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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Journal of Algorithmic Digital Engineering and Networks
Syahrul Ramadhan
Universitas Pembangunan Panca Budi
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance

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Oversampling SMOTE to Handle Imbalance in Multiclass Diabetes Dataset Dika Dika; Syahrul Ramadhan
JADEN : Journal of Algorithmic Digital Engineering and Networks Vol. 1 No. 1 (2025): The Journal of Algorithmic Digital Engineering and Networks
Publisher : Cv. Data Sinergi Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65853/jaden.v1i1.102

Abstract

Diabetes mellitus is a chronic disease with an increasing global prevalence that requires early detection and accurate classification to prevent severe complications. Machine learning has been widely applied in diabetes prediction; however, one of the major challenges lies in the class imbalance problem commonly found in medical datasets. This study focuses on the Multiclass Diabetes Dataset, which consists of 264 samples and exhibits imbalanced distribution among classes (Class 2: 128 samples, Class 0: 96 samples, Class 1: 40 samples). Such imbalance may bias the classifier toward majority classes, reducing its ability to recognize minority classes. The results indicate that SMOTE effectively improved the model’s ability to classify minority classes, with significant increases in recall and F1-score. Among the tested algorithms, Random Forest achieved the best performance, with an overall accuracy of 98% and F1-score above 0.98. Although KNN experienced a slight performance drop after SMOTE, other algorithms, particularly SVM and Logistic Regression, demonstrated notable improvements.
Implementasi Metode Yolo pada Deteksi Objek Manusia Herdianto Herdianto; Hafni Hafni; Darmeli Nasution; Syahrul Ramadhan
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp234-240

Abstract

Until now, the problem of theft of motorbikes and livestock in North Sumatra is still quite high.  Locations for motorbike theft can occur in many places such as schools, homes, parking lots, offices and so on, while for livestock it can occur on pastures and in pens during the day or night and the perpetrators are men. To make this theft a success, various modes are used in varying human positions, from sitting, squatting to standing. To help overcome this, several object detection methods have been developed such as Background Subtraction, Template Matching, Histogram Oriented Gradient (HOG), Deformable Part-based Model (DPM) and Viola Jones (VJ).   Of the many methods that have been used, there are still shortcomings, namely in terms of time, accuracy and various human positions.  For this reason, research was carried out with the aim of improving the time and level of accuracy in detecting human objects using the YOLO method. The research stages carried out in this research include literature study, collecting data, determining training and test data, creating programs, training, and testing. From the trials carried out, it is known that YOLO can detect humans in various positions with a mAP value of 0.99 and an average detection time of 810.01 ms.
Co-Authors Darmeli Nasution Dika Dika Hafni Hafni Herdianto Herdianto