Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning

https://doi.org/10.22146/ijccs.62589

Aditama Azmy Musaddad(1), Arimurti Kriswibowo(2*)

(1) Public Administration Department Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
(2) Public Administration Department Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
(*) Corresponding Author

Abstract


Machine learning approaches have been used to solve various problems. PKH experienced miss-targeting. This study aims to compare the result of big data by SIKS-NG and machine learning based on the same data and measurement indicators. Obtained algorithms Averaged Neural Network with optimal output compared to others. As for data testing obtained on SIKS-NG and machine learning that uses elevated matrix evaluations with the following 3 indicators: 1) Accuracy obtained by SIKS-NG 72.40% increased to 81.18% for Machine Learning; 2) Precision at the center is getting a high percentage of 91,01%, but it is capable of increasing once the data is given Machine Learning to 95,37%; 3) Recall with the cycle was obtained at 75.49%, while Machine Learning obtained a higher yield of 82.19%. Thus, machine learning has been proven to reduce miss-targeting and can be used as an alternative recommendation in automatic decision making and innovative management practices in government circles.


Keywords


Family Hope Program; Miss-Targeting; Big Data; Machine Learning

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References

[1]      D. V. Ferezagia, “Analisis Tingkat Kemiskinan di Indonesia Jurnal Sosial Humaniora Terapan,” J. Sos. Hum. Terap., vol. 1, no. 1, pp. 1–6, 2018.

[2]      W. I. Azizah, Z. Mahmudah, and A. Kriswibowo, “Political Will Pemerintah Kabupaten Jombang Terhadap Penanggulangan Kemiskinan Di Masyarakat Desa,” J. Sos. Ekon. dan Polit., vol. 1, no. 1, 2020.

[3]      Katadata.co.id, “Hasil Studi: PKH dan Bantuan Sembako Tak Tepat Sasaran dan Ganjal.”

[4]      Kominfo.jatimprov.go.id., “DPRD Siap Terima Aduan Masyarakat di Jawa Timur Terkait Bansos Covid-19 Yang Tidak Tepat Sasaran.”

[5]      republikjatim.com, “Panja Covid-19 Dewan Desak Pemkab Sidoarjo Tandai Rumah Penerima Bantuan PKH Dengan Tulisan Cat.”

[6]      Kompas.com, “Distribusi Bansos PKH Tak Tepat Sasaran, Mensos Siapkan Aturan Baru.”

[7]      A. Fiszbein et al., “Conditional Cash Transfers: Reducing Present and Future Poverty, A World Bank Policy Research Report,” The International Bank for Reconstruction and Development / The World Bank, Washington, 2009.

[8]      J. Archenaa and E. A. M. Anita, “A Survey of Big Data Analytics in Healthcare and Government,” Procedia Comput. Sci., vol. 50, pp. 408–413, 2015.

[9]      E. R. E. Sirait, “Implementasi Teknologi Big Data di Lembaga Pemerintahan Indonesia,” J. Penelit. Pos dan Inform., vol. 6, no. 2, pp. 113–136, 2016.

[10]    A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review Paper,” IJCIT (Indonesian J. Comput. Inf. Technol., vol. 5, no. 1, pp. 75–82, 2020.

[11]    E. Fitriani, “Perbandingan Algoritma C4.5 Dan Naïve Bayes Untuk Menentukan Kelayakan Penerima Bantuan Program Keluarga Harapan,” Sist. J. Sist. Inf., vol. 9, no. 1, pp. 103–115, 2020.

[12]    C. A. Sugianto and F. R. Maulana, “Algoritma Naïve Bayes Untuk Klasifikasi Penerima Bantuan Pangan Non Tunai (Studi Kasus Kelurahan Utama),” Techno.Com, vol. 18, no. 4, pp. 321–331, 2019.

[13]    Z. Chang, L. Lei, Z. Zhou, S. Mao, and T. Ristaniemi, “Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era,” IEEE Wirel. Commun., vol. 25, no. 3, pp. 28–35, 2018.

[14]    H. A. Ramadhan and D. A. Putri, “Big Data, Kecerdasan Buatan, Blockchain, dan Teknologi Finansial di Indonesia (Usulan Desain, Prinsip, dan Rekomendasi Kebijakan),” Jakarta, 2018.

[15]    Keputusan Menteri Sosial Republik Indonesia Nomor 146/ HUK/ 2013 Tentang Penetapan Kriteri dan Pendataan Fakir Miskin dan Orang Tidak Mampu. .

[16]    N. Nofriani, “Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status,” J. Pekommas, vol. 4, no. 1, pp. 43–52, 2019.

[17]    B. Buchanan and T. Miller, Machine Learning for Policy Makers What It Is and Why It Matters, no. June. Cambridge: President and Fellows of Harvard College, 2017.

[18]    T. O. Ayodele, Types of Machine Learning Algorithms, New Advances in Machine Learning, Yagang Zha. University of Portsmouth United Kingdom, 2010.

[19]    L. Farokhah, “Implementasi K-Nearest Neighbor Untuk Klasifikasi Bunga Dengan Ekstraksi Fitur Warna RGB,” J. Teknol. Inf. Dan Komun., vol. 7, no. 6, pp. 1129–1136, 2020.



DOI: https://doi.org/10.22146/ijccs.62589

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