Journal
PENGEMBANGAN RISK ASSESSMENT DALAM EVALUASI MANAJEMEN PENANGGULANGAN KEBAKARAN MELALUI FAULT TREE ANALYSIS
Unnes Journal of Public Health · Vol. 5 No. 1 (2016) · pp. 36–47
- ISSN
- 2548-7604, 2252-6781
- Published
- 2016-01-17
- Publisher
- Universitas Negeri Semarang (UNNES) in cooperation with Association of Indonesian Public Health Experts (Ikatan Ahli Kesehatan Masyarakat Indonesia (IAKMI))
- Language
- eng
Abstract
Fault tree analysis adalah suatu penilaian risiko yang mampu mengidentifikasi potensi bahaya secara spesifik, fokus, rinci pada satu kejadian yang tidak diinginkan, dan mengetahui penyebab kejadian tersebut beserta angka probabilitasnya. Tercatat mulai tanggal 1 Januari – 30 April 2014 terjadi kebakaran sebanyak 10 kali di divisi spinning PT. Apac Inti Corpora dengan kasus tertinggi di unit spinning V (50% kejadian).Tujuan penelitian ini adalah mengetahui faktor penyebab kebakaran, mengembangkan penilaian risiko, dan mengetahui gambaran umum sistem manajemen penanggulangan kebakaran di unit spinning V PT. AIC. Desain penelitian ini adalah kualitatif eksplanatoris. Hasil pengembangan bagan fault tree analysis didapatkan 41 basic event, 24 intermediate event, 1 conditioning event, dan 1 undeveloped event yang secara matematis melalui persamaan aljabar boolean akan menghasilkan 35 single minimum cut sets dan 5 double component cut sets, dengan perhitungan angka probabilitas sebesar 0,3552. Hasil kesesuaian sistem manajemen penanggulangan kebakaran didapatkan 76 poin dari 81 poin standar regulasi yang berlaku di Indonesia. Simpulan penelitian ini adalah kemungkinan terjadinya kebakaran di spinning V sebesar 35,52%. Saran dari penelitian ini adalah dilakukannya evaluasi terhadap kegagalan deteksi dini dan kegagalan teknis. Fault tree analysis has advantages in identifying potential hazards in specifics, focus, detail on a potential undesirable event that called top event, and was able to find out the causes with the value of probability. Recorded at January 1th – April 30th, 2014, there had been 10 cases of fires in the spinning PT. Apac Inti Corpora with the highest cases at spinning V (50% event). The purpose of this research was find out the cause of fires, develop a risk assessment, and learn an overview of fires countermeasures management system in spinning V. This research was a qualitative – explanatory design. Result of development fault tree analysis brings about 41 basic event, 24 intermediate event, 1 conditioning event, dan 1 undeveloped event by boolean algebra, mathematically generates 35 single minimum cut sets and 5 double component cut sets, with the value of probability is 0,3552. Whereas the countermeasure of fire management system conformity obtained 76 points is according from 81 points of regulatory standards in Indonesia. Summary, spinning unit V had the possibility of going fires is 35,52%. Advice, conducted evaluation of failure from early detection and technical failures in the production process.
Collaboration network of the authors
19 authors · 22 co-authorship links
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Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Novan Esma Rozanto | Affiliation unknown | 1 |
| Imaduddin Zaid Harviyanto | Affiliation unknown | 1 |
| Galuh Nita Prameswari | Affiliation unknown | 1 |
| Titik Nur Idayani | Affiliation unknown | 1 |
| Halimah Tri Utami | Affiliation unknown | 1 |
| Rahina Esti Nirwana | Affiliation unknown | 1 |
| Dewi Latifatul Janah | Affiliation unknown | 1 |
| Adzillatin 'Alal Mu'miniina | Affiliation unknown | 1 |
| Nurur Rosida | Affiliation unknown | 1 |
| Irwan Budiono | Affiliation unknown | 1 |
| Rengganis Prisklatiwi | Affiliation unknown | 1 |
| Ani Fatkhi Rizki | Affiliation unknown | 1 |
| Muhammad Azinar | Affiliation unknown | 1 |
| Veronica Prila Arlinda | Affiliation unknown | 1 |
| Miftah Fatmawati | Affiliation unknown | 1 |
| Dyah Ayu Bunga Prihastuti | Affiliation unknown | 1 |
| Claudia Ratna Angelina | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.