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All Journal JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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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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.
Implementation of the Apriori Algorithm on Outdoor Equipment Rental Transaction Data Based on Clustering Using the K-Means Algorithm Rizal, Randi; Ruuhwan, Ruuhwan; Al Husaini, Muhammad; Nursamsi, Dede Rizal; M, Meto Rizki
IJAIT (International Journal of Applied Information Technology) Vol 08 No 02 (November 2024)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v8i2.6367

Abstract

Outdoor equipment rental services play a critical role in meeting climbers’ needs prior to expeditions. Sustaining business continuity in this sector requires effective marketing strategies, particularly given the increasing market competition. This study employs data mining techniques to analyze rental transaction data and identify patterns that support strategic decision-making. Specifically, clustering is performed using the K-Means algorithm to group transactions with similar attributes, followed by association rule mining using the Apriori algorithm within each cluster. A dataset comprising 1,276 valid transactions was processed, resulting in three clusters containing 324, 264, and 688 records, respectively, with an accuracy of 0.998. Apriori analysis generated 13 association rules in Cluster 0 and 2 rules in Cluster 1, while no rules met the minimum support and confidence thresholds in Cluster 2 or the overall dataset. These findings demonstrate that clustering prior to association rule mining can uncover meaningful patterns that are not evident in aggregated data. Such insights can inform targeted marketing strategies, including recommendations for item combinations frequently rented together. Future research may integrate alternative algorithms such as ECLAT or FP-Growth and explore framework-based systems to enhance scalability and precision in data-driven decision-making.
Improving Emotion Recognition Accuracy with Combination of Bidirectional and Long Short-Term Memory Models Haerani, Erna; Rahmatulloh, Alam; Rizal, Randi
JICO: International Journal of Informatics and Computing Vol. 1 No. 2 (2025): November 2025
Publisher : IAICO

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Emotions play a vital role in shaping human behavior and mental health, making accurate emotion recognition essential for mitigating potential negative impacts. This study explores the application of Bidirectional Long Short-Term Memory (Bi-LSTM) for recognizing emotions from text-based data. Bi-LSTM extends the standard LSTM by enabling the model to process input sequences in both forward and backward directions, thereby capturing contextual dependencies more effectively. The research methodology consists of data collection, manual emotion labeling, and pre-processing techniques, including stemming, tokenization, and one-hot encoding. Visualization of the dataset and the distribution of labeled emotions was conducted to gain deeper insights into the data. The Bi-LSTM model was trained for 25 epochs, achieving a training accuracy of 0.9954 and validation accuracy of 0.8790, along with a training loss of 0.0133 and validation loss of 0.658. A confusion matrix was used to further evaluate model performance and classification accuracy across various emotion categories. The experimental results confirm that the Bi-LSTM model is highly effective in recognizing emotions from textual input. Its ability to capture long-term dependencies in both directions contribute to improved learning and prediction. However, opportunities for enhancement remain, particularly in refining the model architecture, expanding the dataset, and exploring additional feature extraction techniques. This research demonstrates the potential of Bi-LSTM in building intelligent emotion-aware systems for applications in mental health monitoring, customer feedback analysis, and human-computer interaction.
Web-Based Deepfake Detection Using VERITAS: Integrating Vision-Based Excitation with Transformer-Driven Intelligence Alam Rahmatulloh; Surjono, Herman Dwi; Arifin, Fatchul; Gunawan, Rohmat; Rizal, Randi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7320

Abstract

This study proposes a web-based deepfake detection system that integrates Vision-Based Excitation technology and Transformer-based intelligence, called VERITAS (Vision-based Excitation and Robust Intelligence for Transformer-Assisted Deepfake Detection). The system is designed to automatically detect manipulated images and videos by leveraging the Vision Transformer (ViT) model architecture, equipped with the Grad-CAM mechanism for interpretability of detection results. The study conducted a series of tests to measure the system's performance in various scenarios and ensure its reliability in dealing with various types of input. Load testing results showed that up to 30 simultaneous users, the system can operate with good responsiveness (average response time of 130 ms) without experiencing errors. However, when the number of users reaches 40 or more, the system performance drops drastically with a very high error rate, reflecting limitations in handling server load. Real-world testing showed the system can detect deepfakes with an accuracy of 73.61%, with results varying depending on the quality of the tested images. Furthermore, unit functional testing and coverage analysis demonstrated an excellent test pass rate (85%), with all major functions running smoothly and error handling needed to be fixed in some code sections. Overall, the VERITAS system demonstrates strong potential for web-based deepfake detection, with high reliability under low load and adequate performance in functional testing. However, further optimization is needed to handle higher user loads.
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 Fatchul Arifin Genta Nazwar Tarempa Gunawan, Rohmat Haerani, Erna Herman Dwi Surjono 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