PERBANDINGAN KINERJA ALGORITMA KNN, DECISION TREE, SVM, DAN ANN UNTUK MELAKUKAN KLASIFIKASI PADA CALON PEMBELI MOBIL

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Published: Apr 20, 2025
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Classification, KNN, Decision Tree, SVM, Prospectivebuyers
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Abstract

Business operator sengaged in buying and selling cars are faced within creasingly competitive challenges in the automotive market, especially in identifying the right prospective buyers. to address this issue, this research isutilizing machine learning by implementing classification algorithms, namely KNN, Decision Tree, SVM, and ANN. In aneffortto evaluate the effective nesso feach algorithm, this research compares their performance with achother. This research indicates that the Decision Tree algorithm emerges as the mostsui table choice in the process of classifying potential buyers in the dataset of prospective car buyers. The sefindings provide a strong basis for business operators to enhancemarketing and sales strategies in the car trading business, as well as gain deeperinsights into there levant characteristics of potential buyers.

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Vol. 20 No. 1 (2025): Jurnal Komputer dan Informatika
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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References

[1] Y. Al-Anqoudi, “Using Machine Learning in Business Process Re-Engineering,” Big Data and Cognitive Computing, vol. 5, 2021.

[2] N. S. S. Sugiana dan B. Musty, “Analisis Data Sistem Informasi Monitoring Marketing,” Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 12, pp. 696-708, 2023.

[3] V. Chandgude dan B. Kawade, “Role of Artificial Intelligence and Machine Learning in Decision Making for Business Growth,” International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), vol. 3, no. 1, pp. 54-57, 2023.

[4] M. A. Tripathi, K. Madhavi, P. Kandi, V. K. Nassa, B. Mallik dan K. Chakravarthi, “Machine learning models for evaluating the benefits of business intelligence systems,” The Journal of High Technology Management Research, vol. 34, no. 2, 2023.

[5] H. Ji, X. Xianon, G. Su, J. Wang dan Y. Wang, “Utilizing Machine Learning for Precise AudienceTargeting in Data Science and Targeted Advertising,” Academic Journal of Science and Technology, vol. 9, pp. 216-220, 2024.

[6] J. Nagy dan Z. Jambor, “Competitiveness in global trade: The case of the automobile industry,” ECONOMIC ANNALS, vol. LXIII, pp. 61-84, 2018.

[7] I. Lewaa, “Customer Segmentation Using Machine Learning Model: AnApplication of RFM Analysis,” Journal of Data Science and Intelligent Systems, vol. XX, pp. 1-12, 2023.

[8] F. Ridzuan, W. M. Nazmee dan W. Zainon, “A Review on Data Cleansing Methods for Big Data,” dalam Procedia Computer Science 161, 2019.

[9] M. B. Antor, “A Comparative Analysis of Machine Learning Algorithms to Predict Alzheimer’s Disease,” Journal of Healthcare Engineering, 2021.

[10] S. Uddin, A. Khan, E. M. Hossain dan . M. . A. Moni, “Comparing different supervised machine,” BMC Medical Informatics and Decision Making, pp. 1-16, 2019.

[11] K. Taunk, S. De, S. Verm dan A. Swetapadma, “A Brief Review of Nearest Neighbor Algorithm for Learning and Classification,” dalam Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS 2019), 2019.

[12] E. Oktavianti, M. Agustin dan R. Sari, “MPLEMENTASI ALGORITMA DECISION TREE DENGAN FITUR SELEKSI WEIGHT BY INFORMATION GAIN,” JURNAL MULTINETICS, vol. 9, pp. 118-126, 2023.

[13] C. M. Sitorus, A. Rizal dan M. Jajuli, “Prediksi Risiko Perjalanan Transportasi OnlineDari Data Telematik Menggunakan Algoritma Support Vector Machine,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 6, pp. 254-265, 2020.

[14] J. Cervantes, F. Garcia-Lamon, L. Rodríguez-Mazahua dan A. Lopez, “A comprehensive survey on support vector machine classification: Applications, challenges and trends,” Neurocomputing, vol. 408, pp. 189-205, 2020.

[15] A. Hosameldin dan K. N. Asoke , Condition Monitoring with Vibration Signals: Compressive Sampling and Learning Algorithms for Rotating Machines, IEEE, 2019, pp. 259-277.

[16] D. Kurniasari, R. V. Mahyunis, Warsono dan A. Nurayaman, “IMPLEMENTATION OF ARTIFICIAL NEURAL USING BACKPROPAGATION ALGORITHM BY COMPARING FOUR ACTIVATION FUNCTIONS IN PREDICTING GOLD PRICES,” Kumpulan jurnaL Ilmu Komputer (KLIK), vol. 10, pp. 93-105, 2023.

[17] M. Lendo-Siwicka, K. Zabłocka, E. Soból, A. Markiewicz dan G. Wrzesi ´nski, “Application of an Artificial Neural Network (ANN) Model to Determine the Value of the Damping Ratio (D) of Clay Soils,” Applied Sciences, vol. 13, no. 10, pp. 1-15, 2023.

[18] S. Orozco-Arias, J. S. Piña dan R. Tabares-Soto, “Measuring Performance Metrics of Machine Learning,” MDPI, pp. 1-18, 2020.

[19] W. I. Rahayu, C. Prianto dan E. A. Novia, “PERBANDINGAN ALGORITMA K-MEANS DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRIORITAS PEMBAYARAN TAGIHAN RUMAH SAKIT BERDASARKAN TINGKAT KEPENTINGAN PADA PT. PERTAMINA (PERSERO),” Jurnal Teknik Informatika, vol. 13, no. 2, pp. 1-8, 2021.

[20] S. A. Hicks, . I. Strümke dan V. Thambawita1, “Evaluation metrics for medical applications of actificial intelligence,” natureportofolio, 2022.

[21] T. F. Monaghan, S. N. Rahman,, C. W. Agudelo, A. J. Wein dan J. . M. Laz, “Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value,” MDPI, pp. 1-7, 2021.

[22] P. Fränti dan R. Mariescu-Istodor, “Soft precision and recall,” Pattern Recognition Letters, vol. 167, pp. 115-121, 2023.

[23] D. Chakrabarty, “Arithmetic-Harmonic Mean: A Measure of Central Tendency of Ratio-Type Data,” International Journal of Advanced Research in Science, Engineering and Technology , vol. 8, no. 5, pp. 1-9, 2021.