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All Journal BIOTROPIA - The Southeast Asian Journal of Tropical Biology Journal of Computing Theories and Applications
Ahmad, Aziz
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Immunology & microbiology Veterinary

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Evaluating Open-Source Machine Learning Project Quality Using SMOTE-Enhanced and Explainable ML/DL Models Hamza, Ali; Hussain, Wahid; Iftikhar, Hassan; Ahmad, Aziz; Shamim, Alamgir Md
Journal of Computing Theories and Applications Vol. 3 No. 2 (2025): JCTA 3(2) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14793

Abstract

The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modeling pipeline. The dataset used in this research includes both numerical and categorical attributes representing documentation, testing, architecture, community engagement, popularity, and repository activity. After handling missing values, standardizing numerical features, encoding categorical variables, and addressing the inherent class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), seven different classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Logistic Regression (LR), Support Vector Machine (SVM), and a Deep Neural Network (DNN)—were trained and evaluated. Results show that LR (84%) and DNN (85%) outperform all other models, indicating that both linear and moderately deep non-linear architectures can effectively capture key quality indicators in OSS ML projects. Additional explainability analysis using SHAP reveals consistent feature importance across models, with documentation quality, unit testing practices, architectural clarity, and repository dynamics emerging as the strongest predictors. These findings demonstrate that automated, explainable ML/DL-based quality assessment is both feasible and effective, offering a practical pathway for improving OSS sustainability, guiding contributor decisions, and enhancing trust in ML-based systems that depend on open-source components.
Light-Color-Induced Changes in Fatty Acid Biosynthesis in Chlorella sp. Strain Ks-MA2 in Early Stationary Growth Phase Osman, Siti-Mariam; Chuah, Tse Seng; Loh, Saw Hong; Cha, Thye San; Ahmad, Aziz
BIOTROPIA Vol. 25 No. 1 (2018): BIOTROPIA Vol. 25 No. 1 April 2018
Publisher : SEAMEO BIOTROP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (14.638 KB) | DOI: 10.11598/btb.2018.25.1.685

Abstract

Optimization of light supply remains a critical issue in microalgae biotechnology. The impacts of light color on fatty acid production and biosynthesis in microalgae are poorly understood. The aim of this study was to determine the effect of light color on growth and fatty acid content in Chlorella strain KS-MA2. Cells were cultured on F/2 medium and incubated under blue, green, red, or white light. The cells' growth, fatty acid composition, and the expression levels of the ketoacyl synthase 1 (KAS-1), omega-6 desaturase (ω-6 FAD), and omega-3 desaturase (ω-3 FAD) genes were measured at the early stationary growth phase. Results of this study indicated that light color affected cell density and fatty acid profile produced by Chlorella sp. strain KS-MA2. Cells cultured under blue, red, and white light had higher cell density than those cultured under green light. Palmitic acid (38.62 ± 3.29% of biomass dry weight) and linolenic acid (7.96 ± 0.88% of biomass dry weight) were highly accumulated under white light. Stearic acid was dominant under blue light (11.11 ± 0.14% of biomass dry weight), whereas oleic acid was dominant under red light (30.50 ± 0.14% of biomass dry weight). Linoleic acid was highly produced under green and blue light (28.63 ± 1.36% and 26.00 ± 0.81% of biomass dry weight, respectively). KAS-1 and ω-6 FAD were highly expressed under blue light, whereas ω-3 FAD was highly expressed under green light. The production of particular fatty acids of interest from Chlorella could be achieved by shifting color of light used during the incubation of the cell cultures. Blue light is the most suitable light color for producing biomass and stearic acid by Chlorella strain KS-MA2.
Co-Authors Cha, Thye San Chuah, Tse Seng Hamza, Ali Hussain, Wahid Iftikhar, Hassan Loh, Saw Hong Osman, Siti-Mariam Shamim, Alamgir Md