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p-Index From 2021 - 2026
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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Computing Theories and Applications
Muhamad Riyan Maulana
Universitas Negeri Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Data-driven analysis of growth factors in oyster mushroom cultivation: a case study from Indonesia’s market Yosef Budiman; Gilang Adi Prasetyo; Asma’ Khoirunnisa’; Hanifah Mar’atush Shalihah; Muhamad Riyan Maulana; Yanuar Agung Fadlullah; Sugiri Sugiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3568-3580

Abstract

The oyster mushroom is one of the potential agricultural products that can be developed as an alternative to other agricultural products, to maintain Indonesia's economic condition. However, the production of oyster mushrooms remains low and falls short of the minimum amount of market demand. This study employs a machine learning (ML)–based approach to identify the key parameters influencing oyster mushroom production rates. Recursive feature elimination (RFE) was applied to reduce the initial 19 features to nine, enabling faster processing while maintaining high predictive accuracy. The results showed that agricultural features showed a high contribution rather than environmental, economic, and demographic features. Furthermore, these parameters were related to the train-test analysis to visualize the statistical analysis shown by the best method, adaptive boosting (AdaBoost), with coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE) values of 0.997575, 0.009841, and 0.085884, respectively. Related research relevant to the research findings was analyzed to validate that agricultural product features affect the decline of oyster mushroom production. Other supported research conducted by integrating real-time analysis and twin digital models, which can enhance substrate quality.
Explainable Machine Learning for Predicting Indonesian Vocational School Accreditation: Geographic Validation, Probability Calibration, and Subgroup Auditing Muhamad Riyan Maulana; Putu Sudira; Priyanto Priyanto; Yanuar Agung Fadlullah; Dian Nurdiana; Amir Hamzah bin Sofhi @ Subhi
Journal of Computing Theories and Applications Vol. 4 No. 2 (2026): JCTA 4(2) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17721

Abstract

Vocational school accreditation is a high-stakes institutional classification, yet the geographic transferability and probability reliability of models based on routinely collected administrative data remain underexplored. This study developed a reproducible, leakage-controlled machine-learning framework for classifying the recorded accreditation classes C, B, and A among Indonesian vocational schools. Its main contribution is an integrated evaluation design that combines province-disjoint validation, probability calibration, cluster-aware uncertainty, and subgroup auditing to test reliability beyond conventional random-validation performance. A cross-sectional dataset containing 149,376 program-level records was deterministically aggregated into 14,391 schools, including 14,134 with valid labels. Eight provinces comprising 1,536 schools were locked before feature selection, model comparison, tuning, and calibration; 12,598 schools from 31 provinces supported development. The selected CatBoost model used 35 structural and vocational features with grouped out-of-fold temperature scaling. On the locked holdout, the calibrated model achieved balanced accuracy of 0.586, Macro F1 of 0.533, quadratic weighted kappa of 0.499, Macro ROC-AUC of 0.757, and expected calibration error of 0.049. School size, teacher resources, vocational staffing, program diversity, and status were most in-fluential. Class B had the lowest recall, and performance varied across school groups and provinces. The framework supports calibrated, auditable preliminary screening, but not automated accreditation or replacement of professional assessors.
Co-Authors Amir Hamzah bin Sofhi @ Subhi Asma’ Khoirunnisa’ Dian Nurdiana Gilang Adi Prasetyo Hanifah Mar’atush Shalihah Priyanto, Priyanto Putu Sudira Sugiri Sugiri Yanuar Agung Fadlullah Yosef Budiman