Shafira, Dinda Aulia Ilma (2025) Studi Health, Safety, Environment, and Ergonomics (HSEE) pada Sistem Pembangkit Listrik Tenaga Surya berbasis Adaptive Neuro Fuzzy Inference System (ANFIS). Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Energi surya dikenal sebagai salah satu sumber energi bersih, namun dalam implementasinya ada berbagai tantangan terkait aspek Health, Safety, Environment, and Ergonomics (HSEE) yang perlu diperhatikan. Penelitian ini bertujuan untuk mengidentifikasi sub-variabel dalam aspek HSEE serta menganalisis penilaian HSEE menggunakan metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Hasil identifikasi menunjukkan bahwa aspek health terdiri dari dua sub-variabel, yaitu heat stres dan toxic material, aspek safety meliputi tiga sub-variabel, yaitu risiko listrik, kebakaran, dan terjatuh. Aspek environment terdiri dari tiga sub-variabel, yaitu kerusakan ekosistem, penggunaan lahan, dan daur ulang material. Aspek ergonomics meliputi tiga sub-variabel, yaitu risiko cedera muskuloskeletal, work posture, serta risiko dalam penanganan manual. Sub variabel dikelompokkan dalam kategori probability dan severity untuk melakukan proses penilaian risiko menggunakan pendekatan ANFIS. Model ANFIS yang dikembangkan berdasarkan data kuesioner yang telah dikategorikan ke dalam tiga tingkat risiko penilaian, yaitu baik, cukup, dan buruk. Hasil evaluasi menunjukkan nilai error untuk variabel health sebesar 0.0120 (training) dan 0.0512 (testing); variabel safety sebesar 0.0232 (training) dan 0.1515 (testing); variabel environment sebesar 0.0158 (training) dan 0.0548 (testing); dan variabel ergonomics sebesar 0.0294 (training) dan 0.0327 (testing). Evaluasi performansi dilakukan dengan menggunakan Mean Absolute Percentage Error (MAPE) dan Root Mean Square Error (RMSE). Nilai MAPE sebagai evaluasi model pada variabel health, safety, environment, dan ergonomics masing-masing sebesar 0.89%, 6.64%, 1.48%, dan 0.76%. Sedangkan nilai RMSE pada variabel health, safety, environment, dan ergonomics masing-masing sebesar 0.034, 0.140, 0.045, dan 0.025. Penelitian ini mampu membuktikan bahwa metode ANFIS dapat dijadikan alat bantu pengambilan keputusan untuk menilai performasi HSEE secara sistematis dan adaptif untuk meningkatkan aspek keselamatan, kesehatan, lingkungan, dan ergonomi di tempat kerja.
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Solar energy is recognized as one of the clean energy sources; however, its implementation presents various challenges related to Health, Safety, Environment, and Ergonomics (HSEE) aspects that need to be addressed. This study aims to identify sub-variables within the HSEE aspects and to analyze HSEE assessment using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The identification results show that the health aspect consists of two sub-variables: heat stress and toxic materials. The safety aspect includes three sub-variables: electrical risk, fire hazards, and fall risks. The environmental aspect comprises three sub-variables: ecosystem damage, land use, and material recycling. The ergonomics aspect includes three sub-variables: musculoskeletal injury risk, work posture, and risks in manual handling. The sub-variables are categorized into probability and severity groups to carry out the risk assessment process using the ANFIS approach. The ANFIS model was developed based on questionnaire data that had been categorized into three risk assessment levels: good, fair, and poor. Evaluation results indicate error values for the health variable of 0.0120 (training) and 0.0512 (testing); for the safety variable, 0.0232 (training) and 0.1515 (testing); for the environmental variable, 0.0158 (training) and 0.0548 (testing); and for the ergonomics variable, 0.0294 (training) and 0.0327 (testing). The performance evaluation was conducted using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The MAPE values for the health, safety, environment, and ergonomics variables were 0.89%, 6.64%, 1.48%, and 0.76%, respectively. Meanwhile, the RMSE values for these variables were 0.034, 0.140, 0.045, and 0.025, respectively. This study demonstrates that the ANFIS method can serve as a decision-support tool to systematically and adaptively assess HSEE performance, thereby improving health, safety, environmental, and ergonomic aspects in the workplace.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Health, Safety, Environment, Ergonomics, PLTS, ANFIS, HSEE |
| Subjects: | Q Science > QA Mathematics > QA76.87 Neural networks (Computer Science) Q Science > QA Mathematics > QA248_Fuzzy Sets T Technology > TD Environmental technology. Sanitary engineering > TD194.6 Environmental impact analysis T Technology > TJ Mechanical engineering and machinery > TJ810.5 Solar energy T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1056 Solar power plants. Ocean thermal power plants T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK152.A75 Electrical engineering--Safety measures |
| Divisions: | Faculty of Industrial Technology and Systems Engineering (INDSYS) > Physics Engineering > 30101-(S2) Master Thesis |
| Depositing User: | Dinda Aulia Ilma Shafira |
| Date Deposited: | 06 Aug 2025 06:36 |
| Last Modified: | 06 Aug 2025 06:36 |
| URI: | http://repository.its.ac.id/id/eprint/127791 |
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