Early Diagnosis of Damage to Automatic Injection Motorcycles Using a Decision Tree Algorithm Based on Mobile Application
DOI:
https://doi.org/10.52731/liir.v005.202Keywords:
artificial intelligence, decision tree, injection motorcycle, motorcycle damageAbstract
Indonesia is the country with the most motorcycle users, where nearly a third of the population owns motorcycles. Automatic motorcycles are the most widely used type because of their agility and ability to save fuel. However, if there is sudden damage, it can hamper activity. In addition, most users do not know the cause of the damage they are experiencing. The purpose of this research is to develop a mobile-based application to help users of automatic injection motorcycles in diagnosing earlier the problems they are experiencing. All data related to damage and its solutions were obtained based on interviews with experts and conforming to existing theories. The research method used is to develop applications using the System Development Life Cycle (SDLC) and the artificial intelligence algorithm used is the Decision Tree. Decision Tree has become one of the popular algorithms for diagnosing damage to various systems and devices, including in this context for diagnosing damage to automatic injection motorcycles. This method is used because it is a key path for identifying damage that has rules. The Decision Tree is a technique of exploring data into a branch to produce a solution. The results of this study are that the application is fully functional, both from the user and admin side. In addition, through the application of the Decision Tree Algorithm with an accuracy rate of 100% without any discrepancies. Finally, we can also confirm that the application can be used by users to assist in solving damage problems by accommodating maintenance actions or also choosing repair services.
References
Surahman, A. T. Prastowo, and L. A. Aziz, “Rancang Alat Keamanan Sepeda Motor Honda Beat Berbasis SIM GSM menggunakan Metode Rancang Bangun,” Jurnal Teknologi dan Sistem Tertanam, vol. 03, no. 01, 2022, pp. 17-24.
H. Muhammad, “Kaji Ulang Perbandingan Pemakaian 3 Jenis Bahan Bakar Pertalite, Pertamax, dan Pertamax Turbo terhadap Performa pada Sepeda Motor Matic Sistem Injeksi,” Skripsi Universitas Tridianati, 2021.
K. Saengtabtim, N. Leelawat, J. Tang, W. Treeranurat, N. Wisittiwong, A. Suppasri, K. Pakoksung, F. Imamura, N. Takahashi, and I. Charvet, “Predictive Analysis of the Building Damage from the 2011 Great East Japan Tsunami Using Decision Tree Classification Related Algorithms,” IEEE Access, vol. 9, 2021, pp. 31065-31077.
H. Esmaily, M. Tayefi, H. Doosti, M. G-Mobarhan, H. Nazami, and A. Amirabadizadeh, “A Comparison between Decision Tree and Random Forest in Determining the Risk Factors Associated with Type 2 Diabetes,” Journal of Research in Health Sciences, vol. 18, no. 2, 2018, pp. 1-7.
J. Smith, M. Musharraf, B. Veitch, and F. Khan, “Pilot Study Using Decision Trees to Diagnose the Efficacy of Virtual Offshore Egress Training,” IEEE Transactions on Learning Technologies, vol. 15, no. 6, 2022, pp. 812-826.
A. S. Wiguna and I. Harianto, “Sistem Pakar Diagnosa Kerusakan Sepeda Motor Matic Injeksi menggunakan Metode Forward Chaining berbasis Android,” SMARTICS Journal, vol. 3, no. 1, 2017, pp. 25-30.
D. M. P. Sihombing and H. Fahmi, “Penerapan Sistem Pakar Mendiagnosa Kerusakan Sepeda Motor Automatic Dan Injeksi Berbasis Android Dengan Metode Forward Chaining,” Jurnal Ilmu Komputer dan Sistem Informasi, vol. 4, no. 2, 2021, pp. 59-65.
R. F. Naryanto, M. K. Delimayanti, Kriswanto, A. D. N. I. Musyono, I. Sukoco, and M. N. Aditya, “Development of a Mobile Expert System for the Diagnosis on Motorcycle Damage Using Forward Chaining Algorithm,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 27, no. 3, 2022, pp. 1601-1609.
Rusdiansyah and F. Rantau, “Sistem Pakar Untuk Mendiagnosa Kerusakan Mesin Sepeda Motor Matic Dengan Metode Forward Chaining,” Jurnal PILAR Nusa Mandiri, vol. 14, no. 1, 2018, pp. 35-42.
A. Syaputra and D. Setiadi, “Sistem Pakar Diagnosa Kerusakan Sepeda Motor Yamaha Matic menggunakan Metode Forward Chaining,” Jurnal Sistem Komputer Musirawas, vol. 05, no. 02, 2020, pp. 126-135.
D. Saputra, D. Purwaningtias, and W. Irmayani, “Sistem Pakar Diagnosa Kerusakan Mesin Sepeda Motor Matic Berbasis Web menggunakan Certainty Factor,” Proseding Seminar Nasional Sains dan Teknologi, 2018, pp. 1-11.
B. Setiyaji, T. Susyanto, and D. Remawati, “Aplikasi Diagnosa Kerusakan Motor 4 Tak Honda Vario dengan Algoritma C4.5,” Jurnal TIKomSiN, vol. 6, no. 1, 2018, pp. 29-36.
S. Shafiq, A. Mashkoor, C. M.Dorn, and A. Egyed, “A Literature Review of Using Machine Learning in Software Development Life Cycle Stages,” IEEE Access, vol. 9, 2021, pp. 140896-140920.
A. A. Syahidi, Subandi, and A. Mohamed, “AUTOCAR: A Car Design and Specification as a Work Safety Guide Based on Augmented Reality Technology,” Jurnal Pendidikan Teknologi dan Kejuruan, vol. 26, no. 1, 2020, pp. 18-25.
B. Monsalve, N. Aliane, E. Puertas, and J. F. Andrés, “Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts,” IEEE Access, vol. 11, 2023, pp. 41444-41454.
A. Basuki and I. Syarif, “Decision Tree,” Surabaya: Politeknik. Elektronika Negeri Surabaya, 2003.
