisted by PLN's Jakarta Main Distribution Unit which is located in East Jakarta Regency. Ongoing activities are still carried out manually, resulting in various problems. Therefore, the role of technology is needed to overcome problems related to waste management through scientific media designing the fields of User Interface (UI) and User Experience (UX) by designing waste management applications using design thinking. Design thinking was chosen because it combines priorities based on user needs with appropriate technological capabilities and business needs. Application design is designed on a mobile platform for customers while management is on a website platform. This research produces solutions for application design and usability testing has been carried out. Usability testing according to the ISO 9241-11 standard includes aspects of effectiveness, efficiency and satisfaction. In carrying out usability testing to measure aspects of effectiveness and efficiency using task scenarios, while to measure aspects of satisfaction using System Usability Scale (SUS).

Rani Motor Workshop in North Sumatra faces several problems in its operations, such as inefficient inventory management, late delivery of spare parts, and unstructured distribution planning. This research aims to overcome these problems by integrating the Distribution Requirement Planning (DRP) method in its distribution information system using a research and development (R&D) approach and a waterfall development model. By detailing the flow of data input, distribution planning, parts delivery, and distribution performance recording, this research proposes a framework that integrates the research & development method and the waterfall system development method. Through these steps, this research aims to improve the efficiency and timeliness of delivery. The results of this research are expected to make a positive contribution to the operations of Bengkel Rani Motor, by proving that the implementation of a distribution information system using the DRP method can optimize stock, reduce delays, and increase customer satisfaction.

Batik is one of the cultural heritages that must be preserved and developed. every business actor must experience very tight competition conditions, namely in the batik clothing business. Customer satisfaction is a very important aspect to pay attention to in order to increase business profits. The purpose of this research is to make it easier for Batik Berjaya Labuhanbatu Utara to find out what are the factors that affect customer satisfaction for the future development of Batik Berjaya Labuhanbatu Utara's business, and to find out the rediction results by looking at the accuracy of Naïve Bayes so that Batik Berjaya Labuhanbatu Utara can meet customer satisfaction. This research uses the Naive Bayes Algorithm Method whose results can later facilitate batik business managers in making decisions and improving the quality of products and services provided to customers. By calculating the classification results using the Naive Bayes algorithm with a total of 132 data, as a manual calculation, 30 data are used, namely, 80% of the training data totaling 24, 20% of the test data totaling 6 obtained accuracy 83.33%, precision 100%, recall 75%, specifity 100% and f1-score 85.71. Using the Naive Bayes algorithm helps classification, data in the form of attributes and labels are held training data and test data, where training data labeling must be determined at the beginning in the form of categorical information, namely satisfied and dissatisfied, then predictions are made based on the highest data to get satisfied and dissatisfied labels from test data.

In the market, there are many different brands and types of modified vlegs, making vehicle owners often confused in making the optimal decision. Therefore, it is very important to have a decision-making system that can help vehicle owners in choosing the best modified wheels based on relevant criteria). In this study, researchers will develop a decision-making system that uses the Analytical Hierarchy Process (AHP) and Multi-Objective Optimization by Ratio Analysis (MOORA) methods to help vehicle owners in choosing the best modified wheels that best suit racing needs. In applying the AHP and MOORA algorithms to the SPK for selecting car tire wheels, 4 criteria are used, namely suitability, price, durability and design. Suggestions for further research can be used to add other relevant criteria in the selection of car tire wheels.

Decision support system (DSS) is a computer-based system used to support data analysis and decision modeling, with the aim of increasing the effectiveness of decisions taken. In this research, SPK is needed to determine Outstanding Students. Through this research, it is hoped that the selection process for outstanding students can be optimized by choosing the evaluation method that best suits the student's characteristics and institutional goals. The results of this research also have the potential to improve the quality of graduates by providing fairer and more objective awards to those who excel. The aim of this research is to design and implement the concept of the Simple Additive Weighting (SAW) and Profile Matching methods in a system for determining outstanding students at Universitas Malikussaleh and to find out the ranking results of the two methods (SAW and Profile Matching) in selecting outstanding students at Universitas Malikussaleh. The research methodology used was literature study, data collection, Simple Additive Weighting and Profile Matching calculations, application design, testing and evaluation. The results obtained from this research are the application of the SAW and Profile Matching methods to determine outstanding students resulting in preferences with the highest score of 1 for the SAW method and the highest score of 5 for the Profile Matching method. These two methods can be applied in selecting outstanding students to help decision making because both this method produces the same best alternative

Obtaining student achievement provides benefits for schools for accreditation assessment, curriculum determination, feedback and policy. SMA N 1 Waru is a Senior High School in Indonesia in the National Reference School category which almost every year gets achievements. Currently, management of achievement data has not been carried out optimally, accumulated achievement data cannot be displayed, storage of supporting data is not organized, and the accuracy of the information produced is hampered. This research aims to implement a website-based student achievement application. The method used is in two stages, data collection and application development. The application development technique uses the Rapid Application Development (RAD) model. This research produces an application that has functionalities: master data (period, type of achievement, level of achievement/certification, class), teacher data collection, student data collection, achievement data collection (proposal and approval of achievements), user management, achievement reports, and achievement dashboards, as well as manage user user access. All functionalities have passed testing using the black box testing method. Implementation of applications using RAD can be done quickly and effectively so that the school gets the benefits of collecting achievement data at any time, achievement information can be accessed by anyone who needs it, the information produced is accurate, and has storage media for achievement supporting documents that can be well organized.

Text data compression is done to make the file size smaller. Algorithm is a sequence of steps that aims to solve a problem. In this text data compress research using the Run Length Encoding (RLE) data compress method, it can be used to trim data to minimize the use of storage space so that it can be utilized better. As the function of the data compress itself to trim the file, its use is very beneficial for future technology. The programming applied is a python application, applying the concept of structured programming. Structured programming is a programming concept or paradigm that solves problems structurally, without looking at objects or divisions but must be structured, The result of this research is that the phyton application is able to trim text data so as to minimize the use of storage space so that it can be utilized better.

Marketing strategy in a business is a major factor in the success or failure of a business both micro, medium and macro. With an effective and efficient marketing strategy It is hoped that it can increase income to the maximum. Currently, technological developments are very fast, including carrying out transactions that are directly connected to the database, resulting in very large data growth. The data itself can be used as a source of information to determine the right marketing strategy. The main aim of this research is to maximize the existing marketing strategy at RJ Mart Melaris by utilizing data as a source of information and consideration. The choice of Market Basket Analysis as a method for utilizing data is because these medium-sized businesses need consideration to develop sales by forming effective product packages. Frequent Pattern Growth is used as an effective algorithm to form combinations of product items or what is usually called an association rule. Some of the benefits resulting from this research are knowing how likely a product is to be purchased at the same time as other products, what products are sold the most and the least so that you can maximize stock of goods, and the relationship between products to maximize the placement of goods. This research produced 8 Association Rules or product combinations with 6 different items. The strongest rule that is generated is that if you buy special fried Indomie and chicken curry Indomie, you will definitely also buy special chicken Indomie with an association strength of 8,472. Meanwhile, the item that is most often purchased together with other items is the special fried Indomie, which sells 50% together with the special chicken curry Indomie and special chicken Indomie. Apart from that, this research also produced 2 different groups of items, each item in the group has a sales relationship.

Currently, the interest of comic readers in Indonesia is increasing and has become a popular culture. With so many comics released each year, it makes it difficult for the readers to find comics that fulfill their criteria, therefore the recommendation system becomes a feature that is quite important and has a role in helping the readers. The data used is comic data totaling 1219 comics, with details of 471 physical comics published by Elex Media Komputindo publishers and 748 digital comics released on the Line Webtoon Indonesia platform. This research uses the Content Based Filtering algorithm because it only utilizes title and synopsis data from the comic. The Cosine Similarity method is used to calculate the similarity value of a comic data with the criteria that has been entered and can test the data 10 times until the system successfully gives suitable comic recommendations with an average precision score of 94.86%.

Universitas Islam Negeri Sumatera Utara (UINSU) is one of the growing higher education institutions in Tuntungan, Medan. As a university that continues to improve the quality of education and facilities for its students. Inefficient use of fuel can lead to waste of resources, increased operational costs, as well as a negative impact on the environment. This research aims to develop and implement greedy algorithm in determining efficient travel routes for students, lecturers, and administrative staff to UINSU Tuntungan campus. The results showed that the Greedy algorithm proved effective in determining the shortest route between several points, the test results showed that this application was able to generate the optimal route quickly. Users can easily determine the shortest path and estimate fuel requirements for travel to campus so that this application is very helpful in efficient travel planning.

In today's swiftly changing digital realm, social media has emerged as a pervasive means of communication, yet it has also fostered the rise of cyberbullying, especially among young demographics. This research strives to develop an application that assesses public sentiment on Instagram regarding cyberbullying instances, categorizing sentiments as positive, negative, or neutral. Drawing data from Instagram accounts such as kumparandotcom, merdekadotcom, and okezonedotcom, the approach combines lexicon-based text labeling and sentiment analysis employing Sentistrength. Findings demonstrate the method's effectiveness, achieving accuracy, precision, and recall rates exceeding 85% while offering precise visualization of predictions. This study contributes to combatting cyberbullying, aiming to improve victims' mental well-being by providing clearer insights into social sentiment. The dataset comprises 4500 comments collected through web crawling, categorized into positive (735 entries), negative (2478 entries), and neutral (1288 entries) sentiments. The evaluation highlights the commendable performance of Sentistrength, achieving the highest accuracy at 93.85%.

AC (Air Conditioner) is an air conditioning system that is used anywhere, one of them on the train. Air conditioning is also a major factor in the comfort level of train passengers. However, train air conditioning currently can only be controlled manually from the driver's cabin so that the number of passengers does not affect the air conditioning temperature. What's more, the air conditioner absorbs 30% of the total power in the train. Therefore, this research was compiled with the aim of being able to calculate the number of passengers through the concept of people counting using the You Only Look Once (YOLO) method for the train's temperature control system. This research was built using 1 object class, namely person and the YOLOv5 algorithm, where this method is an object detection method with a high degree of accuracy. This research was designed using the Python programming language with the Google Colaboratory platform and MQTT for the data storage protocol for passenger detection results. The dataset used is passengers in the train as many as 1300 images. The training and validation results model obtained a 99.63% mAP, 95% precision, 100% recall, 97.5% F1 score, and 79.96% average IoU. Then, the model was tested and obtained 99.35% mAP, 95% precision, 99% recall, and 97% F1 score. This research resulted in a fairly good system performance in detecting and counting passengers on trains.

Phishing website merupakan mekanisme kriminal yang menggunakan social engineering serta dalih teknis untuk mengambil data identitas personal dan kredensial akun keuangan dari pelanggan. Di Indonesia sendiri menurut laporan Pengelola Nama Domain Internet Indonesia (Pandi), tercatat jumlah phishing dalam kurun waktu 5 tahun terakhir mencapai 34.622. Jumlah serangan phishing unik yang dilaporkan pada Q3 2022 sebanyak 7.988. Penelitian ini bertujuan untuk mencari algoritma machine learning klasifikasi dengan performa terbaik untuk mendeteksi phishing website menggunakan fitur-fitur URL. Algoritma klasifikasi yang akan dibandingkan adalah decision tree, random forest, dan KNN. Hasil dari penelitian ini adalah model pertama yang menggunakan decision tree didapat akurasi sebesar 0.833, presisi sebesar 0.86, recall sebesar 0.83, dan F1-score sebesar 0.83. Model kedua yang menggunakan algoritma random forest mendapat akurasi sebesar 0.834, presisi sebesar 0.86, recall sebesar 0.83, dan F1-score sebesar 0.83. Model terakhir yang menggunakan algoritma K-Nearest Neighbors mendapat akurasi sebesar 0.482, presisi sebesar 0.24, recall sebesar 0.50, dan F1-score sebesar 0.48. Maka, dari ketiga algoritma tersebut random forest merupakan algoritma terbaik untuk mendeteksi phishing website.

The development of information technology is currently progressing quite rapidly, one implementation of this progress is the creation of web pages or what is known as the web. The web makes it easier for people to disseminate information so that it has an impact on people's activities in meeting their needs for information, including information about study programs (prodi). The Information Systems and Technology Education Study Program is one of the study programs at the Indonesian University of Education, Purwakarta regional campus, which currently cannot publish good information related to the study program itself. Therefore, an information media is needed to be able to publish information as a whole to the wider community. The method used in development is the waterfall method, and the technology used during development includes PHP Laravel, MySQL, PhpMyAdmin, Apache Server, and Visual Studio Code. The results of this research are in the form of a web-based information system that can better present information related to Education Systems and Information Technology study programs..

In Indonesia, the government, through the Indonesian National Police (POLRI), has just released a new regulation, the Electronic Traffic Law Enforcement (ETLE). A traffic ticket policy is carried out electronically through camera monitoring connected directly to the vehicle registration certificates (STNK) database. The government can measure people's likes or dislikes of these public policies through sentiment analysis. There have been studies that have applied sentiment analysis to find out people's responses to ETLE. However, in terms of performance, this model only has an accuracy of 0.42. This study proposes the use of a support vector machine (SVM), term frequency-inversed document frequency (TF-IDF), and mean decrease in impurity (MDI) to evaluate polarization sentiment analysis on ETLE policies. First, we retrieve tweets about ETLE from Twitter. Then we do text analysis pre-processing and the remove stop words process. The next step is to carry out the TF-IDF process. We apply two feature selection methods for our comparison: MDI and recurrent feature elimination (RFE). Next, we compare two classification models, namely naïve Bayes and SVM. Some of the metrics that we use to evaluate the pre-processing stage are the probability density function (PDF) and the t-test. We use the bag of words (BoW) to evaluate the remove stop words stage. Finally, sensitivity, specificity, and the receiver operating curve (ROC) are for evaluating feature selection methods and classification methods. The test results show that TF-IDF produces 1,022 new features. The combination of the methods we used resulted in the six models we compared. SVM+TF-IDF+MDI is the model with the best performance compared to the other five models. Accuracy and area under curve (AUC) scores are 0.99 and 0.97, respectively.

Frequently, the determination of scholarship admission cannot be made with confidence, and the established criteria nevertheless need to be taken into account. The scholarship program's goal is to give students who have academic potential but are struggling financially the chance to finish their education without being burdened by hefty expenditures. However, managing and administering scholarships can be a challenging endeavor for educational institutions. In this situation, using a decision support system can improve the efficacy and efficiency of the process of selecting students for scholarships. The results of this research obtained, cluster 1 got rank 1 with the data characteristics of cluster members based on GPA and high value attendance attendance and medium value UKT data, cluster 2 got rank 2 with the data characteristics of cluster member data based on GPA, UKT and high value attendance attendance and cluster 3 got rank 3 with cluster member data characteristics based on GPA, UKT and attendance which had inconsistent values. This study was able to produce priority rankings for each cluster of scholarship recipients based on important factors, like GPA scores, attendance, and UKT, utilizing the SAW and K-meansmethodologies.

This research compares the performance of two sentiment analysis algorithms, namely VADER (Valence Aware Dictionary and Entiment Reasoner) and RoBERTa (Robustly Optimized BERT Pretraining Approach), using a dataset of public opinions regarding climate change on twitter. Analysis is carried out to determine the sentiment distribution of the tweets described, whether they are positive, negative or neutral. In addition, this research identifies the keywords that appear most frequently from the collection of tweets that have been analyzed. Time series analysis was also carried out to see the distribution of sentiment over 12 months. The relationship between the two models was evaluated using matrix scatter plot analysis for tweets per two months, to assess the correlation and consistency of sentiment results between VADER and RoBERTa. The results show that VADER is more effective in situations that require rapid responses to changes in public sentiment, while RoBERTa is superior in in-depth analysis of more complex and ambiguous content.

One of the innovations that emerged in the environment of the modern boarding school al-azkiya'' is the Azkiya Creative application, which is designed to be an application that contains books / bibles, as well as religious materials. To assess whether the application provided has a usability value for users, it is necessary to conduct an evaluation to measure the level of usability. This evaluation aims to identify the advantages, disadvantages, and potential improvements of the existing system. The research to be carried out consists of several stages, namely literature study, data collection, data analysis, and conclusions. In this study using the SUS (System Usability Scale) method to evaluate the azkiya creative application. Researchers distributed SUS questionnaires that respondents had to fill out, then the data was processed so as to produce output, namely usability testing using the System Usability Scale method. regarding the evaluation of the azkiya creative application, the respondents were teachers who had or were using the azkiya creative application. the results of research conducted on 30 respondents, can be obtained the average score through a questionnaire with a score of 80.5. In terms of Acceptability assessing the azkiya creative application at Pondok Pesantren Modern Al-Azkiya' is categorized as acceptable, Grade Scale assesses the application from the aspect of quality level, the evaluation results show that the azkiya creative application is on grade scale B, and Adjective is categorized as excellent in the azkiya creative application.

The Aglaonema type and quality is difficult to identify due to leaf pattern variety. For this reason, a technique is developed to classify Aglaonema types from leaf images. The Aglaonema is identified using the Local Binary Pattern (LBP) technique. The LBP recognizes objects in the form of pixel neighbor patterns in binary code, which is sensitive to the radius (R) and the number of neighbors (P) pixels. In this article we will study the appropriate radius and number of neighbors so that the LBP code becomes an accurate abject texture attribute. Experimentally, R is varied from 1 to 5 while P is varied from 4 to 24 pixels. Two types of Aglaonema with two varieties taken from each type were used to test the accuracy of the LBP code. The accuracy of the classification results is carried out with the help of K-Nearest Neighbors (KNN). The results show that the greater the number of neighbors in determining the LBP code, the more accurate the classification results. Neighbors with a total of 18 have a stable accuracy reaching a total of 79%. Increasing the number of neighbors does not significantly affect accuracy. The neighbor radius affects the batik type of Aglaonema, the wider the neighbor area, the accuracy increases up to 84%, but for the Lipstick type, the best accuracy is obtained when R=3. By choosing the right R and P, the types of Aglaonema batik and Lipstick can be differentiated well.

Poverty is one of the social problems faced by almost every country in the world. One of the factors causing poverty has not been resolved, namely in an implementation of social assistance policies, the government's survey of the community is still carried out manually so that it is not right on target. So, this research aims to identify the criteria possessed by each group of poor people resulting from data grouping using the K-Means clustering algorithm. By applying the K-Means clustering algorithm to the data of the Targeting for the Acceleration of the Elimination of Extreme Poverty (P3KE) of Sei Litur Tasik Village and modeling the data clustering of the poor population of Sei Litur Tasik Village. The results of testing and evaluating the K-Means Clustering model on the data of the Acceleration of the Elimination of Extreme Poverty (P3KE) are determined to be 2 optimal clusters with an interia value of 0.40 using the Silhouette Score testing method where cluster 1 rich category is 366 families and cluster 2 poor category is 60 families. Modeling of the data clustering system design using the K-Means clustering method was carried out on Google Collaboratory and assisted by supporting literature. The results showed the accuracy of K-Means clustering of 85.92% which means that the accuracy of the analyzed data can be correctly grouped into the appropriate cluster category.

SMK IT Al-Izhar Pekanbaru has implemented an Information System for Managing Report Values to make it easier for teachers to manage student report cards. This report card system has been used since 2015. The behavioral aspect is a very important aspect, because it relates directly to users. The thing that underlies this research is that there are still teachers who do not understand how to input student grades, so they tell other teachers to input grades into the system. The purpose of this study was to determine the factors that most influence user behavior interest based on the UTAUT variable consisting of performance expectancy, effort expectancy, social influence, facilitating conditions and moderated by gender, age and experience variables. The results of this study indicate that the factors that most influence user behavior interest are performance expectancy, effort expectancy, and facilitating conditions. while the moderator variable gender affects user interest by 79.0%. As well as the finding of a gap (GAP) between the expectations of the school and the perception of users. To overcome the GAP, the school must evaluate and monitor the implementation of the report card value management information system at SMK IT Al-Izhar Pekanbaru.

In the midst of the massive and rampant that use in every sector of our life makes Information System is needed. Back then, to entry data in a sector was did mannualy which they had to write it down into books, this thing would create any mistake, data redudancy, or perhaps the book would gone. Therefore, Information system would be necessary whether for company or organization. Gereja Bethel Indonesia Sokaraja Kidul (GBI SOKID) is a church which haven’t using information system in their daily operational activity. Thus, this system is aim to help GBI SOKID to optimalized and increasing value-added from their daily operational activities. This Information System Strategic Planning is using Enterprise Architecture Planning as a method to create some of architectures which will be proposed and also create implementation planning. Beside of it, this research also adding Risk Management using ISO 31000:2018 to map some risks would be come up and also solutions are being proposed. The results form this research are Portofolio of GBI SOKID’s Information System and risk map, risk evaluation, also solutions that will be proposed.

This systematic review paper delves into the evolving landscape of cloud architectures for distributed edge computing, with a particular focus on decentralized and collaborative computing models. The aim of this systematic review is to synthesize recent advancements in decentralization techniques, collaborative scheduling, federated learning, and blockchain integration for edge computing. As edge computing becomes increasingly vital for supporting the Internet of Things (IoT) and other distributed systems, innovative strategies are needed to address challenges related to latency, resource management, and data security.The key findings highlight the benefits of latency-aware task management, autonomous serverless frameworks, and the collaborative sharing of computational resources. Additionally, the integration of federated learning and blockchain technologies offers promising solutions for enhancing data privacy and resource allocation. The versatility of edge computing is showcased through its applications in diverse domains, including healthcare and smart cities. Future research directions emphasize the need for optimized resource management, improved security protocols, standardization efforts, and application-specific innovations. By providing a comprehensive review of these developments, this paper underscores the critical role of decentralized and collaborative models in advancing the capabilities and efficiency of edge computing systems.

Frequently, laptop users forget to disconnect or connect the power supply adapter when the battery needs to be charged or fully charged due to they are too focused on using the laptop. This condition can cause problem with laptop batteries and one of which is overcharging. Thus, keeping the laptop battery healthy is advantageous and one of them is to make it last longer. In addition, when the battery is faulty, the replacement of it also requires a lot of money. To deal with this condition, it is necessary to have a design tool that can automatically cut off the AC voltage on the laptop power supply adapter which will be equipped with software. This current research employed development research method and aimed to create a tool comprising hardware and software that could cut off the AC voltage to the laptop power supply adapter automatically based on the laptop battery percentage status.

According to statistics from the Global Burden of Cancer Study (Globocon) of the World Health Organization (WHO), cancer, particularly breast cancer, is a severe health issue in Indonesia with 68,858 new cases and 22,000 deaths recorded in 2020. Ultrasonography (USG) technology is acknowledged as one of the potentials to support early detection, which is vital in reducing mortality from breast cancer. This study focuses on classifying ultrasound images using the Support Vector Machine (SVM) algorithm, GLCM feature extraction, Min-Max normalization, and Mutual Information with SelectKBest Feature Selection. From several experiments using the SVM algorithm with various combinations of parameter values that have been set and different Tests, namely using a Train/Test Split with a proportion of 80/20 and K-Fold Cross Validation, it shows that the SVM algorithm is capable of classifying ultrasound images of breast cancer. into two categories (Benign Tumor and Malignant Tumor) with the same maximum accuracy of 79% after applying the SMOTE Balancing Data technique or without using the Balancing Data technique. As a result, the Support Vector Machine (SVM) algorithm has the potential to be an effective model for identifying breast cancer ultrasound images, both on data from the original set that has not been balanced and data from the set that has been balanced.

Diabetes is a chronic disease characterized by high levels of sugar (glucose) in excess of normal limits, about 1.5 million deaths each year are directly attributed to diabetes. The purpose of this study is to obtain the optimal cluster analysis results on grouping medical record data on diabetes and obtain the silhouette coefficient value of the optimal cluster analysis results on grouping medical record data on diabetes at TK-II Putri Hijau Kesdam I / B Hospital, Medan-North Sumatra by using the divisive analysis algorithm and determining the variables that cause the patient to develop diabetes. The results showed that the optimal cluster using the divisive analysis algorithm was 2 clusters with a value of 0.468582 which stated that the cluster structure formed in this grouping was the right cluster. And for variables that cause patients to develop diabetes, namely age (X2) and blood sugar level (X8). it is because these two variables have the highest average value among the two clusters and all these variables.

The tourism industry is one of valuable industries in Indonesia that needs a high level of expertise through the knowledge management systems in order to improve the tourism service delivery quality performance. Problems faced by this institution is the complexity and hardness in defining and enhancing knowledge through an accountability report, this is due to the lack of management through the process of applying an accountability report. This attracts us to make a research about the users’ perception towards the lesson learn system prototype where this study will examine the users’ perception in designing Lesson Learn System based on the requirements in order to enhance employees knowledge through an accountability report.. After examining the perception of the lesson learn system users, we will design Lesson Learn System prototype by using User Centered Design that describe the relationship between the lesson learn system and its users by specifying the context of using the lesson learn system in tourism. This also will specify the requirements needed for the lesson learn system that help for designing the Lesson learn system design solution.

E-procurement is a process of procuring goods and services that utilizes information and communication technology. Service providers usually access the Electronic Procurement Service (LPSE) website to carry out the e-procurement process. The LPSE website displays hundreds to thousands of government and non-government tenders, which can often reduce the effectiveness of the tender process due to the amount of information that must be managed. To address this issue, the Tenderplus.id application was developed, which is designed to monitor the latest tender packages in the Indonesian government's LPSE. The app features real-time notifications sent via WhatsApp and Email to service providers according to their company preferences and qualifications. The Tenderplus.id application user experience was measured using the USE Questionnaire (Usefulness, Satisfaction, and Ease of Use Questionnaire) method, which evaluates four main variables: usefulness, ease of use, ease of learning, and satisfaction, using a Likert Scale from one to five. The evaluation results show that the overall user experience obtained a percentage value of 83.6%, which is categorized as very feasible. The percentage details for each variable are usefulness of 87.8%, ease of use of 83.3%, ease of learning of 77.5%, and satisfaction of 84%. Implementation and testing of this application showed very positive results, strengthening the conclusion that Tenderplus.id is an effective solution to the problems faced in managing electronic tenders.

In the era of dynamic business globalization, sales data analysis and visualization are key to strategic decision making. The application of Metabase as the main tool for visualization and analysis of sales data in the context of global retail companies, especially in the online sales sector. XYZ Company became the subject of research with complex challenges in managing extensive and diverse sales data. Metabase was adopted as a solution to deal with this complexity, enabling the company to gain deep insights into sales trends, consumer preferences, and hidden growth opportunities. Data visualization, through Metabase, plays a key role in transforming complex information into easy-to-understand visual representations, helping analysts and business stakeholders spot important patterns and trends. Research results reveal patterns of concurrent product purchases, providing opportunities to increase sales through promotions or product bundling. The identification of product categories that customers are interested in within a single transaction provides important insights for stock management and marketing strategies. Analysis of customer gender preferences opens up opportunities to direct more specific marketing strategies, focusing on the majority of a particular gender. The resulting recommendations include increased promotion or bundling of frequently purchased products together, as well as implementation of more focused marketing strategies based on product category preferences and customer gender. This article aims to contribute to the scientific literature on the practical application of data visualization in the context of sales analysis, with a focus on developing effective business decisions and marketing strategies.

The existence of information and communication technology in this digital era has changed the way people interact with money and finance, especially with the emergence of internet developments such as mobile banking, e-commerce, and fintech which have brought great progress in the way people transact and manage finances. Financial transactions that are increasingly related to digital technology bring new benefits and challenges for UMKM business actors, the introduction of digital financial literacy in the context of UMKM is an important step in facing technology-driven business transformation. However, until now there are still many UMKM who still lack understanding of digital financial literacy. Lack of understanding about online transaction security, selection of appropriate services, and digital data management can hinder growth and potentially increase risks for UMKM later. This makes the need for digital financial literacy for UMKM even more real, this research will focus on helping to provide understanding to UMKM actors, especially Medan City about digital financial literacy through interactive educational applications by applying the User Centered Design Method for the development of their user interface, it is hoped that through this application UMKM actors in the city of Medan can optimize their understanding of digital financial literacy so that they can Take full advantage of financial technology to improve operational efficiency, grow businesses, and participate in the digital economy more effectively.

Flooding is one of the most common natural disasters in Indonesia, including in Merbau Sub-district, North Labuhan Batu Regency, North Sumatra. This disaster can cause huge losses, both material and non-material. One of the efforts to reduce the impact of flooding is to immediately distribute aid to affected communities. The distribution of post-flood aid requires careful planning so that it can run quickly and on target. In this research, we propose the use of greedy algorithm to support the decision of choosing post-flood aid distribution route in Merbau District. The greedy algorithm is an algorithm that chooses the best solution at each step, hoping to find the optimal solution as a whole. This research was conducted using data on the location of disaster points, the location of aid distribution points, and the distance between points. The results showed that the greedy algorithm can produce a faster aid distribution route compared to the conventional route.

Seventy-two percent of the Muslim population in Indonesia is illiterate in Arabic script, thus requiring early childhood education. Young children often struggle to directly learn Arabic script, necessitating the role of instructors. However, most instructors in madrasahs do not teach Arabic script intensively, prompting the need for interactive and engaging learning media for children. Therefore, the development of an Android application for teaching Arabic script to young children is crucial. By employing the Rapid Application Development (RAD) method, an Android application can be developed, incorporating gamification for interactive learning and the AI Life Cycle for developing a 3D Arabic script object detection model useful for automatic correction systems. The AI model is developed using SSD MobileNet v2 FPN Lite 640x640. Sixteen trials are conducted for separate letter modeling and two for connected letters. The separate letter modeling yields the best result in the 8th trial with a total loss of 0.182882 out of 1, using parameters of 16 batch size, 30,000 num steps, and checkpoint from the 7th trial. Connected letter modeling yields the best result in the 2nd trial with parameters of 8 batch size, 14,000 num steps, and checkpoint from the 1st trial. The output is an offline Android application integrating gamification and AI technology, featuring two categories of questions, each with 20 items.

PT. Neora Infrastructure Indonesia is an industrial company engaged in telecommunications and/or telephone transmission network construction services, collaborating with Fiberstar. This necessitates the company to constantly require competent human resources. However, the company must compete with others to acquire competent personnel. Nevertheless, the employee selection process at PT. Neora Infrastructure Indonesia is still manual and takes 2 - 4 weeks, making it difficult for the company to compete in acquiring competent personnel. Therefore, it was decided to develop a decision support system to assist the company in efficiently and effectively selecting employees. The decision support system for employee selection uses the ROC and CPI methods. The ROC method is used to determine the criteria weight values, while the CPI method is used for ranking all alternatives. Quantitative method is the research method used, and several types of UML diagrams including Activity diagram, Class diagram, and Use case diagram are utilized for system design. The RAD method is employed for system development. With the establishment of the decision support system for employee selection using the ROC and CPI methods, it is expected that the company can compete with others in obtaining competent human resources and that the employee selection process becomes efficient and effective

The retail industry has undergone a significant transformation. Jumbo Mart is one of the retail businesses that consistently meet customer needs in Pekanbaru city. The number of similar businesses makes Jumbo Mart need to have a competitive advantage to compete. One of them is by improving the shopping experience, especially in the aspect of goods layout. However, based on observations and interviews, the determination of the layout of goods at Jumbo Mart is still not optimal. The number of products and variety of items sold is one of the reasons. In addition, customer transaction history has not been used further and is only stored as an archive of monthly reports. Whereas analyzing the data can be an effective solution in arranging the layout of goods based on customer behavior. Therefore, this research proposes a data mining technique with the FP-Growth algorithm to find association rules between categories of goods. The implementation results with a minimum support value of 15% and confidence of 50% get 7 association rules, but only 6 are proven valid. The valid association rules are then proposed to be placed close together so that it can make it easier for buyers to find the desired items. In addition, items with the consumption category are dominant as consequents, which can be found in the four association rules. This indicates that items with consumption categories have great appeal and are an important part of customers' shopping behavior and habits.

Most people, especially women, have a great desire to have white, healthy, clean and well-maintained facial skin. However, their knowledge about facial skin types is still limited, even though consulting with an expert requires a lot of time and money which results in someone not paying attention to facial skin type when carrying out treatment. Therefore, an expert system is needed that can help identify facial skin types. A rule base is a rule created based on expert knowledge needed to create an expert system. The forward chaining method is a search method or forward tracing technique that starts from existing information and combines rules to produce a conclusion or goal. The research results show that this application can run well and is suitable for use. Based on the results of system testing from an expert, it was concluded that identifying facial skin types based on facial skin criteria using the forward chaining method had an accuracy rate of 84% where the results of system testing produced several conclusions about the appropriate type of facial skin with the selected criteria data.

The leading cause of human death is heart disease (HD) worldwide which generally occurs when the heart is unable to push enough fresh, oxidized blood to the rest of the body. This disease makes it more difficult for the heart muscle to pump blood efficiently and causes chest pain, chest pressure, shortness of breath, pain in the neck and jaw. The aim of this research is to compare and obtain the best accuracy results from the three methods used, namely Random Forest, Extra Trees Classifier and Naïve Bayes. The results of this research prove that the Extra Trees Classifier method with an accuracy of 86.93% has higher results compared to the Naïve Bayes method with an accuracy of 84.21%, and the Random Forest Classifier method with an accuracy of 84.21%. Meanwhile, the AUC results obtained by the Extra Trees Classifier method are higher than other methods with an AUC of 93.81%.

Technological advances are developing just like scientific advances. One example of the latest technological advances is the online attendance system. At MTsN Binjai, attendance currently uses electronic-based technology, namely fingerprints, but still has several weaknesses. For example, the device cannot be used when the electricity fails, and the attendance process must be carried out alternately due to dependence on one device. In addition, fingerprints only record attendance, do not facilitate the submission of permits or illnesses, so the admin must do double work. The thing that was researched was the use of descriptive methods and the development of a system with the prototype method. The purpose of this study is to generate an Android-based absence application that can be accessed through the personal cellphones of teachers and staff who can facilitate attendance and submission of sick permits. The results of the study are expected to be a reference for schools to improve discipline for attendance.

In accordance with PUPR Ministerial Regulation No. 22 of 2018, the use of Building Information Modeling (BIM) must be applied to non simple state buildings with criteria for a building area of more than 2000 m2 and more than 2 floors. Gedung Layanan Terpadu RSUD Kota Bangkinang has an area of 5,040 m2 and has 4 floors but the calculations still use conventional methods. The aim of this study was to analyze the comparison of the bill of quantity (BOQ) of the Gedung Layanan Terpadu RSUD Bangkinang City using conventional methods and the Tekla Structure application. The integrated services building consists of 4 floors, starting from the basement floor, 1st floor, 2nd floor, 3rd floor and roof floor. The research method used was to carry out manual calculations using Microsoft Excel based on the working plans and modeling the building structure using the Tekla Structure application. From the results of the study it can be concluded that the volume of concrete using the conventional method was 3,193.5 m3 while the calculation using the Tekla Structure application was obtained 3,175.5 m3 where there was a difference of 18 m3 or in a percentage of 0,56 % less than the conventional method. For iron volume, there is a difference in iron volume of 2,687.23 kg or 0,45 % less using the Tekla Structure application compared to conventional methods. The conclusion from this study is that the calculation of the bill of quantity using the conventional method is greater than using the Tekla Structure application.

In its development, many new concrete modifications were found, such as lightweight concrete, fiber concrete, polymer concrete, high-strength concrete and ultra-high-strength concrete. Epoxy resin can accelerate the hardening process of concrete because epoxy itself generates heat so that it helps accelerate hardening. In previous studies, the addition of areca nut shell fiber and 0.8% epoxy resin can increase the split tensile strength of concrete. Based on the description above, it is necessary to conduct research on the split tensile strength of concrete that combines a mixture of pineapple leaf fiber and epoxy resin. So this research uses pineapple leaf fiber and epoxy resin. Pineapple leaf fiber and epoxy resin are expected to be strength-enhancing materials that can produce concrete with more optimal split tensile strength. The purpose of this study was to determine the effect of the addition of epoxy resin and the percentage variation of pineapple leaf fiber on the split tensile strength of concrete and its comparison with normal concrete and to determine the percentage level of the addition of the most optimum pineapple leaf fiber to produce the maximum concrete split tensile strength test value among the planned variations. The method used in this research is experimental. From the results of the average split tensile strength test at 28 days, the epoxy resin variation of 0.8% achieved a strength of 2.87 MPa, the 0.15% variation achieved 2.37 MPa, and the 0.5% variation achieved 2.47 MPa

FCAW welding is an automated welding process that employs a wire roll electrode to melt metal, using flux or powder within the electrode's core for protection. A common issue with FCAW welding is the occurrence of angular distortion and alterations in the length and width dimensions, particularly in thin plate iron materials, due to residual stresses from the welding process. This research aims to assess the impact of static thermal tensioning, preheating, and their combination on angular distortion in SS400 steel corner joints during FCAW welding, considering heat input during welding. True experimental research methodology was employed, with variations in preheating temperatures of 200°C, 250°C, 300°C, static thermal tensioning at 150°C, 200°C, 250°C, transient thermal tensioning at 150°C, 200°C, 250°C, and a reference method with no treatment to determine the welding approach that minimizes angular distortion. The material used was 3mm thick SS400 steel. Data analysis was performed using Minitab 18 software and the One-Way Anova method. The research results indicate a significant influence of static thermal tensioning, preheating, and their combination on angular distortion. The optimal welding methods identified are preheating-static thermal tensioning at 89°55', preheating-transient thermal tensioning at 89°50', static thermal tensioning at 150°C at 89°45', transient thermal tensioning at 150°C at 89°40', and preheating at 200°C at 89°35'.

In Indonesia, the reliance on foreign countries for military components persists, including tank track links which are crucial for combat vehicles. These components require mechanical properties such as wear resistance and toughness to ensure high safety standards. High Strength Low Alloy (HSLA) steel is used, necessitating precise composition and appropriate heat treatment processes. This study varied compositions and heat treatments to achieve desired properties, producing prototypes with five predetermined compositions. The heat treatment process involves initially heating the samples to 860°C for 30 minutes for uniformity, followed by various treatments including quenching in oil, normalizing, and multiple cycles of quenching with tempering at different temperatures and durations to refine microstructure and adjust hardness of the alloy. Results showed optimal mechanical properties in composition no. 4 after normalizing with a blower, achieving a hardness of 31 HRC. This research aimed to optimize mechanical performance during operational conditions through variations in temperatures and holding times.

The substantial expansion of the global economy and the extensive urbanization witnessed in recent decades have rendered bridge infrastructure as a vital component of transportation systems. Conducting structural damage detection is a vital strategy to prevent structural failures and avert bridge collapse. One popular approach for detecting damage is to use mode shapes as characteristics in structural dynamics analysis. In this study, the identification of the mode shapes in the damaged condition will utilize the Modal Assurance Criterion (MAC), while the location of the damage within the simulation will be identified using the Mode Shape Data Base Indicator (MSDBI). The study was carried out with the help of a finite element model using Midas Civil software with a case study of a steel frame bridge. Referring to the report studied, damage identification focused on damage caused by loose bolts. The results of the analysis show that the MAC analysis has consistent values on mode shapes in each damage simulation with the mode shapes produced by the healthy condition model. Meanwhile, damage location is detected by the MSDBI index value in each damage simulation. It can be seen that the MSDBI index changes according to the location of the damage.

Fiber-reinforced composites can be classified into two parts, namely short fiber composites and long fiber composites. Long fibers are generally stronger than short fibers. Long fiber (continuous fiber) is more efficient in laying than short fiber but short fiber is easier to lay than long fiber. Fiber length affects the processability of the fiber composite. Judging from the theory, long fibers can transfer the load and stress from the stress point to the other fiber. In this research, we simulated the effects of volume fraction and fiber orientation in glass fiber-reinforced polyester composites on bending strength to examine the effect of each parameter on the mechanical properties of glass fiber composites. The mechanical properties of the composite were tested using the three-point bending and tensile testing methods. The study expects to find variations in mechanical properties with changes in the glass fiber volume fraction and fiber orientation. In this study, it is planned to function in a relevant environment, the components in this study must be able to operate properly and have been well integrated with prototype manufacturing that has been tested as a test tool function. Notably, the pinnacle of the bending test, measuring 170.41 MPa, was achieved at the specific combination of a 0.5 Fiber Volume Fraction and the 0-90o fiber orientation.

Solar panels are used to convert solar energy into electrical energy. In this study it was applied to electric vehicles which have a very large potential for being constrained by shadows. In fast varying partial shading conditions, the position of the maximum power point is divided into two, namely GMPP and LMPP. This condition makes the MPPT process stuck in LMPP. Therefore, this research proposes the application of Serval Optimization Algorithm (SOA) in MPPT. This method refers to the natural behavior of the serval in nature. The fundamental inspiration of SOA is the serval hunting strategy in two stages of exploration and exploitation. The SOA is implemented in MPPT to change (duty cycle) so that it gets the best value and produces maximum solar panel output power. This SOA method was chosen to complete the partial shading conditions so that MPPT can optimally reach GMPP without going through LMPP. The solar panels used in this system are 2 units with specifications of 25 Wp with a 24V battery load and a 120W BLDC Motor compact in Electric Vehicle - Two Wheeler Scooter. MPPT SOA was tested in a simulation using PSIM and actual Software in 6 variations of normal and partial shading conditions. In the Simulation Test of Partial Shading Conditions, an average accuracy of 99.958% and an average tracking time of 0.492 seconds were obtained. SOA has a higher accuracy than PSO and GWO, which is 99.95%. And it has a faster tracking time of 0.55 seconds. In the SOC Integration Test, the SOA Method obtained an error value of 6.48% better than the GWO Method. On the Road Test with 6 condition, it can slow down the value of the decrease in battery capacity by 16.24%. The application of Single Source on the PV-MPPT-Converter can be implemented with an efficiency value of 76.86%. In previous research where SOA is a new method in Optimizing Problem Solving which has quite good accuracy performance, and in this research it can be implemented in Solar PV Optimization to track MPPT electric vehicles with varied and fluctuating partial shading conditions.

Coronavirus Disease 2019 (COVID-19) is a novel disease previously unidentified in humans. The COVID-19 pandemic has necessitated that all medical personnel and health workers wear personal protective equipment (PPE) while serving patients. The pandemic has caused a global shortage of PPE, especially in Indonesia, leading to the closure of many health service facilities, including several in the Kuranji District, to prevent COVID-19 transmission. In response, the community service team of the Faculty of Medicine, Andalas University (FK UNAND), initiated a PPE procurement project for health workers. This activity aimed to supply PPE to medical personnel at the First Level Health Facility (FLHF) in Kuranji District, Padang City. The PPE, including masks, face shields, and gowns/hazmats, was produced following a feasibility study to ensure safety for health workers. The procurement involved collaboration between the FK UNAND community service team and Micro, Small, and Medium Enterprises (MSMEs). The team designed models of masks, face shields, and gowns/hazmats that met health safety standards, sourced the necessary materials, and passed these designs and materials to MSMEs for production. Prior to production, a meeting with MSMEs was held to discuss the designs. Eight MSMEs were selected to produce the PPE over approximately one month. A total of 400 masks, 180 face shields, and 20 gowns were produced and distributed to 8 FLHFs in Kuranji sub-district. The handover of PPE was met with an extraordinary response from health workers, as it significantly addressed the PPE shortage and allowed FLHFs in Kuranji District to resume operations and provide medical services to the community.

Abstract This mixed-methods study investigates English as a Foreign Language (EFL) lecturers' technostress in low Human Development Index (HDI) contexts, aiming to identify contributing factors, impacts, and coping strategies. The data collection was done through quantitative surveys and qualitative interviews. Twenty-two participants from public and private universities in West Nusa Tenggara, Indonesia, were selected based on their willingness to participate and provided demographic information. The analysis reveals that factors such as techno-complexity, techno-insecurity, and techno-uncertainty significantly contribute to high levels of technostress among EFL lecturers in low HDI contexts. Despite the challenges posed by technostress, participants exhibit adaptive skills in employing coping strategies effectively. The study underscores the need for tailored support programs, such as Technology Professional Development, to enhance EFL lecturers' technological proficiency and well-being. These findings have theoretical implications for understanding technostress in educational settings and practical implications for developing strategies to alleviate technostress and improve teaching outcomes in low-resource contexts. Bahasa Indonesia Abstrak Penelitian dengan metode campuran ini menyelidiki technostress dosen Bahasa Inggris sebagai Penutur Asing (EFL) dalam konteks Indeks Pembangunan Manusia (IPM) yang rendah, yang bertujuan untuk mengidentifikasi faktor penyebab, dampak, dan strategi penanggulangan. Pengumpulan data dilakukan melalui survei kuantitatif dan wawancara kualitatif. Dua puluh dua partisipan dari universitas negeri dan swasta di Nusa Tenggara Barat, Indonesia, dipilih berdasarkan kesediaan mereka untuk berpartisipasi dan memberikan informasi demografis. Analisis menunjukkan bahwa faktor-faktor seperti kompleksitas teknologi, ketidakamanan teknologi, dan ketidakpastian teknologi secara signifikan berkontribusi pada tingginya tingkat stres teknologi di kalangan dosen EFL dalam konteks IPM yang rendah. Terlepas dari tantangan yang ditimbulkan oleh technostress, para peserta menunjukkan keterampilan adaptif dalam menggunakan strategi mengatasi masalah secara efektif. Studi ini menggarisbawahi perlunya program dukungan yang disesuaikan, seperti pengembangan profesionalisme dalam penggunaan teknologi, untuk meningkatkan kemahiran dan kapabilitas dosen EFL. Temuan ini memiliki implikasi teoritis untuk memahami technostress dalam lingkungan pendidikan dan implikasi praktis untuk mengembangkan strategi untuk mengurangi technostress dan meningkatkan hasil pengajaran dalam konteks sumber daya rendah.

- In a world where industrial mechatronic systems evolve from day to day, the intensity of these requirements influences the maximization of efficiency, the optimization of the safety of the working environment including human and the increase of the quality of services. This is why industrial computing is becoming a necessity for manufacturers and even researchers working on innovative and creative subjects.This paper deals with the analysis and the modeling of a flexible manufacturing system. Firstly, the study of the system engineeringthen the composition of the studied system was presented. Second, we present an application of a modeling based on Petri Nets.Finally, the last present conclusion and future work.