Optimasi Algoritma Decision Tree Menggunakan GridSearchCV untuk Klasifikasi Tipe Obesitas
-
Feby Laurent
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Sri Winarno Universitas Dian Nuswantoro, Semarang, Indonesia
- Ika Novita Dewi Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
The rise in obesity cases in various countries, including Indonesia, has become a serious public health problem because it increases the risk of chronic diseases and affects individuals' psychological aspects. One of the main challenges in obesity management is the differences in obesity types in each individual, which are influenced by various factors. Therefore, accurate classification methods are needed to ensure more targeted treatment. In this context, machine learning-based technology is a potential solution for classifying obesity types. However, variations in individual characteristics make the classification process complex, as models often struggle to accurately distinguish obesity types. To overcome this problem, the Decision Tree algorithm was chosen because of its easy-to-interpret results. However, using Decision Tree with default parameters on datasets with many attributes and high variation tends to cause overfitting and decrease accuracy. Furthermore, Decision Tree performance is highly dependent on hyperparameter settings, requiring optimization techniques to achieve optimal results. Based on this, this study aims to optimize the Decision Tree algorithm using GridSearchCV to obtain the most optimal parameters to improve model performance in obesity type classification. The dataset used is from the UCI Machine Learning Repository, consisting of 2,111 rows of data and 17 attributes. Based on the initial test results, the default model achieved 92.58% accuracy, 92.58% recall, 92.66% precision, and 92.56% F1-score. After optimization, the accuracy increased to 95.69%, 95.69% recall, 95.72% precision, and 95.67% F1-score. The 3.1% increase in accuracy demonstrates the effectiveness of GridSearchCV in improving Decision Tree performance, resulting in a more accurate and stable prediction model. This research is expected to contribute as a basis for decision-making in early detection and prevention and treatment of obesity more efficiently and effectively.
Downloads
References
P. S. Nugroho, “Jenis Kelamin Dan Umur Berisiko Terhadap Obesitas Pada Remaja Di Indonesia,” An-Nadaa: Jurnal Kesehatan Masyarakat, vol. 7, no. 2, p. 110, 2020, doi: 10.31602/ann.v7i2.3581.
L. Dewi and Rr. A. Ayuningtyas, A-Z Tentang Obesitas. UGM PRESS, 2023.
S. K. Saraswati et al., “Literature Review : Faktor Risiko lingkungan,” Media Kesehatan Masyarakat Indonesia, vol. 20, no. 1, pp. 70–74, 2021.
Nadia Putri, “10 Negara dengan Tingkat Obesitas Tertinggi di Dunia, Apakah Indonesia Termasuk,” mediaindonesia.com. Accessed: Oct. 12, 2025. [Online]. Available: https://mediaindonesia.com/humaniora/781284/10-negara-dengan-tingkat-obesitas-tertinggi-di-dunia-apakah-indonesia-termasuk
World Obesity Atlas, “World Obesity Atlas 2025 | World Obesity Federation,” worldobesity.org. Accessed: Oct. 12, 2025. [Online]. Available: https://www.worldobesity.org/resources/resource-library/world-obesity-atlas-2025
W. Kurdanti et al., “Jurnal gizi klinik Indonesia.,” Jurnal Gizi Klinik Indonesia, vol. 15, no. 1, pp. 22–27, 2015, [Online]. Available: https://journal.ugm.ac.id/jgki/article/view/34939/24736
D. S. Kohir, A. Murhan, and S. Sulastri, “Skrining Faktor Risiko Obesitas Usia Produktif,” Jurnal Wacana Kesehatan, vol. 9, no. 2, p. 97, 2024, doi: 10.52822/jwk.v9i2.673.
A. B. Yamantri and A. A. Rifa’i, “Penerapan Algoritma C4.5 Untuk Prediksi Faktor Risiko Obesitas Pada Penduduk Dewasa,” Jurnal Komputer Antartika, vol. 2, no. 3, pp. 118–125, 2024, doi: 10.70052/jka.v2i3.341.
M. Yhogha Ismail Ibn Ibrahim and Sandi Badiwibowo Atim, “Klasifikasi Level Obesitas Menggunakan Decision Tree C45 Dalam Menentukan Akurasi Pada Kriteria Information Gain, Gain Ratio, Gini Index,” JIKI (Jurnal llmu Komputer & lnformatika), vol. 5, no. 2, pp. 169–178, 2024, doi: 10.24127/jiki.v5i2.7191.
S. A. Utiarahman and A. M. M. Pratama, “Analisis Perbandingan KNN, SVM, Decision Tree dan Regresi Logistik Untuk Klasifikasi Obesitas Multi Kelas,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 6, pp. 3137–3146, 2024, doi: 10.30865/klik.v4i6.1871.
C. Ihsan, Dasar-Dasar Machine Learning Teori, Algoritma, dan Implementasi. Greenbook Publisher, 2025.
D. Jollyta, Prihandoko, A. Hajjah, E. Haerani, and M. Siddik, Algoritma Klasifikasi untuk Pemula Solusi Python dan RapidMiner. Deepublish, 2023.
M. G. Febrian, R. Ferdiansyah, E. A. Nugraha, D. Satriatama, and R. Kusumastuti, “Prediksi Risiko Diabetes Menggunakan Algoritma Decision Tree Dengan Aplikasi Rapid Miner,” Jurnal Teknologi Informasi, vol. 2, no. November, pp. 14–24, 2024.
P. B. N. Setio, D. R. S. Saputro, and Bowo Winarno, “Klasifikasi Dengan Pohon Keputusan Berbasis Algoritme C4.5,” PRISMA, Prosiding Seminar Nasional Matematika, vol. 3, pp. 64–71, 2020.
H. T. Santoso, F. A. Felmidi, A. N. Fadhila, A. Ristyawan, and E. Daniati, “Analisis Kinerja Algoritma Data Mining pada Klasifikasi Tingkat Obesitas dengan K-Fold Cross Validation dan AUC,” Agustus, vol. 8, pp. 2549–7952, 2024.
H. Syahidah, N. Irsandi, A. N. Ajizah, and Amelia, “Obesity Prediction Using Machine Learning Algorithms,” Indonesian Journal of Applied Technology and Innovation Science, vol. 2, no. February, pp. 53–62, 2025, doi: 10.1201/9781032725444-13.
G. Airlangga, “Machine Learning-Based Obesity Classification: A Comparative Study Using Self-Reported Survey Data and Ensemble Learning Models,” Jurnal Teknologi Informatika dan Komputer, vol. 11, no. 1, pp. 347–361, 2025, doi: 10.37012/jtik.v11i1.2585.
S. S. Al Malaky, A. A. C. Nisa, S. Armiyanti, and R. S. Setyawan, “Comparative Study of Obesity Levels Classification,” JEECS (Journal of Electrical Engineering and Computer Sciences), vol. 10, no. 1, pp. 69–75, 2025, doi: 10.54732/jeecs.v10i1.8.
D. Nasien et al., “Optimization of Body Mass Index Classification Using Machine Learning Approach for Early Detection of Obesity Risk,” Journal of Applied Business and Technology, vol. 6, no. 3, pp. 193–200, 2025, doi: 10.35145/jabt.v6i3.201.
J. T. M. A. Nazanah and M. I. Jambak, “Pemanfaatan Algoritma Decision Tree ID3 Bagi Manajemen Bimbel Untuk Menentukan Faktor Kelulusan Pada Sekolah Kedinasan,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 3, no. 6, pp. 915–924, 2023, doi: 10.30865/klik.v3i6.791.
I. Arfyanti, M. Fahmi, and P. Adytia, “Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 3, pp. 1196–1201, 2022, doi: 10.47065/bits.v4i3.2275.
Z. Maisat Eka Darmawan and A. Fauzan Dianta, “Implementasi Optimasi Hyperparameter GridSearchCV Pada Sistem Prediksi Serangan Jantung Menggunakan SVM,” Teknologi, vol. 13, no. 1, pp. 8–15, 2023, doi: 10.26594/teknologi.v13i1.3098.
A. Yaqin, D. Kurniawan, and J. Zeniarja, “Optimasi Algoritma K-Nearest Neighbors Menggunakan GridSearchCV untuk Klasifikasi Penyakit Diabetes,” Infotekmesin, vol. 16, no. 1, pp. 75–84, 2025, doi: 10.35970/infotekmesin.v16i1.2557.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Optimasi Algoritma Decision Tree Menggunakan GridSearchCV untuk Klasifikasi Tipe Obesitas
Pages: 1705-1716
Copyright (c) 2025 Feby Laurent, Sri Winarno, Ika Novita Dewi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
