JURNAL REVIEW: METODE KLASIFIKASI PADA MACHINE LEARNING
DOI:
https://doi.org/10.53990/xrt1ey36Keywords:
Decision Tree, K-Nearest Neighbor, Machine Learning, Support Vector Machine, Random ForestAbstract
Classification is one of the main problems in Machine Learning, focusing on grouping data into specific classes based on their characteristics or features. This problem has been widely applied in various domains, such as healthcare, education, network security, and decision support systems. This article aims to review classification methods commonly used in Machine Learning, namely Support Vector Machine, Decision Tree, Random Forest, and K-Nearest Neighbor. The research method employed is a literature review by examining scientific articles from indexed national and international journals. The review process analyzes fundamental concepts, applied methods, and research findings from each study. The results indicate that Support Vector Machine performs well on high-dimensional data, Decision Tree excels in interpretability, Random Forest provides more stable and consistent performance, while K-Nearest Neighbor has a simple concept but is sensitive to distance parameters and data volume. The selection of classification methods should be aligned with data characteristics and analytical objectives
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Adithya S, A. G. M. C. (2025). A COMPREHENSIVE SURVEY ON MACHINE LEARNING ALGORITHMS FOR CLASSIFICATION AND REGRESSION. International Research Journal Of Modernization In Engineering Technology And Science (, 12, 1565–1570.
Airlangga, G. (2024). Comparative Analysis Of Machine Learning Models For Intrusion Detection In Internet Of Things Networks Using The RT-Iot2022 Dataset. 4(April), 656–662.
Alzubaidi, L., Zhang, J., Humaidi, A. J., Dujaili, A. Al, Duan, Y., Shamma, O. Al, Santamaría, J., Fadhel, M. A., Amidie, M. Al, & Farhan, L. (2021). Review Of Deep Learning : Concepts , CNN Architectures , Challenges , Applications , Future Directions. In Journal Of Big Data. Springer International Publishing. Https://Doi.Org/10.1186/S40537-021-00444-8
Arifin, N., & Insani, C. N. (2025). Comparative Analysis Of CNN , SVM , Decision Tree , Random Forest , And KNN For Maize Leaf Disease Detection Using Color And Texture Feature Extraction. 6(5), 3572–3586.
Aripradono, H. W. (2025). JTIM : Jurnal Teknologi Informasi Dan Multimedia Perbandingan Support Vector Machine , Random Forest Classi- Fier , Dan K-Nearest Neighbour Dalam Pendeteksian Anomali. 7(1), 23–33.
Bintang, I. G., Budaya, A., & Suniantara, I. K. P. (2024). Comparison Of Sentiment Analysis Algorithms With SMOTE Oversampling And TF-IDF Implementation On Google Reviews For Public Health Centers. 4(July), 1077–1086.
Çakir, M., Yilmaz, M., Atalay, M., Özgür, H., & Oral, O. (2023). Journal Of King Saud University – Science Accuracy Assessment Of Rferns , NB , SVM , And Knn Machine Learning Classifiers In Aquaculture. 35. Https://Doi.Org/10.1016/J.Jksus.2023.102754
Chandran, P., Thierry, F., Odelius, J., Famurewa, S. M., Lind, H., & Rantatalo, M. (2021). Applied Sciences Supervised Machine Learning Approach For Detecting Missing Clamps In Rail Fastening System From Differential Eddy Current Measurements.
Chowdhury, S. (2024). Comparison Of Accuracy And Reliability Of Random Forest , Support Vector Machine , Artificial Neural Network And Maximum Likelihood Method In Land Use / Cover Classification Of Urban Setting. Environmental Challenges, 14(October 2023), 100800. Https://Doi.Org/10.1016/J.Envc.2023.100800
Corea, P. M., Liu, Y., Wang, J., Niu, S., Song, H., & Jul, L. G. (2024). Explainable AI For Comparative Analysis Of Intrusion Detection Models.
Dixit, A. (2023). Data Classification With Multiprocessing.
Faradibah, A., Widyawati, D., Syahar, A. U. T., & Jabir, S. R. (2023). Comparison Analysis Of Random Forest Classifier , Support Vector Machine , And Artificial Neural Network Performance In Multiclass Brain Tumor Classification. 4(2), 55–63.
Fathurrahman, M. A., Prabowo, D. W., Komputer, F. I., Ali, U. D., Tengah, K., & Artikel, I. (2025). Perbandingan Performa Algoritma Random Forest Dan SVM Dalam Mendeteksi Serangan Ddos Di Jaringan Cloud Comparison Of The Performance Of Random Forest And SVM Algorithms In Detecting Ddos Attacks In Cloud Networks. 7(2), 193–202.
Gole, A. (2022). Comparative Analysis Of Machine Learning Algorithms : Random Forest Algorithm , Naive Bayes Classifier And KNN - A Survey. 194–197.
Hamdani, A. U., Setiawati, S., Mentari, Z. D., & Purnomo, M. H. (2024). COMPARISON OF K-NN , SVM , AND RANDOM FOREST ALGORITHM FOR DETECTING HOAX ON INDONESIAN ELECTION 2024 Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI | 167. 13(1), 166–179.
Irawan, Y., Wahyuni, R., & Ordila, R. (2024). Comparative Analysis Of Machine Learning Algorithms With SMOTE And Boosting Techniques In Accuracy Improvement. The Indonesian Journal Of Computer Science, 13(5).
Khatib, J., & Dalam, S. (2024). Indonesian Journal Of Computer Science. 13(1), 2445–2473.
Khikmah, K. N., & Sofro, A. (2025). Comparison Of Basic Statistics And Machine Learning Classification Algorithms In Kalimantan Poverty Prediction With Handling Missing Data. 22(1), 90–101. Https://Doi.Org/10.20956/J.V22i1.44488
Kurniawan, M. F., & Megawaty, D. A. (2025). Comparison Of Logistic Regression , Random Forest , Support Vector Machine ( SVM ) And K-Nearest Neighbor ( KNN ) Algorithms In Diabetes Prediction. 9(5), 2154–2162.
Mulyani, A., Khoerunisa, S., & Kurniadi, D. (2025). Perbandingan Kinerja Algoritma KNN Dan SVM Menggunakan SMOTE Untuk Klasifikasi Penyakit Diabetes. 14, 25–34. Https://Doi.Org/10.22146/Jnteti.V14i1.15198
Nola, A., Kinasih, S., Nur, A., Tri, J., & Salwa, N. (2025). Comparative Analysis Of Decision Tree And Random Forest Classifiers For Structured Data Classification In Machine Learning. 5(2), 13–24.
Nugroho, A., Maulana, D., Engineering, I., & Bangsa, U. P. (2025). COMPARATIVE ANALYSIS OF CLASSIFICATION ALGORITHMS IN HANDLING IMBALANCED DATA WITH SMOTE OVERSAMPLING. 11(2), 487–495. Https://Doi.Org/10.33480/Jitk.V11i2.6956.COMPARATIVE
Pamutha, T., Promthong, W., & Pahlawan, S. (2025). Diabetes Disease Prediction Using Machine Learning Classification Algorithms. 15(4), 1060–1069.
Purwanto, E., Prajadi, B., Utomo, C., Permatasasi, H., & Mohd, F. (2025). Comparative Analysis Of Classification Models For Sales Prediction In E- Commerce : Decision Tree , Random Forest , SVM , Naive Bayes , And KNN. 6(6), 5899–5915.
Putro, I. H. (2025). Evaluating The Performance Of Machine Learning Classifiers For Network Intrusion Detection : A Comparative Study Using The. 14(July), 330–338. Https://Doi.Org/10.34148/Teknika.V14i2.1276
Rahagiyanto, A., Prakoso, B. H., Yunus, M., Vestine, V., Eko, G., Suyoso, J., & Deharja, A. (2025). Perbandingan Kinerja Algoritma KNN-DT-RF-SVM Untuk Deteksi Dini Risiko Kematian Ibu. 6(2), 137–145. Https://Doi.Org/10.25047/J-Remi.V6i2.5658
Siagian, H. R., Setiawan, D., & Abidin, Z. (2025). Comparison Of SVM And KNN Methods For The Integratin Of Myindihome Into Mytelkomsel Application. 5(2), 1037–1045.
Sinshaw, N. T., He, M., Bahiru, T. K., & Mohapatra, S. K. (2025). Blog Data Showdown: Machine Learning Vs Neuro-Symbolic Models For Gender Classification. 2025 International Conference On Information And Communication Technology For Development For Africa (ICT4DA), 229–234. Https://Doi.Org/10.1109/ICT4DA67218.2025.11282773
Siroj, M. N., Zyen, A. K., Wahyu, G., & Wibowo, N. (2025). Optimizing Decision Tree And Random Forest With Grid Search And SMOTE For Malware Classification On Iot Network Traffic. 9(5), 2775–2782.
Sivakumar, S., & Venkataraman, S. (2025). Evaluating Machine Learning Approaches : A Comparative Study Of Random Forest And Neural Networks In Grade Classification. 6(1), 73–80.
Stuart Russell And Peter Norvig. (2022). Artificial Intelligence: A Modern Approach, 4th US Ed. Https://Aima.Cs.Berkeley.Edu/
Swarnalatha, K., Narisetty, N., Kancherla, G. R., Bobba, B., Lakshmaiah, K., & Foundation, E. (2024). Analyzing Resampling Techniques For Addressing The Class Imbalance In NIDS Using SVM With Random Forest Feature Selection. 43, 42–55.
Syahputra, F., Sabrina, E., & Rahmadhani, A. (2025). Tinjauan Literatur Sistematis ( 2019 – 2025 ) Kinerja Decision Tree Dan Neural Network ( Deep Learning ) Serta Perbandingannya Dengan Naive Bayes Dan SVM. 6(4), 51–59. Https://Doi.Org/10.33650/Trilogi.V6i4.13429
Talukder, A., Islam, M., Uddin, A., Hasan, K. F., & Sharmin, S. (2024). Machine Learning ‑ Based Network Intrusion Detection For Big And Imbalanced Data Using Oversampling , Stacking Feature Embedding And Feature Extraction. Journal Of Big Data. Https://Doi.Org/10.1186/S40537-024-00886-W
Vieri, J. K., Munandar, T. A., Srisulistiowati, D. B., & Lestari, T. S. (2023). Comparative Study Of Classification Algorithms For Customer Decisions On Telecommunication Products Using Supervised Learning.
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