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Rahul Sinurat
Universitas HKBP Nommensen, Kota Pematangsiantar
Computer Science & IT Control & Systems Engineering Engineering Social Sciences Other

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Analisis Klasifikasi Penyakit Ginjal Kronis Menggunakan Algoritma K-Nearest Neighbor dan Random Forest Berbasis Orange Data Mining Rahul Sinurat; Kevin Rasi Dauly Pardede; Jeprinus Purba
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.365

Abstract

Chronic Kidney Disease (CKD) is a global health problem with morbidity rates that continue to increase from year to year. The main challenge in the medical management of CKD is delayed diagnosis due to early symptoms that are often unnoticed by patients, thereby requiring rapid, objective, and accurate detection methods. This study aims to analyze and compare the performance of two popular classification algorithms, K-Nearest Neighbor (kNN) and Random Forest, in classifying chronic kidney disease status. Experiments were conducted using Orange Data Mining software by utilizing a patient clinical medical record dataset. The model evaluation was rigorously tested using the 10-Fold Cross Validation method to ensure the validity of the results. The main contribution of this study is to present an in-depth comparative analysis regarding the evaluation metrics of ensemble trees compared to distance-based approaches to minimize the risk of missed diagnosis in clinical decisions. The results showed that the Random Forest algorithm produced the best and superior performance with an accuracy rate (Accuracy) reaching 99.0%, a Precision value of 99.3%, and an AUC value of 0.999. Conversely, the K-Nearest Neighbor algorithm obtained a lower accuracy rate of 96.0% with an AUC of 0.994. These findings indicate that the ensemble tree approach is more adaptive in handling clinical data characteristics, and has great potential to be integrated as a clinical decision support system for medical personnel in hospitals.
Perbandingan Kinerja Random Forest, Decision Tree, dan Naive Bayes Menggunakan Metode 10-Fold Cross Validation untuk Prediksi Status Akademik Mahasiswa Jeprinus Purba; Rahul Sinurat
Jurnal Ilmu Komputer, Teknologi Dan Informasi Vol 4 No 2 (2026): Juli
Publisher : CV. Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/jurikti.v4i2.367

Abstract

Predicting students' academic status is an important effort that higher education institutions can undertake to identify students who are at risk of experiencing academic decline, delayed graduation, or dropout. Early identification enables institutions to provide appropriate academic and non-academic interventions, thereby improving student retention and graduation rates. This study aims to compare the performance of the Random Forest, Decision Tree, and Naive Bayes algorithms in predicting students' academic status using the Predict Students Dropout and Academic Success dataset obtained from the UCI Machine Learning Repository. The dataset consists of 4,424 student records with 36 predictor attributes and three target classes: Dropout, Enrolled, and Graduate. The research methodology includes data exploration, feature selection, model development using Orange Data Mining, and model evaluation through the 10-Fold Cross Validation method. Model performance was assessed using Accuracy, Precision, Recall, F1-Score, Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), Confusion Matrix, and ROC Curve. The experimental results indicate that the Random Forest algorithm achieved the best performance, with an Accuracy of 77.4%, Precision of 0.760, Recall of 0.774, F1-Score of 0.759, AUC of 0.897, and MCC of 0.624, outperforming both Decision Tree and Naive Bayes. This study contributes a comparative evaluation of three widely used classification algorithms on a publicly available higher education dataset and demonstrates that Random Forest is the most effective algorithm for supporting accurate and reliable student academic status prediction systems.
Co-Authors Jeprinus Purba Kevin Rasi Dauly Pardede