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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) sudo Jurnal Teknik Informatika Hello World Blend Sains Jurnal Teknik Deli Sains Informatika Buletin Utama Teknik Hanif Journal of Information Systems
Mhd. Zulfansyuri Siambaton, Mhd. Zulfansyuri
Magister Teknik Informatika, Universitas Sumatera Utara
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Journal : Hanif Journal of Information Systems

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Implementation of Linear Regression Algorithm in a Web-Based Major Prediction System for New Student Applicants at SMK N 1 Percut Sei Tuan Pulungan, Sabrina Meylani; Siambaton, Mhd. Zulfansyuri; Santoso, Heri
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.47

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.
A Decision Support System for Determining Optimal Concrete Quality Using the Simple Additive Weighting (SAW) Algorithm (Case Study: UISU Concrete Laboratory) Rianto, Muhammad Aulia Abdi; Siambaton, Mhd. Zulfansyuri; Santoso, Heri
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.48

Abstract

This study aims to design a decision support system to determine the best concrete quality using the Simple Additive Weighting (SAW) algorithm. Concrete is the primary material in construction, possessing various mechanical properties and characteristics that define its quality. At the Concrete Laboratory of Universitas Islam Sumatera Utara (UISU), the determination of concrete quality is still conducted manually, relying on subjective experience, which can lead to inconsistencies in assessment. Therefore, developing a system based on the SAW algorithm is necessary to enhance efficiency and objectivity in selecting the best concrete. The research process begins with data collection on concrete samples, covering parameters such as compressive strength, water volume, setting time, cement content, and aggregate quantity. Each criterion is assigned a weight based on its importance, followed by normalization to align scale values. The SAW algorithm is then applied to calculate the final preference values for each concrete sample, ultimately generating a recommendation for selecting the highest-quality concrete. The study results show that Concrete C achieves the highest final score (0.94706), followed by Concrete A (0.88328) and Concrete B (0.76292). The study concludes that the SAW algorithm effectively enhances objectivity and accuracy in determining the best concrete quality.
Co-Authors Amanda, Kiki Andika Syahputra, Andika Antoni Antoni Daivan, Fajri Damanik, Doni Kusuma Haramaini, Tasliyah Hasyim, Alwi Heri Santoso Lubis, Nadhira Luthfi, Muhammad Azmi Maharudinsah, Maharudinsah Muhammad Fakhriza, Muhammad Nasution, Ghiffari Ikhsan Nasution, Indah Khairani Oris Krianto Sulaiman Pulungan, Sabrina Meylani Rachmat Aulia, Rachmat Rianto, Muhammad Aulia Abdi Ryandi, Essanov Saripurna, Darjat Syahwin, Syahwin