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All Journal Journal Medical Informatics Technology Journal of Soft Computing Exploration
Efendi, Ade Irfan Efendi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health

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Hepatitis Prediction Using K-NN, Naive Bayes, Support Vector Machine, Multilayer Perceptron and Random Forest, Gradient Boosting, K-Means Dwi Saputra, Heru; Efendi, Ade Irfan Efendi; Rudini, Edwin; Riana, Dwiza; Hewiz, Alya Shafira
Journal Medical Informatics Technology Volume 1 No. 4, December 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i4.21

Abstract

Hepatitis is a serious disease that causes death throughout the world. It is responsible for inflammation in the human liver. If we manage to detect this life-threatening disease early, we can save many lives from it. In this research paper, we predict hepatitis disease using data mining techniques. We have attempted to propose a feasible approach to improve the performance of our prediction models in our research. We address the problem of missing values in the dataset by replacing them with the mean value. Nine algorithms were applied to the hepatitis disease dataset to calculate prediction accuracy. We measure accuracy, precision, recall, ROC and best score, and we compare them with random search hyperparameter tuning. It is hoped that by using them we will find the optimal combination of hyperparameters to improve the performance of machine learning models which helps us compare the performance of classification models.
Predicting willingness to pay for urban rail transit using machine learning : Evidence from jakarta MRT Kusuma, Wisnu Wardana; EFENDI, ADE IRFAN EFENDI; Prakosa, Dandun; Montanasyah, M. Popik Montanasyah; wanadi, adil; Rizal, Yus
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.18

Abstract

The development of urban transportation requires an efficient, reliable and sustainable system, so fare determination is an important factor in the success of the Jakarta MRT service. In this context, understanding the user's Willingness to Pay (WTP) is crucial because it is not only influenced by economic ability, but also perception and preference for services. This study aims to analyze and predict the WTP of MRT users by integrating transportation economics approaches and machine learning methods. The research data is in the form of primary data from a survey of 296 MRT users which includes socio-economic characteristics, transportation costs, frequency of use and Ability to Pay (ATP). The methodology used includes descriptive analysis and regression modeling using various algorithms, namely Linear Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Regression (SVR) and XGBoost. Model evaluation was carried out using MAE, RMSE and determination coefficient (R²). The results showed that the value of WTP was relatively homogeneous compared to variations in income and transportation costs, which indicated that willingness to pay was not entirely determined by economic ability. The performance of the model shows that no algorithm is consistently superior, with R² values that tend to be low. The feature importance analysis identified income, transportation costs and ATP as the main factors. This research contributes through the application of a multi-model machine learning framework and policy implications that MRT fare determination needs to consider economic aspects and user preferences in a balanced manner.
Co-Authors Dwi Saputra, Heru Dwiza Riana Hewiz, Alya Shafira M. Adil Wanadi Montanasyah, M. Popik Montanasyah Prakosa, Dandun rizal, Yus Rudini, Edwin Wisnu Wardana Kusuma