Unsafe Conditions Identification Using Social Networks in Power Plant Safety Reports
DOI:
https://doi.org/10.30812/matrik.v23i2.3883Keywords:
Occupational Health and Safety, Social Network Analysis, Unsafe ConditionAbstract
Power plants in Indonesia grapple with significant challenges in managing occupational health and safety. Power generation companies urgently need to reduce workplace accidents every year and need an application for reporting every potential workplace hazard. The huge reporting data in applications such as IZAT requires thorough analysis to find out the pattern and distribution. This research aims to facilitate the company in hazard mitigation by identifying reported unsafe conditions and building a semantic association network to understand the nature of unsafe conditions between Paiton and Indramayu generating units. The research method uses social network analysis, which is carried out by preprocessing the data using programming to remove noise and then converting the data into a readable format. Then, semantic relationships between words were analyzed, and the data was visualized using the ForceAtlas2 algorithm. The findings revealed a different focus between the two units, where 6.597 reports from the Paiton generating unit mainly highlighted team response and accident-prone workplace conditions, while 5.840 reports from the Indramayu unit emphasized specific conditions, locations, and equipment that pose accident risks
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https://doi.org/10.1093/intqhc/mzy080.
[18] F. N. Hasan, A. S. Aziz, and Y. Nofendri, “Utilization of Data Mining on MSMEs using FP-Growth Algorithm for Menu
Recommendations,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 22, no. 2, pp. 261–270,
mar 2023, https://doi.org/10.30812/matrik.v22i2.2166.
[19] N. Prabowo, “Social Network Analysis for User Interaction Analysis on Social Media Regarding E-Commerce Business,†IJIIS:
International Journal of Informatics and Information Systems, vol. 4, no. 2, pp. 95–102, sep 2021, https://doi.org/10.47738/ijiis.
v4i2.106.
[20] A. Kumari, R. K. Behera, K. S. Sahoo, A. Nayyar, A. Kumar Luhach, and S. Prakash Sahoo, “Supervised link prediction using
structuredbased feature extraction in social network,†Concurrency and Computation: Practice and Experience, vol. 34, no. 13,
jun 2022, https://doi.org/10.1002/cpe.5839.
[21] M. K. Bratawisnu and A. Alamsyah, “Social Network Analysis Untuk Analisa Interaksi User Di Media Sosial Mengenai Bisnis
E-Commerce,†Almana : Jurnal Manajemen dan Bisnis, vol. 2, no. 2, pp. 107–115, aug 2018, https://doi.org/http://journal.unla.
ac.id/index.php/almana/article/view/143/107.
[22] A. J. Al-Bayati, “Impact of Construction Safety Culture and Construction Safety Climate on Safety Behavior and Safety Motivation,â€
Safety, vol. 7, no. 2, p. 41, may 2021, https://doi.org/10.3390/safety7020041.
[23] M. Mambwe, E. M.Mwanaumo,W. D. Thwala, and C. O. Aigbavboa, “Evaluating Occupational Health and Safety Management
Strategy Success Factors for Small-Scale Contractors in Zambia,†Sustainability, vol. 13, no. 9, p. 4696, apr 2021, https:
//doi.org/10.3390/su13094696.
[24] E. J. Tetzlaff, K. A. Goggins, A. L. Pegoraro, S. C. Dorman, V. Pakalnis, and T. R. Eger, “Safety Culture: A Retrospective
Analysis of Occupational Health and Safety Mining Reports,†Safety and Health at Work, vol. 12, no. 2, pp. 201–208, jun 2021,
https://doi.org/10.1016/j.shaw.2020.12.001.
[25] D. I. Af’idah, D. Dairoh, S. F. Handayani, R. W. Pratiwi, and S. I. Sari, “Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan
Bidirectional Long Short Term Memory,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer,
vol. 21, no. 3, pp. 607–618, jul 2022, https://doi.org/10.30812/matrik.v21i3.1402.
[26] D. C. U. Lieharyani and R. Ambarwati, “Visualisasi Data Tweet di Sektor Pendidikan Tinggi Pada Saat Masa Pandemi,†Building
of Informatics, Technology and Science (BITS), vol. 4, no. 1, pp. 116–123, jun 2022, https://doi.org/10.47065/bits.v4i1.1551.
[27] Z. Hou, F. Cui, Y. Meng, T. Lian, and C. Yu, “Opinion mining from online travel reviews: A comparative analysis of Chinese
major OTAs using semantic association analysis,†Tourism Management, vol. 74, no. January, pp. 276–289, oct 2019, https:
//doi.org/10.1016/j.tourman.2019.03.009.
[28] A. N. Navisha, R. Ambarwati, and M. Hariasih, “Twitter Social Network Interaction As Customer Engagement In Competition
For E-Commerce E-Health Performance In Indonesia,†Jurnal Manajerial, vol. 10, no. 02, p. 303, jun 2023, https://doi.org/10.
30587/jurnalmanajerial.v10i02.5279.
[29] E. Muller and R. Peres, “The effect of social networks structure on innovation performance: A review and directions for
research,†International Journal of Research in Marketing, vol. 36, no. 1, pp. 3–19, mar 2019, https://doi.org/10.1016/j.ijresmar.
2018.05.003.
[30] L. Yuliana and D. Ardhyaksa, “Analysis Of Unsafe Action And Unsafe Condition Based On Occupational Health And Safety
Reporting Programs,†Journal of Global Research in Public Health, vol. 4, no. 2, pp. 78–86, 2019, https://doi.org/10.30994/
jgrph.v4i2.40.
[31] B. Fakhruddin, J. Kirsch-Wood, D. Niyogi, L. Guoqing, V. Murray, and N. Frolova, “Harnessing risk-informed data for disaster
and climate resilience,†Progress in Disaster Science, vol. 16, no. September, p. 100254, dec 2022, https://doi.org/10.1016/j.
pdisas.2022.100254.
[32] S. Azad and S. Devi, “Tracking the spread of COVID-19 in India via social networks in the early phase of the pandemic,â€
Journal of Travel Medicine, vol. 27, no. 8, pp. 1–9, dec 2020.
[33] A. S. Rachma, R. Ambarwati, and M. Yani, “Comparison of Twitter Users’ Perception of Content Marketing Effectiveness and
Service Quality in Two Online Transportation,†Almana : Jurnal Manajemen dan Bisnis, vol. 7, no. 1, pp. 134–146, apr 2023,
https://doi.org/10.36555/almana.v7i1.2132.
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2024-03-16
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[1]
A. Mubarokah, R. Ambarwati, D. Dedy, and M. T. Alimova, “Unsafe Conditions Identification Using Social Networks in Power Plant Safety Reports”, MATRIK, vol. 23, no. 2, pp. 389–404, Mar. 2024, doi: 10.30812/matrik.v23i2.3883.
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