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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Applied Data Sciences
Maharina, Maharina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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E-government in the public health sector: kansei engineering method for redesigning website Zonyfar, Candra; Maharina, Maharina
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11648

Abstract

The role of government health websites as a source of referrals and credible health information is very important, especially now that everything is digital. People use the internet and make health websites as the first step in finding health information, government policies related to health, and public health services. So it is very important to consider the user aspect in designing the appearance of an appropriate health website. This study utilizes the Kansei Engineering KEPack type 1 in analyzing various emotional factors related to the e-government website interface in the health sector. So that it can be found that the psychological emotional factors of users are important and become the main recommendations in the design of the website interface. We are focuses on user preferences for the e-government site interface of the Karawang District Health Office with the Kansei Engineering Type I approach. The Kansei Engineering study was conducted to analyze various emotional factors related to the user interface by comparing 5 specimens of e-Government sites in the health sector. A total of 20 kansei words were identified which were then processed using the multivariate statistical method Cronbach's Alpha (CA), Coefficient Correlation Analysis (CCA), Factor Analysis (FA). The result is that 4 kansei words have a high influence and successfully present a matrix of design element recommendations with 7 main elements and 45 sub-criteria for specific design elements.
Machine Learning Models for Predicting Flood Events Using Weather Data: An Evaluation of Logistic Regression, LightGBM, and XGBoost Maharina, Maharina; Paryono, Tukino; Fauzi, Ahmad; Indra, Jamaludin; Sihabudin, Sihabudin; Harahap, Muhammad Khoiruddin; Rizki, Lutfi Trisandi
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.503

Abstract

This study examines flood prediction in Jakarta, Indonesia, a pressing concern due to its significant implications for public safety and urban management. Machine Learning (ML) presents promising methodologies for accurately forecasting floods by leveraging weather data. However, flood prediction in Jakarta remains challenging due to the city’s highly variable weather patterns, including fluctuations in rainfall, humidity, temperature, and wind characteristics. Existing methods often struggle with these complexities, as they rely on traditional ML models such as K-Nearest Neighbors (KNN), which may not capture certain patterns or provide high accuracy and robustness. Therefore, this study proposes three ML methods—Logistic Regression (LR), LightGBM, and XGBoost—to predict floods accurately. Five performance metrics (i.e., accuracy, area under the curve (AUC), precision, recall, and F1-score) were used to measure and compare the accuracy of the algorithms. The proposed method consists of three main processes. The first process involves data preprocessing and evaluation using 14 different ML models. In the second process, additional feature engineering is applied to improve the quality of the data. Finally, the third process combines the previous steps with oversampling techniques and cross-validation methods. This structured approach aims to enhance the overall performance of the analysis. The experimental results show that Process 3 significantly improves performance compared to Processes 1 and 2. The model predicts floods with an accuracy score of 93.82% for LR, 96.67% for XGBoost, and 96.81% for LightGBM, respectively. Thus, the proposed model offers a solution for operational decision-making in flood risk management, including flood mitigation planning.
Co-Authors AA Sudharmawan, AA Indra, Jamaludin Muhammad Khoiruddin Harahap Paryono, Tukino Rizki, Lutfi Trisandi Sihabudin Sihabudin, Sihabudin Zonyfar, Candra