Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm


  • Hao Chieh Chiua National Taiwan University
  • Robbi Rahim Sekolah Tinggi ilmu Manajemen Sukma
  • Mahmud Mustapa Universitas Negeri Makassar
  • Kamaruddin Universitas Teknologi Akba Makassar
  • Akbar Hendra Universitas Teknologi Akba Makassar
  • Asnimar Universitas Teknologi Akba Makassar
  • Omita Abigail Universitas Teknologi Akba Makassar


Adaptive Boosting, Decision Tree, Data Mining, Classification, Diabetes Mellitus


One of the data mining processes that is often used in machine learning is the data classification process. A decision tree is a classification algorithm that has the advantage of being easy to visualize because of its simple structure. However, the decision tree algorithm is quite susceptible to incorrect classification calculations due to the presence of noise in the data or imbalance in the data, which can reduce the overall level of accuracy. Therefore, the decision tree algorithm should be combined with other methods that can increase the accuracy of classification performance. Machine Learning is used through an artificial intelligence approach to solve problems or carry out optimization. Adaptive Boosting is used to optimize classification calculations. This study aims to examine the performance of Adaptive Boosting in the process of classifying second-degree diabetes mellitus patients using the Decision Tree algorithm. Diabetes mellitus is known as a chronic condition of the human body, the cause of which is an increase in the body's blood sugar levels because the body is unable to produce insulin or is unable to utilize insulin effectively, which is usually referred to as hyperglycemia.. By using a 60:40 data split, the Decision Tree algorithm produces an accuracy value of 95.71%, while the Adaptive Boosting-based Decision Tree results reach a value of 98.99%.


Download data is not yet available.


Argina, AM (2020). Application of the K-Nearest Neighbor Classification Method on a Dataset of Diabetes Patients. Indonesian Journal of Data and Science, 1(2), 29–33.

Aziz, MI, Fanani, AZ, & Affandy, A. (2023). Analysis of Ensemble Methods in Decision Tree Based Heart Disease Classification. Budidarma Media Informatics Journal, 7(1), 1–12.

Bisri, A., & Wahono, RS (2015). Application of Adaboost to resolve class imbalances in determining student graduation using the Decision Tree method. In Computer Science (Vol. 1, Issue 1). Computer Science. com.

Byna, A., & Basit, M. (2020). Application of the Adaboost Method to Optimize Stroke Prediction Using the Na{"i}ve Bayes Algorithm. Sisfokom Journal (Information and Computer Systems), 9(3), 407–411.

Horizon. (2021, September 9). Cost of Illness in Type 2 Diabetes Mellitus Patients - Unair News. Newssunair.

Domas, ZKS, & Rakhmadi, R. (2022). Improving Decision Tree Performance with AdaBoost for Classifying Lack of Transparency of Anti-Corruption Information. AppliedInformationSystemsandManagement (AISM), 5(2), 75–82.

Etika, K., Pandu, A., Syafar, F., Akbar, I., Arman, P., & Robbi, R. (2023). Application of forward selection strategy using C4. 5 algorithms to improve the accuracy of classification's data set. Application of Forward Selection Strategy Using C4. 5 Algorithms to Improve the Accuracy of Classification's Data Sets, 30(1), e14--e23.

Fajri, M. (2015). Splitting Rule and Application of Bagging in Classification Trees. Faculty of Mathematics and Natural Sciences, Sepuluh Nopember Institute of Technology.

Fatimah, RN (2015). DIABETES MELLITUS TYPE 2. Majority Journal, 4(5).

Hana, FM (2020). Classification of Diabetes Patients Using the C4 Decision Tree Algorithm. 5. SISKOM-KB Journal (Computer Systems and Artificial Intelligence), 4(1), 32–39.

Iftikar, MA, & Sibaroni, Y. (2022). Twitter Sentiment Analysis: Handling the Covid-19 Pandemic Using the Hybrid Method of Na{"i}ve Bayes, Decision Tree, and Support Vector Machine. EProceedings of Engineering, 9(3).

Iskandar, A. (2023). Introduction to Data Analysis With R Studio. Indonesian Digital Innovation Scholars Foundation.

Junita, V., & Bachtiar, F.A. (2019). Human Activity Classification using the C4 Decision Tree Algorithm. 5 and Information Gain for Feature Selection. Journal of Information Technology and Computer Science Development, 3(10), 9426–9433.

Kong, M., Zhang, H., Cao, X., Mao, X., & Lu, Z. (2020). Higher levels of neutrophil-to-lymphocyte are associated with severe COVID-19. Epidemiology & Infection, 148.

Kusuma, IGNA, Pradipta, IM, Santosa, IMA, Dharmendra, IK, & others. (2023). Handling Data Imbalances in the Classification of Public Complaints. Journal of Information and Computer Technology, 9(5).

Lorena Br Ginting, S., Zarman, W., & Darmawan, A. (2015). Data Mining Technique for Predicting Student Study Period Using the K-Nearest Neighborhood Algorithm. KOMPUTIKA-UNIKOM Computer Systems Journal, 3(2).

Muhlisin, A. (2019, February 22). Hyperglycemia - Causes, Symptoms, & Treatment | HonestDocs. Honestdocs.

Nusrhendratno, SS (2022). Synthesis of Density Based Feature Selection (DBFS) and AdaBoots Features with XGBoost to Improve Prediction Model Performance. Proceedings of the National Science and Technology Seminar, 12(1), 305–313.

Pebrianti, L., Aulia, F., Nisa, H., & Saputra, K. (2022). Implementation of the Adaboost Method to Optimize Diabetes Classification with the Naive Bayes Algorithm. JUSTINDO (Indonesian Journal of Information Systems and Technology), 7(2), 122–127.

Permana, AA, Wahyuddin, S., Santoso, LW, Wibowo, GWN, & Wardhani, AK (nd). Ahmad Jurnaidi Wahidin Gusti Eka Yuliastuti Elisawati Rima Rizqi Wijayanti Abdurrasyid.

Pirmansyah, F., & Wahyudi, T. (2023). Implementation of Data Mining Using the C4 Algorithm. 5 To Predict Evaluation of Security Unit Members Case Study Pt. Yimm Pulogadung. Indonesian Journal: Information and Communication Management, 4(3), 1566–1580.

Prabowo, NA, Ardyanto, TD, Hanafi, M., Kuncorowati, NDA, Dyanneza, F., Apriningsih, H., & Indriani, AT (2021). Increasing Diabetes Diet Knowledge, Diabetes Self Management and Reducing Stress Levels During Dieting in Type 2 Diabetes Mellitus Patients at Sebelas Maret University Hospital. LPM Newsletter, 24(2), 285–296.

Pradana, E. (2018). Analysis of the Application of Adaptive Boosting (Adaboost) in Improving the Performance of the C4 Algorithm. 5. Pelita Bangsa College of Technology.

Sibarani, RP (2023, January 3). Recognizing the Symptoms of Type 2 Diabetes Mellitus that You Need to Know - EMC Healthcare - SAME. Emc.

Sudargo, T., Freitag, H., Kusmayanti, NA, & Rosiyani, F. (2018). Diet and obesity. UGM press.

Suprayitna, M., Hajri, Z., Fatmawati, BR, Prihatin, K., & Nadrati, B. (2023). Early Detection of Diabetes Mellitus (DM) Through "DM Awareness." BERNAS: Journal of Community Service, 4(3), 2291–2296.

Suryati, NI, & Kep, M. (2021). Effective Nursing Exercise Book for Diabetes Mellitus Patients Based on Research Results. Deepublish.

Tangkelayuk, A. (2022). The Water Quality Classification Using KNN, Na{"i}ve Bayes, and Decision Tree Methods. JATISI (Journal of Informatics Engineering and Information Systems), 9(2), 1109–1119.

Ula, M., Ulva, AF, Mauliza, M., Sahputra, I., & Ridwan, R. (2021). Implementation of Machine Learning in Determining Nutritional Status using the Complete Linkage Agglomerative Hierarchical Clustering Method. Mantic Journal, 5(3), 1910–1914.

Urva, G., Albanna, I., Sungkar, MS, Gunawan, IMAO, Adhicandra, I., Ramadhan, S., Rahardian, RL, Handayanto, RT, Ariana, AAGB, Atika, PD, & others. (2023). APPLICATION OF DATA MINING IN VARIOUS FIELDS: Concepts, Methods, and Case Studies. PT. Sonpedia Publishing Indonesia.


DOI: 10.56134/jst.v2i2.47
Published: 2023-12-06

How to Cite

Hao Chieh Chiua, Robbi Rahim, Mahmud Mustapa, Kamaruddin, Akbar Hendra, Asnimar, & Abigail, O. (2023). Application of the Adaptive Boosting Method to Increase the Accuracy of Classification of Type Two Diabetes Mellitus Patients Using the Decision Tree Algorithm. Ceddi Journal of Information System and Technology (JST), 2(2), 10–21.