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[IDENTIFYING POTENTIAL CREDIT CARD PAYMENT DEFAULTS USING GMDKNN WITH LOF AS OUTLIER HANDLING ]]>
<![CDATA[Dewi, Liony Puspita]]>;
<![CDATA[Chrisnanto, Yulison Herry]]>;
<![CDATA[Yuniarti, Rezki]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2341
<![CDATA[In classifying data, accuracy results are greatly influenced by outliers. The presence of outliers can cause a low level of accuracy in the classification process. The Generalised Mean Distance K-Nearest Neighbor (GMD-KNN) algorithm is a classification technique that shows advantages in terms of flexibility and responsiveness to attribute variations. This research aims to classify credit card data between current and bad payments by handling outliers using the Local Outlier Factor (LOF). The data used is 30,000 credit card transaction data taken from the UCI Machine Learning Repository. This research method uses several stages, namely data collection, data pre-processing carried out to detect and clean outliers with LOF, classification process with GMD-KNN, and evaluation to calculate the accuracy of classification results. As a result, the model shows the best performance at 80%:20% data sharing ratio with k=5 value, achieving 77.60% accuracy, 74.97% precision, 82.57% recall, 78.58% F1-Score, and 77.48% G-Mean.]]>
<![CDATA[COMPARISON OF K-NEAREST NEIGHBORS AND NAÏVE BAYES CLASSIFIER ALGORITHMS IN SENTIMENT ANALYSIS OF USER REVIEWS FOR INTERMITTENT FASTING APPLICATIONS ]]>
<![CDATA[Kusuma, Muhammad Varhan]]>;
<![CDATA[Juanita, Safitri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2345
<![CDATA[Applications that focus on health, especially obesity prevention, are scattered in the Google Play Store, one of which is the "Intermittent Fasting" application, which, according to the developer, aims to help users maintain a healthy lifestyle and regulate eating habits. With the increasing number of similar health applications, this research focuses on sentiment analysis of user reviews of "Intermittent Fasting" to find out how users respond. The purpose of this research is to find the best algorithm to analyze sentiment on user reviews on the Google Play Store against the "Intermittent Fasting" application, as well as provide recommendations for new or old users and for application developers based on the results of processing review data. The data mining methodology used in this research is CRISP-DM, using a dataset collected on user reviews on the Google Play Store for five years (2019-2024), which is annotated with three sentiment labels (positive, negative, and neutral) based on user ratings, then modeling using two algorithms K-Nearest Neighbors (KNN) and Naïve Bayes Classifier (NBC). The contribution of this research is to test, evaluate, and compare the two algorithms (KNN and NBC) using two testing models (Split and K-Fold Cross Validation) and then provide recommendations for the best algorithm. The research concludes that the NBC algorithm is superior to KNN with an accuracy value of 80%, while the KNN algorithm has an accuracy value of only 71.43%. In addition, the K-Fold Cross Validation testing model is more optimal in improving the accuracy of the algorithm's performance than the Split model.]]>
<![CDATA[Comparative Analysis of Decision Tree, Random Forest, Svm, and Neural Network Models for Predicting Earthquake Magnitude ]]>
<![CDATA[Turino, Turino]]>;
<![CDATA[Saputro, Rujianto Eko]]>;
<![CDATA[Karyono, Giat]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.2378
<![CDATA[This study conducts a comparative analysis of four machine learning algorithms—Decision Tree, Random Forest, Support Vector Machine (SVM), and Neural Network—to predict earthquake magnitudes using the United States Geological Survey (USGS) earthquake dataset. The analysis evaluates each model's performance based on key metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The Random Forest model demonstrated superior performance, achieving the lowest MAE (0.217051), lowest RMSE (0.322398), and highest R² (0.574261), indicating its robustness in capturing complex, non-linear relationships in seismic data. SVM also showed strong performance, with competitive accuracy and robustness. Decision Tree and Neural Network models, while useful, had comparatively higher error rates and lower R² values. The study highlights the potential of ensemble learning and kernel methods in enhancing earthquake magnitude prediction accuracy. Practical implications of the findings include the integration of these models into early warning systems, urban planning, and the insurance industry for better risk assessment and management. Despite the promising results, the study acknowledges limitations such as reliance on historical data and the computational intensity of certain models. Future research is suggested to explore additional data sources, advanced machine learning techniques, and more efficient algorithms to further improve predictive capabilities. By providing a comprehensive evaluation of these models, this research contributes valuable insights into the effectiveness of various machine learning techniques for earthquake prediction, guiding future efforts to develop more accurate and reliable predictive models.]]>
<![CDATA[LEARNING RATE AND EPOCH OPTIMIZATION IN THE FINE-TUNING PROCESS FOR INDOBERT’S PERFORMANCE ON SENTIMENT ANALYSIS OF MYTELKOMSEL APP REVIEWS ]]>
<![CDATA[Zaidan, Muhammad Naufal]]>;
<![CDATA[Sibaroni, Yuliant]]>;
<![CDATA[Prasetyowati, Sri Suryani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2396
<![CDATA[With the advancement of the digital era, the growth of mobile applications in Indonesia is rapidly increasing, particularly with the MyTelkomsel app, one of the leading applications with over 100 million downloads. Given the large number of downloads, user reviews become crucial for improving the quality of services and products. This study proposes a sentiment analysis approach utilizing the Indonesian language model, IndoBERT. The main focus is on optimizing the learning rate and epochs during the fine-tuning process to enhance the performance of sentiment analysis on MyTelkomsel app reviews. The IndoBERT model, trained with the Indo4B dataset, is the ideal choice due to its proven capabilities in Indonesian text classification tasks. The BERT architecture provides contextual and extensive word vector representations, opening opportunities for more accurate sentiment analysis. This study emphasizes the implementation of fine-tuning with the goal of improving the model's accuracy and efficiency. The test results show that the model achieves a high accuracy of 96% with hyperparameters of batch size 16, learning rate 1e-6, and 3 epochs. The optimization of the learning rate and epoch values is key to refining the model. These results provide in-depth insights into user sentiment towards the MyTelkomsel app and practical guidance on using the IndoBERT model for sentiment analysis on Indonesian language reviews.]]>
<![CDATA[CLASSIFICATION OF RICE ELIGIBILITY BASED ON INTACT AND NON-INTACT RICE SHAPES USING YOLO V8-BASED CNN ALGORITHM ]]>
<![CDATA[Hastari, Nazwa Putri]]>;
<![CDATA[Rohana, Tatang]]>;
<![CDATA[Masruriyah, Anis Fitri Nur]]>;
<![CDATA[Wahiddin, Deden]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2413
<![CDATA[The large amount of unfit rice has an impact on the quality of rice provided to the community. This is due to the lack of supervision of the quality of existing rice, so that the quality of rice distributed to the community has a lot of unfit quality. Rice production for public consumption reached 21.69 million tons in 2021, according to data from the Central Statistics Agency (BPS). Rice is the main food of the Indonesian people because most Indonesians are farmers and the vast amount of agricultural land makes Indonesia one of the largest rice producing countries in Southeast Asia, this has a huge impact on people's habits in consuming rice as the main food provider. The Government of the Republic of Indonesia started a Social Assistance rice distribution program through the Ministry of Social Affairs in 2018. This program is named Prosperous Rice Social Assistance (Bansos Rastra). Classification of rice eligibility can be the first step to ensure that the rice received from the government is of high quality and can meet the daily needs of households in Indonesia. CNN algorithm based on YOLOv8 system can automatically recognize the form of rice given by the government whether it is feasible or not. In the research stages there are dataset collection, preprocessing, training models to evaluation. Based on the results obtained in this study, the accuracy achieved is 79% for the Eligible class and 79% for the Ineligible class with Confidence score reaching a value of 1.00. The results of this study can be used as a decent and unfit rice classification detection model by looking at the shape of the rice. So that the rice distributed to the community has decent rice quality.]]>
<![CDATA[INFORMATION SYSTEM AUDITING USING COBIT 5 ON PRADITA UNIVERSITY E-LEARNING ASWAYA ]]>
<![CDATA[Panduwitama, Aldira]]>;
<![CDATA[Atmojo, Wahyu Tisno]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2416
<![CDATA[Learning management sytems (LMS) play a crucial role to an academic process in a modern university, LMS systems facilitate an online learning process and help teach and it students to connect. This system is considered vital by its nature to ensure a smooth academic process, that’s why keeping the sensitive data and information that are contained in it are a must and ensuring the security are top priority.Whereheas LMS that are used in Pradita University havent got its system information audited. By using COBIT 5 developed by ISACA gives a solution to align IT with the organization goals, ensure its security, manage risk and threats and also manage compliance to a current policy. The main goal of this research is to understand its IT management, Especially on Security sector and Service request incident that contained in COBIT 5 domains like DSS02, DSS04, DSS05 and how this information system compliance to external regulations that already stated by an external organization as contained in MEA03 domain. And the result of its maturity level that have been assesed on Domain DSS02 is 2 DSS04 level 3 DSS05 level 5 and MEA03 is on level 3. That makes LMS aswaya have an average of 2.5 on its maturity level and have 0,5 gap from the expected level of 3. This shown because most of the processes on LMS Aswaya are Repeatable but inituitive.]]>
<![CDATA[ENSEMBLE MACHINE LEARNING WITH NEURAL NETWORK STUNTING PREDICTION AT PURBARATU TASIKMALAYA ]]>
<![CDATA[Al-Husaini, Muhammad]]>;
<![CDATA[Lukmana, Hen Hen]]>;
<![CDATA[Rizal, Randi]]>;
<![CDATA[Puspareni, Luh Desi]]>;
<![CDATA[Hoeronis, Irani]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2421
<![CDATA[This research uses an ensemble model and neural network method that combines several machine learning algorithms used in the prediction of stunting and nutritional status children in Purbaratu Tasikmalaya. This ensemble method is complemented by a combination of the prediction results of several algorithms used to improve accuracy. The data used is anthropometry-based calculations of 195 toddlers with 39% of related stunting from 501 total data in Purbaratu Tasikmalaya City; high rates of stunting this research urgent to make a stable model for prediction. The results of this study are significant as they provide a more accurate and efficient method for predicting stunting and nutritional status in children, which can be crucial for early intervention and prevention strategies in public health and nutrition. The best accuracy value for some of these categories is 98, 21% for the Weight/Age category with the xGBoost algorithm, 97.7% of the best accuracy results with the Random Forest and Decision Tree algorithms for the Height/Age category, the Weight/Height category with the best accuracy of 97.4% for the Random Forest and xGBoost algorithms, and the use of neural network models resulted in an accuracy of 99.19% for Weight/Age and Height/Age while for Weight/Height resulted in an accuracy of 91.94%..]]>
<![CDATA[IMPLEMENTATION OF DEEP LEARNING FOR DETECTING PHISHING ATTACKS ON WEBSITES WITH COMBINATION OF CNN AND LSTM ]]>
<![CDATA[Raihan, Ahmad]]>;
<![CDATA[Fadhli, Mohammad]]>;
<![CDATA[Lindawati, Lindawati]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2446
<![CDATA[Phishing attacks represent significant cyber threats to internet users, particularly on websites. These attacks are conducted by perpetrators seeking to acquire victims' data by impersonating legitimate websites. To address this threat, a solution is proposed using deep learning with a combined algorithm of convolutional neural network and long short-term memory. The research methodology included data collection comprising phishing and legitimate website links, pre-processing through tokenization, padding, and labeling, and splitting data into training and testing sets. The models were then trained, and grid search was employed to identify the optimal hyperparameters for each algorithm. The algorithm’s performance was calculated by accuracy, precision, recall, and F1-score metrics. The outcomes indicated that using the combination algorithm achieved 95.63% accuracy, 94.60% precision, 96.81% recall, and 95.78% f1-score. This paper concludes the proposed algorithm is effective in detecting phishing attacks on websites.]]>
<![CDATA[IMPLEMENTATION OF REST API ARCHITECTURE FOR FEELSQUEST ONLINE COURSE FEATURE IN FEELSBOX APPLICATION USING LARAVEL FRAMEWORK ]]>
<![CDATA[Riawan, Faza Alexander]]>;
<![CDATA[Kusumo, Dana Sulistyo]]>;
<![CDATA[Selviandro, Nungki]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.5.2493
<![CDATA[Feelsbox is a digital-based startup that focuses on the importance of mental health issues and offers innovative solutions to help people maintain their mental health. FeelsBox took the initiative to develop an online course feature "FeelsQuest" with the aim of providing education and helping prevent and overcome mental health problems to the wider community, especially teenagers. The development of this feature uses the PHP programming language with the Laravel framework and implements the REST API architecture. The choice of REST API architecture is based on the concept of separation of responsibilities so that the API can be reused on different platforms. In addition, a suitable test is needed to test the REST API that has been built. Testing of the REST API that has been built is done with the API testing method which is focused on aspects of functionality and performance using Postman to ensure that the API built produces responses and behaves according to the needs of the FeelsQuest feature of the FeelsBox application. The test results show that the implementation of the REST API on the FeelsQuest feature is in accordance with the functional requirements and successfully applies the concept of separation of concerns and meets the non-functional needs of the FeelsQuest feature related to the response time of each API, which is under 3 seconds.]]>
<![CDATA[Performance Evaluation of Transformer Models: Scratch, Bart, and Bert for News Document Summarization ]]>
<![CDATA[Holle, Khadijah Fahmi Hayati]]>;
<![CDATA[Munna, Daurin Nabilatul]]>;
<![CDATA[Ekaputri, Enggarani Wahyu]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2025.6.2.2534
<![CDATA[This study evaluates the performance of three Transformer models: Transformer from Scratch, BART (Bidirectional and Auto-Regressive Transformers), and BERT (Bidirectional Encoder Representations from Transformers) in the task of summarizing news documents. The evaluation results show that BERT excels in understanding the bidirectional context of text, with a ROUGE-1 value of 0.2471, ROUGE-2 of 0.1597, and ROUGE-L of 0.1597. BART shows strong ability in de-noising and producing coherent summaries, with a ROUGE-1 value of 0.5239, ROUGE-2 of 0.3517, and ROUGE-L of 0.3683. Transformer from Scratch, despite requiring large training data and computational resources, produces good performance when trained optimally, with ROUGE-1 scores of 0.7021, ROUGE-2 scores of 0.5652, and ROUGE-L scores of 0.6383. This evaluation provides insight into the strengths and weaknesses of each model in the context of news document summarization.]]>
