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Analysis of Student Graduation Prediction Using Machine Learning Techniques on an Imbalanced Dataset: An Approach to Address Class Imbalance Hermanto, Dedy; Desy Iba Ricoida; Desi Pibriana; Rusbandi; Muhammad Rizky Pribadi
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.5528

Abstract

Purpose: Machine learning is a key area of artificial intelligence, applicable in various fields, including the prediction of timely graduation. One method within machine learning is supervised learning. However, the results are influenced by the distribution of data, particularly in the case of imbalanced classes, where the minority class is significantly smaller than the majority class, affecting classification performance. Timely graduation from a university is crucial for its sustainability and accreditation. This research aims to identify a suitable method to address the issue of predicting timely graduation by managing class imbalance using SMOTE (Synthetic Minority Oversampling Technique). Methods: This study uses a five-year dataset with 26 attributes and 1328 records, including status labels. The preprocessing stages involve applying five classification algorithms: Decision Tree (DT), Naive Bayes (NB), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Random Forest (RF). Each algorithm is used both with and without SMOTE to handle the class imbalance. The dataset indicates that 60.84% of the cases represent timely graduations. To mitigate the imbalance, over/under-sampling methods are employed to balance the data. The evaluation metric used is the confusion matrix, which assesses the classification performance. Result: Without SMOTE, the accuracies were 89.12% for DT, 79.65% for NB, 89.47% for LR, 87.72% for KNN, and 90.88% for RF. With SMOTE, the accuracies were 88.89% for DT, 81.48% for NB, 91.05% for LR, 92.59% for KNN, and 89.81% for RF. The algorithms NB, LR, and KNN showed improvement with SMOTE, with KNN yielding the best results. Novelty: Based on the comparison results, a comparison of five algorithms with and without SMOTE can reasonably classify several of the algorithms being compared.
HUBUNGAN POLA MAKAN DAN GAYA HIDUP DENGAN HIPERTENSI DI SAWANGAN, DEPOK Siti Chasani; Susi Dewiasih Kusumawati; Suyono; Rusbandi
Jurnal Media Akademik (JMA) Vol. 4 No. 1 (2026): JURNAL MEDIA AKADEMIK Edisi Januari
Publisher : PT. Media Akademik Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62281/3271p027

Abstract

Hipertensi merupakan masalah kesehatan utama yang berkontribusi terhadap meningkatnya risiko penyakit kardiovaskular. Pola makan dan gaya hidup sering dikaitkan sebagai faktor risiko hipertensi, namun hasil penelitian sebelumnya menunjukkan temuan yang tidak konsisten. Penelitian ini bertujuan untuk menganalisis hubungan pola makan dan gaya hidup dengan kejadian hipertensi pada kelompok middle age di wilayah kerja Puskesmas Sawangan, Kota Depok. Penelitian ini menggunakan desain kuantitatif dengan pendekatan potong lintang. Sampel penelitian berjumlah 53 responden yang diambil dari populasi sebanyak 516 pasien. Data dikumpulkan menggunakan kuesioner terstruktur dan dianalisis dengan uji Chi-Square untuk mengetahui hubungan antarvariabel serta Odds Ratio untuk melihat kecenderungan risiko. Hasil penelitian menunjukkan bahwa sebagian besar responden memiliki pola makan buruk (79,2%) dan gaya hidup tidak sehat (83,0%), dengan proporsi kejadian hipertensi sebesar 50,9%. Hasil uji Chi-Square menunjukkan tidak terdapat hubungan yang signifikan antara pola makan dengan kejadian hipertensi (p = 0,473; OR = 0,154) maupun antara gaya hidup dengan kejadian hipertensi (p = 0,501). Meskipun tidak signifikan secara statistik, nilai Odds Ratio menunjukkan kecenderungan bahwa pola makan dan gaya hidup yang lebih baik bersifat protektif terhadap kejadian hipertensi. Kesimpulan penelitian ini menunjukkan bahwa secara statistik pola makan dan gaya hidup belum terbukti berhubungan dengan kejadian hipertensi pada responden, namun tetap memiliki relevansi klinis dalam upaya pencegahan hipertensi. Penelitian lanjutan disarankan menggunakan sampel yang lebih besar dan mempertimbangkan faktor risiko lain.
Co-Authors Dedy Hermanto Desy Iba Ricoida Muhammad Rizky Pribadi Pibriana, Desi Siti Chasani Susi Dewiasih Kusumawati SUYONO