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All Journal Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah Jurnal Tekno Insentif JURTEKSI Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma Electronic Journal of Education, Social Economics and Technology Jurnal Informatika: Jurnal Pengembangan IT Journal of Informatics and Interactive Technology (JIITE) INJECT Interdisciplinary Journal of Communication Journal of Digital Market and Digital Currency
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : Journal of Applied Data Sciences

 1 
Novel Predictive Framework for Student Learning Styles Based on Felder-Silverman and Machine Learning Model Maulana Baihaqi, Wiga; Eko Saputro, Rujianto; Setyo Utomo, Fandy; Sarmini, Sarmini
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.408

Abstract

This study analyzes data from the Open University Learning Analytics Dataset to evaluate how students' interactions with Virtual Learning Environment (VLE) materials influence their final outcomes. This research aims to formulate and build a novel predictive framework based on the Felder-Silverman and Machine Learning Model for student learning styles. Based on these objectives, this research provides novelty and contributions since it enhances student data analysis, uses a learning model using Felder-Silverman Learning Style Model (FSLSM) to give a more comprehensive understanding of students' learning styles, and improves prediction accuracy by introducing Artificial Neural Network (ANN) and feature selection using Random Forest. The data used includes 3 main files: vle.csv, which contains information about the materials and activities in the VLE; studentVle.csv, which records students' interactions with the materials; and studentInfo.csv, which provides demographic information of students and their final outcomes. The analysis process involved data merging and processing, including handling of missing values, data type conversion, as well as mapping activity types to learning style features based on the FSLSM. We use the Random Forest feature selection method, as well as data imbalance handling techniques such as oversampling, to improve model performance. The applied classification models include Logistic Regression, K-Nearest Neighbor, Random Forest, Support Vector Machine (SVM), and ANN. The analysis results showed that after tuning, the Random Forest model achieved 97% accuracy, while SVM achieved 97% accuracy as well, with better performance than previous studies. This research highlights the importance of comprehensive data integration and appropriate processing techniques in improving the accuracy of student learning style prediction. Based on the increase in accuracy results, it can be beneficial for more effective personalized learning and improve our understanding of students' learning style preferences. The research advances knowledge and provides practical applications for educators to tailor their teaching strategies.
Enhancing the Robustness of Adaptive Class Activation Mapping (AD-CAM) Against Noisy Facial Expression Data Using Preprocessing and Adaptive Normalization Sugianto, Dwi; Hariguna, Taqwa; Utomo, Fandy Setyo
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.v7i1.1005

Abstract

In real-world computer vision applications, visual data is often corrupted by noise, reducing both the accuracy and interpretability of deep learning models. This study proposes an enhanced AD-CAM framework that integrates noise-aware preprocessing and adaptive normalization to improve robustness in both prediction and visual explanation. Experiments were conducted on the FER2013 facial expression dataset augmented with Gaussian, salt-and-pepper, and speckle noise. Using ResNet-50 as the backbone, the proposed method demonstrated significant gains across multiple evaluation metrics, including Robust Accuracy (RA), Drop Coherence (DC), Area Under Robustness Curve (AURC), and Signal-to-Noise Ratio (SNR). Compared to the baseline, the model achieved over 10% accuracy improvement and up to 0.16 DC reduction under noise. Qualitative visualizations showed that the improved model consistently highlighted semantically relevant facial regions, maintaining interpretability even under severe input degradation. These results support the adoption of noise-aware interpretability frameworks for more reliable and trustworthy deployment in real-world vision systems.
Co-Authors Adiya, Az Zahra Dwi Nur Afit Ajis Solihin Aisha Hukama Setyowati Aji Saeful Aji Septa, Adrian Ajis Solihin, Afit Amar Al Farizi Anas Nur Khafid Anggini, Melisa Anggraeni, Mutia Dwi Anggraini, Nova Anggriani, Epri Anies Indah Hariyanti Azhari Shouni Barkah Azmi, Mohd Sanusi Bagus Adhi Kusuma Bahari, Aris Ridky Setiya Baihaqi, Wiga Maulana Balit, Muhamad Naufal Burhanuddin Berlilana Berlilana Berlilana Burhanuddin Balit, Muhamad Naufal Churil Aeni, Agustina Chyntia Raras Ajeng Widiawati Chyntia Raras Ajeng Widiawati Darmono Dedi Purwanto, Dedi Didi Prasetyo Dwi Krisbiantoro, Dwi Dzaky Candy Fahrezy Fadhilah, Siti Nur Febriansyah Husni Adiatma Giat Karyono Giat Karyono Hanif Hidayatulloh Hendra Marcos, Hendra hidayatulloh, hanif Ilham, Rifqi Arifin Imam Tahyudin Indriyani, Ria Jamie Mayliana Alyza Kafilla, Princess Iqlima Kusuma, Bagus Adhi Kusuma, Velizha Sandy Lasmedi Afuan Lubna, Zuhriyatul Lukita, Dita Maulana Baihaqi, Wiga Mohd Fairuz Iskandar Othman Mohd Nazrin Muhammad Mohd Sanusi Azmi Muaziz, Imam Muhamad Naufal Burhanuddin Balit Muhtyas Yugi Murtiyoso Murtiyoso Nandang Hermanto Nanna Suryana Nikmah Trinarsih Nugroho, Khabib Adi Nur Cholis Romadhon Octavia, Annisa Suci Prayoga, Fandhi Dhuga Pungkas Subarkah Purbo, Yevi Septiray Purwidiantoro, Moch. Hari Pyawai, Hero Galuh R. Vitto Mahendra Putranto Ramadhan, Aziz Ramadhan, Rio Fadly Rifqi Arifin Ilham RR. Ella Evrita Hestiandari Rujianto Eko Saputro Sagita, Selvi Samsul Arifin Sarmini - Sarmini Sarmini Sarmini Sekhudin Sekhudin Setiabudi, Rizki Setiawan, Ito Shafira, Lulu Shendy Filanzi Slamet Widodo Sofa, Nur Sri Hartini Subarkah, Pungkas Sugianto, Dwi Suryana, Nanna Taqwa Hariguna Titi Safitri Maharani Trinarsih, Nikmah Turino, Turino Utomo, Dadang Wahyu Wahid, Arif Mu'amar Wibisono, Arif Cahyo Wiga Maulana Baihaqi Yuli Purwat Yuli Purwati Yuli Purwati Yulianto, Koko Edy