Taufiqi, Dewi Priscilia (2026) Analisis Penerimaan Pengguna Sistem Vibration Monitoring Berbasis Artificial Intelligence: Studi Kasus pada PT Semen Tonasa dan PT Semen Baturaja (Persero) Tbk. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
PT Semen Tonasa dan PT Semen Baturaja telah menerapkan sistem vibration monitoring berbasis Artificial Intelligence (AI) pada aset kritis. Namun, implementasinya masih terbatas dan direncanakan untuk diperluas ke unit operasional lainnya. Sebelum perluasan dilakukan, perusahaan perlu mengetahui apakah sistem yang telah diterapkan benar-benar diterima dan dimanfaatkan oleh pengguna dalam aktivitas pemantauan dan pemeliharaan. Penelitian ini bertujuan untuk mengevaluasi penerimaan pengguna terhadap sistem vibration monitoring berbasis AI pada kedua perusahaan dengan menggunakan kerangka Technology Acceptance Model (TAM). Penelitian ini menggunakan pendekatan kuantitatif eksplanatori dengan pengumpulan data primer melalui penyebaran kuesioner kepada pengguna sistem di lingkungan operasional dan pemeliharaan. Analisis data dilakukan menggunakan Partial Least Squares–Structural Equation Modeling (PLS-SEM) karena model penelitian terdiri atas konstruk laten dengan beberapa indikator dan melibatkan jumlah responden yang relatif terbatas. Pengujian mencakup evaluasi outer model, inner model, serta perbandingan hasil antara kedua perusahaan untuk mengidentifikasi perbedaan pengaruh konstruk pada masing-masing konteks penggunaan. Hasil penelitian diharapkan dapat memberikan gambaran empiris mengenai hubungan antara persepsi kemudahan penggunaan, persepsi manfaat, sikap terhadap penggunaan, niat perilaku, dan penggunaan aktual dalam menentukan penerimaan sistem. Temuan penelitian juga diharapkan dapat menjadi dasar penyusunan rekomendasi bagi perusahaan dalam mengoptimalkan pemanfaatan sistem serta menilai kesiapan perluasan implementasi vibration monitoring berbasis AI ke unit operasional lainnya.
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PT Semen Tonasa and PT Semen Baturaja have implemented an Artificial Intelligence (AI)-based vibration monitoring system on critical assets. However, its implementation remains limited and is planned to be expanded to other operational units. Prior to the expansion, the companies need to determine whether the implemented system has been effectively accepted and utilized by users in monitoring and maintenance activities. This study aims to evaluate user acceptance of the AI-based vibration monitoring system in both companies using the Technology Acceptance Model (TAM) framework. This research adopts an explanatory quantitative approach, with primary data collected through questionnaires distributed to system users in operational and maintenance departments. The data are analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM), as the research model consists of latent constructs with multiple indicators and involves a relatively limited number of respondents. The analysis includes an evaluation of the outer model, inner model, and a comparison of the results between the two companies to identify differences in the influence of the constructs across their respective usage contexts. The results are expected to provide empirical insights into the relationships among perceived ease of use, perceived usefulness, attitude toward using, behavioral intention, and actual system use in determining system acceptance. Furthermore, the findings are expected to serve as a basis for recommendations to assist the companies in optimizing system utilization and assessing their readiness to expand the implementation of AI-based vibration monitoring to other operational units.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Technology Acceptance Model, Vibration Monitoring, Artificial Intelligence, Predictive Maintenance, PLS-SEM |
| Subjects: | Q Science > QA Mathematics > QA336 Artificial Intelligence T Technology > T Technology (General) > T58.6 Management information systems T Technology > TA Engineering (General). Civil engineering (General) > TA355 Vibration. T Technology > TJ Mechanical engineering and machinery > TJ174 Maintenance and repair of machinery |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Industrial Engineering > 26201-(S1) Undergraduate Thesis |
| Depositing User: | Dewi Priscilia Taufiqi |
| Date Deposited: | 15 Jul 2026 08:36 |
| Last Modified: | 15 Jul 2026 08:43 |
| URI: | http://repository.its.ac.id/id/eprint/135008 |
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