Application of the support vector machine algorithm in the classification of livable houses
Article Sidebar
Main Article Content
Abstract
Home is a basic need for humans in living life. Humans need a house to live and mingle with family. Having a decent home is the dream of every family. However, due to economic limitations, livable houses are difficult to realize. The government made the Rutilahu (Uninhabitable House) policy to reduce the number of uninhabitable houses. However, in practice there are still many misdirected targets. The Village Government is still carrying out the data classification process manually to determine which houses are livable and which are not. Processes that are still manual are old and inaccurate. For this reason, it is necessary to have a system to classify suitable and ineligible houses using the Support Vector Machine algorithm to make it more detailed so that later the assistance will not be misdirected. Support Vector Machine is a technique for maximizing margins, namely the distance that separates data classes by finding the best hyperplane. Determination of the classification of livable houses is based on four main indicators, namely the structure of the building, its area, sanitation, and clean water. This study took 642 data with 513 training data and 129 testing data and by using validation techniques using the confusion matrix obtained an accuracy of 80%. Thus the system built with the Support Vector Machine algorithm is quite good in the classification of livable houses
Downloads
Article Details
Aji, S. A. P., Oktavianto, H., & A’yun, Q. (2020). KLASIFIKASI PENERIMA BANTUAN DANA DESA MENGGUNAKAN METODE KNN (K-NEAREST NEIGHBOR).
Aunurrofiq, M., Yudhistira, I., & Nur Abdullah, F. (n.d.). Panas Bumi Dieng Untuk Masyarakat.
Darnila, E., Mauliza, & Ula, M. (2019). Aplikasi Teknologi Sistem Pakar Berbasis Fuzzy Clustering. yayasan kita menulis.
Elvira, V. F., & Badrah, S. (2023). Buku Ajar Sanitasi Perumahan dan Permukiman. Deepublish Digital.
Fikriani, A., Asror, I., & Murti, Y. R. (2019). Klasifikasi Kepribadian Berdasarkan Data Twitter dengan Menggunakan Metode Support Vector Machine. E-Proceeding of Engineering, 6(3), 10436–10450.
Furqan, M., Kurniawan, R., & HP, K. (2020). Evaluasi Performa Support Vector Machine Classifier Terhadap Penyakit Mental. Jsinbis, 10(2), 203–210.
Ghaniaviyanto Ramadhan, N., & Khoirunnisa, A. (2021). JURNAL MEDIA INFORMATIKA BUDIDARMA Klasifikasi Data Malaria Menggunakan Metode Support Vector Machine. 5, 1580–1584.
Iman, Q., & Wahyu, A. (2021). Klasifikasi Rumah Tangga Penerima Beras Miskin ( Raskin )/ Beras Sejahtera ( Rastra ) di Provinsi Jawa Barat Tahun 2017 dengan Metode Random Forest dan Support Vector Machine Classification of Poor Rice ( Raskin )/ Prosperous Rice ( Rastra ) Recipient Hou. 09(2), 178–184.
Isa, I. G. T., Elfaladonna, F., & Ariyanti, I. (2022). Buku Ajar Sistem Pendukung Keputusan. PT. Nasya Expanding Mangement.
Jakaria, A., Hendriadi, A. A., & Sulistiyowati, N. (2019). Aplikasi Penentuan Tunjangan Kinerja dan Rekomendasi Pegawai Universitas Singaperbangsa Karawang. Jurnal Media Informatika Budidarma, 3 No 3, 192.
Khairuni, Z. I., Matondang, Z., Nurhayani, U., & Atika, L. (2022). Karakteristik dan Desain Rumah Tanggap Bencana. CV Bintang Semesta Media.
Khasanah, N. (2019). KLASIFIKASI PENERIMA BANTUAN RUTILAHU (RUMAH TIDAK LAYAK HUNI) DI DESA PLUMBON TEMON KULON PROGO YOGYAKARTA DENGAN METODE NAÏVE BAYES. JURNAL INFORMATIKA, 1–8.
Kurniawan, D. (2020). Pengenalan Dengan Machine Learning Dengan Python. PT. Elex Media Komputindo.
Lasmiatun, Solehudin, & Anindita, M. (2023). Manajemen dan Analisis Data (H. B. A. Safrizal (ed.)). PT. Global Eksekutif Teknologi.
M Prawirosusanto, K. (2021). Mimpi Kemakmuran Dalam Pemukiman (kepenertiban dan perubahan sosiokultural orang suku laut di kepulauan riau) (Lulu (ed.)). Gadjah Mada University Press.
Mardawani. (2020). Praktis Penelitian Kualitatif Teori Dasar Dan Analisis Data Dalam Perspektif Kualitatif. Deepublish pubhliser.
Muhammad, A. C., Ariana, A. A. G. B., & Intan, I. (2023). Dasar-Dasar Pembelajaran Mesin (Foundations of Machine Learning) (Syafrizal (ed.)). PT. Sada Kurnia Pustaka.
Setyawati, E., Wibowo, A., & Adilla, A. (2023). Pngantar Pengujian dan Implementasi Sistem (F. Wongso (ed.)). Pt. Mafi Media Literasi Indonesia.
Sukmawati, A. S., Rusmayadi, G., & Amalia, M. M. (2023). Metode Penelitian Kuantitatif (Teori dan Penerapan Praktis Analisis Data Berbasis Studi Kasus) (Efitra & Sepriono (eds.)). PT. Sonpedia Publishing Indonesia.
Susilowati, E., Sabariah, M. K., & Gozali, A. A. (2015). Implementasi Metode Support Vector Machine untuk Melakukan Klasifikasi Kemacetan Lalu Lintas Pada Twitter. E-Proceeding of Engineering, 2(1), 1478–1484.
Vijayakumar S, W. . (1999). Sequential Support Vector Classifiers and Regression. Proc. International Conference on Soft Computing.
Wahab. (2019). Eksiklopedia Kebutuhan Manusia (U. Munaji (ed.)). ALPRIN.
Werdiningsih, I., Nuqoba, B., & Muhammadun. (2020). Data Mining Menggunakan Android, Weka dan Spss. Airlangga University Press.
Yoga Tursilarini, T., & Trilaksmi Udiati. (2020). Dampak Bantuan Rumah Tidak Layak Huni (Rtlh) Bagi Kesejahteraan Sosial Keluarga Penerima Manfaat Di Kabupaten Bangka.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.