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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Ilmiah FIFO
Pinem, Tuahta Hasiholan
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Computer Science & IT Electrical & Electronics Engineering

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Evaluasi Kinerja Algoritma Klasifikasi Deep Learning dalam Prediksi Diabetes Pinem, Tuahta Hasiholan; Putra, Zico Pratama
Jurnal Ilmiah FIFO Vol 17, No 1 (2025)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2025.v17i1.003

Abstract

Penelitian yang bertujuan untuk mengembangkan dan mengevaluasi algoritma model prediksi diabetes telah dilakukan dengan menggunakan algoritma model K-Nearest Neighbor Classifier, Naive Bayes, Regresi Logistik, SVM, dan Neural Network. Dataset yang digunakan didapatkan dari Kaggle yang terdiri dari 768 data pasien yang dibagi menjadi data training 60%, data validation 20%, dan data test 20%. Hasil penelitian menunjukkan bahwa akurasi tertinggi diperoleh oleh model Regresi Logistik dan Neural Network, masing-masing sebesar 73% dan 72%. Model Regresi Logistik unggul dalam presisi untuk kelas non-diabetes dan recall untuk kelas diabetes, sedangkan model Neural Network menunjukkan keseimbangan performa yang baik antara presisi dan recall untuk kedua kelas. Model Naive Bayes juga menunjukkan performa yang kompetitif dengan akurasi 72% dan recall tinggi untuk kelas diabetes, model ini dapat menjadi pilihan yang baik dalam situasi yang memprioritaskan deteksi positifKinerja yang lebih rendah ditunjukkan oleh model KNN dan SVM jika dibandingkan dengan model lainnya. Masalah utama yang diangkat dalam penelitian ini adalah pentingnya meningkatkan akurasi prediksi diabetes untuk mendukung deteksi dini dan pengobatan. Secara keseluruhan, model Regresi Logistik dan Neural Network diidentifikasi sebagai model yang paling potensial untuk prediksi diabetes, dengan Regresi Logistik menunjukkan efektivitas yang tinggi dalam mengidentifikasi kasus non-diabetes, sementara Neural Network memberikan keseimbangan performa yang baik di kedua kelas.
Integrating Multiple Machine Learning Models to Predict Heart Failure Risk Pinem, Tuahta Hasiholan; Rianto, M., Yan
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.13006

Abstract

The research aims to create and evaluate machine learning models for the prognosis of heart failure based on patient medical information. Various predictive models have been created employing algorithms like logistic regression, decision trees, random forests, K-nearest neighbors, naive Bayes, support vector machines (SVMs), neural networks, and ensemble voting classifiers. The dataset utilized comprises diverse clinical characteristics from patients diagnosed with heart failure. The data underwent division into training and testing sets in an 80:20 ratio. Metrics including accuracy, Cross Validation Score, and ROC_AUC Score score were used to assess the models' performance. The findings reveal that the Voting Classifier, amalgamating the Logistic Regression and Support Vector Classifier models, demonstrated superior performance with an accuracy of 88.04%, a cross-validation score of 91.01%, and a ROC_AUC score of 88.00%. Further scrutiny suggested that blood pressure and cholesterol levels serve as substantial indicators of heart failure. This study presents a notable advancement in the utilization of machine learning models for heart failure prediction by scrutinizing diverse algorithms and pinpointing the most pertinent clinical characteristics. These outcomes hint at the potential for the development of machine learning-driven clinical tools to facilitate early detection and enhance medical interventions.
Enhancing The Accuracy of Small Object Detection In Traffic Safety Attributes Using Yolov11 And Esrgan: Peningkatan Akurasi Deteksi Objek Kecil pada Atribut Keselamatan Berkendara Menggunakan Yolov11 dan ESRGAN Pinem, Tuahta Hasiholan; Haris, Muhammad
Telematika Vol 22 No 3 (2025): Edisi Oktober 2025
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v22i3.14800

Abstract

This study aims to detect motorcycle rider attributes, specifically helmets and side mirrors, using a deep learning approach combining YOLOv11 and ESRGAN models. The proposed model addresses challenges in attribute detection under real-world conditions, such as low-resolution images, varying angles, and uneven lighting. The dataset comprises images of motorcycle riders captured by surveillance cameras (CCTV), which underwent preprocessing, augmentation, and resolution enhancement using ESRGAN to improve input quality. The results demonstrate that ESRGAN significantly enhances the performance of YOLOv11, particularly for high-resolution images. The YOLOv11 + ESRGAN model with 300 epochs achieved the best performance, with precision of 75.8%, recall of 69.1%, and an F1-score of 0.7 during testing. During validation, the model reached a precision of 0.826 and recall of 0.797, indicating good generalization capabilities. Compared to the YOLOv11 model without ESRGAN, this combination significantly improved accuracy, especially in detecting small attributes such as side mirrors. This study suggests further exploration with larger and more diverse datasets and fine-tuning to enhance detection accuracy. Additionally, integrating the model into real-world systems based on edge computing can accelerate real-time inference and reduce reliance on cloud-based servers. With broader implementation, this model has the potential to improve the efficiency and safety of AI-powered traffic monitoring systems.
Co-Authors MUHAMMAD HARIS Putra, Zico Pratama Rianto, M., Yan