Determining recipients of uninhabitable house rehabilitation program assistance using the classification method
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Published:
May 30, 2024
Keywords:
Assistance program eligibility determination system Classification, Naïve Bayes, Uninhabitable House
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Computer Science
Statistics Article
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Abstract
The data used in this study amounted to 15182 datasets consisting of 14 variables. Existing variables are divided into basic variables and additional variables. The basic variables consist of 5 variables namely Home ownership, Roof type, Wall type, Floor type, Defecation facilities. While the additional variables consist of 9 variables, namely employment, having money / livestock / jewelry deposits and others, welfare deciles, education, recipients of non-cash food assistance, recipients of productive assistance for micro enterprises, recipients of cash social assistance, recipients of family hope programs, and recipients of basic necessities. Using the naïve bayes algorithm classification method, the values of accuracy, precision, recall, and f-measure are 67.61%, 67.97%, 93.71% and 78.79%. The addition of additional variables to the basic variables resulted in an accuracy of 68.29% in the additional variables of education. This shows that by adding additional variables, the accuracy results are higher than using only basic variables, so that this study can be used as a recommendation in decision making on the implementation of determining the beneficiaries of the rehabilitation program for uninhabitable houses
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How to Cite
Silfiyah, C., Kusumawati, R. and Crysdian, C. . (2024) “Determining recipients of uninhabitable house rehabilitation program assistance using the classification method”, Jurnal Mantik, 8(1), pp. 734-747. doi: 10.35335/mantik.v8i1.5184.
References
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Data P3KE (Penyasaran Percepatan Penghapusan Kemiskinan Ekstrem) tahun 2022 Kabupaten Probolinggo (Inventaris RTLH DPPKP (Dinas Perumahan Kawasan Permukiman dan Pertanahan) Kabupaten Probolinggo.
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Furqan, M., Hasibuan, M. S., & Sapitri, B. (2023). Jurnal Mantik Application of the support vector machine algorithm in the classification of livable houses. 7(3).
Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 Or 80/20 Relation Between Training And Testing Sets?: A Pedagogical Explanation. Departmental Technical Reports (CS), 1209, 1–6.
Halim, A. A. D., & Anraeni, S. (2021). Analisis Klasifikasi Dataset Citra Penyakit Pneumonia menggunakan Metode K-Nearest Neighbor (KNN). Indonesian Journal of Data and Science, 2(1), 01–12. https://doi.org/10.33096/ijodas.v2i1.23
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INPRES Nomor 4 Tahun 2022 tentang Percepatan Penghapusan Kemiskinan Ekstrem.
Marga, N. S. (2022). Sentimen Analisis Tentang Kebijakan Pemerintah Terhadap Kasus Corona Menggunakan Metode Naive Bayes. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 2(4), 453–463. https://doi.org/10.33365/jatika.v2i4.1602
Maulana Rosyid, E. (2017). Implementasi Peraturan Menteri Perumahan Rakyat Republik Indonesia Nomor 06 Tahun 2013 Tentang Pedoman Pelaksanaan Bantuan Stimulan Perumahan Swadaya Di Kelurahan Mondokan Kecamatan Tuban Kabupaten Tuban. Publika, 5(5).
Megawati, S., Ma’ruf, M. F., Fanida, E. H., Niswah, F., & Oktariyanda, T. A. (2020). Strengthening Family Resilience through Financial Management Education in Facing the Covid-19 Pandemic. Journal La Bisecoman, 1(5), 8–15. https://doi.org/10.37899/journallabisecoman.v1i5.246
Mubarak, M. F., & Hidayat, N. (2019). Rekomendasi Perbaikan Rumah Tidak Layak Huni Menggunakan Metode TOPSIS Studi Kasus Badan Keswadayaan Masyarakat Di Kelurahan Bekasi Jaya. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(4), 3390–3395.
Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, & Michael Indrawan. (2024). Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z. Journal of Applied Computer Science and Technology, 5(1), 16–25. https://doi.org/10.52158/jacost.v5i1.715
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI), 5(2), 697–711.
Pahlevi, O.-, Amrin, A.-, & Handrianto, Y.-. (2023). Implementasi Algoritma Klasifikasi Random Forest Untuk Penilaian Kelayakan Kredit. Jurnal Infortech, 5(1), 71–76. https://doi.org/10.31294/infortech.v5i1.15829
Peraturan Bupati Nomor 7 Tahun 2020 tentang Pedoman Pelaksanaan Pemberian Bantuan Rumah Tidak Layak Huni.
Siahaan, M. L., & Situmorang, Z. (2023). Algoritma Naïve Bayes Dalam Penentuan Bantuan Renovasi Rumah Di Desa Sialang Buah. KAKIFIKOM (Kumpulan Artikel Karya …, 05(02), 71–81. https://ejournal.ust.ac.id/index.php/KAKIFIKOM/article/view/3102%0Ahttps://ejournal.ust.ac.id/index.php/KAKIFIKOM/article/view/3102/2508
Sinaga, A. S. R., & Simanjuntak, D. (2020). Sistem Pakar Deteksi Gizi Buruk Balita Dengan Metode Naïve Bayes Classifier. Jurnal Inkofar, 1(2). https://doi.org/10.46846/jurnalinkofar.v1i2.110
Song, M., Zhu, Z., Wang, P., Wang, K., Li, Z., Feng, C., & Shan, M. (2023). An Alternative Rural Housing Management Tool Empowered by a Bayesian Neural Classifier. Sustainability (Switzerland), 15(3). https://doi.org/10.3390/su15031785
Surahman, A., & Hayati, U. (2023). Implementasi Algoritma Naïve Bayes Untuk Prediksi Penerima Bantuan Sosial. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 347–352. https://doi.org/10.36040/jati.v7i1.6302
Wijaya, N. (2020). Evaluation of Naïve Bayes and Chi-Square performance for Classification of Occupancy House. International Journal of Informatics and Computation, 1(2), 46. https://doi.org/10.35842/ijicom.v1i2.20
Yustina, E., Hariyadi, M. A., & Crysdian, C. (2024). Recommendation of Prospective Construction Service Providers in Government Procurement Using Decision Tree. International Journal of Advances in Data and Information Systems, 5(1), 39–48. https://doi.org/10.59395/ijadis.v5i1.1316
Undang -Undang Nomor 1 Tahun 2011 tentang Perumahan dan Permukiman bahwa dalam penyelenggaraan perumahan dan kawasan permukiman, setiap orang berhak menempati, menikmati, dan atau memiliki atau memperoleh rumah yang layak dalam lingkungan yang sehat, aman, serasi, dan teratur
Argina, A. M. (2020). Penerapan Metode Klasifikasi K-Nearest Neigbor pada Dataset Penderita Penyakit Diabetes. Indonesian Journal of Data and Science, 1(2), 29–33. https://doi.org/10.33096/ijodas.v1i2.11
Data P3KE (Penyasaran Percepatan Penghapusan Kemiskinan Ekstrem) tahun 2022 Kabupaten Probolinggo (Inventaris RTLH DPPKP (Dinas Perumahan Kawasan Permukiman dan Pertanahan) Kabupaten Probolinggo.
Fakhrun Nisa, A., & Setyadharma, A. (2020). Benefit Incidence Analysis of Uninhatitable Houses Rehabilitation Program in Indonesia. KnE Social Sciences. https://doi.org/10.18502/kss.v4i6.6690
Febrisa Sidabutar, A., Habibi, R., & Isti Rahayu, W. (2023). Perbandingan Metode Klasifikasi Untuk Pengelompokan Risiko Magang Mahasiswa. JATI (Jurnal Mahasiswa Teknik Informatika), 7(3), 2071–2076. https://doi.org/10.36040/jati.v7i3.7026
Furqan, M., Hasibuan, M. S., & Sapitri, B. (2023). Jurnal Mantik Application of the support vector machine algorithm in the classification of livable houses. 7(3).
Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 Or 80/20 Relation Between Training And Testing Sets?: A Pedagogical Explanation. Departmental Technical Reports (CS), 1209, 1–6.
Halim, A. A. D., & Anraeni, S. (2021). Analisis Klasifikasi Dataset Citra Penyakit Pneumonia menggunakan Metode K-Nearest Neighbor (KNN). Indonesian Journal of Data and Science, 2(1), 01–12. https://doi.org/10.33096/ijodas.v2i1.23
Heliyanti Susana. (2022). Penerapan Model Klasifikasi Metode Naive Bayes Terhadap Penggunaan Akses Internet. Jurnal Riset Sistem Informasi Dan Teknologi Informasi (JURSISTEKNI), 4(1), 1–8. https://doi.org/10.52005/jursistekni.v4i1.96
INPRES Nomor 4 Tahun 2022 tentang Percepatan Penghapusan Kemiskinan Ekstrem.
Marga, N. S. (2022). Sentimen Analisis Tentang Kebijakan Pemerintah Terhadap Kasus Corona Menggunakan Metode Naive Bayes. Jurnal Informatika Dan Rekayasa Perangkat Lunak, 2(4), 453–463. https://doi.org/10.33365/jatika.v2i4.1602
Maulana Rosyid, E. (2017). Implementasi Peraturan Menteri Perumahan Rakyat Republik Indonesia Nomor 06 Tahun 2013 Tentang Pedoman Pelaksanaan Bantuan Stimulan Perumahan Swadaya Di Kelurahan Mondokan Kecamatan Tuban Kabupaten Tuban. Publika, 5(5).
Megawati, S., Ma’ruf, M. F., Fanida, E. H., Niswah, F., & Oktariyanda, T. A. (2020). Strengthening Family Resilience through Financial Management Education in Facing the Covid-19 Pandemic. Journal La Bisecoman, 1(5), 8–15. https://doi.org/10.37899/journallabisecoman.v1i5.246
Mubarak, M. F., & Hidayat, N. (2019). Rekomendasi Perbaikan Rumah Tidak Layak Huni Menggunakan Metode TOPSIS Studi Kasus Badan Keswadayaan Masyarakat Di Kelurahan Bekasi Jaya. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(4), 3390–3395.
Muhammad Daffa Al Fahreza, Ardytha Luthfiarta, Muhammad Rafid, & Michael Indrawan. (2024). Analisis Sentimen: Pengaruh Jam Kerja Terhadap Kesehatan Mental Generasi Z. Journal of Applied Computer Science and Technology, 5(1), 16–25. https://doi.org/10.52158/jacost.v5i1.715
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. Jurnal Sains Komputer & Informatika (J-SAKTI), 5(2), 697–711.
Pahlevi, O.-, Amrin, A.-, & Handrianto, Y.-. (2023). Implementasi Algoritma Klasifikasi Random Forest Untuk Penilaian Kelayakan Kredit. Jurnal Infortech, 5(1), 71–76. https://doi.org/10.31294/infortech.v5i1.15829
Peraturan Bupati Nomor 7 Tahun 2020 tentang Pedoman Pelaksanaan Pemberian Bantuan Rumah Tidak Layak Huni.
Siahaan, M. L., & Situmorang, Z. (2023). Algoritma Naïve Bayes Dalam Penentuan Bantuan Renovasi Rumah Di Desa Sialang Buah. KAKIFIKOM (Kumpulan Artikel Karya …, 05(02), 71–81. https://ejournal.ust.ac.id/index.php/KAKIFIKOM/article/view/3102%0Ahttps://ejournal.ust.ac.id/index.php/KAKIFIKOM/article/view/3102/2508
Sinaga, A. S. R., & Simanjuntak, D. (2020). Sistem Pakar Deteksi Gizi Buruk Balita Dengan Metode Naïve Bayes Classifier. Jurnal Inkofar, 1(2). https://doi.org/10.46846/jurnalinkofar.v1i2.110
Song, M., Zhu, Z., Wang, P., Wang, K., Li, Z., Feng, C., & Shan, M. (2023). An Alternative Rural Housing Management Tool Empowered by a Bayesian Neural Classifier. Sustainability (Switzerland), 15(3). https://doi.org/10.3390/su15031785
Surahman, A., & Hayati, U. (2023). Implementasi Algoritma Naïve Bayes Untuk Prediksi Penerima Bantuan Sosial. JATI (Jurnal Mahasiswa Teknik Informatika), 7(1), 347–352. https://doi.org/10.36040/jati.v7i1.6302
Wijaya, N. (2020). Evaluation of Naïve Bayes and Chi-Square performance for Classification of Occupancy House. International Journal of Informatics and Computation, 1(2), 46. https://doi.org/10.35842/ijicom.v1i2.20
Yustina, E., Hariyadi, M. A., & Crysdian, C. (2024). Recommendation of Prospective Construction Service Providers in Government Procurement Using Decision Tree. International Journal of Advances in Data and Information Systems, 5(1), 39–48. https://doi.org/10.59395/ijadis.v5i1.1316
Undang -Undang Nomor 1 Tahun 2011 tentang Perumahan dan Permukiman bahwa dalam penyelenggaraan perumahan dan kawasan permukiman, setiap orang berhak menempati, menikmati, dan atau memiliki atau memperoleh rumah yang layak dalam lingkungan yang sehat, aman, serasi, dan teratur
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