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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat Kaizen : Management Systems and Industrial Engineering Journal Barometer Jurnal Aplikasi Dan Inovasi Ipteks SOLIDITAS Profisiensi : Jurnal Program Studi Teknik Industri Jurnal Airaha JUSTE (Journal of Science and Technology)
Agung K Henaulu
Universitas Darussalam Ambon
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Dynamics of Job Satisfaction Among Contract-Based Employees: The Role of Commitment and Work Environment at the Education Office of South Buru Regency Wahyudi, Indra; Afsoh, Fradana Firdiantoni; Zakaria, Syawal; Ollong, Kingsly Awang; Latocinsina, Yudhy Muhtar; Henaulu, Agung K.; latuconsina, Bay
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 23, No 1 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v23i1.38639

Abstract

The vital role of contract-based employees (honorer) in supporting the operational and administrative functions of local government education offices is often accompanied by employment uncertainties, potentially affecting their job satisfaction. This study aims to analyze the influence of organizational commitment and the work environment on the job satisfaction of contract employees at the Education Office of South Buru Regency. Employing a quantitative associative approach, data were collected from 100 respondents and analyzed using multiple linear regression. The results indicate that both employee commitment and the work environment have a significant positive effect on job satisfaction. However, the work environment demonstrates a stronger influence (β = 0.739, p = 0.000) compared to organizational commitment (β = 0.094, p = 0.016). The regression model explains 91.6% of the variance in job satisfaction (R² = 0.916). This study concludes that to enhance the job satisfaction of contract employees, management interventions should prioritize creating a supportive and conducive work environment while simultaneously fostering organizational commitment. These improvements are essential for enhancing individual well-being and the overall quality of educational services.
COMPARISON OF LINEAR REGRESSION AND ARTIFICIAL NEURAL NETWORK MODELS FOR PREDICTING FISH CATCH VOLUME IN URENG VILLAGE, CENTRAL MALUKU Kasriana, Kasriana; Ode, Rasid; Lukman, Eryka; Henaulu, Agung K.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1743-1756

Abstract

This study aims to develop a predictive model for fish catch volume in Ureng Village, Central Maluku, using a mathematical modeling approach based on artificial intelligence with the Scikit-Learn and TensorFlow libraries. The research dataset consists of 24 monthly data records collected from July 2024 to June 2025. The data were obtained through a combination of primary and secondary collection methods. Primary data were gathered through interviews, field observations, and fishermen’s catch records, while secondary data included oceanographic parameters such as sea surface temperature, weather conditions, and current velocity. Two main models were developed: a linear regression model using Scikit-Learn as the baseline and a neural network model using TensorFlow as the comparator, both trained and evaluated on the same dataset to ensure consistency. The testing results show that the linear regression model produced a Mean Squared Error (MSE) of 0.8821 and a coefficient of determination (R²) of 0.682, while the neural network model achieved an MSE of 0.5423 and an R² of 0.815. These findings indicate that the neural network model is more capable of capturing nonlinear patterns among temperature, weather, and current variables, resulting in higher prediction accuracy than the linear model. Nevertheless, this study is limited by the relatively small sample size and the need for a more detailed description of the data period and measurement units to allow a more objective evaluation of the model’s performance. Overall, this AI-based approach has the potential to support more efficient, adaptive, and sustainable decision-making in fishery planning for coastal communities.
Co-Authors Abd. Samad Rumagia Abdi Ansyah Solissa Achmad Jais Ely Afsoh, Fradana Firdiantoni Ahmad A. Latuponu Ahmad A. Latuponu Akbar Rumuar Aliah Rahman Aminah Soleman Ardian, Sony Awia Conang Besmin Jahawadan Ely, Achmad Jais Eryka Lukman Farida Mony Ferdimon Kainama Indra Wahyudi Indra Wahyudi, Indra Janaria Rumbouw John Waldi Ch. Karuwal Kasriana, Kasriana Kimin Kimin Kisman Ady Kolengsusu, Haris Latocinsina, Yudhy Muhtar latuconsina, Bay Masda Tuhuteru Maulana Nur Fazri Angkotasan Mony, Farida Muhammad Alvuad Mualo Muhammad Ramdhani Ohorella Nasrulyati, Tri Siwi Ollong, Kingsly Awang Rahma W. Sowakil Rahul Lestaluhu Rapiah Sarfa Marasabessy Rasid Ode Reza Abdulmudy Rina Latuconsina Sitnah Aisyah Marasabessy Sony Ardian Sri Pitrianti Marasabessy Syawal Zakaria Tahir Tuasikal Yenni Sofyan Yudhy Muchtar Latuconsina Yudhy Muhtar Latuconsina