Analysis of Covid-19 Cash Direct Aid (BLT) Acceptance Using K-Nearest Neighbor Algorithm

https://doi.org/10.22146/ijccs.70801

Ahmad Ari Aldino(1*), Ryan Randy Suryono(2), Riyama Ambarwati(3)

(1) Department of Informatics, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia
(2) Department of Information System, Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia
(3) Department of Mathematics Education, Faculty of Tarbiyah and Teacher Training, UIN Raden Intan Lampung
(*) Corresponding Author

Abstract


During the COVID-19 pandemic, the government imposed Large-Scale Social Restrictions (PSBB) to reduce or slow down the spread of COVID-19. This causes people to be unable to work as usual, and not even a few people have lost their jobs. This prompted the government to launch the Covid-19 direct cash assistance (BLT) program. One of the areas affected by the PSBB is Batu Ampar Village, which distributing BLT is considered less effective by residents because there are BLTs that are not well-targeted. The cause of the ineffectiveness of the distribution of aid was assessed because the data was out of sync; it was difficult to verify and validate the new data due to the size of the area and the constantly changing number of underprivileged residents. To overcome these problems, a model is needed to predict the recipients of this Covid-19 BLT. This study uses the K-Nearest Neighbor (K-NN) algorithm and RapidMiner tools to make predictions and validate using Cross-Validation. The data used are 711 lines with 474 training data and 237 testing data resulting in an accuracy of 89.68% for training data and 88.61% for testing data.

Keywords


COVID-19; Data Mining; K-NN; Cross-Validation

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References

[1] U. Lestari and M. Targiono, “Sistem Pendukung Keputusan Klasifikasi Keluarga Miskin Menggunakan Metode Simple Additive Weighting (Saw) Sebagai Acuan Penerima Bantuan Dana Pemerintah (Studi Kasus: Pemerintah Desa Tamanmartani, Sleman),” Jurnal TAM (Technology Acceptance Model), vol. 8, no. 1, pp. 70–78, 2017.

[2] R. Wowiling, “PERAN PEMERINTAH DESA DALAM PENYALURAN BANTUAN LANGSUNG TUNAI PADA MASYARAKAT DI MASA PANDEMI COVID–19 DI KECAMATAN …,” JURNAL POLITICO, 2021.

[3] D. Herdiana, I. Wahidah, N. Nuraeni, and A. N. Salam, “Implementasi Kebijakan Bantuan Langsung Tunai (BLT) Dana Desa Bagi Masyarakat Terdampak COVID-19 di Kabupaten Sumedang: Isu dan Tantangan,” Jurnal Inspirasi, vol. 12, no. 1, 2021.

[4] B. Iping, “PERLINDUNGAN SOSIAL MELALUI KEBIJAKAN PROGRAM BANTUAN LANGSUNG TUNAI (BLT) DI ERA PANDEMI COVID-19: TINJAUAN PERSPEKTIF EKONOMI DAN SOSIAL,” JURNAL MANAJEMEN PENDIDIKAN DAN ILMU SOSIAL, vol. 1, no. 2, 2020, doi: 10.38035/jmpis.v1i2.290.

[5] S. Du and J. Li, "Parallel Processing of Improved KNN Text Classification Algorithm Based on Hadoop," 2019 7th International Conference on Information, Communication and Networks, ICICN 2019, pp. 167–170, 2019, doi: 10.1109/ICICN.2019.8834973.

[6] M. Dixit, R. Sharma, S. Shaikh, and K. Muley, "Internet traffic detection using naïve bayes and K-Nearest neighbors (KNN) algorithm," 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, no. Iciccs, pp. 1153–1157, 2019, doi: 10.1109/ICCS45141.2019.9065655.

[7] C. Chethana, "Prediction of heart disease using different KNN classifier," Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021, no. Iciccs, pp. 1186–1194, 2021, doi: 10.1109/ICICCS51141.2021.9432178.

[8] Q. Yunneng, "A new stock price prediction model based on improved KNN," Proceedings - 2020 7th International Conference on Information Science and Control Engineering, ICISCE 2020, pp. 77–80, 2020, doi: 10.1109/ICISCE50968.2020.00026.

[9] R. L. Hasanah, M. Hasan, W. E. Pangesti, F. F. Wati, and W. Gata, “KLASIFIKASI PENERIMA DANA BANTUAN DESA MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR),” Jurnal Techno Nusa Mandiri, vol. 16, no. 1, 2019, doi: 10.33480/techno.v16i1.25.

[10] I. Arfanda, W. Ramdhan, and R. A. Yusda, “Naive Bayes Dalam Menentukan Penerima Bantuan Langsung Tunai,” Digital Transformation Technology, vol. 1, no. 1, 2021, doi: 10.47709/digitech.v1i1.1091.

[11] A. Pratama, F. Ali, I. Ade, and R. Rinaldi, “Klasifikasi Penerima Beasiswa Dengan Menggunakan Algoritma K Nearest Neighbor,” Jurnal Data Science & Informatika (JDSI), vol. 1, no. 1, 2021.

[12] A. Rahman Isnain, A. Indra Sakti, D. Alita, and N. Satya Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm,” Jdmsi, vol. 2, no. 1, pp. 31–37, 2021.

[13] Z. Nabila, A. Rahman Isnain, and Z. Abidin, “Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means,” Jurnal Teknologi dan Sistem Informasi (JTSI), vol. 2, no. 2, p. 100, 2021.

[14] I. G. Mustafa, “Studi Tentang Pemberian Insentif Dalam Meningkatkan Kinerja Pegawai Di Sekretariat Daerah Provinsi Kalimantan Timur,” Jurnal Paradigma (JP), vol. 1, no. 3, pp. 373–388, 2017.

[15] D. Alita, I. Sari, A. R. Isnain, and S. Styawati, “Penerapan Naïve Bayes Classifier Untuk Pendukung Keputusan Penerima Beasiswa,” Jurnal Data Mining Dan Sistem Informasi, vol. 2, no. 1, pp. 17–23, 2021.

[16] R. I. Borman and M. Wati, “Penerapan Data Maining Dalam Klasifikasi Data Anggota Kopdit Sejahtera Bandarlampung Dengan Algoritma Naïve Bayes,” Jurnal Ilmiah Fakultas Ilmu Komputer, vol. 9, no. 1, pp. 25–34, 2020.

[17] Aprilla Dennis, "Belajar Data Mining dengan RapidMiner," Innovation and Knowledge Management in Business Globalization: Theory & Practice, Vols 1 and 2, vol. 5, no. 4, pp. 1–5, 2013, doi: 10.1007/s13398-014-0173-7.2.

[18] I. G. Mustafa, “Imam, Studi Tentang Pemberian Insentif Dalam Meningkatkan Kinerja Pegawai … 373,” pp. 373–388.

[19] 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, 2019, doi: 10.33633/tc.v18i4.2587.

[20] H. Sulistiani and A. A. Aldino, "Decision Tree C4.5 Algorithm for Tuition Aid Grant Program Classification (Case Study: Department of Information System, Universitas Teknokrat Indonesia)," Edutic - Scientific Journal of Informatics Education, vol. 7, no. 1, pp. 40–50, 2020, doi: 10.21107/edutic.v7i1.8849.



DOI: https://doi.org/10.22146/ijccs.70801

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