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All Journal JOIV : International Journal on Informatics Visualization JURNAL ILMIAH INFORMATIKA Warta Pengabdian Jurnal Informatika Universitas Pamulang JUMANJI (Jurnal Masyarakat Informatika Unjani) MIND (Multimedia Artificial Intelligent Networking Database) Journal Jurnal ICT : Information Communication & Technology JISKa (Jurnal Informatika Sunan Kalijaga) IJAIT (International Journal of Applied Information Technology) JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Ilmu Komputer untuk Masyarakat Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Journal of Informatics and Computing
Rizal, Randi
Informatika, Universitas Siliwangi, Tasikmalaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
VISUAL ENTITY OBJECT DETECTION SYSTEM IN SOCCER MATCHES BASED ON VARIOUS YOLO ARCHITECTURE Althaf Pramasetya Perkasa, Mochamad; El Akbar, R. Reza; Al Husaini, Muhammad; Rizal, Randi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.2015

Abstract

In this study, a performance comparison between the YOLOv7, YOLOv8, and YOLOv9 models in identifying objects in soccer matches is conducted. Parameter adjustments based on GPU storage capacity were also evaluated. The results show that YOLOv8 performs better, with higher precision, recall, and F1-score values, especially in the "Ball" class, and an overall accuracy (mAP@0.5) of 87.4%. YOLOv9 also performs similarly to YOLOv8, but YOLOv8's higher mAP@0.5 value shows its superiority in detecting objects with varying degrees of confidence. Both models show significant improvement compared to YOLOv7 in overall object detection performance. Therefore, based on these results, YOLOv8 can be considered as the model that is close to the best performance in detecting objects in the dataset used. This study not only provides insights into the performance and characteristics of the YOLOv7, YOLOv8, and YOLOv9 models in the context of object detection in soccer matches but also results in a dataset ready for additional analysis or for training deep learning models.
ENSEMBLE MACHINE LEARNING WITH NEURAL NETWORK STUNTING PREDICTION AT PURBARATU TASIKMALAYA Al-Husaini, Muhammad; Lukmana, Hen Hen; Rizal, Randi; Puspareni, Luh Desi; Hoeronis, Irani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2421

Abstract

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%..
MAnTra: A Transformer-Based Approach for Malware Anomaly Detection in Network Traffic Classification Rizal, Randi; Darmawan, Muhamad Aditya; Selamat, Siti Rahayu; Rahmatulloh, Alam; Haerani, Erna; Tarempa, Genta Nazwar
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5462

Abstract

Cybersecurity is a critical priority in the ever-evolving digital era, particularly with the emergence of increasingly sophisticated and difficult to detect malware. Traditional detection techniques, such as static and dynamic analysis, are often limited in their ability to recognize novel and concealed malware that poses a threat to security systems. Consequently, this study investigates the potential of Transformer models for network traffic classification to detect anomalies associated with malware activity. The proposed approach emphasizes retrospective analysis, wherein the model is evaluated across various platforms and datasets encompassing different virus variants. By incorporating diverse types of malwares into the training data, the model is better equipped to identify a range of attack patterns. The Transformer model employed in this study was trained over 30 epochs. The evaluation results demonstrated excellent performance, achieving a training accuracy of 99.16% and a test accuracy of 99.32%. The very low average loss value of 0.01 indicates that the model effectively reduces classification errors. These findings underscore the potential of Transformer models as an efficient method for malware detection, offering greater accuracy and speed compared to traditional approaches. The results further reveal that the Transformer exhibits strong capabilities in handling sequential data, which is highly relevant to the dynamic nature of network traffic. For future research, it is recommended to explore the scalability of this method in larger network environments and assess its effectiveness in real-time detection scenarios. Expanding its application could establish the Transformer model as a more reliable and efficient solution for identifying evolving malware threats, thereby enhancing overall network security. This approach presents a robust framework for protecting systems and data against increasingly complex cyber threats.
Comparative Sentiment Analysis of YouTube Comments on Indonesia's Electric Vehicle Incentive Policy Using TF-IDF and IndoBERTweet Models Chairat, Arief Suardi Nur; Rizal, Randi; Himawan, Hidayatulah
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5499

Abstract

Indonesia’s battery electric vehicle (KBLBB) incentives aim to accelerate low-carbon mobility, yet public reactions regarding affordability, charging infrastructure readiness, and subsidy equity remain highly heterogeneous. This research systematically compares classical machine learning and transformer-based models for classifying sentiment in 1,516 YouTube comments discussing the incentive policy and broader EV ecosystem. Comments are collected via web scraping and processed through filtering, case folding, normalization, tokenization, stopword removal, stemming, lexicon-based sentiment labelling, TF-IDF bigram vectorization, random oversampling, and hyperparameter optimization with GridSearch. Support Vector Machine and Random Forest serve as baseline models, while Logistic Regression with TF-IDF bigram and IndoBERTweet represent advanced approaches that exploit richer feature representations. Results show that the baseline models achieve around 65–66% accuracy, Logistic Regression improves performance to 88%, and IndoBERTweet attains the highest accuracy of 94% with balanced precision, recall, and F1-score across sentiment classes. Sentiment distribution indicates that 63.3% of comments are negative, dominated by concerns over limited charging networks, high upfront costs, and perceived unfairness of public subsidies, while 36.7% of comments highlight support for cleaner transportation, technological innovation, and national industrial competitiveness. These findings demonstrate that transformer-based contextual embeddings substantially enhance sentiment classification for noisy Indonesian social media text and provide a scalable informatics tool for continuous monitoring of EV policy reception. The resulting empirical evidence can inform more targeted refinements of incentive design, infrastructure planning, and communication strategies, thereby supporting inclusive, data-driven, and sustainable KBLBB adoption across diverse demographic groups and evolving policy scenarios nationwide over time.
Co-Authors Al Husaini, Muhammad Al-Husaini, Muhammad Alam Rahmatulloh Aldya, Aldy Putra Althaf Pramasetya Perkasa, Mochamad Amien, Nashihun Candra, Fikri Chairat, Arief Suardi Nur Darmawan, Muhamad Aditya Dina Lestari, Dina El-Akbar, R Reza Faisal Muhammad Dzikry Faiz, Muhammad Genta Nazwar Tarempa Gunawan, Rohmat Haerani, Erna Hidayatulah Himawan Impron, Ali Indra Karyana Irani Hoeronis Komarudin, Arif Maulana Linda Sutriani Lukmana, Hen Hen M, Meto Rizki Mas’ud, Mohd. Zaki Nugraha, Cindera Syaiful Nur Widiyasono, Nur Nursamsi, Dede Rizal Nursihab, Cucu Nusamsi, Dede Rizal Oumarou, Hayatou Pendit, Ulka Chandini Purwayoga, Vega Puspareni, Luh Desi R. Reza El Akbar Rahmi Nur Shofa Ramli, Nuraminah Rasi Nuraeni Rianto, Rianto Rifki Maulana Insan Rizal Kurniawan Rochim, Rachma Verina Ruuhwan Ruuhwan Selamat, Siti Rahayu Septian Chandra Siti Annisa, Siti Siti Yuliyanti, Siti Sudiarjo, Aso Syadza Anggraini Tiara Komala Sutra, Melanda Yahdi Siradj Yusuf Sumaryana