Comparison of Machine Learning Algorithms for Classification of Drug Groups

Purwono Purwono, Anggit Wirasto, Khoirun Nisa


The stages of clinical trials need to be carried out when determining a new drug group for patient management. This stage is considered quite long and requires a lot of money. Medical record system data continues to grow all the time. The data can be analyzed to find a pattern of grouping drugs used in the treatment of patients based on their body condition. Utilization of artificial intelligence (AI) technology can be done to classify drug data used during patient care. Machine learning as a branch of science in the AI field can be a solution to deal with these problems. Machines will learn, analyze, and predict drug requirements quickly with less cost. Based on related research, we contribute to comparing the performance of the best machine learning algorithms that can be used as drug classification models. The results of this study are the accuracy of the support vector machine algorithm is 94.7% while the random forest and decission tree algorithms are 98.2%. This shows that the algorithms that can be considered as a drug classification model are random forest and decision tree. This model needs to be tested on a larger dataset to produce the best accuracy value.


Classification; Machine; Learning; Healthcare; Drugs;

Full Text:

PDF (Indonesian)


Awwalu, J. (2019). On Holdout and Cross Validation: A Comparison between Neural Network and Support Vector Machine. International Journal of Trend in Research and Development, 6(2).

Bießmann, F., Rukat, T., Schmidt, P., Naidu, P., Schelter, S., Taptunov, A., … Salinas, D. (2019). DataWig: Missing value imputation for tables. Journal of Machine Learning Research, 20, 1–6.

Drugsite Trust. (2020). Drug Database Index A to Z. Retrieved from

Hackeling, G. (2014). Mastering Machine Learning with scikit-learn. Birmingham: Packt Publishing.

Hartini, E. (2017). Classification of Missing Values Handling Method During Data Mining: Review. Sigma Epsilon, 21(2), 49–60.

Hendrawati, M., Agushybana, F., & Kartini, A. (2021). The Influence of Electronic Medical Record Toward Drug Planning Quality at the Pharmacy Department of the Hospital ‘ X .’ Public Health Persepective Journal, 6(1269).

Holland, S. M. (2016). Principal Components Analysis (Pca) (University of Georgia). Retrieved from

Lin, W. C., Chen, J. S., Chiang, M. F., & Hribar, M. R. (2020). Applications of artificial intelligence to electronic health record data in ophthalmology. Translational Vision Science and Technology, 9(2).

Maulidah, M., Gata, W., Aulianita, R., & Agustyaningrum, C. I. (2020). Algoritma Klasifikasi Decision Tree Untuk Rekomendasi Buku Berdasarkan Kategori Buku. Jurnal Ilmiah Ekonomi Dan Bisnis, 13(2), 89–96.

Maulina, D., & Sagara, R. (2018). Klasifikasi Artikel Hoax Menggunakan Support Vector Machine Linear Dengan Pembobotan Term Frequency – Inverse Document Frequency. Mantik Penusa, 2(1), 35–40.

Meiliana, A., Dewi, N. M., & Wijaya, A. (2019). Artificial intelligent in healthcare. Indonesian Biomedical Journal, 11(2), 125–135.

Patel, L., Shukla, T., Huang, X., Ussery, D. W., & Wang, S. (2020). Machine Learning Methods in Drug Discovery. Molecules, 25(22).

Potdar, K., Pardawala, T. S., & Pai, C. D. (2017). A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers. International Journal of Computer Applications, 175(4), 7–9.

Primajaya, A., & Sari, B. N. (2018). Random Forest Algorithm for Prediction of Precipitation. Indonesian Journal of Artificial Intelligence and Data Mining, 1(1), 27.

Rahman, M. F., Alamsah, D., Darmawidjadja, M. I., & Nurma, I. (2017). Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode Bayesian Regularization Neural Network (RBNN). Jurnal Informatika, 11(1), 36.

Retnoningsih, E., & Pramudita, R. (2020). Mengenal Machine Learning Dengan Teknik Supervised Dan Unsupervised Learning Menggunakan Python. Bina Insani Ict Journal, 7(2), 156.

Ritonga, A. S., & Purwaningsih, E. S. (2018). Penerapan Metode Support Vector Machine ( SVM ) Dalam Klasifikasi Kualitas Pengelasan Smaw ( Shield Metal Arc Welding ). Ilmiah Edutic, 5(1), 17–25.

Saputra, I., & Rosiyadi, D. (2019). Perbandingan Kinerja Algoritma K-Nearest Neighbor , Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp. Faktor Exacta, 12(2), 101–111.

Suyanto. (2018). Machine Learning Tingkat Dasar dan Lanjut. Bandung: Informatika.

Tripathi, P. (2020). Drug Dataset. Retrieved from

Umar, R., Riadi, I., & Purwono, P. (2020). Klasifikasi Kinerja Programmer pada Aktivitas Media Sosial dengan Metode Support Vector Machines. CYBERNETICS, 4(1), 32.

Wahyu Adi Kurniawan. (2019). Sistem Pendukung Keputusan Pencarian Universitas di Malang Menggunakan Weight Product dengan Pembobotan Weighted Sum Model. Jurnal Ilmiah Informatika, 4(2), 103–110.

Wan, X. (2019). Influence of feature scaling on convergence of gradient iterative algorithm. Journal of Physics: Conference Series, 1213(3).

Wang, S., Tang, J., & Liu, H. (2016). Feature Selection. In Encyclopedia of Machine Learning and Data Mining.


Article Metrics

Abstract view : 346 times
PDF (Indonesian) - 286 times


  • There are currently no refbacks.

Copyright (c) 2021 SISFOTENIKA

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Badan Pengelola Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (SISFOTENIKA) STMIK Pontianak.


Jurnal Ilmiah SISFOTENIKA terindex di :











ISSN Printed : 2087-7897

ISSN Online : 2460-5344


Jurnal Ilmiah SISFOTENIKA Terakreditasi Peringkat Empat


Partners & Co-Organizers:

Lisensi Creative Commons

Jurnal Ilmiah SISFOTENIKA: STMIK Pontianak Online Journal ISSN Printed (2087-7897) - ISSN Online (2460-5344) licensed under a Lisensi Creative Commons Atribusi 4.0 Internasional. Flag Counter

View My Stats>