Penentuan Kelayakan Penerima Bantuan Program Keluarga Harapan Menggunakan Algoritma Support Vector Machine
Abstract
Technological developments, particularly in the field of machine learning, have had a significant impact on supporting data-driven decision making. One of the challenges faced in implementing PKH in Tiang Pumpung Kepungut Subdistrict, Musi Rawas Regency, is [A1] the process of determining aid recipients, which is still done manually. Data is still manually recorded into Excel based on data obtained during the population census. This often causes errors and mistakes during the aid distribution process. To overcome this problem, this study proposes the use of the Support Vector Machine algorithm in the PKH beneficiary classification process. Support Vector Machine is an effective classification method for handling complex and non-linear data with a high degree of accuracy. This study aims to develop a Support Vector Machine-based system to improve efficiency, accuracy, and transparency in the selection process for determining the eligibility of aid recipients. A total of 250 PKH data sets were successfully obtained. The data obtained or collected included several variables, namely name, age, number of family members, occupation, income, number of dependent children, and status. The data was then divided into two sets: 80% for training and 20% for testing from a total of 250 data points. After cleaning the data, the number of data points became 244, with 195 for training and 49 for testing. The results of this study showed that 39 families were eligible to receive assistance and 10 families were not eligible. It is hoped that the resulting system can serve as an innovative solution to support more targeted social assistance distribution in the region.
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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, doi: 10.36040/jati.v8i5.11050.
Y. Nur, K. M. Hindrayani, and A. T. Damaliana, “Optimasi Elastic Net Regression dengan Optuna untuk Analisis Faktor Kemiskinan di Indonesia,” JATI(Jurnal Mhs. Tek. Inform., vol. 9, no. 5, pp. 8583–8590, 2025, doi: 10.36040/jati.v9i5.15179.
T. P. Septiya Nuraeni, Harliana, “Analisis Akurasi Naïve Bayes dan KNN dalam Penentuan Penerima PKH di Lombok Utara,” J. Inf. Syst. Manag., vol. 5, no. 2, pp. 121–126, 2024, doi: 10.24076/joism.2024v5i2.1205.
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.
Sasela Astri Wayuni, Sofia E. Pangemanan, and Frans Singkoh, “Kontribusi Program Keluarga Harapan Dalam Menunjang Pendidikan Siswa Kurang Mampu di Kecamatan Tuminting,” J. Gov., vol. 1, no. 2, pp. 1–12, 2021, [Online]. Available: https://ejournal.unsrat.ac.id/index.php/governance/article/view/35368
K. A. Mustari, P. Assiroj, B. Hartati, and F. Samuel, “Implementasi Data Mining pada Instansi Pemerintahan (Systematic Literature Review),” JATI(Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 3137–3142, 2024, doi: 10.36040/jati.v8i3.9618.
I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),” Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.
D. Alita, Y. Fernando, and H. Sulistiani, “Implementasi Algoritma Multiclass SVM pada Opini Publik Berbahasa Indonesia di Twitter,” J. TEKNOKOMPAK, vol. 14, no. 2, pp. 86–91, 2020.
V. Westley, D. Thomas, and F. Rumaisa, “Analisis Sentimen Ulasan Hotel Bahasa Indonesia Menggunakan Support Vector Machine dan TF-IDF,” J. MEDIA Inform. BUDIDARMA, vol. 6, no. 3, pp. 1767–1774, 2022, doi: 10.30865/mib.v6i3.4218.
K. I. Gunawan and J. Santoso, “Multilabel Text Classification Menggunakan SVM dan Doc2Vec Classification pada Dokumen Berita Bahasa Indonesia,” J. Inf. Syst. Graph. Hosp. Technol., vol. 03, no. 01, pp. 29–38, 2021, doi: 10.37823/insight.v3i01.126.
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, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/10801
S. Suryani and M. Mustakim, “Estimasi Keberhasilan Siswa dalam Pemodelan Data Berbasis Learning Menggunakan Algoritma Support Vector Machine,” Bull. Informatics Data Sci., vol. 1, no. 2, pp. 81–88, 2022, doi: 10.61944/bids.v1i2.36.
H. S. Wafa, A. I. Hadiana, and F. R. Umbara, “Prediksi Penyakit Diabetes Menggunakan Algoritma Support Vector Machine (SVM),” Informatics Digit. Expert, vol. 4, no. 1, pp. 40–45, 2022, doi: 10.36423/index.v4i1.895.
F. Martin, T. Sun, and C. D. Westine, “A Systematic Review of Research on Online Teaching and Learning from 2009 to 2018,” Comput. Educ., vol. 159, p. 104009, 2020, doi: 10.1016/j.compedu.2020.104009.
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, pp. 147–156, 2021, doi: 10.25126/jtiik.202183944.
D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform., vol. 5, no. 2, pp. 697–711, 2021, doi: 10.30645/j-sakti.v5i2.369.
H. Sanjaya, R. Kurniawan, and Rusdiyanto, “Pemanfaatan Pengenalan Citra Kematengan Jengkol Untuk Saran Masakan Menggunakan Metode Algoritma Deep Learning,” Bull. Comput. Sci. Res., vol. 4, no. 4, pp. 356–367, 2024, doi: 10.47065/bulletincsr.v4i4.358.
A. A. Kurniawan and M. Mustikasari, “Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia,” J. Inform. Univ. Pamulang, vol. 5, no. 4, pp. 544–552, 2021, doi: 10.32493/informatika.v5i4.7760.
F. Azimah and K. Rizky Nova Wardani, “Sistem Pendeteksi Gejala Awal Covid-19 dengan Penggunaan Metode Al Project Cycle,” J. Locus Penelit. dan Pengabdi., vol. 1, no. 6, pp. 405–418, 2022, doi: 10.36418/locus.v1i6.135.
C. A. Aurelia and D. A. Prasetya, “Implementasi Pembelajaran AI Mastery Program dalam Mengembangkan Aplikasi Berbasis Web di PT. Orbit Ventura Indonesia,” J. Pengabdi. dan Pemberdaya. Masy., vol. 4, no. 1, pp. 1–10, 2023, doi: 10.37802/society.v4i1.299.
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