Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial

Penulis

  • Elly Firasari STMIK Nusa Mandiri
  • Umi Khultsum STMIK Nusa Mandiri
  • Monikka Nur Winnarto STMIK Nusa Mandiri
  • Risnandar Risnandar Pusat Penelitian Informatika-LIPI

DOI:

https://doi.org/10.25126/jtiik.0813087

Abstrak

Kemiskinan bagi pemerintah Indonesia termasuk masalah yang sulit untuk diselesaikan. Upaya yang dilakukan pemerintah dalam mengatasi kemiskinan di Indonesia yaitudengan  program bantuan sosial meliputiBLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), dan lain lain. Dalam Pelaksanaan program bantuan sosial saat masih sangat terbatas sehingga dalam penerimaan program bantuan tidak tepat sasaran. Data mining membantu untuk menentukan keputusan dalam memprediksi data di masa yang akan datang. Gradient Boosted Trees dan K-NN merupakan salah satu metode data mining untuk klasifikasi data. Masing-masing metode tersebut memiliki kelemahan. Gradient Boosted Trees menghasilkan nilai persentase akurasi lebih rendah dibanding metode K-NN. Dari permasalahan tersebut maka diusulkan metode kombinasi K-NN dan Gradient Boosted Trees untuk meningkatkan akurasi pada pelaksanaan program bantuan sosial agar tepat sasaran. Metode K-NN, Gradient Boosted Trees, K-NN-Gradient Boosted Treesdilakukan pengujian pada data yang sama untuk mendapatkan hasil perbandingan nilai akurasi. Hasil pengujian membuktikan bahwa kombinasi tersebut menghasilkan nilai persentase yang tinggi dibanding metode K-NN atau Gradient Boosted Trees yaitu 98.17%.

Abstract

Poverty for the Indonesian government is a problem that is difficult to solve. The efforts made by the government in overcoming poverty in Indonesia are through social assistance programs including BLT (Bantuan Langsung Tunai), PKH (Program Keluarga Harapan), Raskin (Beras Miskin), and others. In the implementation of the social assistance program when it was still very limited, the acceptance of the aid program was not on target. Data mining helps to determine decisions in predicting data in the future. Gradient Boosted Trees and K-NN are data mining methods for data classification. Each of these methods has weaknesses. Gradient Boosted Trees produce lower accuracy percentage values than the K-NN method. From these problems, a proposed method of combination of K-NN and Gradient Boosted Trees is used to improve the accuracy of the implementation of social assistance programs so that it is right on target. The K-NN, Gradient Boosted Trees, and K-NN-Gradient Boosted Trees methods are tested on the same data to get a comparison of the accuracy values. The test results prove that the combination produced a high percentage value compared to the K-NN or Gradient Boosted Trees method that is 98.17%.

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Referensi

Amra, Ihsan A Abu & Ashraf Y.A. Maghari. (2017). Student Performance Prediction Using KNN and Naïve Bayesian. 2017 7th International Conference on Information Technology (ICIT), 909–913.

Anggraina, A., Primartha, R., & Wijaya, A. (2019). The Combination of Logistic Regression and Gradient Boost Tree for Email Spam Detection The Combination of Logistic Regression and Gradient Boost Tree for Email Spam Detection. 1–6. https://doi.org/10.1088/1742-6596/1196/1/012013

Bilal, M., Israr, H., Shahid, M., & Khan, A. (2015). Sentiment classification of Roman-Urdu opinions using Navie Baysian, Decision Tree and KNN classification techniques. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES. https://doi.org/10.1016/j.jksuci.2015.11.003

BPS. 2019. Jumlah Dan Tingkat Penduduk Miskin Indonesia 1970 – Sep 2018

Devika, R., Avilala, S. V., & Subramaniyaswamy, V. (2019). Comparative Study of Classifier for Chronic Kidney Disease prediction using Naive Bayes , KNN and Random Forest. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), (Iccmc), 679–684.

Li, X., Yang, S., Fan, R., Yu, X., & Chen, D. (2018). Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors ( kNN ) and support vector machine ( SVM ) classifiers. Optics and Laser Technology, 102, 233–239. https://doi.org/10.1016/j.optlastec.2018.01.028

Okfalisa, Gazalba, I., Mustakim, & Reza, N. G. I. (2018). Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. Proceedings - 2017 2nd

International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017, 2018–Janua, 294–298. https://doi.org/10.1109/ICITISEE.2017.8285514

Pan, C., Tan, J., & Feng, D. (2018). Identification of Power Quality Disturbance Sources Using Gradient Boosting Decision Tree. 2018 Chinese Automation Congress (CAC), 2589–2592. https://doi.org/10.1109/CAC.2018.8623162

Pertiwi, M. W., Adiwisastra, M. F., & Supriadi, D. (2019). Analisa Komparasi Menggunakan 5 Metode Data Mining dalam Klasifikasi Persentase Wanita Sudah menikah di Usia 15-49 yang Memakai Alat KB ( Keluarga Berencana ). Jurnal Khatulistiwa Informatika, VII(1), 37–42.

Saikin, & Kusrini. (2019). MODEL DATA MINING UNTUK KAREKTERISTIK DATA TRAVELLER PADA PERUSAHAAN TOUR AND TRAVEL ( Studi Kasus : Lombok Ceria Holiday ). Jurnal Manajemen Informatika & Sistem Informasi, 2(2), 61–68.

Sari, M. K., Ernawati, & Pranowo. (2015). KOMBINASI METODE K-NEAREST NEIGHBOR DAN NAÏVE BAYES UNTUK KLASIFIKASI DATA. Seminar Nasional Teknologi Informasi Dan Multimedia 2015, 6–8.

Sheng, P., Chen, L., & Tian, J. (2018). Learning-based Road Crack

Detection Using Gradient Boost Decision Tree. 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 1228–1232.

Tian, D., He, G., Wu, J., Chen, H., & Jiang, Y. (2016). An Accurate Eye Pupil Localization Approach Based on Adaptive Gradient Boosting Decision Tree. 27–30. https://doi.org/978-1-5090-5316-2/2016

Yunus, A., Akbar, M., & Andri. (2019). DATA MINING UNTUK MEMEPREDIKSI HASIL PRODUKSI NUAH SAWIT PADA PT BUMI SAWIT SAUKSES (BSS) MENGGUNAKAN METODE K-NEAREST NEIGHBOR. Bina Darma Conference on Computer Science, 198–207.

Diterbitkan

02-12-2020

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Kombinasi K-NN dan Gradient Boosted Trees untuk Klasifikasi Penerima Program Bantuan Sosial. (2020). Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(6), 1231-1236. https://doi.org/10.25126/jtiik.0813087