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All Journal Journal of Computing Theories and Applications JUMPA : Jurnal Manajemen Pendidikan
Bimo Haryo Setyoko
Universitas Islam Negeri Salatiga
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences

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Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting Ella Budi Wijayanti; De Rosal Ignatius Moses Setiadi; Bimo Haryo Setyoko
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10057

Abstract

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection De Rosal Ignatius Moses Setiadi; Arnold Adimabua Ojugo; Octara Pribadi; Etika Kartikadarma; Bimo Haryo Setyoko; Suyud Widiono; Robet Robet; Tabitha Chukwudi Aghaunor; Eferhire Valentine Ugbotu
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12698

Abstract

Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To address class imbalance, the training set was balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, an LSTM encoder extracted non-linear latent features from the selected features. A fusion strategy was applied by concatenating the statistical and latent features, followed by re-selection of the top 30 features. The final classification was performed using a Support Vector Machine (SVM) with RBF kernel and evaluated using 5-fold cross-validation and a held-out test set. Experimental results showed that the proposed method achieved an average training accuracy of 98.13%, F1-score of 98.13%, and AUC-ROC of 99.55%. On the held-out test set, the model reached an accuracy of 99.30%, precision of 100%, and F1-score of 99.05%, with an AUC-ROC of 0.9973. The proposed pipeline demonstrates improved generalization and interpretability compared to existing methods such as LightGBM-PSO, DHH-GRU, and ensemble deep networks. These results highlight the effectiveness of combining statistical selection and LSTM-based latent feature encoding in a balanced classification framework.
Digital Heritage Knowledge Governance for Islamic Higher Education: A Stakeholder-Oriented WebGIS for Historic Mosques in Central Java Emawati Emawati; Bimo Haryo Setyoko
JUMPA : Jurnal Manajemen Pendidikan Vol 6, No 2 (2025)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jumpa.v6i2.16964

Abstract

Historic mosques carry valuable Islamic cultural knowledge, but their historical, architectural, and spatial records often stay scattered across separate sources. This condition weakens the ability of educational and heritage stakeholders to organize and reach that knowledge. This study developed a stakeholder-oriented WebGIS for the digital heritage knowledge governance of historic mosques in Central Java. It applied Research and Development through the Design Science Research Methodology, moving from needs assessment, field observation, and GPS-based mapping to system development, implementation, and user evaluation. Data were examined descriptively using frequencies, percentages, field verification, and functional assessment. Historical information was the content most respondents wanted, chosen by four of six participants, while five of six regarded interactive mapping as important or very important, and easy navigation ranked as the leading function. The resulting WebGIS joined hierarchical geographic navigation with historical, architectural, and visual records, and an AI-assisted feature was integrated as a supporting analytical layer. Preliminary users reported no negative assessments, though most ratings stayed moderately positive. These results imply that a mosque WebGIS can serve educational institutions as knowledge-governance infrastructure that strengthens heritage information management and widens access to Islamic cultural knowledge.
Co-Authors Arnold Adimabua Ojugo De Rosal Ignatius Moses Setiadi Eferhire Valentine Ugbotu Ella Budi Wijayanti Emawati Emawati Etika Kartikadarma Octara Pribadi Robet Robet Suyud Widiono Tabitha Chukwudi Aghaunor