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All Journal International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Journal Medical Informatics Technology
Rudini, Edwin
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Computer Science & IT Dentistry Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health

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Combination of Technology Acceptance Model and Decision-making Process to Study Retentive Consumer Behavior on Online Shopping Rudini, Edwin; Riana, Dwiza; Hadianti, Sri
International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) Vol. 4 No. 1 (2023): INJIISCOM: VOLUME 4, ISSUE 1, JUNE 2023
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/injiiscom.v4i1.9414

Abstract

During the spread of the Covid-19 virus, generally the Indonesian people began to switch from conventional markets to buying and selling goods and services online with various features and conveniences offered to users. The purpose of this study is to find out the extent to which indicators of satisfaction and trust influence consumer attitudes and behavior when deciding to make transactions at online shops. The study method uses a combination of TAM (Theory Acceptance Model) and DMP (Decision Making Process) models using a sampling of 110 student respondents and the public who have made transactions in online shops. Data analysis using SEM (Structural Equation Modeling) theory. The results showed that satisfaction and trust will influence consumers in shaping.
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.
Co-Authors Dwi Saputra, Heru Dwiza Riana Efendi, Ade Irfan Efendi Hewiz, Alya Shafira Sri Hadianti