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All Journal Jurnal Sains dan Teknologi Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Telematics and Informatics Jurnal Simantec Jurnal sistem informasi, Teknologi informasi dan komputer Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnal Informatika dan Teknik Elektro Terapan Jurnas Nasional Teknologi dan Sistem Informasi CESS (Journal of Computer Engineering, System and Science) Journal of Animation & Games Studies JOIN (Jurnal Online Informatika) Sistemasi: Jurnal Sistem Informasi Jurnal Pengabdian UntukMu NegeRI JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JRSI (Jurnal Rekayasa Sistem dan Industri) Jurnal Pilar Nusa Mandiri Faktor Exacta Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Information System JURNAL ILMIAH INFORMATIKA SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ULTIMATICS Surya Abdimas MIND (Multimedia Artificial Intelligent Networking Database) Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Antivirus : Jurnal Ilmiah Teknik Informatika JUTIS : Jurnal Teknik Informatika JISKa (Jurnal Informatika Sunan Kalijaga) JATI (Jurnal Mahasiswa Teknik Informatika) Joined Journal (Journal of Informatics Education JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of Innovation Information Technology and Application (JINITA) Jurnal Bakti Masyarakat Indonesia Innovation in Research of Informatics (INNOVATICS) Jurnal Dinamis Jurnal Teknik Informatika (JUTIF) Informatics and Digital Expert (INDEX) JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Ilmu Komputer untuk Masyarakat Abdi Teknoyasa Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Informatika dan Multimedia "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Tekmulogi: Jurnal Pengabdian Masyarakat SATIN - Sains dan Teknologi Informasi Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Intelmatics NERO (Networking Engineering Research Operation) Jurnal Informatika: Jurnal Pengembangan IT PADIMAS: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Siliwangi Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) International Journal of Informatics and Computing Jasmine : Journal of Intelligent Systems and Machine Learning El-Khidmat; Jurnal Pengabdian Masyarakat
Alam Rahmatulloh
Program Studi Informatika, Fakultas Teknik, Universitas Siliwangi
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Workshop Penerapan Teknologi Qrcode pada Aplikasi Elivestock di Bumdes Panji Boma Ciamis Irfan Darmawan; Alam Rahmatulloh; Rohmat Gunawan; Randi Rizal; Erna Haerani
ABDI KARYA : Pengabdian Kepada Masyarakat Vol. 3 No. 1 (2026): Februari: ABDI KARYA : Jurnal Pengabdian Kepada Masyarakat
Publisher : Akademi Kesejahteraan Sosial Ibu Kartini Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69697/abdikarya.v3i1.365

Abstract

Goat farming in Indonesia, especially at the small and medium farmer level, is still largely carried out traditionally. Recording of livestock data related to pedigree, birth, weight and others, has not been done well by some farmers. Good livestock management is important because it has a direct impact on productivity, livestock health, cessation of livestock businesses and the welfare of farmers. Based on these problems, in this community service activity, training was carried out on the use of livestock recording applications (eLivestock) with the application of QRCode technology. There are three main activities carried out in this community service activity, including: preparation, implementation, evaluation and reporting. The community service activity was carried out on Monday, June 16, 2025, starting at 13:30 until finished, located at Panji Boma Farm, Werasari Village, Sadananya District, Ciamis Regency, West Java. After carrying out this community service activity, the partners were interested in using the application, and planned to make several preparations related to: information flow, data availability, report formats, software & hardware support and management officers.
Comparative Analysis of LightFM and Neural Collaborative Filtering for Anime Recommendation Systems Farras, Daffa Haidar; Rahmatulloh, Alam
Intelmatics Vol. 6 No. 1 (2026): January-June (In Progress)
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v6i1.24815

Abstract

The rapid growth of digital entertainment content such as movies and anime poses challenges in providing relevant viewing recommendations to users. Recommendation systems are a crucial solution to improve the user experience when exploring thousands of titles across various platforms. This study develops and compares two main approaches to recommendation systems: LightFM and the Collaborative Method . Classic Filtering (CF), as well as Neural Collaborative Deep- based Filtering (NCF) learning . Evaluation was conducted using Precision@K and Recall@K metrics . The test results showed that NCF was able to provide more relevant recommendations, with a Precision@5 value of 0.7983, much higher than LightFM which only reached 0.1721. Although LightFM showed a high AUC value (0.9134), its performance in generating Top-K recommendations was still low. Thus, it can be concluded that modern neural network -based approaches such as NCF are more effective than classical methods in the context of anime recommendation systems.
IMPLEMENTASI INDOROBERTA UNTUK KLASIFIKASI SENTIMEN PADA MEDIA SOSIAL X TERHADAP PROGRAM MAKAN BERGIZI GRATIS Salma Nur Fithriyah; Alam Rahmatulloh
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 6, No 1 (2026): JURNAL JRIS EDISI JANUARI 2026
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol6no1.1066

Abstract

The MBG (Free Nutritious Meals) program remains a hot topic on social media, sparking a variety of comments, both positive and negative, regarding the policy. The diverse responses and comments on social media serve as a relevant data source and can be used as research objects. This study aims to classify sentiment in netizen posts and comments regarding the MBG program implemented in Indonesia. The classification model uses the IndoRoBERTa method, implemented within a sentiment analysis scheme, to classify sentiment in text. The process includes collecting social media comment text data, preprocessing, training the IndoRoBERTa model, and evaluating its performance. The results show that the developed sentiment classification model achieved an accuracy of 85.3% and an F1-score of 82.6%. Sentiment classification tends to produce negative sentiments from the overall text data.Program MBG (Makan Bergizi Gratis) masih menjadi salah satu topik yang masif di media sosial dan memicu berbagai komentar pro dan kontra terhadap kebijakan tersebut. Beragam respon dan komentar yang muncul di media sosial menjadi sebuah sumber data yang relevan dan bisa dijadikan sebagai sebuah objek penelitian. Penelitian ini, bertujuan untuk melakukan klasifikasi terhadap sentimen pada postingan dan komentar warganet terhadap program MBG yang diterapkan di Indonesia. Model Klasifikasi menggunakan metode IndoRoBERTa yang diimplementasikan dalam skema analisis sentimen untuk mengklasifikasi sentimen pada teks. Proses yang dilakukan mencakup pengumpulan data teks komentar di media sosial, tahap preprocessing, lalu implementasi model IndoRoBERTa dan mengukur hasil dari evaluasi kinerja model yang dibangun. Hasil penelitian menunjukkan nilai evaluasi model klasifikasi sentimen yang dikembangkan mencapai tingkat akurasi sebesar 85,3% dan nilai F1-score sebesar 82,6%. Klasifiksi sentimen cenderung menghasilkan sentimen berlabel negatif dari keseluruhan data teks.
Implementation of ECDSA and MLDSA Digital Signature Schemes for Transaction Authentication on Blockchain Ridwan Muhammad Raihan; Alam Rahmatulloh
ULTIMATICS Vol 18 No 1 (2026): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v18i1.4516

Abstract

Blockchain technology serves as a core foundation for decentralized systems that demand high levels of security, transparency, and data integrity. However, the advancement of quantum computing introduces substantial risks to classical cryptographic algorithms such as the Elliptic Curve Digital Signature Algorithm (ECDSA), which is vulnerable to attacks targeting the discrete logarithm problem. This study proposes a hybrid digital signature scheme that integrates ECDSA with the Module Lattice-based Digital Signature Algorithm (MLDSA) to strengthen blockchain transaction authentication against both classical and quantum threats. Experimental results shown that the hybrid scheme achieves efficient performance, with an average verification time of 0.647 ms and a signing time of 0.806 ms. Although the resulting signature size reaches 6761 bytes due to the concatenation of two signatures, the hybrid approach successfully provides dual-layered cryptographic protection capable of maintaining transaction authenticity and integrity in a post-quantum environment. These findings highlight the feasibility of adopting hybrid digital signatures in blockchain systems. Future work may focus on optimizing signature size through compression techniques to improve scalability and reduce payload overhead.
Comparison of SVM, Naive Bayes, and Logistic Regression for LinkedIn Reviews Sentiment Analysis Nadhilah Hazrati; Alam Rahmatulloh
JASMINE: Journal of Intelligent Systems and Machine Learning Vol. 1 No. 1 (2026)
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.v1i1.10214

Abstract

The rapid development of digital technology has transformed the way people search for jobs, with LinkedIn emerging as the world’s largest professional social media platform. Many users express their opinions about the application through reviews on the Google Play Store, reflecting both positive and negative sentiments regarding their experiences. This study aims to conduct sentiment analysis on LinkedIn user reviews by comparing three classification algorithms: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. The research process involves data collection, text preprocessing, feature extraction, and model evaluation using accuracy, precision, recall, and F1-score metrics. The results indicate that all three algorithms are capable of classifying sentiments effectively, with Logistic Regression achieving the best performance, obtaining an accuracy of 88.53%, a precision of 94% for negative reviews and 83% for positive reviews, as well as a recall of 84% for negative reviews and 94% for positive reviews. In comparison, SVM achieved an accuracy of 87.79%, while Naïve Bayes reached 83.44%. These findings highlight that Logistic Regression outperforms the other models in sentiment analysis of LinkedIn reviews, making it a reliable method for understanding user perceptions and supporting application improvement.
ANALISIS DAMPAK PERMAINAN MOBA TERHADAP SIKAP MANUSIA MENGGUNAKAN K-MEANS CLUSTERING Geri Nugraha; Alam Rahmatulloh
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 1 (2025): JATI Vol. 9 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i1.12368

Abstract

In this study, we explore the impact of MOBA games, specifically Mobile Legends and Honor of Kings, on players' attitudes and behaviors. The main focus discussed is the tendency for anger and aggressiveness to emerge during play. The purpose of this study is to understand the relationship between frequency of play and social attitudes and interactions between players. The methods we applied included a survey to collect data, as well as the application of the K-Means Clustering algorithm to group players based on their game characteristics. The results of the analysis showed that 65% of respondents felt less irritable after playing, while 70% reported improved teamwork skills. Visualization of the data through scatter plots, heatmaps, and centroid plots provided clear insights into player behavior patterns and stress levels experienced after playing. Through the application of K-Means Clustering, we found a significant trend between playing frequency and aggressive attitudes, with 55% of frequent players exhibiting high levels of anger. Thus, this study is expected to provide a deeper understanding of how MOBA games affect individual attitudes as well as social interactions among young players.
ANALISIS KERENTANAN CVE-2025-6019 PADA LIBBLOCKDEV LINUX MENGGUNAKAN PENDEKATAN VULNERABILITY ASSESSMENT Ardhan Dimas Nuryadin; Alam Rahmatulloh
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 1 (2026): JATI Vol. 10 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i1.16648

Abstract

Sistem operasi Linux merupakan salah satu fondasi utama dalam infrastruktur komputasi modern, sehingga keamanan setiap komponen intinya menjadi aspek yang sangat krusial. Salah satu ancaman yang paling sering muncul pada ekosistem ini adalah Local Privilege Escalation (LPE), di mana pengguna dengan hak terbatas dapat memperoleh akses administratif penuh melalui celah logika dalam sistem. Penelitian ini menyajikan studi kasus teknis mengenai kerentanan CVE-2025-6019, sebuah celah LPE yang ditemukan pada pustaka libblockdev, yang berperan penting dalam manajemen perangkat blok dan diakses melalui layanan udisks2 dan Polkit. Dengan menggunakan pendekatan Vulnerability Assessment, penelitian ini menilai karakteristik teknis, tingkat risiko, serta efektivitas mitigasi yang diterapkan vendor. Data dianalisis dari sumber resmi seperti Red Hat Security Advisory, Bugzilla, dan National Vulnerability Database (NVD), disertai verifikasi empiris melalui Proof-of-Concept (PoC) pada lingkungan terisolasi. Hasil penelitian menunjukkan bahwa kerentanan ini memungkinkan pengguna lokal mengeksekusi berkas SUID-root melalui mekanisme mount udisks2 tanpa validasi hak akses yang memadai, sehingga menghasilkan eskalasi hak ke konteks root. Berdasarkan perhitungan CVSS v3.1, kerentanan ini dikategorikan High Severity dengan skor 7.0, berdampak pada kerahasiaan, integritas, dan ketersediaan sistem.
Evaluasi Efektivitas Penggunaan FastText Embedding dan LSTM Networks dalam Deteksi Phishing Email Sheptianna Healtha Rukiman; Alam Rahmatulloh
Faktor Exacta Vol 18, No 2 (2025)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v18i2.26769

Abstract

Phishing emails represent a significant cyber threat, necessitating advanced detection methods. This study evaluates a model combining FastText word embedding and a Long Short-Term Memory (LSTM) neural network to identify these threats. Using a public dataset from Kaggle, the model was trained on 80% of the data and tested on the remaining 20%. The methodology included data preprocessing, vectorization with FastText to capture sub-word information, and sequential pattern recognition using the LSTM architecture. Performance was evaluated using accuracy, precision, recall, and F1-Score, with the model achieving a 92% detection accuracy. Key challenges identified include class imbalance and high computational requirements. Future research could focus on model optimization and data augmentation techniques to further enhance detection performance and address these limitations.
Web-Based Deepfake Detection Using VERITAS: Integrating Vision-Based Excitation with Transformer-Driven Intelligence Alam Rahmatulloh; Herman Dwi Surjono; Fatchul Arifin; Rohmat Gunawan; Randi Rizal
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.
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.
Co-Authors Aditya, Hafish Naufal Aemy, Nandhitta Albi Fitransyah Aldy Putra Aldya Alpan Hikmat Muharram Permana Amaludin, Luthfi Andi Nur Rachman Andi Nurachman Anggi Putri Meriani Anggi Putri Meriani Anjar Ginanjar ANWAR, FAHMI Aradea, A Ardhan Dimas Nuryadin Asep Kurniawan Asep Rizki Maulana Asih, Dwi Ramti Aulia, Karina A’izzah, Virra Retnowati Budi Permana Darmawan, Muhamad Aditya Dewi Rahmawati Dita Pramesti Dodi Muhamad Kodar Dwi Ramti Asih Eka Wahyu Hidayat Eka Wahyu Hidayat El Akbar, R Reza El-Akbar, R Reza Elmeftahi, Souhayla Ernawati, Rita Sri Faisal Al Isfahani Faisal Muhammad Dzikry Faisal, Fikri Ahmad Faridah Hanifah Farras, Daffa Haidar Fatchul Arifin Ficry Cahya Ramdani Firdaus, Muhamad Akbar Firmansah, Teguh Anugrah Firmansyah MSN Firmansyah, Faldi Ramadhan Fuji Nugraha Gagan Akhmad Fauzi Galih Permana Genta Nazwar Tarempa Geri Nugraha Ginting, Muliani Guna, Nandana Surya Gunawan, Rohmat Gunawan, Rohmat Gunawan, Rohmat Haerani, Erna Heni Sulastri Heni Sulastri Herlambang, Andriana Herman Dwi Surjono Hidayat, Deri Kurnia Hidayat, Eka Wahyu Hilman Septian Husen Husen husen husen Husen, - Ihsanuddin Ihsan Ikhsan Nur Rizkiana Ilham Yuslin Anugrah Indra Sontana Iqbal Muhammad Fajar Nuralam Irfan Darmawan Ivang Fahmi Fauzi Kodar, Dodi Muhamad Komara Kusumah, R Herick Fauzi Kusumah, R Herick Fauzi Komara Laely Armiyati Lapandu, Raihan Azhar Leka Destrilia Leviana, Rika Maulana, Dikri Merdiriyani, Sindy Meriani, Anggi Putri Mochamad Dzikri Daely Muhammad Quraish Shihab Muhammad Ramadhan, Galih Muhammad Saiful Anwar Nadhilah Hazrati Neng Ika Kurniati Neng Ika Kurniati Neng Ika Kurniati Neng Ika Kurniati Nida, Siti Nabilah Nugraha, Cindera Syaiful Nugroho, Rizal Nur Widiyasono Nur Widiyasono, Nur Perkasa, Mochamad Althaf Pramasetya Popy, Popy Anisa Purwayoga, Vega Putra, Aldy R. Reza El Akbar R. Wahjoe Witjaksono Rachman, Andi Nur Rahmi Nur Shofa Rahmi Nur Shofa Rahmi Nur Shofa Rakhman, Maulana Decky Ramdani, Setiadi Randi Rizal Rianto, Rianto Ridwan Muhammad Raihan Ridwan Nur Qomar Rifki Mubarok Riki Ahmad Fauji Rita Sri Ernawati Rizal Nugroho Rizal, Randi Rizal, Randi Rizkie Irfan Awaludin Rochim, Rachma Verina Rohmat Gunawan Rohmat Gunawan Ruuhwan Salma Nur Fithriyah Salman, Ade Selamat, Siti Rahayu Septian, Hilman Setiawan, Muhamad Bayu Setiawan, Perdi Sheptianna Healtha Rukiman Shofa, Rahmi Nur Sholihat Ruhaedi, Hedy Sulastri, Heni Sulastri, Heni Sulastri, Heni Virra Retnowati A’izzah Visi Tinta Manik Warih Puspitasari Yuliasari, Silpani Yuzar, Arnefia ZK Abdurahman Baizal