Analisis Perbandingan Algoritma Klasifikasi Data Mining untuk Penentuan Lokasi Perumahan
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Andi Ernawati
*
Universitas Pembangunan Pancabudi, Medan,
Indonesia
- Muhammad Iqbal Universitas Pembangunan Pancabudi, Medan, Indonesia (*) Corresponding Author
Abstract
This study aims to analyze the application of C5.0 and K-Nearest Neighbor (K-NN) algorithms in the classification process for determining the optimal location for housing. The classification process involves several factors such as land price, accessibility, public facilities, crime rate, infrastructure, land availability, and consumer preferences. The research conducted tests on both algorithms to compare their performance in generating accurate predictions. The results show that the C5.0 algorithm outperforms K-NN, achieving an accuracy rate of 100%, compared to K-NN, which achieved an accuracy of 66.67%. This demonstrates that C5.0 is more effective in modeling data and producing more precise classifications. Therefore, it can be concluded that the use of data mining algorithms, particularly C5.0, greatly assists in the classification process for determining housing locations, providing more optimal results compared to K-NN.
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References
Mesran et al., Data Mining for Decision Support System, 1st ed. Jawa Tengah: Pena Persada Kerta Utama, 2024.
Y. R. S. Winanjaya, “Implementasi Data MiningDalamMengelompokkan Jumlah Penduduk MiskinBerdasarkan Provinsi Menggunakan AlgoritmaK-Means,” KESATRIA J. Penerapan Sist. Inf. (Komputer Manajemen), vol. 2, no. 2, pp. 125–132, 2021.
I. Nasution, A. P. Windarto, and M. Fauzan, “Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi,” vol. 2, no. 2, pp. 76–83, 2020.
B. Bangun and A. K. Karim, “Pengembalian Data Yang Hilang Pada Dataset Dengan Menggunakan Algoritma K-Nearest Neighbor Imputation Data Mining,” J. Media Inform. Budidarma, vol. 8, no. 3, p. 1706, 2024, doi: 10.30865/mib.v8i3.8014.
A. Karim, S. Esabella, K. Kusmanto, M. Hidayatullah, and S. Suryadi, “Penerapan Data Mining Untuk Pengelompokan Terhadap Kualitas Kinerja Karyawan Dengan Menggunakan Algoritma K-Medoids Clustering,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 1001, 2024, doi: 10.30865/mib.v8i2.7445.
M. S. Pangestu and M. A. Fitriani, “Perbandingan Perhitungan Jarak Euclidean Distance, Manhattan Distance, dan Cosine Similarity dalam Pengelompokan Data Bibit Padi Menggunakan Algoritma K-Means,” Sainteks, vol. 19, no. 2, p. 141, 2022, doi: 10.30595/sainteks.v19i2.14495.
A. D. Adhi Putra, “Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 2, pp. 636–646, 2021, doi: 10.35957/jatisi.v8i2.962.
A. T. Yuliandari, Z. Sari, and V. R. S. Nastiti, “Pemetaan Mata Kuliah Yang Berpengaruh Pada Kelulusan Tidak Tepat Waktu Mahasiswa Informatika UMM Menggunakan SOM,” J. Repos., vol. 3, no. 1, pp. 111–120, 2024, doi: 10.22219/repositor.v3i1.31016.
T. Permana, A. M. Siregar, A. F. N. Masruriyah, and A. R. Juwita, “Perbandingan Hasil Prediksi Kredit Macet pada Koperasi Menggunakan Algoritma KNN dan C5.0,” Conf. Innov. Appl. Sci. Technol., vol. 3, no. 1, pp. 737–746, 2020.
F. N. Umma, B. Warsito, and D. A. I. Maruddani, “Klasifikasi Status Kemiskinan Rumah Tangga Dengan Algoritma C5.0 Di Kabupaten Pemalang,” J. Gaussian, vol. 10, no. 2, pp. 221–229, 2021, doi: 10.14710/j.gauss.v10i2.29934.
E. Novianto, A. Hermawan, and D. Avianto, “Klasifikasi Algoritma K-Nearest Neighbor, Naive Bayes, Decision Tree Untuk Prediksi Status Kelulusan Mahasiswa S1,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 8, no. 2, pp. 146–154, 2023, doi: 10.36341/rabit.v8i2.3434.
D. P. Indini, Mesran, and Dito Putro Utomo, “Penerapan Data Mining Dalam Pengelompokan Data Reseller di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means,” J. Ilm. Media Sisfo, vol. 17, no. 2, pp. 189–202, 2023, doi: 10.33998/mediasisfo.2023.17.2.1391.
M. Mesran, M. Syahrizal, S. Sarwandi, S. Aripin, D. P. Utomo, and A. Karim, “A comparison of the performance of data mining classification algorithms on medical datasets with the application of data normalization,” AIP Conf. Proc., vol. 3048, no. 1, 2024, doi: 10.1063/5.0207994.
U. R. Amanda and D. P. Utomo, “Penerapan Data Mining Algoritma Hash Based Pada Data Pemesanan Buah Impor Cv. Green Uni Fruit,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 5, no. 1, 2021.
B. S. Pranata and D. P. Utomo, “Penerapan Data Mining Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel Motor (Study Kasus Bengkel Sinar Service),” Bull. Inf. Technol., vol. 1, no. 2, pp. 83–91, 2020.
I. Arfyanti, M. Fahmi, and P. Adytia, “Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1196–1201, 2022, doi: 10.47065/bits.v4i3.2275.
N. H. Harani and F. S. Damayanti, “Implementasi Algoritma C5.0 Untuk Menentukan Pelanggan Potensial Di Kantor Pos Cimahi,” J. SITECH Sist. Inf. dan Teknol., vol. 4, no. 1, pp. 69–76, 2021, doi: 10.24176/sitech.v4i1.6281.
D. Fitrianah, W. Gunawan, and A. Puspita Sari, “Studi Komparasi Algoritma Klasifikasi C5.0, SVM dan Naive Bayes dengan Studi Kasus Prediksi Banjir Comparative Study of Classification Algorithm between C5.0, SVM and Naive Bayes with Case Study of Flood Prediction,” Februari, vol. 21, no. 1, pp. 1–11, 2022.
R. N. Amalda, N. Millah, and I. Fitria, “Implementasi Algoritma C5.0 Dalam Menganalisa Kelayakan Penerima Keringanan Ukt Mahasiswa Itk,” Teorema Teor. dan Ris. Mat., vol. 7, no. 1, p. 101, 2022, doi: 10.25157/teorema.v7i1.6692.
A. Putri et al., “Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 20–26, 2023, doi: 10.57152/malcom.v3i1.610.
J. Supriyanto, D. Alita, and A. R. Isnain, “Penerapan Algoritma K-Nearest Neighbor (K-NN) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 4, no. 1, pp. 74–80, 2023, doi: 10.33365/jatika.v4i1.2468.
R. Sari, “Analisis Sentimen Pada Review Objek Wisata Dunia Fantasi Menggunakan Algoritma K-Nearest Neighbor (K-Nn),” EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 10–17, 2020, doi: 10.31294/evolusi.v8i1.7371.
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