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All Journal ComEngApp : Computer Engineering and Applications Journal JSI: Jurnal Sistem Informasi (E-Journal) Annual Research Seminar CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Informatik : Jurnal Ilmu Komputer Data Science: Journal of Computing and Applied Informatics Building of Informatics, Technology and Science Jurnal Generic Malcom: Indonesian Journal of Machine Learning and Computer Science ABDIKOM
Ahmad Rifai
Universitas Sriwijaya
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Other

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Journal : journal of applied informatics and computing

 1 
Hybrid Machine Learning for Knowledge Discovery in E-Commerce Reviews Jeremiah Alwin Siahaan; Lailla Syal Syabilla; M. Thoriqul Fadli; Mei Intan Natasyah; Allsela Meiriza; Ken Ditha Tania; Ahmad Rifai
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12635

Abstract

The rapid growth of e-commerce platforms like Tokopedia has triggered a massive accumulation of over 65,000 customer reviews, yet it is often accompanied by information pollution in the form of non-informative reviews that hinder consumer decision-making processes. This research aims to extract new knowledge regarding these review characteristics through the implementation of the Knowledge Discovery in Database (KDD) framework, integrating a hybrid K-Means Clustering and Random Forest algorithm. Diverging from conventional classification approaches, this study utilizes K-Means as an exploratory instrument to naturally map six latent topic patterns of reviews based on their textual structure. Experiments were conducted on 35,000 data samples using TF-IDF features enriched by cluster labels as structural predictors. The results indicate that the hybrid model achieves 94.41% accuracy with an F1-score of 0.90 for the non-informative class, showing high stability via 5-Fold Cross-Validation (94.56% ± 0.19%) . The most crucial knowledge discovery is evidenced through SHAP analysis, where the cluster feature ranks 7th out of 1,001 predictor features, confirming that semantic grouping provides a richer structural context than pure lexical features . Furthermore, error analysis reveals specific linguistic challenges such as sarcasm and semantic ambiguity as constraints in automated review detection . This research provides a managerial contribution to e-commerce platforms in enhancing information quality and mitigating information overload issues.
Comparative Analysis of the Performance of Machine Learning Methods and Text Embedding Techniques in Classifying Toxic Conversations in the Roblox Game Octa Dama Yanti; Syifa Alfariani; Syifa Naura Milla Celesta; Ken Dhita Tania; Ahmad Rifai
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12646

Abstract

Online games have evolved into digital social spaces where player interactions often include toxic communication, potentially affecting user experience and psychological well-being, especially among younger players. This research is intended to examine and compare the performance of various machine learning algorithms in classifying toxic chat on the Roblox platform and to identify underlying linguistic patterns. The dataset consists of 7,119 Indonesian-language chat data labeled into six categories: identity_hate, insult, obscene, severe_toxic, threat, and toxic. The methodology includes data preprocessing, text representation using Bag-of-Words (BoW) and TF-IDF, and classification using Naive Bayes, Support Vector Machine (SVM), and Random Forest. To assess how well the model performs, several metrics are used, including accuracy, precision, recall, F1-score, and 3-fold cross-validation. The results show that SVM with TF-IDF achieves the best performance with 84.48% accuracy, followed closely by SVM with BoW. The findings indicate that while classical machine learning models remain effective, challenges persist in distinguishing linguistically similar categories.
Knowledge Discovery of AI Usage Dependency Patterns in Learning Activities Using Random Forest, XGBoost, Logistic Regression with SHAP-Based Interpretation Fidela Tertia Alfino; Puti Chalisa Wardhana; A. Salwa Aurelya Putri; Athiyyah Nuha Rotifa; Ken Ditha Tania; Ahmad Rifai; Dedy Kurniawan
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12745

Abstract

The increasing use of Artificial Intelligence (AI) in education has influenced various learning activities. However, excessive AI usage has the potential to create dependency patterns that may affect students’ learning independence and critical thinking abilities. This study aims to analyze patterns of AI usage dependency in learning activities using a machine learning approach and to interpret the factors influencing such dependency. The analysis was conducted using a publicly available dataset representing usage intensity, session duration, AI assistance level, repeated usage behavior, and students’ academic characteristics. The research stages consisted of data preprocessing, categorical variable encoding, feature engineering, the construction of the Knowledge Dependency Level variable, class imbalance handling using SMOTE, and model evaluation using Stratified 5-Fold Cross Validation. The dataset was divided into 80% training data and 20% testing data, then modeled using Logistic Regression, Random Forest, and XGBoost. The results showed that XGBoost achieved the best performance with an accuracy of 0.6845, precision of 0.7288, recall of 0.6845, F1-score of 0.7028, and an AUC value of 0.860, indicating better discrimination capability compared to Random Forest and Logistic Regression. To support the knowledge discovery process, an interpretative analysis using SHAP was conducted to identify the contribution of each feature to the classification results. The interpretation revealed that SatisfactionRating was the most dominant feature influencing the prediction of AI usage dependency levels, followed by FinalOutcome, while academic factors such as StudentLevel and Discipline contributed relatively less. These findings transform previously implicit AI usage behavior patterns into explicit knowledge.
Co-Authors A. Salwa Aurelya Putri Abd. Rasyid Syamsuri Adelia Rizki Putri Ahmad Fadhil Rizqi Al Amin Mulya Al Farissi Ali Ibrahim Alifa Putri Shahabiyah Aliya Faiza Allsela Meiriza, Allsela Allsella Meiriza Alsella Meiriza Alsella Meiriza Athiyyah Nuha Rotifa Aulia Pinkasari Bagus Prihantoro Bambang Tutuko Danny Matthew Saputra Dedy Kurniawan Dhio Pratama Wiransyah Dinda Lestarini Dinna Yunika Hardiyanti Donny Giovanna Karo Karo Edo Wicaksono Eka Prasetyo Ariefin Endang Lestari Ruskan Fathoni - Fidela Tertia Alfino Fransiska Prihatini Sihotang, Fransiska Gabriel Sebastian Santoso Gibral Abdurahman Haniifah Putriani Hardini Novianti Hardini Novianti Hardini Novianti Hardini Novianti Huda Ubaya Jaidan Jauhari Jeremiah Alwin Siahaan Kemahyanto Exaudi Ken Dhita Tania Ken Dhita Tania Ken Ditha Tania Kesuma, Lucky Indra Lailla Syal Syabilla Lina Oktarina M Raykah Alam Ramadan M. Rudi Sanjaya M. Thoriqul Fadli Mei Intan Natasyah Meiyin Monica Amilia Putri Melisa Tri Cahya Ningsih Mira Afrina Muhammad Bayu Samudra Muhammad Dzaky Hasyim Muhammad Fachri Nuriza Muhammad Iqbal Disriansyah Muhammad Mayda Ary Pratama Muhammad Naufal Rachmamtullah Muhammad Rafly Muhammad Rendi Muhammad Wahyu Hikmalsyah Octa Dama Yanti Osvari Arsalan Pacu Putra Pascal Adhi Kurnia Tarigan Pibriana, Desi Purwita Sari Puti Chalisa Wardhana Putri Eka Sevtiyuni Putri Rahel Alifia Rahmad Fadli Isnanto Rahmat Izwan Heroza Rahmat Izwan Heroza Rayya Ramadhan Simangunsong Richa Pratiwi Rizka Dhini Kurnia Rossi Passarella Samsuryadi - Sarifah Putri Raflesia Sarmayanta Sembiring Satria Ramadhani Shafa Aurelliza Arian Sutarno - Sutarno Sutarno Syifa Alfariani Syifa Naura Milla Celesta Winda Kurnia