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Jurnal Ilmu Komputer, Teknologi Dan Informasi
Published by CV. Graha Mitra Edukasi
ISSN : -     EISSN : 29630169     DOI : https://doi.org/10.62866/jurikti.v2i1
Core Subject : Science, Education, Social, Engineering,
Computer Science & IT Control & Systems Engineering Engineering Social Sciences Other
Jurnal Ilmu Komputer, Teknologi Dan Informasi, ini memiliki bidang kajian: 1. Manajemen Informatika, 2. Sistem Informasi, 3. Game Design, 4. Multimedia System, 5. Sistem Pembelajaran Berbasis Multimedia, 6. GIS, 7. Mobile Programming, 8. Database Design, 9. Network Programming, 10. Distributed System, 11. Data Mining, 12. Sistem Pakar, 13. Kriptografi, dan 14. Sistem Pendukung Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 44 Documents
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Prediksi Risiko Penyalahgunaan Narkoba pada Remaja Menggunakan Algoritma Random Forest Berdasarkan Faktor Sosial, Demografis, dan Lingkungan Bryan Yohan Manalu
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.v4i2.363

Abstract

Drug abuse among adolescents is a highly complex social and public health problem, influenced by multifactorial interactions between social, demographic, psychological, and environmental factors. Current prevention efforts are generally generalist and educational in nature, thus significantly limiting their ability to identify individuals with specific risk vulnerabilities early. Furthermore, existing predictive models often rely on costly and time-consuming clinical measurement instruments, making them difficult to implement on a large scale in schools. To address these challenges, this study aims to develop and evaluate a predictive model for the risk of drug abuse in adolescents using the Random Forest algorithm. This approach focuses on utilizing social, demographic, and environmental variables in a more practical and accessible manner. The dataset used comprises 10,000 respondents, with methodological steps including data preprocessing, feature engineering for classification of three risk classes (Low, Medium, High) using a quantile approach, and dividing the dataset into 80% training data and 20% testing data. The model was comprehensively evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the Random Forest model achieved an accuracy rate of 33% in the multi-class classification scheme, and significantly increased to 51.95% in the binary classification scheme. Furthermore, feature importance analysis revealed that smoking prevalence, peer influence, and family background conditions were the predictors with the most dominant contribution in mapping adolescent vulnerability. The main contribution of this study is the design of a more granular risk categorization scheme and providing an initial foundation for the development of an adaptive, practical, and highly scalable machine learning-based early warning system to support preventive interventions in educational institutions.
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.
Analisis Perbandingan Algoritma Random Forest dan Support Vector Machine pada Prediksi Customer Churn Menggunakan IBM Telco Customer Churn Dataset Saudurma Seven Septiana Sidabutar; Ningsih Septi Uli Purba; Wulan Liviana Simbolon; Betharya Tampubolon; Jaya Tata Hardinata
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.v4i2.377

Abstract

Customer churn is a condition in which customers decide to discontinue the services provided by a company. In the telecommunications industry, a high customer churn rate can reduce company revenue and customer loyalty. Therefore, an accurate prediction method is needed to identify customers who are likely to churn so that preventive strategies can be implemented at an early stage. This study aims to compare the performance of the Random Forest and Support Vector Machine (SVM) algorithms in predicting customer churn using the IBM Telco Customer Churn Dataset. The research stages include data collection, data preprocessing, model development using Orange Data Mining, model evaluation through Test and Score, Confusion matrix, and Receiver operating characteristic (ROC), as well as data visualization using Microsoft Power BI. The results indicate that both algorithms are capable of classifying customer data; however, the Random Forest algorithm achieves better performance than the Support Vector Machine based on the evaluation metrics obtained. Furthermore, data visualization using Microsoft Power BI provides a clearer understanding of customer characteristics and supports the interpretation of the research findings. Therefore, Random Forest is recommended as a more effective algorithm for customer churn prediction in the telecommunications sector.

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