Studi Komparatif Metode Boosting Dalam Pengklasifikasian Penerima Bantuan Program Keluarga Harapan (PKH)
DOI:
https://doi.org/10.15575/telka.v11n3.315-326Kata Kunci:
Boosting, Ensemble Learning, Program Keluarga HarapanAbstrak
Ensemble Learning adalah paradigma pembelajaran mesin dimana beberapa model (biasanya disebut "weak learners") dilatih untuk memecahkan masalah yang sama dan digabungkan untuk mendapatkan hasil yang lebih baik. Salah satu model Ensemble, yaitu model boosting. Beberapa metode boosting yang digunakan dalam penelitian ini, yaitu Gradient Boosting Machines (GBM), Extreme Gradient Boosting Machine (XGBM), Light Gradient Boosting Machine (LGBM), dan CatBoost. Penelitian ini akan mengklasifikasikan Rumah Tangga (RT) yang menerima bantuan Program Keluarga Harapan (PKH). Pengklasifikasian PKH sangat penting dilakukan, karena saat ini pemberian PKH belum optimal dan masih banyak yang tidak tepat sasaran. Hasil penelitian menunjukkan bahwa metode LGBM menunjukkan performa terbaik ketika jumlah data latih berukuran besar, yaitu 90% dengan akurasi sebesar 67,97%, sedangkan untuk data latih kecil yaitu 60:40, LGBM memiliki performa yang kurang baik, dengan nilai balanced accuracy terendah dibandingkan metode boosting lainnya, yaitu sebesar 54,43%. Keunggulan LGBM ini disebabkan karena kemampuannya dalam mengelola data besar dan kompleks yang sesuai dengan karakteristik data sosial ekonomi rumah tangga penerima PKH. Dua fitur yang memiliki peran penting untuk pengklasifikasian PKH dalam model terbaik yaitu LGBM adalah faktor ekonomi dan jumlah anggota rumah tangga.
Ensemble Learning is a machine learning paradigm in which multiple models (commonly referred to as "weak learners") are trained to solve the same problem and combined to achieve better results. One of the Ensemble models is the boosting model. Several boosting methods used in this study include Gradient Boosting Machines (GBM), Extreme Gradient Boosting Machine (XGBM), Light Gradient Boosting Machine (LGBM), and CatBoost. This study aims to classify households (RT) that receive assistance from the Program Keluarga Harapan (PKH). The classification of PKH recipients is crucial because the distribution of PKH aid has not been optimal, with many cases of misallocation. The results of the study indicate that the LGBM method demonstrates the best performance when the latih dataset is large (90%), achieving an accuracy of 67.97%. However, when the latih dataset is small (60:40), LGBM performs poorly, recording the lowest balanced accuracy among the boosting methods, at 54.43%. The superiority of LGBM is attributed to its ability to handle large and complex data, which aligns with the socio-economic characteristics of PKH recipient households. Two key features that play a significant role in PKH classification using the best-performing model, LGBM, are economic factors and the number of household members.
Referensi
T. C. Nokeri, Data Science Solutions with Python. 2022.
L. M. Cendani and A. Wibowo, “Perbandingan Metode Ensemble Learning pada Klasifikasi Penyakit Diabetes,” J. Masy. Inform., vol. 13, no. 1, pp. 33–44, 2022, doi: 10.14710/jmasif.13.1.42912.
N. I. Nella, N. Y. Setiawan, and D. E. Ratnawati, “Klasifikasi Penerima Bantuan Program Keluarga Harapan menggunakan Algoritme Decision Tree C4. 5 (Studi Kasus: Desa Mlirip Kabupaten Mojokerto),” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 3, pp. 1332–1339, 2022.
D. Haidar, B. Irawan, and A. Bahtiar, “Penerapan Deep Learning Model Random Forest Untuk Prediksi Penerima Bantuan Program Keluarga Harapan (Pkh),” JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 6, pp. 3564–3571, 2023, doi: 10.36040/jati.v7i6.8250.
I. A. Sobari and R. A. Zuama, “Pendeketan Machine Learning dalam Memprediksi Keluarga Penerima Program PKH,” J. Tek. Komput. AMIK BSI, vol. 9, no. 1, pp. 61–64, 2023, doi: 10.31294/jtk.v4i2.
N. Alfiah, “Klasifikasi Penerima Bantuan Sosial Program Keluarga Harapan Menggunakan Metode Naive Bayes,” J. Teknol. Inf., vol. 16, no. 1, pp. 32–40, 2021, doi: 10.35842/jtir.v16i1.386.
M. N. Isyam, D. Indrayana, and W. Apriandari, “Klasifikasi Penerima Bantuan Program Keluarga Harapan Menggunakan Support Vector Machine,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 5, pp. 10416–10421, 2024.
W. H. Vidila, R. Kurniawan, and S. Anwar, “Optimization of Social Assistance Recipient Determination using Gradient Boosting Algorithm,” vol. 4, no. 2, 2025.
R. D. Mendrofa, M. H. Siallagan, D. P. Pakpahan, and J. Amalia, “Credit Risk Analysis With Extreme Gradient Boosting and Adaptive Boosting Algorithm,” J. Inf. Syst. Hosp. Technol., vol. 5, no. 1, pp. 1–7, 2023, doi: 10.37823/insight.v5i1.233.
E. R. Putri and D. B. Arianto, “Perbandingan Performa Algoritma Metode Bagging dan Boosting pada Prediksi Konsentrasi PM10 di Jakarta Utara,” J. Nas. Teknol. dan Sist. Inf., vol. 10, no. 1, pp. 72–81, 2024, doi: 10.25077/teknosi.v10i1.2024.72-81.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002, doi: 10.1613/jair.953.
P. Liashchynskyi and P. Liashchynskyi, “Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS,” arXiv Prepr., no. 2017, pp. 1–11, 2019, [Online]. Available: http://arxiv.org/abs/1912.06059.
J. H. Friedman, “Stochastic gradient boosting,” Comput. Stat. Data Anal., vol. 38, no. 4, pp. 367–378, 2002, doi: 10.1016/S0167-9473(01)00065-2.
T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., vol. 13-17-Augu, pp. 785–794, 2016, doi: 10.1145/2939672.2939785.
G. Ke et al., “LightGBM: A highly efficient gradient boosting decision tree,” 31st Conf. Neural Inf. Process. Syst., pp. 1–9, 2017.
M. J. Sai, P. Chettri, R. Panigrahi, A. Garg, A. K. Bhoi, and P. Barsocchi, “An Ensemble of Light Gradient Boosting Machine and Adaptive Boosting for Prediction of Type-2 Diabetes,” Int. J. Comput. Intell. Syst., vol. 16, no. 1, pp. 1–20, 2023, doi: 10.1007/s44196-023-00184-y.
L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, “Catboost: Unbiased boosting with categorical features,” 32nd Conf. Neural Inf. Process. Syst., pp. 1–11, 2018.
A. Desiani et al., “Penerapan Metode Support Vector Machine Dalam Klasifikasi Bunga Iris,” IJAI (Indoneisan J. Appl. Informatics), vol. 7, no. 1, pp. 12–18, 2022.
A. Sharma and W. J. M. I. Verbeke, “Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch Dataset (n = 11,081),” Front. Big Data, vol. 3, no. April, pp. 1–11, 2020, doi: 10.3389/fdata.2020.00015.
C. Yang, E. A. Fridgeirsson, J. A. Kors, J. M. Reps, and P. R. Rijnbeek, “Impact of random oversampling and random undersampling on the performance of prediction models developed using observational health data,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-023-00857-7.
O. P. Moerdyanto and I. K. D. Nuryana, “Prediksi Kelulusan Tepat Waktu Menggunakan Pendekatan Pohon Keputusan Algoritma Decision Tree,” J. Informatics Comput. Sci., vol. 05, no. 1, pp. 90–96, 2023, [Online]. Available: https://ejournal.unesa.ac.id/index.php/jinacs/article/view/55329.
