K-Means Algorithm for Clustering The Location Of Accident-Prone On The Highway

K-Means Algorithm for Clustering The Location Of Accident-Prone On The Highway

• By Puspitasari, Diah; Wahyudi, Mochamad; Rizaldi, Muhammad; Nurhadi, Acmad; Ramanda, Kresna; Sumanto,;
• Seminar Internasional Bereputasi
• 351
• 1-6

 In Indonesia, there is a highway which connects Jakarta and Bogor, some accidents have occurred at the highway every year. This paper aims to analyze the location of the accident to classify the high and low levels of accident vulnerability in the Jakarta Bogor Highway. First, the extracted data is then grouped based on some of the same characteristics in the dataset namely cause, location, minor injuries, serious injuries and death. Second, the grouping results are visualized in the form of highway maps that can help highway managers in identifying and evaluating several accident-prone points on Jakarta Bogor Highway. The method to be used in data processing in this study is the K-Means clustering algorithm which is expected to produce useful information for Jakarta Bogor Highway managers. The results of this study indicate that accidents that often occur are in cluster 3 with a total of 80 accidents and at least there are in cluster 2 with a total of 57 accidents, the location of accidents that often occurs in cluster 1 is in KM 24, while cluster 2 is in KM 41 and cluster 3 located at KM 10.6

• Katakunci
• URL https://iopscience.iop.org/article/10.1088/1742-6596/1641/1/012086/pdf
• Penerbit ,

REFERENSI

[1] IW. Aprianti and J. Permadi, 2018, “K-Means Clustering Untuk Data Kecelakaan Lalu Lintas Jalan Raya Di Kecamatan Pelaihari,” J. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 5, pp. 613–620. [2] E. Wicaksono, Kusrini, and E. T. Lutfi, 2017, “Analisis Data Kerawanan Kecelakaan Lalu Lintas Menggunakan Metode K-Means (Study Kasus Polres Bantul),” Semin. Nas. Teknol. Inf. dan Multimed. 2017, vol. 5, no. 1, pp. 110–114. [3] D. Puspitasari, S. S. Al Khautsar, and W. P. Mustika, 2019, “Algoritma Na¨ıve Bayes Untuk Memprediksi Kredit Macet Pada Koperasi Simpan Pinjam,” J. Inform. Upgris, vol. 4, no. 2. [4] R. T. Wulandari, 2017, Data Mining Teori dan Aplikasi Rapidminer, 1st ed. Yogyakarta: Gava Media. [5] Ardiyansyah, P. A. Rahayuningsih, and R. Maulana, 2018, “Analisis Perbandingan Algoritma Klasifikasi Data Mining Untuk Dataset Blogger Dengan Rapid Miner,” J. Khatulistiwa Inform., vol. VI, no. 1, pp. 20–28. [6] E. Purwaningsih, 2019, “Analisis Kecelakaan Berlalu Lintas Di Kota Jakarta Dengan Menggunakan Metode K-Means,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 5, no. 1, pp. 139–144. [7] A. Chandra, 2017, “Penerapan Data Mining Menggunakan Pohon Keputusan Dengan Algoritma C4.5 Dalam Menentukan Kecelakaan Penerbangan,” Semin. Nas. Teknol. Inf. dan Multimed. 2017, vol. 5, no. 1, pp. 91–96. [8] C. Silvia, Y. Wilandari, and A. Hoyyi, 2015, “Ketepatan Klasifikasi Tingkat Keparahan Korban KecelakaanLalu Lintas Menggunakan Metode Regresi Logistik Ordinal Dan Fuzzy K-nearest Neighbor In Every Class,” vol. 4, no. Data Mining, pp. 441–451. [9] B. Yang, Z. Zhao, and J. Ma, 2018, “Marine accidents analysis based on data mining using K-medoids clustering and improved A priori algorithm,” IOP Conf. Ser. Earth Environ. Sci., vol. 189, no. 4. [10] A. D. R. Pradipta, M. Awaluddin, and A. L. Nugraha, 2018, “Pemetaan Daerah Rawan Kecelakaan Di Kota Semarang Dengan Menggunakan Metode Cluster Analysis (Studi Kasus: Kecamatan Banyumanik Dan Tembalang),” J. Geod. Undip, vol. 7, no. 4, pp. 185–194. [11] L. Shen, J. Lu, M. Long, and T. Chen, 2019, “Identification of accident blackspots on rural roads using grid clustering and principal component clustering,” Math. Probl. Eng., vol. 2019. [12] S. Adinugroho and Y. A. Sari, 2018, Implementasi Data Mining Menggunakan Weka, 1st ed. Malang: UB Press. [13] W. S. Azis and D. Atmajaya, 2016, “Pengelompokan Minat Baca Mahasiswa menggunakan Metode KMeans,” Ilk. J. Ilm., vol. 8, no. 2, pp. 89–94.