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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 RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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) Journal of Innovation Information Technology and Application (JINITA) 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) Padma: Jurnal Pengabdian Kepada Masyarakat 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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Performance Comparison Of Xgboost Lightgbm And Lstm For E-Commerce Repeat Buyer Prediction Nugroho, Lustiyono Prasetyo; Saputro, Rujianto Eko; Utomo, Fandy Setyo
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5746

Abstract

Repeat buyer behavior is a critical indicator of customer retention success in e-commerce platforms. However, accurately predicting repeat buyers remains a challenging problem due to the complexity of user behavior patterns and the temporal characteristics embedded in interaction data. Existing studies often focus on single modeling approaches or limited sequence exploration, resulting in insufficient comparative insight between ensemble-based machine learning and sequence-based deep learning models. Therefore, this study aims to systematically compare the performance of tree-based ensemble models (XGBoost and LightGBM) and a sequence-based deep learning model (LSTM) in predicting repeat buyers using user behavior data. To ensure fair evaluation, data preprocessing and feature engineering were carefully designed to prevent data leakage by utilizing user behavior prior to the first purchase. Model performance was evaluated using Accuracy, F1-score, and ROC–AUC metrics. Experimental results show that XGBoost and LightGBM achieve stable classification performance with accuracy values of 86.11% and 85.84%, respectively, while the LSTM model attains the highest ROC–AUC value of 0.937, indicating superior capability in capturing temporal behavioral patterns. This study provides valuable insights for e-commerce platforms seeking to optimize predictive models for repeat buyers, contributing to more effective customer retention strategies.
Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process Rujianto Eko Saputro; Fandy Setyo Utomo; Linda Perdana Wanti
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.
Utilitarian vs Human-Centered AI Acceptance: Explaining Students’ Adoption of ChatGPT in Higher Education Dwi Angesti Dinda Parameswara; Berlilana Berlilana; Rujianto Eko Saputro
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.
Co-Authors Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adiya, Az Zahra Dwi Nur Afriansyah, Fery Aimah, Samsul Anugerah Bagus Wijaya Aptana, Naufal Yogi Arif Mu'amar Wahid Aulia Hamdi Azhari Shouni Barkah Bagaskoro, Galih Baihaqi, Wiga Maulana Berlilana Berlilana Berlilana Bunga Asriandhini 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 Dwi Angesti Dinda Parameswara Ely Purnawati Ely Purnawati, Ely Embong Octavianto Fandy Setyo Hutomo Fandy Setyo Utomo Fandy Setyo, Utomo Fatudin, Arif Faturama, Rafi Febriansyah Husni Adiatma Febrianti, Diah Ratna Fery Afriansyah Fiby Nur Afiana 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 Linda Perdana Wanti Lughri Wijaya Pamungkas Maharani, Revalyna Octavia Maulana Baihaqi, Wiga Millatul Izza, Nia Mohamad, Siti Nurul Mahfuzah Mohd. Hafiz Zakaria Muhammad Nur Faiz Munandar, Irwansyah Nanjar, Agi Ndari, Arum Vika Nia Millatul Izza Novita Eka Ramadhani Nugroho, Lustiyono Prasetyo Nur Rahma Keysha Maharani Maharani Nurfaizi, Maulana Nurmalitasari, Gupita Octavianto, Embong Pandu W, Muhammad Arfianto Prasetyo, Agung Priaji Januardi Primandani Arsi Pungkas Subarkah Purwadi Purwadi Purwadi Purwadi R. Vitto Mahendra Putranto Radeta Tea Makdatuang Ramadhan, Rio Fadly Ranggi Praharaningtyas Aji Ria Indriyani Rida Purnama Sari 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 Wasihatun Hasanah Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy Yusoff, Azizul Mohd Zanuar Rifai