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Abstrak Penentuan jenis tanah pada kedalaman tertentu untuk kebutuhan perencanaan pembangunan perumahan dilakukan berdasarkan data Cone Penetration Test. Tujuan penelitian ini untuk mengkomparasi Algoritma C4.5, Naive Bayes, and Neural Network sehingga ditemukan pemodelan yang terbaik untuk mengklasifikasikan tanah. Hasil dari penelitian ini didapatkan algoritma terbaik yaitu Algoritma C4.5. Algoritma C4.5 dalam klasifikasi dua kelas mencapai akurasi 98,45% dan AUC 0,981. Dalam klasifikasi tiga kelas C4.5 juga mencapai akurasi tertinggi (93,21%), demikian juga pada klasifikasi tujuh kelas (83,40%). Hasil penelitian ini menyimpulkan bahwa Algoritma C 4.5 dapat dijadikan pilihan dalam mengklasifikasi tanah untuk pembangunan perumahan. Kata Kunci : Data Mining, Klasifikasi Tanah, C4.5, Naive Bayes, Neural Network Abstract Determining the type of soil at a certain depth to the needs of residential development planning is done based on the data Cone Penetration Test. The purpose of this research to compare the data mining algorithm C4.5, Naive Bayes, and Neural Network to find the best modeling can be used for land classification. The results of this research, the best algorithm is C4.5. Algoritma C45 in binary-class classification accuracy reaches 98% and AUC 0,981. In the three-class classification C4.5 also have scored the highest accuracy (93.21%), as well as on the seven-class classification (83.40%). The results of this research concluded that the algorithm C 4.5 can be selected in classifying soil for residential development. Keywords: Data mining, C4.5, Naive Bayes, Neural Network, Soil Classification.

Komparasi Algoritma C4.5, Naïve Bayes Dan Neural Network Untuk Klasifikasi Tanah