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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Anton Yudhana
Universitas Ahmad Dahlan, Yogyakarta, Indonesia
Computer Science & IT

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The UTAUT Model for Measuring Acceptance of the Application of the Patient Registration System Tugiman Tugiman; Herman Herman; Anton Yudhana
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 2 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2844

Abstract

The Covid-19 pandemic forced hospitals to innovate so that services comply with health protocols in the new adaptation period. Electronic Health (E-Health) such as online patient registration is expected to be a solution for hospitals with high patient visit rates. The purpose of this study was to analyze the level of user acceptance and the factors that influence the implementation of the online patient registration system for hospital patients. This research was conducted at PKU Muhammadiyah Gombong Hospital, which has implemented an online patient registration system based on Android since May 2020. The evaluation model uses Unified Theory of Acceptance and Use of Technology(UTAUT) and the analysis uses the Structural Equation Model (SEM) method using smart PLS. The results of the research show that all the hypotheses formed show valid values. So it can be said that the application of SIPENDOL in hospitals has been well received by users.
Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image Miftahuddin Fahmi; Anton Yudhana; Sunardi Sunardi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 3 (2023)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i3.2819

Abstract

Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies.
Co-Authors Herman Miftahuddin Fahmi Sunardi Sunardi Tugiman Tugiman