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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Indonesian Health Issue
Bambang Widoyono
Informatics, Universitas Sebelas Maret, Indonesia
Computer Science & IT Dentistry Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Nursing Public Health Veterinary

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Digital Public Sentiment Toward Out-of-Wedlock Pregnancy and Its Implications for Health Policy in Indonesia Herliana Riska; Bambang Widoyono; Dewi Sartika; Nur Khasanah
Indonesian Health Issue Vol. 5 No. 1 (2026): FEBRUARY
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/inhis.v5i1.160

Abstract

Background: Unmarried pregnancy in Indonesia remains closely associated with gendered moral norms and social stigma, which may limit women’s access to equitable maternal health services. Digital platforms such as YouTube function as public arenas where these norms are reproduced, contested, and potentially influence informal policy environments. Purpose: This study aims to analyze digital public sentiment toward unmarried pregnant women and to examine its implications for gender-responsive and intersectional maternal health policy in Indonesia. Methods: A qualitative descriptive study was conducted using content analysis of publicly available YouTube comments posted between January 2024 and 2026. Comments from highly engaged videos discussing unmarried pregnancy were anonymized and categorized into negative, positive, and neutral sentiment groups. Descriptive statistics were used to present proportional distributions. Results: Negative sentiment dominated the discourse (47.25%), followed by positive sentiment (39.32%) and neutral sentiment (13.44%). Although moral judgment and stigma remain prominent, a substantial proportion of comments express empathy and support. Conclusions: Digital public sentiment reflects persistent gender stigma surrounding unmarried pregnancy, alongside emerging supportive narratives. Integrating intersectional and rights-based approaches aligned with WHO recommendations is essential to strengthen inclusive maternal health policy and ensure equitable access to care for all women, regardless of marital status.
Evaluating Single and Hybrid Feature Selection for Rainfall Prediction Using XGBoost Bambang Widoyono; Muhammad Fahmy Nadhif; Ridha Adjie Eryadi
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rainfall prediction is challenging due to the complex and nonlinear nature of meteorological data. Previous studies using XGBoost with feature selection have demonstrated superior performance compared to other models, but evaluations have focused solely on error metrics (RSME, SME, MAE). Recent research suggests that predictive models should be evaluated for generalization, stability, interpretability, and computational efficiency to ensure their reliability. To close this gap, this study uses 8,750 hourly records obtained from Open-Meteo with 81 engineered features to evaluate XGBoost under three scenarios: without feature selection, single feature selection (MI, Boruta, SHAP, mRMR, ReliefF), and hybrid feature selection. Our findings demonstrate that accuracy is not always increased by feature selection. It does, however, increase interpretability, decrease overfitting, and improve computational efficiency. SHAP provides the most reliable performance among single methods, achieving lower RMSE (0.72632) and improved stability. Hybrid feature selection produces the most balanced performance gap = 0.01325, and stable variance = 0.03315 while reducing feature complexity to 35 variables. This study theoretically shows the value of multidimensional evaluation that goes beyond error metrics. In practical terms, this study suggests a feature selection method for rainfall prediction systems that are effective, reliable, and simple to understand.
Optimization of User-Based Collaborative Filtering Movie Recommendation System Using Mean-Centering and Overlap Weighting on Cosine Similarity Arif Rohmadi; Ery Permana Yudha; Bambang Widoyono
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3315

Abstract

Rapid technological advancements have changed the way people enjoy entertainment. Viewers can now watch movies online without having to go to the cinema. The rapid development of online movie streaming services has increased the need for systems capable of providing content recommendations based on user preferences. This study aims to optimize a User-Based Collaborative Filtering (UBCF)-based recommendation system using the MovieLens 100K dataset containing 100,000 ratings from 943 users for 1,682 movies. The evaluation was conducted by dividing the data into 80% training data and 20% testing data. The system was optimized through two main approaches: user average normalization (mean-centering) in the KNN method to reduce bias in rating scale differences between users, and the application of overlap weighting on cosine similarity to give greater weight to user pairs with a greater number of shared item ratings. Based on experiments, a value of k = 50 was chosen as the optimal trade-off point in predicting ratings. The experimental results show that KNN with mean centering (KNNWithMeans) consistently outperforms standard KNN. The cosine + KNNWithMeans model produced an RMSE of 0.9701 and an MAE of 0.7567, lower than cosine + KNN (RMSE 1.0377; MAE 0.8226). Further overlap weighting was shown to improve prediction accuracy, with the combination of weighted cosine with α = 1 and KNNWithMeans providing the best performance with an RMSE of 0.9686 and an MAE of 0.7556.
Content-Based Recommendation System for Non-Textbook using TF-IDF, Cosine Similarity, and Educational Level Filtering Arif Rohmadi; Ristu Saptono; Brilyan Hendrasuryawan; Bambang Widoyono; Akhmad Syaifuddin
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 4 (2026): JUTIF Volume 7, Number 4, August 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.4.5425

Abstract

Non-textbook educational resources refer to books designed to enrich readers’ knowledge, insights, and skills, serving as complementary materials to formal textbooks. These may include fiction, non-fiction, biographies, self-help books, and other supplementary literature. However, the broad range of available non-textbooks targeting diverse educational levels often presents challenges for students in selecting materials that are appropriate to their academic stage. This study aims to develop a content-based recommendation system capable of recommending non-textbooks based on the reader's educational level. The recommendation process employs Term Frequency–Inverse Document Frequency (TF-IDF) for feature extraction and Cosine Similarity to calculate semantic similarity between user search queries and book content. To improve relevance, a filtering mechanism based on education level is introduced prior to feature extraction. Experimental results show that applying this filtering process significantly improves the recommendation's performance, yielding an average precision of 100%. In contrast, models without the filtering process achieve only 50% precision. These findings highlight the effectiveness of contextual filtering in improving the accuracy of non-textbook recommendation systems.  
Co-Authors Akhmad Syaifuddin Arif Rohmadi Arif Rohmadi Brilyan Hendrasuryawan Dewi Sartika Ery Permana Yudha Herliana Riska Muhammad Fahmy Nadhif Nur Khasanah Ridha Adjie Eryadi Ristu Saptono