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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH)
Irawati, Okta
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Computer Science & IT Decision Sciences, Operations Research & Management

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Analisis Pengaruh Kualitas Layanan Website Pembinaan Sekolah Menengah Kejuruan (PSMK) Terhadap Kepuasan Pengguna Dengan Menggunakan Metode Webqual Istiqomah, Iis; Irawati, Okta
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6444

Abstract

This study aims to evaluate the factors influencing the quality of the Pembinaan Sekolah Menengah Kejuruan (PSMK) website using a user satisfaction approach. The PSMK website is a critical component of the Education Management Information System; however, users often encounter issues related to information quality, ease of interaction, usability, and interface design. These challenges may hinder the effectiveness of technology-based educational services. As a solution, this study employs the Modified WebQual 4.0 method to analyze four key aspects: information quality, interaction, usability, and interface design. Data were collected through a survey of 100 PSMK website users and analyzed using multiple linear regression. This approach helps identify the most significant factors influencing user satisfaction. The results indicate that all four factors—information quality, interaction, usability, and interface design—positively and significantly affect user satisfaction. Enhancing these aspects can significantly improve the user experience. These findings provide valuable insights for educators and policymakers to improve the quality of the PSMK website, support more effective educational services, and promote the integration of technology in vocational education. By addressing these factors, the PSMK website can better support the achievement of educational goals at the vocational school level.
Implementasi dan Optimalisasi Metode Naive Bayes Dalam Sistem Deteksi Dini Penyakit Tiroid Nurhasanah, Nurhasanah; Asyiah, Nilovar; Irawati, Okta
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7940

Abstract

This study aims to develop an early detection system for thyroid disease using the Naive Bayes algorithm. The dataset used is the Thyroid Disease Dataset from the UCI Machine Learning Repository, consisting of thousands of patient records. Prior to model training, the data undergoes preprocessing steps such as handling missing values, numerical normalization, and categorical encoding. The classification process involves calculating the prior probability, likelihood, and posterior probability for each class: normal, hypothyroid, and hyperthyroid. The system also presents the probability percentage for each class as an automated diagnosis result. Model accuracy is evaluated using a Confusion Matrix, achieving an accuracy score of 98.01% on the test data. These results indicate that the proposed approach can effectively and accurately classify thyroid conditions for early diagnosis purposes.
Co-Authors Asyiah, Nilovar Istiqomah, Iis Nurhasanah Nurhasanah