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[WEBSITE VULNERABILITY TESTING USING THE PENETRATION TESTING METHOD REFERRING TO NIST SP 800 – 155 (CASE STUDY (Astonprinter.com Domain)) ]]>
<![CDATA[Agustinus, Ari]]>;
<![CDATA[Sembiring, Irwan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3859
<![CDATA[Information security is a very important aspect in maintaining the confidentiality, integrity and availability of data on a system, especially on websites that are vulnerable to various cyber threats. This research aims to test website vulnerabilities using the penetration testing method by referring to the NIST SP 800-115 standard. The case study used in this research is the astonprinter.com website. The penetration testing method applied in this research follows the NIST SP 800-115 guidelines which include the Planning, Discovery, Attacking and Reporting stages. The results of the research show that the astonprinter.com website has 20 vulnerabilities that can be exploited, with details of 2 vulnerabilities which are in the high threat level, namely DNS Server Spoofed Request Amplification Ddos and Path Traversal, then it has 7 vulnerabilities which are in the medium threat level, including DNS Server Chace Snooping Remote Information Disclosure and Vulnerable Js Library and 11 vulnerabilities that are in the low threat level including ICMP Timestamp Request Remote Date Disclosure, SSH Server CBC Mode Ciphers Enabled, , Cookie No HttpOnly Flag and Cookie without SameSite Attribute. These findings can provide valuable insight for website managers in strengthening security systems and reducing the risk of cyber attacks in the future.]]>
<![CDATA[OPTIMIZING ANDROID MALWARE DETECTION USING NEURAL NETWORKS AND FEATURE SELECTION METHOD ]]>
<![CDATA[Bintoro, Jevan]]>;
<![CDATA[Rafrastara, Fauzi Adi]]>;
<![CDATA[Latifah, Ines Aulia]]>;
<![CDATA[Ghozi, Wildani]]>;
<![CDATA[Yassin, Warusia]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3898
<![CDATA[Malware poses a serious threat to Android security systems. In recent years, Android malware has rapidly evolved, employing obfuscation techniques such as polymorphic and metamorphic. Unfortunately, signature-based malware detection cannot identify modern variants of Android malware. This study aims to compare various feature selection methods and machine learning algorithms to identify the most effective and efficient combination for classifying Android malware. The dataset used in this research is the Drebin dataset. Four classification algorithms are used in this comparison: Naive Bayes, Logistic Regression, Neural Network, and Random Forest. The best-performing algorithm is then implemented in three different scenarios: without feature selection, with Information Gain, and with Chi-Squared (X²). In the latter two scenarios, the appropriate number of features was selected using the backward elimination method. Both feature selections achieved the same performance, but Information Gain required fewer features. The evaluation metrics used in this study include AUC, accuracy, F1-score, training time, and testing time. Measuring training and testing time benefits the model by making it more efficient, thus allowing for faster detection in real-world applications. The results show that the combination of the Information Gain feature selection method and the Neural Network algorithm achieves the highest performance, with an accuracy and F1-Score of 98.6%. Additionally, this combination achieves a training time of 81.135 seconds and a testing time of 1.095 seconds. Compared to the Neural Network algorithm without feature selection, this combination results in a 17.7597 % reduction in training time and a 57.9977 % reduction in testing time while maintaining the same performance values. This research contributes to improving the speed and accuracy of malware detection systems, enhancing mobile security.]]>
<![CDATA[IMPROVING MALWARE DETECTION USING INFORMATION GAIN AND ENSEMBLE MACHINE LEARNING ]]>
<![CDATA[Ramadhani, Arsabilla]]>;
<![CDATA[Rafrastara, Fauzi Adi]]>;
<![CDATA[Rosyada, Salma]]>;
<![CDATA[Ghozi, Wildanil]]>;
<![CDATA[Osman, Waleed Mahgoub]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3903
<![CDATA[Malware attacks pose a serious threat to digital systems, potentially causing data and financial losses. The increasing complexity and diversity of malware attack techniques have made traditional detection methods ineffective, thus AI-based approaches are needed to improve the accuracy and efficiency of malware detection, especially for detecting modern malware that uses obfuscation techniques. This study addresses this issue by applying ensemble-based machine learning algorithms to enhance malware detection accuracy. The methodology used involves Random Forest, Gradient Boosting, XGBoost, and AdaBoost, with feature selection using Information Gain. Datasets from VirusTotal and VxHeaven, including both goodware and malware samples. The results show that Gradient Boosting, strengthened with Information Gain, achieved the highest accuracy of 99.1%, indicating a significant improvement in malware detection effectiveness. This study demonstrates that applying Information Gain to Gradient Boosting can improve malware detection accuracy while reducing computational requirements, contributing significantly to the optimization of digital security systems.]]>
<![CDATA[CLASSIFICATION OF PUBLIC SENTIMENT TOWARDS STUNTING PREVENTION PROGRAM USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE ON X APPLICATION ]]>
<![CDATA[Fitria, Desi]]>;
<![CDATA[Suryono, Ryan Randy]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.3998
<![CDATA[Indonesia has serious health problems, one of which is stunting among children. Stunting is caused by chronic malnutrition that affects a child's physical and cognitive growth. To address its impact, the government launched a prevention program that focuses on improving nutrition, improving sanitation, and health education. Public response to these programs has varied, with some supportive and others skeptical. In the digital age, public opinion is expressed through social media, making sentiment analysis important to understand public perception. This research aims to classify public sentiment towards the stunting prevention program using Naive Bayes and Support Vector Machine (SVM) methods. Data preprocessing includes cleaning, case folding, tokenizing, stopwords, and stemming, ensuring the text data is ready for analysis. The dataset consists of 5907 tweets divided by a ratio of 80:20, resulting in 4725 tweets for training data and 1182 tweets for testing data. The analysis results show that the Naive Bayes model achieved an accuracy of 95.34% for training data and 84.52% for testing data, while SVM achieved an accuracy of 95.43% for training data and 96.74% for testing data, indicating the performance of the SVM model is better than the Naïve Bayes model. The important impact of this research is to assist policymakers in understanding the public's perception of government programs so that they can design communication strategies.]]>
<![CDATA[SENTIMENT ANALYSIS OF CHATGPT APP USER REVIEWS USING SVM AND CNN METHODS ]]>
<![CDATA[Widaad, Nurul]]>;
<![CDATA[Anggraini, Dina]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4010
<![CDATA[The rapid development of Artificial Intelligence (AI) has significantly impacted various sectors, including user interactions with natural language-based applications such as ChatGPT. This study aims to analyze user sentiment towards ChatGPT amidst the emergence of alternative AI technologies like Gemini (Google Deep Mind), Claude (Anthropic AI), and LLaMA (Meta AI). ChatGPT was chosen as the research subject due to its role as a pioneer in public AI usage. The research focuses on uncovering user sentiments—positive, negative, or neutral. A total of 155,529 reviews from the Google Play Store were analyzed using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms. The research process involved data collection (data scraping), preprocessing (emoji removal, case folding, punctuation removal, tokenization, stopword removal, stemming, and normalization), sentiment labeling, data splitting (80% training and 20% testing), and model evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that the SVM model achieved an accuracy of 85%, with a precision of 0.83, recall of 0.55, and an F1-score of 0.58. Meanwhile, the CNN model recorded an accuracy of 84%, with a precision of 0.68, recall of 0.59, and an F1-score of 0.62. Among the analyzed reviews, 75% expressed positive sentiment, 18.22% negative, and 6.71% neutral. The dominance of positive sentiment reaffirms ChatGPT's position as a preferred choice among users, although certain aspects need improvement to maintain its competitiveness amidst growing AI competition. This study provides valuable insights for developers to identify the strengths and weaknesses of ChatGPT based on user feedback, enabling them to optimize the application's features to create a more satisfying and relevant user experience in the future.]]>
<![CDATA[IMPLEMENTATION OF JSON WEB TOKEN IN THE DEVELOPMENT OF VILLAGE MONOGRAPH DATABASE BASED ON RESTAPI ]]>
<![CDATA[Ramadhan, Akhmad]]>;
<![CDATA[Mulyati, Sri]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4028
<![CDATA[Currently, both central and local government institutions have been utilizing technological advancements to optimize services, strengthen partnerships, and maximize management efficiency. However, this has led to discrepancies in data across various government platforms due to the absence of an integrated database that can be linked with multiple institutional platforms. The development of an integrated database has become crucial to ensure data consistency and prevent discrepancies across different platforms. This study aims to develop an integrated database for village monographs by leveraging data transfer technology based on REST API and implementing security using JSON Web Token (JWT). The database development was carried out using the prototype development method, and testing was conducted using the black-box testing method. The results show that this research successfully developed a database capable of operating effectively on two different platforms, and the system developed adheres to the security standard of JSON Web Token. Therefore, this study can improve the efficiency of managing village monograph data while ensuring data protection during the transfer process between platforms.]]>
<![CDATA[COMPARISON OF NAÏVE BAYES CLASSIFIER, SUPPORT VECTOR MACHINE, RANDOM FOREST ALGORITHMS FOR PUBLIC SENTIMENT ANALYSIS OF KIP-K PROGRAM ON TWITTER ]]>
<![CDATA[Ali, Humaidi]]>;
<![CDATA[Hendrastuty, Nirwana]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4030
<![CDATA[The Kartu Indonesia Pintar Kuliah (KIP-K) program has become a hot topic of conversation on social media Twitter, with various public sentiments regarding its implementation. The program is regulated through Minister of Education and Culture Regulation (Permendikbud) No. 10/2020, which notes an increase in the number of recipients from 552,706 in 2020 to 985,577 in 2024. However, controversy has arisen due to the alleged misuse of KIP-K funds by some influencers to support lavish lifestyles. This study aims to compare the performance of Naive Bayes, Support Vector Machine, and Random Forest algorithms in classifying public sentiment towards the KIP-K program. The research dataset was obtained from Twitter with a total of 6,842 tweets collected using crawling techniques in the time span of 2023 to 2024. The dataset was then processed through the preprocessing stage to produce clean data. The three algorithms were tested to evaluate the accuracy of each model in predicting public sentiment. The test results show that the Random Forest algorithm has the best performance with 100% accuracy, followed by Support Vector Machine with 99% accuracy, and Naive Bayes with 91% accuracy after optimization (SMOTE). Based on these findings, Random Forest proved to be the most effective algorithm in classifying sentiments related to the KIP-K program. It is hoped that the results of this research can help the management of the KIP-K program to be more targeted by providing a better understanding of public perception.]]>
<![CDATA[OPTIMIZATION OF BACKTRACKING ALGORITHM WITH HEURISTIC STRATEGY FOR LOGIC-BASED SORTING PUZZLE GAME SOLVING ]]>
<![CDATA[Nuranti, Eka Qadri]]>;
<![CDATA[Intizhami, Naili Suri]]>;
<![CDATA[Hasanah, Primadina]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4031
<![CDATA[Puzzle Game Sorting is a logic-based puzzle game where players must transfer colored balls into tubes until each tube contains only one color. Although it appears simple, the game becomes increasingly challenging at higher levels, testing players’ logical thinking and patience. This study proposes using the backtracking algorithm combined with optimization strategies, such as conflict heuristics and lookahead, to address players’ challenges at advanced levels. The test results indicate that the optimized backtracking algorithm can solve the game faster and with more efficient steps compared to manual methods. Specifically, heuristic optimization strategies significantly improved performance, reducing execution time by up to 91.4% and the number of steps by up to 76.9% at the most complex levels. These findings demonstrate that combining the backtracking algorithm and optimization strategies is an effective solution for solving puzzles in Sorting, particularly at levels with increasing complexity.]]>
<![CDATA[ANALYSIS OF WIRELESS NETWORK SIMULATION BASED ON OPENWRT AND PFSENSE WITH QUALITY OF SERVICE INDICATORS ON LOW-COST NETWORK INFRASTRUCTURE ]]>
<![CDATA[Mishbahuddin, Ahmad]]>;
<![CDATA[Hatta, Puspanda]]>;
<![CDATA[Budiyanto, Cucuk Wawan]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4047
<![CDATA[The need for high network traffic in Indonesia with the challenge of geographical topology that is difficult to reach makes the majority of internet network users access via wireless networks with limited budgets, resulting in poor internet QoS in Indonesia. An available solution to optimize low-cost network quality is to use the OpenWRT, open source router operating system with a focus on ease of application implementation in network projects. Another solution is pfSense, which is an open source router operation with a firewall network security focus to prevent intrusion. These two operating systems have different comparison methods in performance testing, making it difficult to make decisions about the performance differences between the two operating systems. This study aims to analyze the difference in performance and significance of open source operating systems with different development focuses on low-cost wireless network services. Analysis obtained from the quality of service measurement method on OpenWRT and Pfsense router operating systems on users that connected to a simulated wireless network topology. Data was collected by sending data between a number of users to the server and vice versa using iperf3 and mtr tools. The data consisted of QoS parameters: troughput, delay, jitter, and packet loss. The data shows that there are significant differences in several QoS parameters in the service of a number of users between the OpenWRT and PfSense operating systems. The results of this study show the limitations of each operating system in its implementation in low-cost wireless networks.]]>
<![CDATA[COMPARATIVE ANALYSIS OF LSTM, BILSTM, GRU, CNN, AND RNN FOR DEPRESSION DETECTION IN SOCIAL MEDIA ]]>
<![CDATA[Muhammad Huda, Alam]]>;
<![CDATA[Shidik, Guruh Fajar]]>;
<![CDATA[Praskatama, Vincentius]]>
<![CDATA[ Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024 ]]>
Publisher : <![CDATA[Informatika, Universitas Jenderal Soedirman ]]>
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DOI: 10.52436/1.jutif.2024.5.6.4060
<![CDATA[The prevalence of mental health issues and the increasing use of social media provide an opportunity to leverage technology for early detection of depression. This study evaluates and compares five deep learning models, LSTM, BiLSTM, GRU, CNN, and RNN for detecting depressive tendencies from over 10,000 annotated social media messages. These models were trained on preprocessed data using standard techniques, including cleansing, tokenization, and padding. Evaluation metrics such as accuracy, precision, recall, and F1-score were utilized. BiLSTM emerged as the best-performing model with an accuracy of 98.45% and an F1-score of 96.37%, attributed to its bidirectional architecture for contextual analysis. In contrast, CNN achieved high precision (98.55%) but struggled with recall (15.14%), while RNN and GRU exhibited limitations in capturing complex patterns, with GRU showing no measurable performance. These findings establish BiLSTM as a robust tool for mental health monitoring. Future research could explore transformer-based models such as BERT or multilingual datasets for enhanced applicability.]]>
