Optimasi Algoritma Genetika Dalam Memprediksi Minat Baca Siswa Pada Perpustakaan SMK Negeri 1 Gantar Dengan Metode Decision Tree
DOI:
https://doi.org/10.37366/jpcs.v2i1.949Keywords:
Reading interest, library, library, C4.5 algorithm, genetic algorithmAbstract
Students are one part of the world of education that cannot be separated from reading activities. Each school certainly seeks to provide reading facilities such as school libraries, as well as libraries owned by SMK Negeri 1 Gantar aim to be able to foster and increase students' interest in reading books in the school library. But at the moment the library of SMK Negeri 1 Gantar tends to be minimal in number of visitors, this could be due to the lack of student awareness of the importance of reading books or there are other factors that can influence such as service, type of book, comfort, collection of books and so on. Then conducted a study that aims to find out how much interest in reading students of SMK Negeri 1 Gantar library using genetic algorithm optimization with the decision tree method. The data used in this study are visitor data owned by the library of SMK Negeri 1 Gantar as many as 290 data, the process of testing the method using RapidMiner 9.2. Based on the results of testing on research in predicting students' reading interest in the library of SMK Negeri 1 Gantar, the results obtained from the C4.5 algorithm or decision tree are accuracy by 84.48% and after being optimized using genetic algorithms the accuracy increases by 12.07% so that the accuracy value obtained from optimization of 96.55%. Then it can be concluded that the genetic algorithm optimization technique in value succeeded in increasing the accuracy of the C4.5 algorithm or decision tree in predicting students' interest in reading at the SMK Negeri 1 Gantar library.
Keyword: Reading interest, library, C4.5 algorithm, genetic algorithm.
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References
Fatmawati. (2016). Perbandingan Algoritma Klasifikasi Data Mining Model C4.5 Dan Naive Bayes Untuk Prediksi Penyakit Diabetes, XIII(1), 50–59.
Febriarini, A. S., & Astuti, E. Z. (2019). Penerapan Algoritma C4. 5 untuk Prediksi Kepuasan Penumpang Bus Rapid Transit (BRT) Trans Semarang. Jurnal Eksplora Informatika, 8(2), 95–103.
Galih. (2019). Data Mining di Bidang Pendidikan untuk Analisa Prediksi Kinerja Mahasiswa dengan Komparasi 2 Model Klasifikasi pada STMIK Jabar, 2(1), 23–30.
Iriadi, N., & Nuraeni, N. (2016). kajian penerapan metode klasifikasi data mining algoritma C4. 5 untuk prediksi kelayakan kredit pada bank mayapada jakarta. Jurnal Teknik Komputer Amik BSI, 2(1), 132–137.
Meilani, B. D., & Asadulloh, M. (2015). Data Mining Untuk Menggali Pola Mahasiswa Baru Menggunakan Metode Frequent Pattern Growth (Studi Kasus : Institut Teknologi Adhi Tama Surabaya), 269–276.
Raharjo, S., & Mada, U. G. (2014). Klasterisasi, klasifikasi dan peringkasan teks berbahasa indonesia, 8(Kommit), 391–401.
Singestecia, R., Handoyo, E., Isdaryanto, N., Politik, J., Sosial, F. I., & Negeri, U. (2018). Partisipasi Politik Masyarakat Tionghoa dalam Pemilihan Kepala Daerah di Slawi Kabupaten Tegal, 2(1), 63–72.
Sugeha, I. H., Inkiriwang, R. L., & Pratasis, P. A. K. (2019). Optimasi penjadwalan menggunakan metode algoritma genetika pada proyek rehabilitasi puskesmas minanga. Jurnal Sipil Statik, 7(12).
Utami, R. D., Wibowo, D. C., & Susanti, Y. (2018). Analisis minat membaca siswa pada kelas tinggi di sekolah dasar negeri 01 belitang. Jurnal Pendidikan Dasar Perkhasa, 4(1), 179–188.
Yatun, S. (2015). Menumbuhkan minat baca siswa melalui perpustakaan, X(2), 171–187.
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