Jurnal Teknik Informatika (JUTIF)
Jurnal Teknik Informatika (JUTIF) is an Indonesian national journal, publishes high-quality research papers in the broad field of Informatics, Information Systems and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology. Jurnal Teknik Informatika (JUTIF) is published by Informatics Department, Universitas Jenderal Soedirman twice a year, in June and December. All submissions are double-blind reviewed by peer reviewers. All papers must be submitted in BAHASA INDONESIA. JUTIF has P-ISSN : 2723-3863 and E-ISSN : 2723-3871. The journal accepts scientific research articles, review articles, and final project reports from the following fields : Computer systems organization : Computer architecture, embedded system, real-time computing 1. Networks : Network architecture, network protocol, network components, network performance evaluation, network service 2. Security : Cryptography, security services, intrusion detection system, hardware security, network security, information security, application security 3. Software organization : Interpreter, Middleware, Virtual machine, Operating system, Software quality 4. Software notations and tools : Programming paradigm, Programming language, Domain-specific language, Modeling language, Software framework, Integrated development environment 5. Software development : Software development process, Requirements analysis, Software design, Software construction, Software deployment, Software maintenance, Programming team, Open-source model 6. Theory of computation : Model of computation, Computational complexity 7. Algorithms : Algorithm design, Analysis of algorithms 8. Mathematics of computing : Discrete mathematics, Mathematical software, Information theory 9. Information systems : Database management system, Information storage systems, Enterprise information system, Social information systems, Geographic information system, Decision support system, Process control system, Multimedia information system, Data mining, Digital library, Computing platform, Digital marketing, World Wide Web, Information retrieval Human-computer interaction, Interaction design, Social computing, Ubiquitous computing, Visualization, Accessibility 10. Concurrency : Concurrent computing, Parallel computing, Distributed computing 11. Artificial intelligence : Natural language processing, Knowledge representation and reasoning, Computer vision, Automated planning and scheduling, Search methodology, Control method, Philosophy of artificial intelligence, Distributed artificial intelligence 12. Machine learning : Supervised learning, Unsupervised learning, Reinforcement learning, Multi-task learning 13. Graphics : Animation, Rendering, Image manipulation, Graphics processing unit, Mixed reality, Virtual reality, Image compression, Solid modeling 14. Applied computing : E-commerce, Enterprise software, Electronic publishing, Cyberwarfare, Electronic voting, Video game, Word processing, Operations research, Educational technology, Document management.
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962 Documents
<![CDATA[LOBSTER AGE DETECTION USING DIGITAL VIDEO-BASED YOLO V8 ALGORITHM ]]>
<![CDATA[Nusman, Bayu]]>;
<![CDATA[Rahman, Aviv Yuniar]]>;
<![CDATA[Putera, Rangga Pahlevi]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2144
<![CDATA[Lobster is an aquatic animal that has high economic value in the fishing industry. Demand for lobster in both domestic and export markets continues to increase thanks to its delicious meat and a variety of desirable dishes. Indonesia, especially Java Island, contributes significantly to the national lobster production. However, the current manual determination of lobster age has limitations such as complexity, time required, and subjectivity in assessment.To overcome this problem, this research proposes the detection of lobster age using the YOLO (You Only Look Once) method, specifically the YOLOv8 version. This algorithm is known to be able to perform image and video recognition quickly and produce high accuracy. YOLOv8 can be run using a GPU, enabling parallel operations that significantly increase the speed of object detection compared to using a CPU alone. The data processing in this study involves several stages, starting from pre-processing in the form of image extraction and bounding from lobster videos. Next, the YOLOv8 algorithm was used to train the model with customized grid and bounding box parameters. The trained model is then validated and tested using lobster image and video data. The results of the test show that the developed YOLOv8 model has a precision of 0.997, recall of 0.998, mAP50 of 0.995, and mAP50-95 of 0.971. This shows that the model is able to detect and determine the age of the lobster with very high accuracy, providing a more efficient and objective solution than the manual method.]]>
<![CDATA[IMPLEMENTATION OF A WEB-BASED SPP PAYMENT INFORMATION SYSTEM AT SMA NEGERI 1 WATUBANGGA USING THE WATERFALL METHOD TO ENHANCE EFFICIENCY AND TRANSPARENCY ]]>
<![CDATA[Bantun, Suharsono]]>;
<![CDATA[Sari, Indri Purnama]]>;
<![CDATA[Assagaf, Sayyed]]>;
<![CDATA[Sari, Jayanti Yusmah]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2154
<![CDATA[This research aims to develop and implement a website-based Education Development Contribution (SPP) payment information system at SMA Negeri 1 Watubangga using the Waterfall method. The general problem faced is the management of conventional SPP payments which is inefficient, prone to errors, and lacks transparency. In particular, SMA Negeri 1 Watubangga experienced difficulties in manually recording SPP payments which resulted in data accumulation, loss of payment cards, and errors in presenting reports. Needs analysis was carried out through interviews and observations with related parties, resulting in functional requirements such as student data management, SPP bill management, printing proof of payment, and payment reports that can be accessed by the school principal. Coding is done using HTML, CSS, JavaScript, and PHP, with a MySQL database. The WhiteBox test results show that this system is free from errors with cyclomatic complexity, region and independent path values of 52. A total of 14 flowgraphs received good validation. User Acceptance Test (UAT) testing with 36 respondents, including treasurers, school principals and students, showed positive results with a final score of 64%, indicating this system is good and feasible to implement. Implementation of this system increases efficiency and transparency in managing SPP payments, as well as making access to information easier for students and schools. Challenges in user adaptation to new systems can be overcome with intensive training. This research proves that information technology can provide an effective solution in managing SPP payments and can be used as a model for other schools that face similar challenges.]]>
<![CDATA[EVALUATION EXECUTION TIME FEATURES OF SIMPATI WEB-BASED MONITORING AND EVALUATION APPLICATION USING AUTOMATION TESTING ]]>
<![CDATA[Arifin, Nur]]>;
<![CDATA[Agustin, Ninik]]>;
<![CDATA[Anggoro, Tri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2158
<![CDATA[One of the data collection methods employed in monitoring and evaluation is a survey. The advantages of information technology-based surveys include a reduced risk of data loss and the ability to access the survey system from any location. Consequently, it is imperative to devise a web-based monitoring and evaluation system. In this study, the monitoring and evaluation system was developed using an Agile methodology and implementing automation testing. The Agile development method was utilized to create this application through the stages of design, development, testing, deployment, and feedback. The system was developed using the PHP programming language and the MySQL database. Automation testing was conducted using Katalon Studio. The design and implementation of the web-based SIMPATI application resulted in a system capable of monitoring and evaluating higher education with main features such as multi-user support, online surveys, a real-time database, and data visualization. Automation testing using Katalon Studio demonstrated that all features in the system run well and are stable. However, there is a discrepancy in the execution time for each test case. This discrepancy is attributed to the intricacy of the test cases and external factors such as system load, server performance, and network conditions. The fastest execution time for the login feature is 11 seconds, while the longest execution time for the add new user feature is 4 minutes and 29 seconds. In conclusion, automation testing in web-based system development employing agile methods can assist in the rapid and repeatable evaluation of system performance.]]>
<![CDATA[VGG-16 ARCHITECTURE ON CNN FOR AMERICAN SIGN LANGUAGE CLASSIFICATION ]]>
<![CDATA[Meitantya, Mutiara Dolla]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Rachmawanto, Eko Hari]]>;
<![CDATA[Ali, Rabei Raad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2160
<![CDATA[Every country has its sign language such as in Indonesia there are 2 types namely Indonesian Sign Language System called SIBI and BISINDO (Indonesian Sign Language). American Sign Language (ASL) is a sign language that is widely used in the world. In this research, the classification of American Sign Language (ASL) using the Convolutional Neural Network (CNN) method using VGG-16 architecture with Adam optimizer. The data used is 14000 ASL image data with 28 classes consisting of letters A to Z plus space and nothing with a division of 90% training data and 10% validation data. From this research, the overall accuracy is obtained with a value of 98% and the accuracy value of validation data evaluation is 89.07%.]]>
<![CDATA[ANDROID BASED MULTIMEDIA APPLICATION FOR RECOGNIZING LETTERS AND SENTENCES FOR DEAF ]]>
<![CDATA[Fahrul, Fahrul]]>;
<![CDATA[Nirsal, Nirsal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2162
<![CDATA[This research aims to create an Android-based guide to reading letters and sentences for deaf children at UPT Special School (SLB) Negeri 1 Palopo. This research process aims to help deaf students read letters and sentences, so with this letter and sentence reading guide application, it can make it easier for deaf students to learn anywhere and anytime. The type of research used in this research is Research and Development (R&D), using the ADDIE model. This system design uses MIT App Inventor Software. This Android-based letter and sentence recognition application for deaf children has been tested by media experts using black box testing. The results of research validation from two validators obtained an average result of 3.85 with a very feasible category. The conclusion of this research shows that Android-based learning media has been proven to be very feasible. The results of the research were implemented in the form of an Android application as a learning medium for recognizing letters and sentences for deaf children as a teaching medium that can be used at UPT Special School (SLB) Negeri 1 Palopo.]]>
<![CDATA[DESIGN AND CONSTRUCTION OF E-LEARNING MEDIA MOBILE BASED USING ANDROID STUDIO ]]>
<![CDATA[Aminuddin, Harun]]>;
<![CDATA[Nirsal, Nirsal]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2163
<![CDATA[In the increasingly developing digital era, technological transformation has had a significant impact on various sectors, including education. This phenomenon gives rise to a new paradigm in the teaching and learning process, which is specifically known as educational technology or e-learning. This research aims to design an e-learning application. The main aim of this application is so that schools can keep up with technological developments that are developing rapidly in the world of education and also make it easier for teachers and students to provide information and increase learning hours that can be accessed by teachers and students anytime and anywhere. The type of research used is the Research and Development (R&D) method. The application development model used in this research involves four stages, namely observation, interviews and literature study. The application used in the creation system is Android Studio. The programming languages used in designing this e-learning are HTML/CSS, PHP, Java Script and Kotlin. The result of this research is an application used by teachers and students at SDN 7 Ponjalae in the teaching and learning process using an electronic learning system to make it easier to access learning anywhere and anytime. This e-learning application has been tested using a black box by obtaining media expert validation test results of 3.90, including in the Very Good category.]]>
<![CDATA[CLASSIFICATION OF ORGANIC AND NON-ORGANIC WASTE WITH CNN-MOBILENET-V2 ]]>
<![CDATA[Oktayaessofa, Eqania]]>;
<![CDATA[Sari, Christy Atika]]>;
<![CDATA[Rachmawanto, Eko Hari]]>;
<![CDATA[Yaacob, Noorayisahbe Mohd]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2165
<![CDATA[Data from the Ministry of Environment and Forestry shows that the amount of organic and non-organic waste in 2023 has started to decline compared to the previous year. However, waste management in the central landfill is still not optimal. This is a problem for the community and the environment because it can cause pollution and disrupt public health around the disposal site. The reason for the difficulty of waste management at the landfill is that people still dispose of waste without separating it first. In addition, it is also due to a lack of public awareness and knowledge. One of the things that can be done to help overcome the problem of waste and its management is to develop an application that can help people understand the importance of waste selection and facilitate socialization in the community. For that, a model is needed that can classify waste based on its type with accurate accuracy. In this study, we propose a deep learning model, CNN with mobilenetV2 architecture, to classify organic and non-organic waste. This model uses a dataset consisting of 4380 images of organic and non-organic waste. Then 3 preprocessing stages were carried out, namely resize, normalization, and augmentation. From this process, data training was carried out and researchers obtained model evaluation results with 98.47% accuracy, 97% precision, 97% recall, and 97% F1 Score evaluation results. These results show that the proposed model is categorized as excellent.]]>
<![CDATA[IMPLEMENTATION OF THE K-NEAREST NEIGHBORS METHOD FOR DETERMINING FETAL HEALTH STATUS ]]>
<![CDATA[Mawaddah, Maulidatul]]>;
<![CDATA[Homaidi, Ahmad]]>;
<![CDATA[Lidimillah, Lukman Fakih]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2173
<![CDATA[Determining the health status of the fetus is a crucial aspect of pregnancy monitoring to reduce the risk of complications and increase the safety of the mother and baby. The K-Nearest Neighbors (KNN) method has been implemented as a classification technique in determining fetal health status based on cardiotocography (CTG) data. This study describes the use of the KNN algorithm to analyze various CTG parameters, including fetal heart rate and uterine contraction frequency, to classify fetal health status into three categories: normal, suspect, and pathologic. The implementation process involves collecting normalized data, selecting relevant features, and using the KNN algorithm with varying K values to determine the most optimal value. The research results show that the KNN method with the right K value can achieve high accuracy in classifying fetal health status, with accuracy reaching up to 89%. These findings indicate that KNN is an effective and reliable method in supporting medical personnel to make decisions based on CTG, which can ultimately improve the quality of maternal and infant health care. In addition, the implementation of this method is relatively simple and can be integrated into existing health systems without requiring large computing resources. Further research is recommended to compare the performance of KNN with other machine learning methods such as Support Vector Machine(SVM) and Random Forest to identify the best method in this context. The use of larger and more diverse data is also expected to increase the accuracy and generalization of the model in various clinical conditions.]]>
<![CDATA[TECHNOLOGY TREND OF DIGITAL IDENTITY: A BIBLIOMETRIC APPROACH ]]>
<![CDATA[Tika Riskawati]]>;
<![CDATA[Suryanegara, Muhammad]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2176
<![CDATA[The future of digital ecosystem requires various supporting technologies, one of which is digital identity. A necessary validation tool not only for individuals but also for organizational institutions which later will be used in digital economic activities. Indonesia as a country with large citizen urgently needs digital identity to protect the people and to upheld national security systems. However, we need to figure out the overall development of digital identity before adopting the technology. This article conducts a bibliometric analysis to investigate the future that digital identity holds. The investigations revealed that digital identity will eventually evolve to four technologies such as decentralized identity, verifiable credentials, self-sovereign identity, and metaverse. The findings will be a catalyst for the information technology and telecommunications industry to adopt digital identity technology.]]>
<![CDATA[DESMOCAM (DETECTION SMOKING CAMERA): INTEGRATION OF IOT AND MACHINE LEARNING FOR ACTIVE SMOKER DETECTION TO SUPPORT SMART CITIES IN INDONESIA ]]>
<![CDATA[Abdillah, Annas]]>;
<![CDATA[Nayu, Balqist Kharisma]]>;
<![CDATA[Setianingsih, Susi]]>;
<![CDATA[Hidayat, Galih B.]]>;
<![CDATA[Ahmad, Tuhfa R.]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.4.2181
<![CDATA[Cigarettes are an addictive substance that kills around 8 million people every year, as of 2022 there will be around 8,67 million deaths in the world caused by cigarettes and other tobacco products with resulting economic losses of around 2 trillion USD. Efforts to reduce losses due to smoking in Indonesia have been implemented through various regulations and rules that have been established, such as Law Number 36 of 2009 Article 115 concerning non-smoking areas. The target for non-smoking areas (NSA) regulations in Indonesia will reach 100% by 2023. However, currently, only 86% of regions have NSA regulations and must continue to monitor and evaluate through regulations set by the government. One solution to emphasize non-smoking areas with the latest technology connections to support Smart City is a smoke detection system using IoT. DesMoCam (Detection Smoking Camera) applies the latest machine learning model, InceptionResNet2, which has high accuracy and has the ability to detect smokers precisely in a Non-Smoking Area (NSA). DesMoCam uses a Raspberry Pi with ESP32-CAM to capture situations in a smoking-free room and warnings through the speaker. Machine learning modeling includes data acquisition with smoking and non-smoking images, data preprocessing, two-way modeling with and without a freeze layer, and analysis of model results. The InceptionResnet2 model used for image identification and classification, achieved an accuracy of 92.75%.]]>
