Implementation of Linear Regression Algorithm in a Web-Based Major Prediction System for New Student Applicants at SMK N 1 Percut Sei Tuan
DOI:
https://doi.org/10.56211/hanif.v3i1.47Keywords:
Linear Regression; Major Prediction; Web-Based System; SMK N 1 Percut Sei Tuan; PPDB
Abstract
This study aims to develop a web-based major prediction system by applying a linear regression algorithm to enhance transparency and accuracy in the selection process. The system predicts 14 available majors at SMK N 1 Percut Sei Tuan, including: Civil Construction and Housing Engineering, Modeling and Building Information Design, Geomatics Engineering, Electrical Installation Engineering, Electrical Power Network Engineering, Heating, Air Conditioning and Refrigeration Engineering, Audio Video Engineering, Machining Engineering, Welding Engineering, Light Vehicle Engineering, Motorcycle Engineering, Software Engineering, Computer and Network Engineering, and Television Production and Broadcasting. The system uses report card scores from the 5th and 6th semesters of junior high school as predictor variables, including Bahasa Indonesia, Mathematics, Science, and English. The system development method includes data collection through observation, literature study, and interviews, as well as system design using PHP, HTML, JavaScript, MySQL database, and XAMPP. System modeling was carried out using UML (Unified Modeling Language), which includes use case diagrams, sequence diagrams, and activity diagrams. The linear regression algorithm is implemented by calculating subject averages, regression coefficients, and intercepts to predict student acceptance. The results of the study, based on five student data samples, show that M. Dafi and Ahmad Suhendra were not eligible for any major. Adellya Saputri and Alfit Septian were accepted into one major, Television Production and Broadcasting. Meanwhile, Ummi qualified for five majors: Modeling and Building Information Design, Audio Video Engineering, Welding Engineering, Light Vehicle Engineering, and Television Production and Broadcasting.
Downloads
References
J. Homepage, A. Roihan, P. A. Sunarya, and A. S. Rafika, “Pemanfaatan Machine Learning dalam Berbagai Bidang: Review Paper,” IJCIT (Indonesian Journal on Computer and Information Technology), 2019.
C. Chazar and B. E. Widhiaputra, “Machine Learning Diagnosis Kanker Payudara Menggunakan Algoritma Support Vector Machine,” INFORMASI (Jurnal Informatika dan Sistem Informasi), vol. 12, pp. 67–80, 2020. DOI: https://doi.org/10.37424/informasi.v12i1.48
R. R. Pratama, “Analisis Model Machine Learning Terhadap Pengenalan Aktivitas Manusia,” Jurnal MATRIK, vol. 19, pp. 302–311, May 2020. DOI: https://doi.org/10.30812/matrik.v19i2.688
B. Mahesh, “Machine Learning Algorithms — A Review,” International Journal of Science and Research, 2018, doi: 10.21275/ART20203995. DOI: https://doi.org/10.21275/ART20203995
M. Ula, A. F. Ulva, and Mauliza, “Implementasi Machine Learning dengan Model Case Based Reasoning dalam Mendiagnosa Gizi Buruk pada Anak,” Jurnal Informatika Kaputama (JIK), vol. 5, pp. 333–339, Jul. 2021. DOI: https://doi.org/10.59697/jik.v5i2.267
B. G. Pijls, “Machine Learning Assisted Systematic Reviewing in Orthopaedics,” Journal of Orthopaedics, vol. 48, pp. 103–106, Feb. 2024, doi: 10.1016/j.jor.2023.11.051. DOI: https://doi.org/10.1016/j.jor.2023.11.051
S. Jahandideh, G. Ozavci, B. W. Sahle, A. Z. Kouzani, F. Magrabi, and T. Bucknall, “Evaluation of Machine Learning-Based Models for Prediction of Clinical Deterioration: A Systematic Literature Review,” International Journal of Medical Informatics, vol. 175, Jul. 2023, doi: 10.1016/j.ijmedinf.2023.105084. DOI: https://doi.org/10.1016/j.ijmedinf.2023.105084
O. Alshaikh, S. Parkinson, and S. Khan, “Exploring Perceptions of Decision-Makers and Specialists in Defensive Machine Learning Cybersecurity Applications: The Need for a Standardised Approach,” Computers & Security, p. 103694, Dec. 2023, doi: 10.1016/j.cose.2023.103694. DOI: https://doi.org/10.2139/ssrn.4582920
A. X. Wang, S. S. Chukova, and B. P. Nguyen, “Synthetic Minority Oversampling Using Edited Displacement-Based K-Nearest Neighbors,” Applied Soft Computing, vol. 148, Nov. 2023, doi: 10.1016/j.asoc.2023.110895. DOI: https://doi.org/10.1016/j.asoc.2023.110895
Mulyadi, Implementasi Organisasi. Yogyakarta: Gadjah Mada University Press, 2015.
M. Kani, Algoritma dan Pemrograman. Tangerang Selatan: Universitas Terbuka, 2020.
A. Kurniadi and Y. Novianto, "Penerapan Metode Regresi Linier untuk Memprediksi Kebiasaan Pelanggan Studi Kasus: PT Mensa Binasukses," Jurnal Ilmiah Mahasiswa Teknik Informatika, vol. 2, no. 2, p. 107, 2020.
A. Bode, "Perbandingan Metode Prediksi Support Vector Machine dan Linear Regression Menggunakan Backward Elimination pada Produksi Minyak Kelapa," Jurnal Sistem Informasi dan Teknologi Komputer, vol. 4, no. 2, pp. 104–107, 2019. DOI: https://doi.org/10.51876/simtek.v4i2.57
D. A. Trianggana, "Peramalan Jumlah Siswa-Siswi Melalui Pendekatan Metode Regresi Linear," Jurnal Media Infotama, vol. 16, no. 2, pp. 115–120, 2020. DOI: https://doi.org/10.37676/jmi.v16i2.1149
V. W. Sujarweni, Sistem Akuntansi. Yogyakarta: Pustaka Baru Press, 2019.
Mulyadi, Sistem Informasi Akuntansi. Jakarta: Salemba Empat, 2018.
Srisulistiowati, "Sistem Informasi Prediksi Penjualan Alat Tulis Kantor dengan Metode FP-Growth (Studi Kasus Toko Koperasi Sekolah Bina Mulia)," unpublished.
C. Mashuri, Sistem Informasi Prediksi dengan Fuzzy dan RFID pada VMI. Perkumpulan Rumah Cemerlang Indonesia, 2022.
J. Winanjar and D. Susanti, "Rancang Bangun Sistem Informasi Administrasi Desa Berbasis Web Menggunakan PHP dan MySQL," Prosiding Seminar Nasional Sains dan Teknologi (SNAST), pp. 97–105, 2021.
T. Lesmana and M. Silalahi, “Jurnal Comasie,” Comasie, vol. 3, no. 3, pp. 21–30, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Sabrina Meylani Pulungan, Mhd. Zulfansyuri Siambaton, Heri Santoso

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish in Hanif Journal of Information Systems agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Most read articles by the same author(s)
- Aulia Ichsan, Mhd. Zulfansyuri Siambaton, Khairuddin Nasution, Android-Based Practical Work Student Registration Form Application System Design , Hanif Journal of Information Systems : Vol. 1 No. 1 (2023): August Edition
- Muhammad Aulia Abdi Rianto, Mhd. Zulfansyuri Siambaton, Heri Santoso, A Decision Support System for Determining Optimal Concrete Quality Using the Simple Additive Weighting (SAW) Algorithm (Case Study: UISU Concrete Laboratory) , Hanif Journal of Information Systems : Vol. 3 No. 1 (2025): August Edition
Similar Articles
- Oktaviana Nirmala Purba, Dian Novianti Sitompul, Tua Holomoan Harahap, Sri Rahmah Dewi Saragih, Rizka Fahruza Siregar, Application of Fuzzy C-Means Algorithm for Clustering Customers , Hanif Journal of Information Systems : Vol. 1 No. 1 (2023): August Edition
- Fachriza Habibi, Isnaini Faiz Qathrunada, Thamita Anggraini, Design and Build a Tourism Website Using Shopify Framework , Hanif Journal of Information Systems : Vol. 1 No. 1 (2023): August Edition
- Aulia Jannah, Abdillah Husaini, Aulia Ichsan, Mulkan Azhari, Implementation of Fuzzy K-Nearest Neighbor Method in Dengue Disiase Classification , Hanif Journal of Information Systems : Vol. 1 No. 2 (2024): February Edition
You may also start an advanced similarity search for this article.









