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All Journal Jurnal Informatika Kaputama (JIK) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi)
Mubarok, Rifqi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Evaluasi Kepuasan Pengguna Aplikasi E-Book-Ku Universitas Amikom Purwokerto Dengan Metode UTAUT Susilo, Deni Dwi; Pratama, Riski Adhi; Mubarok, Rifqi; Haqqi, Matsnan; Setiawan, Ito
Jurnal Informatika Kaputama (JIK) Vol 9 No 1 (2025): Volume 9, Nomor 1, Januari 2025
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v9i1.923

Abstract

This research aims to analyze user experience on the level of user satisfaction of the application E-Book-Ku application at Amikom Purwokerto University, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Theory of Acceptance and Use of Technology (UTAUT) model. This research using a quantitative approach with data collection through a questionnaire distributed to students of Amikom Purwokerto University as users of the E-Book-Ku application. E-Book-Ku application. The variables studied consisted of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Behavioral Influence (BC), and Behavioral Behavior (BC). (SI), Facilitating Conditions (FC), Behavioral Intention (BI), and Use Behavior (UB). Data were analyzed using validity test, reliability, and hypothesis testing to test the relationship between variables using SmartPLS. using SmartPLS. The results showed that all hypotheses accepted, where Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions have a positive and significant effect on Behavioral Intention. and significant on Behavioral Intention. In addition, Behavioral Intention is proven to have a positive influence on Use Behavior. This finding indicates that user satisfaction in using the E-Book-Ku application is influenced by ease of use. E-Book-Ku application is influenced by the ease of use of the application, social support from the surrounding environment, and the availability of facilitating conditions. environment, and the availability of conditions that facilitate the use of application. This research contributes to application managers to improve the aspects that influence user experience in order to increase the level of satisfaction and use of the application among students. Keywords: User Satisfaction, Application E-Book-Ku, UTAUT Method, SmartPLS.
Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means Mubarok, Rifqi; Syahputra, Akhmal Angga; Permana, Abdillah Teguh; Sholiah, Lifa; Tarwoto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3438

Abstract

The development of digital technology has transformed the way businesses recruit employees online. This study aims to create an interactive dashboard that facilitates job seekers and companies, using clustering methods with the K-Means algorithm to analyze job posting data in the United States. The data from the Kaggle LinkedIn Job Postings 2023 dataset, consisting of 33,000 records, is processed using the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The clustering analysis results in four job categories: low-mid-level general jobs, high-level executive jobs, time-based jobs, and mid-high-level professional jobs. Model evaluation shows good clustering quality with a Silhouette Coefficient of 0.78 and a Davies-Bouldin Index of 0.55. The developed dashboard helps companies plan recruitment and job seekers find positions matching their skills and salary expectations. The practical contribution of this study is modernizing the recruitment process, assisting companies and recruitment agencies in screening candidates more efficiently, and improving job matching through deeper data analysis.
Implementasi Data Mining untuk Clustering Lowongan Pekerjaan Menggunakan Metode Algoritma K-Means Mubarok, Rifqi; Syahputra, Akhmal Angga; Permana, Abdillah Teguh; Sholiah, Lifa; Tarwoto
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3438

Abstract

The development of digital technology has transformed the way businesses recruit employees online. This study aims to create an interactive dashboard that facilitates job seekers and companies, using clustering methods with the K-Means algorithm to analyze job posting data in the United States. The data from the Kaggle LinkedIn Job Postings 2023 dataset, consisting of 33,000 records, is processed using the CRISP-DM phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The clustering analysis results in four job categories: low-mid-level general jobs, high-level executive jobs, time-based jobs, and mid-high-level professional jobs. Model evaluation shows good clustering quality with a Silhouette Coefficient of 0.78 and a Davies-Bouldin Index of 0.55. The developed dashboard helps companies plan recruitment and job seekers find positions matching their skills and salary expectations. The practical contribution of this study is modernizing the recruitment process, assisting companies and recruitment agencies in screening candidates more efficiently, and improving job matching through deeper data analysis.
Co-Authors Haqqi, Matsnan Permana, Abdillah Teguh Pratama, Riski Adhi Setiawan, Ito Sholiah, Lifa Susilo, Deni Dwi Syahputra, Akhmal Angga Tarwoto