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All Journal Techno.Com: Jurnal Teknologi Informasi PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic IJoICT (International Journal on Information and Communication Technology) Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System Faktor Exacta Jurnal Komtika (Komputasi dan Informatika) Jurnal Cendikia JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal Teknologi Terpadu Jurnal ICT : Information Communication & Technology Competitive JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Sains Teknologi dalam Pemberdayaan Masyarakat Journal of Students‘ Research in Computer Science (JSRCS) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jitu: Jurnal Informatika Utama Competitive Journal of Computer Science Contributions (Jucosco) Journal of Informatics and Information Security Jurnal Riset Informatika dan Teknologi Informasi (JRITI) Jurnal Komtika (Komputasi dan Informatika) Jurnal Bersama Pengabdian Kepada Masyarakat
Prima Dina Atika
Fakultas Ilmu Komputer; Universitas Bhayangkara Jakarta Raya
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Comparative Study of Logistic Regression, Neural Network, and Deep Learning in Predicting Hypertension Risk Prima Dina Atika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 13 No. 2 (2025): September 2025
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v13i2.11646

Abstract

Hypertension is a major risk factor for cardiovascular diseases, and early detection is crucial for effective management. This study compares the predictive performance of three modeling techniques—Logistic Regression (LR), Neural Network (NN), and Deep Learning (DL)—in estimating the risk of hypertension. The dataset, obtained from Kaggle, consists of demographic and clinical variables with binary labels indicating the presence or absence of hypertension. Each model was trained and evaluated using RapidMiner, with performance assessed through accuracy and Root Mean Squared Error (RMSE). The results indicate that the Neural Network outperformed both Deep Learning and Logistic Regression, achieving the highest accuracy (99.88%) and the lowest RMSE (0.124). These findings suggest that shallow neural networks can provide reliable and efficient predictions for hypertension risk, sometimes even surpassing more complex deep learning architectures.  
A Comparative Study of Machine Learning-Based Student Dropout Risk Prediction Prima Dina Atika
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12299

Abstract

Student dropout is a critical issue in higher education, affecting both institutional performance and student success. This study aims to develop a classification model for predicting student dropout risk and to compare the performance of several machine learning algorithms. A quantitative experimental approach was employed using a dataset that integrates academic records and Learning Management System (LMS) activity. The dataset exhibits imbalanced characteristics, with approximately 20% of instances belonging to the dropout class. The classification algorithms evaluated in this study include Naïve Bayes, Decision Tree, Random Forest, and K-Nearest Neighbor (KNN). Model performance was assessed using Accuracy, Precision, Recall, and ROC-AUC metrics to ensure a comprehensive evaluation. The results indicate that Naïve Bayes achieved the best performance with an accuracy of 86.40% and a ROC-AUC value of 0.934, followed by Random Forest with a ROC-AUC of 0.907. All models demonstrated high recall values (above 90%), indicating strong capability in identifying students at risk of dropout. These findings highlight the importance of selecting appropriate algorithms and evaluation metrics when dealing with imbalanced datasets. This study contributes by utilizing a more realistic dataset with noise and imbalance, as well as integrating academic and behavioral data to improve prediction performance. The proposed approach can support early intervention strategies to reduce student dropout rates in higher education.
Co-Authors .S.T., M.Kom., Suhadi Afifah Hafshah, Aidah Afzil Ramadian Ahmad Fathurrozi Ahmad Fathurrozi Aida Fitriyani, Aida Ajif Yunizar Pratama Yusuf Alfiansyah, Rizky AlHakim, Abdu Malik Almajid, Nafis Alviansyah, Mohammad Anita Setyowati Srie Gunarti Beno Aditya Sanusi Dadan Irwan Damar Wijati Dani Yusuf Darusman, Rasyid Dwipa Handayani Dzulfiqar, Muhammad Akmal Ekawati, Inna Endang Retnoningsih Faisal Adi Saputra Fata Nidaul Khasanah Fathurrozi, Ahmad Fauzan, Zaki Nur Ferdiansyah, Mohamad Diandra Fitria Nurapriani Galih Apriansha Pradana Gedhe Hilman Wakhid Gilby Lionska Wenas Haryono Haryono Hendharsetiawan, Andy Achmad Herlawati Herlawati Indah Dwijayanthi Nirmala Ira Wardani Ismaniah Ismaniah Joni Warta Julianto Khairunnisa Fadhilla Ramdhania Kusmara, Hadi Lestari, Panca Indah Maimunah, Maimunah Malikus Sumadyo Mamang Jhulianawati Mega Wahyu Rhamadani Mugiarso Mugiarso Muhamad Galih Muhammad Riky Sudrajat Muhammad Yazid Mukhlis Nur Amanda Pratiwi Nurfauzi, Ridwan Priatna , Wowon Prihatin, Sandy Satyo Putri, Fadia Amelia Rafika Sari RAFIKA SARI Rahmadya Trias Handayanto Ramadhan, Alif Izzuddin Ramadhan, Tedi Ramdhania, Khairunnisa Fadhilla Rasim Rasim Resty Nandya Retna Ayu Puspitasari Retno Nugroho Whidhiasih Ridwan Ridwan Robertoi . Samuel, Federick Dedi Sandy Satyo Prihatin Saputro, Septyo Sari , Rafika Septia, Dwi Yoga Seta Samsiana Seta Samsiana Shofa Shofia Siti Setiawati SITI SETIAWATI Sri Rejeki Sri Rejeki Sugeng Murdowo Sugiyatno Sugiyatno Sugiyatno Sugiyatno Sugiyatno Syahbaniar Rofiah Tambun, Jerisman Jhon Wesli Tyastuti Sri Lestari Tyastuti Sri Lestari Wijaya, Rian Wijaya, Rifky Putra Yusuf, Ajif Yunizar Pratama