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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Alfaridzi, Gemma Tahmid
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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A Comparison of Machine Learning Algorithms in Predicting Students' Academic Performance Baye, Juanda Alra; Alfaridzi, Gemma Tahmid; Abdurrahim, Hilmy; Adinda, Abid Aziz; Athallah, Muhammad Rakha; Ramadhan, Muhammad Zahid
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 2: PREDATECS January 2026
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i2.1861

Abstract

Predicting students’ academic performance enables early interventions and data-driven planning in education. We compare five machine-learning algorithms Decision Tree, K-Nearest Neighbor, Naive Bayes, Random Forest, and Support Vector Machine on a publicly available dataset of 1,001 students, evaluated with Accuracy, Precision, Recall, and F1-Score. The Decision Tree achieved the highest performance, with perfect scores on this dataset, while SVM (?82% F1) and Random Forest (?81% F1) were competitive. These results suggest that simple, interpretable models can be highly effective when features are clean and predictive; however, the Decision Tree’s perfection also indicates potential overfitting and warrants further validation on larger, more diverse samples. The study underscores how model choice should reflect dataset characteristics and practical deployment goals in educational settings, informing early-warning systems and targeted support programs.
Inggris Alfaridzi, Gemma Tahmid; Nur Salisah, Febi; Permana, Inggih
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.765

Abstract

Pharmacy sales transaction data contain valuable information on customer purchasing patterns; however, in practice, such data are often used merely as operational records, making relationships between purchased drugs difficult to identify. This study analyzes drug purchasing patterns using the Apriori and FP-Growth algorithms based on sales transaction data from Apotek Gadi Lamba Condet for the period January to June 2025. The transaction data were processed through data cleaning, drug name standardization, and transformation into transaction format, resulting in 7,038 transactions with 1,495 drug items. Association rule mining was performed using a minimum support of 0.01 and a minimum confidence of 0.17. The results show that the Apriori and FP-Growth algorithms generate ten identical association rules with the same support, confidence, and lift values, and all rules have lift values greater than one. Paracetamol 500 MG emerges as the most frequently involved drug in the association rules. These findings demonstrate that, for medium-scale pharmacy transaction datasets, Apriori and FP-Growth have equivalent capability in identifying drug purchasing patterns, with the primary difference lying in computational efficiency rather than the quality of the generated patterns.
Co-Authors Abdurrahim, Hilmy Adinda, Abid Aziz Athallah, Muhammad Rakha Baye, Juanda Alra Febi Nur Salisah Inggih Permana Ramadhan, Muhammad Zahid