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All Journal NOTARIUS MATEMATIKA Media Wisata Khizanah al-Hikmah : Jurnal Ilmu Perpustakaan, Informasi, dan Kearsipan Jurnal Bulletin Ganaya: Jurnal Ilmu Sosial dan Humaniora Majalah Ilmiah Pengkajian Industri Ideas: Jurnal Pendidikan, Sosial dan Budaya Aksen : Journal of Design and Creative Industry Jurnal Pengabdian Dharma Bakti Respati Kepariwisataan: Jurnal Ilmiah SABBHATA YATRA : Jurnal Pariwisata dan Budaya Automotive Experiences Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Novi Irawati
Jurusan Matematika FMIPA Universitas Diponegoro Semarang
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Journal : Automotive Experiences

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Enhancing Brake System Evaluation in Periodic Testing of Goods Transport Vehicles through FTA-FMEA Risk Analysis Ansori, Irfan; Waskito, Dwitya Harits; Mutharuddin, Mutharuddin; Irawati, Novi; Nugroho, Sinung; Mardiana, Tetty Sulastri; Subaryata, Subaryata; Siregar, Nurul Aldha Mauliddina
Automotive Experiences Vol 6 No 2 (2023)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.8394

Abstract

Failure of the braking system is one of the factors causing traffic accidents, therefore periodic testing of goods transport vehicles is very important. In fact, the incidence rate is still very high despite routine testing. Standard Operating Procedures (SOP) for periodic testing must be updated to reduce the risk of possible accidents. Therefore, procedures for updating the SOP for periodic brake system testing are presented in this article. The Fault Tree Analysis (FTA) and Failure Mode and Effect Analysis (FMEA) methods were applied based on accident investigation data from the National Transportation Safety Committee (NTSC) from 2017 to 2022. FTA is used for risk identification, while FMEA is used for risk analysis to find the highest-risk failure cases. The results of our analysis showed that 13 failure cases were classified as intolerable so additional SOPs were required for each case. Finally, the results of this study provide new insights for stakeholders to revise the rules regarding periodic vehicle testing.
The Road Safety: Utilising Machine Learning Approach for Predicting Fatality in Toll Road Accidents Mutharuddin, Mutharuddin; Rosyidi, M.; Karmiadji, Djoko Wahyu; Fitri, Hastiya Annisa; Irawati, Novi; Waskito, Dwitya Harits; Mardiana, Tetty Sulastry; Subaryata, Subaryata; Nugroho, Sinung
Automotive Experiences Vol 7 No 2 (2024)
Publisher : Automotive Laboratory of Universitas Muhammadiyah Magelang in collaboration with Association of Indonesian Vocational Educators (AIVE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/ae.11082

Abstract

Road safety is one of the critical government transportation concerns, especially on the toll roads. With the increasing number of toll roads as part of infrastructure planning, road traffic accidents are significantly escalating. Developing a system that predicts accidents on toll roads will benefit to reduce the harm that is caused by traffic accidents. This study will propose a method for analysing toll road accidents in Indonesia using historical toll road accident data as a dataset to become a pattern to examine the frequency of accidents. This dataset consists of various parameters from three main factors that cause accidents: human, environmental, and road infrastructure factors. Machine learning technique will be mainly used to determine the most influencing factors by employing classifiers such as Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbors (KNN) can construct the prediction model. Fourteen subfactors from the data were used to predict the future fatalities caused by accidents, which allowed the system to forecast the accident fatality. The results show accuracy performance on the test set with LR, DT, KNN, and GNB models, 85.3%, 79.4%, 87.1%, and 77.1%, respectively. The KNN Classifier model has the most minor error value of 0.6 compared to the other models. The study’s findings will help analyse the causal factors involved in toll road accidents and could be utilised by road authorities to employ risk control options to mitigate the ramifications.
Co-Authors Ahmad Sobari, Haris Amin Kiswantoro Ansori, Irfan Ardhanareswari, Emma Asep Syaiful Bahri, Asep Syaiful Athar, Nathan Baluk, Maria Avilla Golu Deskarina, Rekta Dwi Budi Santoso Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Eko Sugiarto Evrita Lusiana Utari, Evrita Lusiana Fiantika, Thiya Fitri, Hastiya Annisa Hendratono, Tonny Ikhwan Mustiadi Karmiadji, Djoko Wahyu Mardiana, Tetty Sulastri Mardiana, Tetty Sulastry Meiana Maulida Hikmawati Mutharuddin, Mutharuddin Ngasifudin, Ngasifudin Nugroho, Sinung Nur Rohman Nuraisya, Nissa Prakoso, Aditha Agung Prakoso, Aditha Agung Prasetyo, Hendi Pratiwi, Nindy Nadia Pribudi, Anggarani Priyanto, Sabda Elisa Putu Susastriawan, Anak Agung Rando, Ayustina Silvia Rizki Makiya, Kiki Robertus Heri Sulistyo Utomo Rosyidi, M. Saputra, Yova Febrian Sari, Vivin Dewita Satriawati, Zahrotun Siregar, Nurul Aldha Mauliddina Slamet Hani, Slamet Sri Wahyuni Subaryata, Subaryata Sugiarto S Susanto, Dwiyono Rudi Tosofu, Irwan Waskito, Dwitya Harits Widodo, Janu Putra Tri Cahyo Yuliana Pinaringsih Kristiutami Zumar Rahafuna, Muhammad