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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) 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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Optimized Skill Mastery Prediction for Adaptive Test Decision-Making Linda Perdana Wanti; Rujianto Eko Saputro; Fandy Setyo Hutomo; Muhammad Nur Faiz
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3171

Abstract

Adaptive testing systems require accurate and timely estimation of students' skill mastery levels to support decision-making in question selection and assessment flow determination. However, uncertainty in students' knowledge levels and variations in learning behavior often limit the performance of conventional predictive models. This study proposes an optimized predictive modeling framework for skill mastery to enhance decision-making quality in adaptive testing. The proposed framework integrates a machine learning-based skill mastery prediction model with an optimization mechanism to improve model accuracy and stability, while accommodating the sequential nature and uncertainty of student responses. Learning interaction data is used to dynamically model the development of skill mastery levels, which are then utilized as decision-support input in the adaptive testing system. The proposed predictive skill mastery model shows strong and consistent performance with an AUC value of 0.822, Average Precision of 0.868, accuracy of 0.757, and a precision balance of 0.834, recall of 0.788, and F1-score of 0.810, supported by well-calibrated probabilities and the ability to respond adaptively to student learning dynamics, making it suitable for use to support decision-making in adaptive test decision-making systems. The results of this study confirm the potential of integrating predictive analytics and optimization techniques in developing intelligent adaptive assessment systems.
KLASIFIKASI JENIS PERMASALAHAN APLIKASI GOJEK PADA GOOGLE PLAY STORE BERDASARKAN ULASAN PENGGUNA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE DAN NAIVE BAYES Nur Rahma Keysha Maharani Maharani; Rujianto Eko Saputro
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8029

Abstract

User reviews of the Gojek application on the Google Play Store contain various types of information regarding problems experienced by users. However, most previous studies have focused on sentiment analysis and have not been able to identify specific problem types. This study aims to classify problem types in Gojek user reviews into five categories: login, transaction, system disruption, feature error, and other issues. Data were collected through web scraping from the Google Play Store between January 2024 and April 2026, targeting 5,000 reviews. After removing empty and duplicate records, 3,753 reviews were retained for analysis. Labeling was performed using a keyword-based rule-based approach, followed by preprocessing stages including case folding, cleaning, tokenization, stopword removal, and stemming. Feature representation was conducted using TF-IDF with a maximum of 2,500 features. Class imbalance in the training data was addressed using Random Over Sampling (ROS), while the dataset was split using an 80:20 ratio through stratified sampling. This study compares the performance of Support Vector Machine (SVM) and Naïve Bayes classifiers using accuracy, precision, recall, F1-score, and ROC-AUC metrics, with model validation performed through 5-fold Stratified K-Fold Cross Validation. The results show that SVM achieved the best performance, with an accuracy of 0.846, precision of 0.872, recall of 0.846, F1-score of 0.856, ROC-AUC of 0.903, and an average cross-validation F1-score of 0.8509. In contrast, Naïve Bayes achieved an accuracy of 0.555, precision of 0.828, recall of 0.555, F1-score of 0.635, ROC-AUC of 0.836, and an average cross-validation F1-score of 0.6281. These results indicate that SVM performs better in classifying problem types in Gojek user reviews.
Pemanfaatan Teknologi untuk Meningkatkan Pembelajaran Inklusif bagi Dosen Fakultas Ilmu Komputer Universitas Amikom Purwokerto Rujianto Eko Saputro; Arif Mu'amar Wahid; Novita Eka Ramadhani; Lughri Wijaya Pamungkas
JURPIKAT Vol 6 No 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2629

Abstract

Pengabdian ini menjawab isu kurangnya pemahaman dan keterampilan dosen di Fakultas Ilmu Komputer Universitas AMIKOM Purwokerto terkait implementasi teknologi untuk mendukung pendidikan inklusif, khususnya bagi mahasiswa dengan Gangguan Spektrum Autisme (ASD) dan disabilitas penglihatan. Tujuan kegiatan ini adalah untuk meningkatkan kompetensi dosen dalam memanfaatkan teknologi bantu, seperti aplikasi pembelajaran multimedia, melalui webinar dan workshop yang dipimpin oleh Ts. Syariffanor Hisham dari Universiti Teknikal Malaysia Melaka (UTeM). Metode yang digunakan adalah pelatihan partisipatif dengan evaluasi melalui kuesioner umpan balik. Hasil menunjukkan respons peserta yang sangat positif, dengan nilai rata-rata evaluasi di atas 4.0 pada skala 1-5 untuk semua aspek. Ini membuktikan bahwa program berhasil memberikan wawasan baru dan meningkatkan pemahaman peserta, sekaligus menunjukkan relevansi topik dan efektivitas kolaborasi internasional.
Audit Data Leakage dan Evaluasi Model Machine Learning untuk Prediksi Capaian Pembelajaran Lulusan dalam Outcome-Based Education Priaji Januardi; Rujianto Eko Saputro; Fandy Setyo Utomo
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

Outcome-Based Education (OBE) implementation requires continuous evaluation of graduate learning outcomes (CPL). While machine learning models are widely used for predicting academic performance, most studies overlook fundamental issues like data leakage and class imbalance. This study evaluates the impact of data leakage audits on the performance of four models (Logistic Regression, Random Forest, XGBoost, and Deep Neural Network) in predicting CPL. The novelty lies in applying a structured audit framework prior to model comparison. Experiments utilized 27,262 academic records with stratified cross-validation. Audit results proved that proxy features caused unrealistic performance (Accuracy 0.999). After removing leaked features, Logistic Regression achieved the highest discrimination stability (AUC 0.772), while DNN recorded the highest F1-score. Wilcoxon tests confirmed no statistically significant performance difference among the models (α=0.05). In conclusion, on leakage-free OBE data, simple linear models remain highly competitive and suitable as the foundation for early warning systems.
Labeling Optimization and Hybrid CNN Model in Sentiment Analysis of Movie Reviews with Slang Handling Saputra, Alfin Nur Aziz; Saputro, Rujianto Eko; Saputra, Dhanar Intan Surya
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.2025.6.6.4465

Abstract

This research focuses on the development of a hybrid Convolutional Neural Network (CNN) model for sentiment analysis of movie comments, specifically designed to overcome the challenges of handling nonstandard language and slang. Slang is often an obstacle in sentiment analysis due to its non-standard nature and is difficult to recognize by traditional algorithms. By utilizing an kamusalay as a data preprocessing step, this research successfully converts slang words into standardized forms, thus improving the quality of data used in modeling. The data was collected through YouTube Data API on the comments of the movie “Pengabdi Setan 2: Communion” and processed using tokenization, stemming, stopwords removal, and TF-IDF feature extraction techniques. The hybrid model combines machine learning algorithms such as Naive Bayes, Logistic Regression, and Random Forest with CNN's ability to extract complex spatial patterns from text data. The evaluation results show that this model is able to achieve up to 95% accuracy, with consistently high precision, recall, and F1-score. This approach not only improves the accuracy of sentiment analysis, but also provides an effective solution for handling non-standard language variations, making it relevant for application in digital opinion analysis on social media.
Comparison of the Accuracy Levels of Naive Bayes, Random Forest, and Long Short-Term Memory (LSTM) Methods in Predicting Gold Jewelry Sales Pandu W, Muhammad Arfianto; Saputro, Rujianto Eko; Purwadi, Purwadi; Rohmah, Umdah Aulia
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.2026.7.1.5139

Abstract

Gold has long been recognized as a safe haven asset, especially during economic uncertainty. Accurate prediction of gold jewelry sales is essential for inventory management and business strategy, particularly in high-demand regions such as Imogiri. This study aims to compare the accuracy levels of three machine learning methods—Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM)—in predicting gold jewelry sales using historical transaction data from Toko Emas Parimas. The dataset comprises 4,595 records from January 2022 to December 2024. The research employs data preprocessing, including data cleaning, feature transformation, and normalization, followed by classification into sales categories. Two data-splitting schemes (80:20 and 70:30) were implemented to evaluate model generalization. The models were trained and tested using performance metrics such as accuracy, precision, recall, and F1-score. The results show that Random Forest achieved perfect classification with an accuracy of 1.00 in both schemes, outperforming the other models. Naïve Bayes also performed well with accuracy up to 0.98, while LSTM showed moderate results with accuracy ranging from 0.82 to 0.88. These findings indicate that Random Forest is the most reliable model for sales prediction of gold jewelry, especially for static classification tasks. The study provides practical insights for retailers and decision-makers in selecting suitable analytical models, and it highlights the importance of aligning analytical methods with data characteristics to improve decision support systems in retail.
User Acceptance Analysis of SINAGA Digital Attendance System Using Integrated UTAUT and SCT Models with PLS-SEM for Civil Servants in Purbalingga Regency Latif, Imam Sofarudin; Saputro, Rujianto Eko; Barkah, Azhari Shouni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5584

Abstract

This study combines two main theories, namely the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), to analyze the level of user acceptance of the SINAGA digital attendance system among civil servants in Purbalingga Regency. This study aims to identify factors that influence technology adoption through an integrated UTAUT approach with SCT moderation, particularly self-efficacy. The method used was a survey of 102 respondents, with analysis using Partial Least Squares-Structural Equation Modeling (PLS-SEM) involving testing of outer and inner models through the Slovin approach. The results show that factors such as Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) significantly influence Behavioral Intention (BI). Self-Efficacy (SE) and Outcome Expectancy (OE) also act as moderating factors that strengthen the relationship between PE and BI, as well as EE and BI. With an R2 value of 78%, this model has a high explanatory power regarding users' behavioral intentions in adopting the system. This study contributes to the development of technology acceptance theory in the public sector, particularly for e-government systems, and suggests improving users' digital competence and optimizing infrastructure to support further technology acceptance with the integration of artificial intelligence (AI) technology in the system for more efficient dynamic monitoring. The main contribution of this research is the development of digital systems within the Indonesian government, in line with the sustainability of technology adoption in the public sector.
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 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 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 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 Tea Makdatuang, Radeta Titi Safitri Maharani Toni Anwar Turino, Turino Wasihatun Hasanah Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy Yusoff, Azizul Mohd Zanuar Rifai