Implementation Of Decision Tree (ID3) For Student And Alumni Profiling

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Artikel (Bahasa Indonesia)
Published Dec 20, 2023
DOI : https://doi.org/10.32664/smatika.v13i02.898
  • Halaman : 250 - 259
  • Abstract views : 144
  • PDF views : 135

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Salsabila Vebi Natasya
Universitas Logistik dan Bisnis Internasional
Rolly Maulana Awangga
Teknik Informatika, Universitas Logistik dan Bisnis Internasional
M Yusril Helmi etyawan
Teknik Informatika, Universitas Logistik dan Bisnis Internasional

Abstract

Profiling of students and alumni is a study that aims to identify the characteristics of individuals who enter and leave an educational institution. Through the analysis of the collected data, this study can provide valuable information for the institution namely the University of Logistics and International Business to improve the programs offered and help students and alumni to plan their careers. The data used in this study were obtained from scraping on the linkedin web, which totaled 300 data. With the ratio of the distribution of testing data and test data as much as 75%: 25%. The purpose of this research is to apply the decision tree method to student and alumni profiling at the International Logistics and Business University to see how accurate the profiling is produced using the id3 decision tree method. Conclusions that can be drawn from the results and discussion of the research that has been done. So the most influential attributes for getting students and alumni to get jobs are organization and certification skills. After forming a decision tree rule with the id3 algorithm, pre-pruning and post-pruning is done to ensure that the resulting decision tree is not overfitting. Then the accuracy obtained for the train data is 0.64 or 64% and the test data is 0.61 or 61%. With a recall of 0.36 or 36% and a precision of 0.6 or 60%.

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Issue
Vol 13 No 02 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal
Section
Articles
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References
[1] Akbar, F., Saputra, H. W., Maulaya, A. K., Hidayat, M. F., & Rahmaddeni, R. (2022). Implementasi Algoritma Decision Tree C4. 5 dan Support Vector Regression untuk Prediksi Penyakit Stroke: Implementation of Decision Tree Algorithm C4. 5 and Support Vector Regression for Stroke Disease Prediction. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 2(2), 61-67.
[2] Ananto, R. P., Purwanto, Y., & Novianty, A. (2017). Deteksi Jenis Serangan Pada Distributed Denial Of Service Berbasis Clustering dan Classification Menggunakan Algoritma Minkowski Weighted K-Means dan Decision Tree. eProceedings of Engineering, 4(1).
[3] Arfyanti, I., Fahmi, M., & Adytia, P. (2022). Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah. Building of Informatics, Technology and Science (BITS), 4(3), 1196-1201.
[4] Lishania, I., Goejantoro, R., & Nasution, Y. N. (2020). Perbandingan Klasifikasi Metode Naive Bayes dan Metode Decision Tree Algoritma (J48) pada Pasien Penderita Penyakit Stroke di RSUD Abdul Wahab Sjahranie Samarinda. EKSPONENSIAL, 10(2), 135-142.
[5] Majid, M. B. A., Cani, Y. M., & Enri, U. (2022). Penerapan Algoritma K-Means dan Decision Tree Dalam Analisis Prestasi Siswa Sekolah Menengah Kejuruan. Jurnal Sistem Komputer dan Informatika (JSON), 4(2), 355-364.
[6] Permana, A., Wibowo, A., Hananto, A., & Nugroho, S. B. (2021). Komparasi performa algoritma ID3, C4. 5, CHAID dalam profiling tersangka kasus narkoba di jawa barat. Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi, 6(1), 1-8.
[7] Rahmawati, T. D., Adnan, F. N., & CS, M. (2016). Penentuan Produk Asuransi BPJS Berdasarkan Profil Pelanggan Dengan Pendekatan K-Nearest Neighbor Manhattan Distance. Journal of Information System, 1(2), 167-180.
[8] Aradea, S. A., Ariyan, Z., & Yuliana, A. (2011). Penerapan Decision Tree untuk penentuan pola data Penerimaan Mahasiswa Baru. Jurnal Penelitian Sitrotika, 7(1), 1693-9670.
[9] Santoso, T. B. (2014). Analisa Dan Penerapan Metode C4. 5 Untuk Prediksi Loyalitas Pelanggan. Jurnal Ilmiah Fakultas Teknik LIMIT’S, 10(1), 1-10.
[10] Srimenganti, I., Taufik, I., & Mulyana, E. (2019, January). Implementasi Algoritma Decision Tree (ID3) Untuk Penyakit Campak. In Prosiding Seminar Nasional Teknik Elektro UIN Sunan Gunung Djati Bandung (pp. 235-242).
[11] Sulianta, F. (2014). Customer Profiling Pada Supermarket Menggunakan Algoritma K-Means Dalam Memilih Produk Berdasarkan Selera Konsumen Dengan Daya Beli Maksimum. Jurnal Ilmiah Teknologi Infomasi Terapan, 1(1).
[12] Sunardi, S., Fadlil, A., & Kusuma, N. M. P. (2022). Implementasi Data Mining dengan Algoritma Naïve Bayes untuk Profiling Korban Penipuan Online di Indonesia. JURNAL MEDIA INFORMATIKA BUDIDARMA, 6(3), 1562-1572.
[13] Widhyastuti, L. P. W., Sukajaya, I. N., & Aryanto, K. Y. E. (2022). The Customer Profiling berdasarkan Model RFM dengan Metode K-Means pada Institusi Pendidikan untuk menunjang Strategi Bisnis di Masa Pandemi Covid-19. JTIM: Jurnal Teknologi Informasi dan Multimedia, 4(2), 94-108.
[14] Widiyati, D. K., Wati, M., & Pakpahan, H. S. (2018). Penerapan Algoritma ID3 Decision Tree Pada Penentuan Penerima Program Bantuan Pemerintah Daerah di Kabupaten Kutai Kartanegara. J. Rekayasa Teknol. Inf, 2(2), 125.