Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOMLAI: Journal of Machine Learning and Artificial Intelligence
Poningsih Poningsih
STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
Computer Science & IT Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Artificial Neural Network Predicts Motorcycle Sales Level Using Back-propagation Method Reza Pratama; Poningsih Poningsih; Anjar Wanto
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.498 KB) | DOI: 10.55123/jomlai.v1i4.1670

Abstract

Motorcycles are everyone's choice as a means of transportation because they are affordable and can be used for a long time. The high level of motorcycle sales made CV Apollo Motor dealers experience difficulties in procuring motorcycle variants to be sold. The large number of motorcycle variants in one manufacturer makes sales different for each of these variants; there are variants with high and low sales. Therefore predictions about this matter are essential as information material for the company. Input data was obtained from CV Apollo Siantar from 2018 to 2022 as a sales prediction target consisting of 10 data based on Honda motorcycles. Each data has seven variables and one target. This data will later be transformed into data between 0 to 1 before training and testing are carried out using the Back-propagation algorithm artificial neural network. This study uses the back-propagation algorithm. Based on the analysis results, the best architectural model is 7-3-5-1 because it has the highest level of accuracy compared to other models, which is 100%. MSE Testing of 0.08501.
OPTIMIZATION OF SVM ALGORITHM FOR OBESITY CLASSIFICATION WITH SMOTE TECHNIQUE AND HYPERPARAMETER TUNING Khairun Nisa Arifin Nur; Anjar Wanto; Poningsih Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6878

Abstract

Excessive fat accumulation that impairs personal health and raises the risk of chronic diseases is the hallmark of obesity, a global health issue. Decision Tree (DT) has been widely used for obesity classification, but it tends to suffer from overfitting and poor performance on imbalanced datasets. To overcome these limitations, this study proposes an optimization of the Support Vector Machine (SVM) algorithm using Synthetic Minority Over-sampling Technique (SMOTE) and Hyperparameter Tuning. SMOTE was applied to balance the class distribution, whereas Grid Search was utilized to determine the optimal combination of hyperparameters (C, gamma, and kernel). The dataset employed in this research comprises multiple features related to individual health and lifestyle, with obesity level as the target class. The experimental results demonstrate that the optimized SVM model demonstrated strong classification performance, attaining 97% in accuracy, precision, recall, and F1-score. This high performance is significant because it enables more accurate early detection of obesity risk, which can support timely medical intervention and personalized treatment planning, ultimately contributing to better public health outcomesThese findings suggest that incorporating SMOTE and Hyperparameter Tuning substantially improves SVM performance, establishing it as a robust approach for obesity classification and early risk detection.
Co-Authors Khairun Nisa Arifin Nur Reza Pratama Wanto, Anjar