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Journal of Engineering and Science Application
Published by Institute Of Advanced Knowledge and Science
ISSN : 30470544     EISSN : 30469627     DOI : https://doi.org/10.69693/jesa
Core Subject : Science, Engineering,
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT
Journal of Engineering and Science Application (JESA) is published by the Institute Of Advanced Knowledge and Science in helping academics, researchers, and practitioners to disseminate their research results. JESA is a blind peer-reviewed journal dedicated to publishing quality research results in the fields of Applied Sciences, Engineering and Information Technology. All publications in the JESA Journal are open access which allows articles to be available online for free without any subscription. JESA is a national journal with e-ISSN: 3046-9627, and is have fee of charge in the submission process and review process. Journal of Engineering and Science Application publishes articles periodically twice a year, in April and October. JESA uses Turnitin plagiarism checks, Mendeley for reference management and supported by Crossref (DOI) for identification of scientific paper.
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 23 Documents
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Comparison of Naive Bayes Method and Support Vector Machine in Classifying Diabetes Mellitus Disease Sari, Indah Kusuma; Wijaya, Rizky Putra
Journal of Engineering and Science Application Vol. 2 No. 2 (2025): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v2i2.33

Abstract

Diabetes mellitus is a chronic disease that occurs due to excessively high blood glucose levels resulting in the absence of insulin. In the period of data at the Siti Khadijah Islamic Hospital in Palembang, which is influenced by the number of patients undergoing health checks such as diabetes mellitus, it affects the classification of data that will complicate the hospital. So by utilizing data mining, classification to determine patients who have undergone examinations including diabetes sufferers or not. With these problems, the author conducted a comparative analysis of two algorithms, namely the naïve Bayes algorithm and the support vector machine algorithm for the classification of diabetes by using the WEKA tool with the Cross Validation and Confusion Matrix options tools with the highest accuracy results, namely the support vector machine algorithm with a polynomial kernel, the results of which are 96.2704% and an error rate of 3.7296%, it can be concluded that the most accurate algorithm in the classification of diabetes is the support vector machine algorithm with a polynomial kernel.
Evaluating Imputation Approaches and Support Vector Regression Parameters in Weather Forecasting Priyatno, Arif Mudi; Ningsih, Yunia
Journal of Engineering and Science Application Vol. 2 No. 2 (2025): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v2i2.34

Abstract

Rainfall plays a vital role in various sectors such as transportation, agriculture, and industry. Having accurate rainfall information enables stakeholders in these fields to take proper measures and minimize potential losses caused by inaccurate data. This study focuses on identifying an effective method for rainfall forecasting by examining imputation techniques in data preprocessing and parameter settings within Support Vector Regression (SVR). The experimental findings indicate that the most effective imputation method for SVR is determined using the Mean Squared Error (MSE) and Mean Absolute Error (MAE) evaluation metrics. Based on MSE, the k-nearest neighbor method proves to be the most reliable approach for data imputation preprocessing. The preprocessing results were then applied to Polynomial SVR with parameters C = 1000, tolerance = 0.001, epsilon = 0.01, and unlimited iterations. Conversely, MAE results highlight Artificial Neural Network (ANN) as the optimal imputation method. ANN, when combined with a radial basis function kernel, gamma = 0.001, C = 1000, tolerance = 0.001, and unlimited iterations, was further tested using RBF SVR under the same parameter settings.
Predictive Modeling of Unilever Indonesia’s Stock Prices with Linear Regression: A RapidMiner-Based Approach Ependi, Zulfan; Firmananda, Fahmi Iqbal
Journal of Engineering and Science Application Vol. 2 No. 2 (2025): October
Publisher : Institute Of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/jesa.v2i2.35

Abstract

Investment decision-making in the capital market requires accurate analysis to minimize risk and maximize returns. This study focuses on PT Unilever Indonesia Tbk, a leading Fast-Moving Consumer Goods (FMCG) company listed on the Indonesia Stock Exchange, to evaluate its stock price prediction using the linear regression method supported by RapidMiner software. Historical stock data were collected from January 2, 2018, to June 27, 2023, including attributes such as opening price, closing price, lowest price, highest price, and trading volume. The dataset was processed using data screening and modeling techniques to construct a linear regression model for prediction. Various scenarios with different proportions of training and testing data (70/30, 80/20, 60/40, and 90/10) were tested to analyze the impact of data distribution on model performance. The evaluation results showed that the 80% training and 20% testing scenario provided the lowest Root Mean Squared Error (RMSE) of 56.699, indicating better predictive accuracy compared to other scenarios. Nevertheless, the linear regression model still produced a relatively high error rate, with the best RMSE value suggesting limitations of this approach for complex market prediction. This study concludes that while linear regression can provide a basic framework for stock price forecasting, incorporating additional economic, fundamental, and external factors could significantly improve predictive reliability. The findings offer practical insights for investors and researchers in understanding the potential and limitations of linear regression in stock market analysis

Page 3 of 3 | Total Record : 23

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