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All Journal International Journal of Electrical and Computer Engineering Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal of Education Technology Jurnal Tekno Insentif JURTEKSI EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) Bulletin of Social Informatics Theory and Application Jurnal Riset Teknik Komputer CSRID Southeast Asia Journal on Open and Distance Learning
Rujianto Eko Saputro
Universitas Amikom Purwokerto
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process Saputro, Rujianto Eko; Utomo, Fandy Setyo; Wanti, Linda Perdana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1065

Abstract

Cognitive Diagnosis Models (CDMs) in Computerized Adaptive Testing (CAT) are widely used to assess students’ cognitive abilities; however, existing approaches face significant limitations. The Latent Trait Model often suffers from specification errors due to its complexity, the Diagnostic Classification Model encounters difficulties in integrating hierarchical structures, and Deep Learning Models demand substantial computational resources. To address these challenges, this study introduces Salmon Salar Optimization (SSO) to enhance CDM performance and integrates the Partially Observable Markov Decision Process (POMDP) to improve dynamic question selection. The proposed adaptive testing framework comprises three components: preprocessing, CDM, and a selection algorithm. Experimental results on the ASSISTments 2009-2010 dataset demonstrate that SSO outperforms representative baselines from both deep learning: Neural CD and Latent Trait Model: MIRT approaches. Using 5-fold cross-validation, the proposed model achieved superior predictive performance with 75.51% accuracy and an AUC of 0.8191, highlighting its robustness compared to existing state-of-the-art methods. Furthermore, adaptive test simulations reveal that the SSO- and POMDP-based model delivers superior outcomes, attaining 80.3% accuracy with a reward of 8.03 for 10-question exams and 79.8% accuracy with a reward of 11.97 for 15-question exams. These findings confirm the effectiveness of the proposed model in enhancing cognitive diagnosis and adaptive testing performance.
Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X Slamet Endro Prianto; Berlilana Berlilana; Rujianto Eko Saputro
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1371

Abstract

The rapid growth of e-commerce has made personalized product recommendations a crucial aspect of enhancing customer satisfaction and boosting sales. However, many small-to-medium-sized retail businesses, like Adiva Fashion Store, still rely on manual product selection through customer searches or seller recommendations, which often leads to challenges in meeting customer preferences. This study presents a case study of Adiva Fashion Store, where the Collaborative Filtering method was implemented to develop a personalized clothing product recommendation system. The item-based Collaborative Filtering approach was employed to calculate the similarity between products based on customer ratings and transaction history. These similarity values were then used to predict customer preferences for products that had not yet been purchased. The system was developed using the Waterfall methodology, which involved needs analysis, system design, implementation, testing, and maintenance. The results show that the recommendation system significantly improved the relevance of product suggestions, helping customers make better purchasing decisions and increasing sales effectiveness. This case study illustrates how data-driven recommendation systems can be effectively integrated into small-to-medium-sized retail environments, providing valuable insights for other businesses aiming to adopt similar strategies.
Utilitarian vs Human-Centered AI Acceptance: Explaining Students’ Adoption of ChatGPT in Higher Education Parameswara, Dwi Angesti Dinda; Berlilana, Berlilana; Saputro, Rujianto Eko
Jurnal Pendidikan Informatika (EDUMATIC) Vol 10 No 1 (2026): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v10i1.34218

Abstract

The growing use of generative artificial intelligence (AI) in higher education raises questions about how students assess and adopt these systems, particularly whether traditional utilitarian models are sufficient to explain their use. This study compares the Technology Acceptance Model (TAM) and the Human-Centered AI Acceptance Model (HCAIAM) in explaining students’ behavioral intention to use ChatGPT, while examining how functional and human-centered factors operate within the same framework. A cross-sectional design was used, involving 100 undergraduate students in Indonesia selected through convenience sampling, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that TAM provides stronger explanatory power and better model fit (R² = 0.765; SRMR = 0.073) than HCAIAM (R² = 0.709; SRMR = 0.136). Perceived usefulness and perceived ease of use emerge as the main drivers of intention, indicating that students tend to use ChatGPT primarily as a tool to support academic tasks. In contrast, human-centered factors such as transparency and ethical alignment influence intention indirectly through trust and attitude. The autonomy construct shows weak reliability and overlaps with other variables, suggesting limitations in its measurement. These findings indicate that utilitarian factors remain central in this context, while human-centered aspects play a more conditional role, and point to a layered pattern of AI acceptance in which different types of factors operate at different levels.
Pengembangan Sistem E-Learning Inklusif Cerdas untuk Tuna Netra dengan Integrasi Teknologi Voice Command dan Text-To-Speech Fandy Setyo, Utomo; Saputro, Rujianto Eko; Baihaqi, Wiga Maulana; Sarmini; Berlilana; Aptana, Naufal Yogi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.132

Abstract

Aksesibilitas menjadi tantangan utama dalam e-learning bagi penyandang tunanetra karena keterbatasan visual dalam memahami konten digital. Penelitian ini bertujuan mengembangkan sistem e-learning inklusif dengan fitur voice command dan text-to-speech untuk mendukung interaksi non-visual. Pengembangan sistem dilakukan menggunakan metode Agile Scrum dalam beberapa siklus sprint, yang mencakup tahapan product backlog, sprint planning, daily scrum, sprint review, dan sprint retrospective. Sistem dirancang dalam arsitektur tiga lapisan, dengan React.js pada sisi klien dan Node.js pada sisi server, serta mengintegrasikan layanan API Gemini untuk pemrosesan suara dan teks ke audio. Validasi sistem dilakukan secara internal melalui dokumentasi sprint review dan skenario pengujian teknis. Hasil dokumentasi sprint menunjukkan bahwa fitur ini berfungsi sesuai dengan skenario pengujian internal dan berpotensi meningkatkan aksesibilitas. Meskipun belum dievaluasi langsung oleh pengguna tunanetra, hasil pengembangan awal ini memberikan fondasi penting untuk pengujian lebih lanjut dan pengembangan sistem e-learning yang lebih inklusif.   Absctract Accessibility is a major challenge in e-learning for visually impaired individuals due to visual limitations in understanding digital content. This study aims to develop an inclusive e-learning system with voice command and text-to-speech features to support non-visual interaction. The system was developed using the Agile Scrum method in several sprint cycles, which included the product backlog, sprint planning, daily scrum, sprint review, and sprint retrospective stages. The system is designed with a three-layer architecture, using React.js on the client side and Node.js on the server side, and integrates the Gemini API service for voice and text-to-audio processing. System validation was conducted internally through sprint review documentation and technical testing scenarios. The sprint documentation results indicate that this feature functions according to internal testing scenarios and has the potential to improve accessibility. Although it has not yet been directly evaluated by visually impaired users, these initial development results provide an important foundation for further testing and the development of a more inclusive e-learning system.
Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS Yusoff, Azizul Mohd; Salam, Sazilah; Mohamad, Siti Nurul Mahfuzah; Saputro, Rujianto Eko
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i3.pp1319-1328

Abstract

The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.
Perancangan Video Animasi 3D Menggunakan Metode MDLC untuk Meningkatkan Pemahaman Materi IPAS Wasihatun Hasanah; Rujianto Eko Saputro; Dinar Mustofa
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3277

Abstract

Pembelajaran Ilmu Pengetahuan Alam dan Sosial (IPAS) pada materi Tata Surya memiliki karakteristik abstrak sehingga sering menimbulkan kesulitan pemahaman bagi siswa tingkat sekolah dasar apabila disampaikan melalui media konvensional seperti buku teks dan Lembar Kerja Siswa (LKS). Penelitian ini bertujuan untuk merancang media pembelajaran berupa video animasi tiga dimensi (3D) menggunakan metode Multimedia Development Life Cycle (MDLC) sebagai upaya meningkatkan pemahaman siswa terhadap bentuk dan susunan Tata Surya. Pengembangan media dilakukan melalui tahap MDLC yang menghasilkan animasi 3D yang menggambarkan Matahari serta delapan planet beserta ciri-ciri masing-masing. Efektivitas media dievaluasi dengan menggunakan desain pretest–posttest pada siswa kelas VI di MI Nurul Iman Glempang. Hasil pengujian menunjukkan peningkatan nilai rata-rata kelas dari 52,7 pada pretest menjadi 80,9 pada posttest, dengan kenaikan sekitar 28 poin. Temuan ini mengindikasikan bahwa penggunaan video animasi 3D yang didasarkan pada MDLC dapat secara efektif meningkatkan pemahaman konseptual siswa pada materi IPAS yang memiliki karakteristik abstrak. Penelitian ini memberikan kontribusi sebagai acuan untuk pengembangan media pembelajaran digital yang berbasis visualisasi ruang dan dapat dijadikan pilihan alternatif untuk bahan ajar yang inovatif dalam pembelajaran IPAS di jenjang Madrasah Ibtidaiyah.
Co-Authors Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adiya, Az Zahra Dwi Nur Afriansyah, Fery Aimah, Samsul Aptana, Naufal Yogi Arif Mu'amar Wahid Aulia Hamdi Azhari Shouni Barkah Bagaskoro, Galih Baihaqi, Wiga Maulana Berlilana Berlilana Berlilana Cahyo, Samsul Dwi Chyntia Raras Ajeng Widiawati Damayanti, Wenti Risma Dani Arifudin Darmono Deasy Komarasary Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dinar Mustofa Ely Purnawati Ely Purnawati, Ely Embong Octavianto Fandy Setyo Utomo Fandy Setyo, Utomo Fatudin, Arif Faturama, Rafi Febriansyah Husni Adiatma Febrianti, Diah Ratna Fery Afriansyah Giat Karyono Hasna Salsa Dhia hidayatulloh, hanif Ikmah Ikmah Ikmah, Ikmah Ilham, Rifqi Arifin Indriyani, Ria Irwansyah Munandar Ismail, Dimas Shafa Malik Junianto, Haris Kusuma, Bagus Adhi Latif, Imam Sofarudin Lughri Wijaya Pamungkas Maharani, Revalyna Octavia Maulana Baihaqi, Wiga Millatul Izza, Nia Mohamad, Siti Nurul Mahfuzah Mohd. Hafiz Zakaria Munandar, Irwansyah Nanjar, Agi Ndari, Arum Vika Nia Millatul Izza Novita Eka Ramadhani Nurfaizi, Maulana Nurmalitasari, Gupita Octavianto, Embong Pandu W, Muhammad Arfianto Parameswara, Dwi Angesti Dinda Prasetyo, Agung Pungkas Subarkah Purwadi Purwadi R. Vitto Mahendra Putranto Radeta Tea Makdatuang Ramadhan, Rio Fadly Ria Indriyani Rizqi Aulia Widianto Rohmah, Umdah Aulia Rosana Fadila Sari safitri feriawan, Titi Salam, Sazilah Salsa Dhia, Hasna Samsul Aimah Saputra , Dhanar Intan Surya Saputra, Alfin Nur Aziz Saputri, Inka Sari, Rida Purnama Sarmini Sarmini - Sarmini Sarmini Sarmini Sazilah Salam Serli, Serli Shendy Filanzi Slamet Endro Prianto Sofa, Nur Sri Hartini Suliswaningsih, Suliswaningsih Syahputra, Akhmal Angga Tanzilla, Armeyta Putri Tarwoto, T Tea Makdatuang, Radeta Titi Safitri Maharani Toni Anwar Turino, Turino Wahyuni, Irmawati Tri Wanti, Linda Perdana Wasihatun Hasanah Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy Yusoff, Azizul Mohd