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All Journal Transformasi: Jurnal Pengabdian Masyarakat Infoman's: Jurnal Ilmu-Ilmu Manajeman dan Informatika Infotronik : Jurnal Teknologi Informasi dan Elektronika JATI (Jurnal Mahasiswa Teknik Informatika) Teknologika Populer: Jurnal Penelitian Mahasiswa Jurnal Teknik Mesin, Industri, Elektro dan Informatika Indonesian Journal of Education And Computer Science Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Jurnal Intelek Dan Cendikiawan Nusantara Jasmine : Journal of Intelligent Systems and Machine Learning RUMI
David Setiadi
Universitas Sebelas April
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Social Sciences Other

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Journal : jasmine journal of intelligent systems and machine learning

 1 
Decision Tree-Based Early Warning System for Academic Failure: Comparative Analysis with Random Forest and Logistic Regression Virzan Pasa Nugraha; Fidi Supriadi; David Setiadi
JASMINE: Journal of Intelligent Systems and Machine Learning Vol. 1 No. 1 (2026)
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.v1i1.10123

Abstract

A Decision Tree–based early warning system for academic failure was evaluated using a dataset of 649 student observations and compared with Random Forest and Logistic Regression models through nested ten-fold cross-validation. The mean accuracy of the Decision Tree model was 0.9169, compared to 0.9260 for the Random Forest model and 0.8922 for the Logistic Regression model. Although the Random Forest model achieved the highest raw accuracy, paired statistical testing indicates that its performance difference with the Decision Tree model is not statistically significant (paired t-test, p = 0.104950). The difference between the Decision Tree model and the Logistic Regression model is statistically significant before Bonferroni correction (p = 0.01734) but becomes non-significant after adjustment (Bonferroni-adjusted p = 0.05201). The Decision Tree model was therefore selected to balance competitive predictive performance with interpretability through explicit and readable decision rules. In out-of-fold evaluation, the Random Forest model achieved the strongest results, with an accuracy of 0.9245, high receiver operating characteristic and precision–recall performance, a balanced accuracy of 0.8409, a minority-class recall of 0.7200, and twenty-eight false-negative predictions. Feature-importance analysis showed that G2 was the most influential variable with a relative importance of 0.2417, followed by G1 with 0.1992. Shapley value analysis and an ablation study further demonstrated that removing G1 and G2 substantially reduced overall accuracy and minority-class recall. These findings support the use of the Decision Tree model in educational contexts, where transparent rule-based decisions can guide early academic interventions while maintaining performance comparable to more complex ensemble methods.
Global Food Waste Prediction (2018-2024): Trend Analysis and Random Forest Regression Model Development kemal pramayuda kemal; Fidi Supriadi; David Setiadi
JASMINE: Journal of Intelligent Systems and Machine Learning 2026: Articles in Press
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jasmine.vi.10124

Abstract

Food waste remains a major global sustainability challenge due to its environmental, economic, and social impacts. Understanding food waste patterns and their contributing factors is essential for supporting effective mitigation strategies. This study aims to (1) investigate food waste trends and patterns through Exploratory Data Analysis (EDA) and (2) develop a predictive model for estimating total food waste using Random Forest Regression. The study utilizes a publicly available dataset containing 5,000 records from 20 countries, covering eight food categories over the period 2018–2024. The dataset includes variables such as food category, economic loss, population, average waste per capita, and household waste percentage. Exploratory analysis reveals variations in waste generation across food categories and countries, with fruits and vegetables contributing a substantial share of total waste. A Random Forest Regression model was developed and evaluated, achieving a coefficient of determination (R²) of 0.9582. In addition to predictive performance, the study highlights the importance of examining key contributing variables to better understand food waste patterns. The findings demonstrate the potential of machine learning techniques as decision-support tools for food waste analysis and management, while also acknowledging the limitations associated with secondary public datasets.
Co-Authors Abdul Muhaemin, Muhamad Nurdin Alip Rizky Firdaus Annisha Maharany Atep Ruhiat Dede Wulan Rahayu Dwi Yuniarto Fidi Supriadi Fidi Supriadi Fidi Supriadi Fidi Supriadi Finka Mambo Gita Fitria Haifan Ghani Raihan Santos Irma Rahayu kemal pramayuda kemal Leni Nurhayati Leni Nurhayati Maya Suhayati Muhamad Nurdin Abdul Muhaemin Rafli Ahmad Zulfikri Raka Rey Ferdian Reny Rian Marliana Sri Bekti Handayani Ningsih Virzan Pasa Nugraha Yopi Hidayatul Akbar