Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Instal : Jurnal Komputer The Journal of Information Technology, Computer Science, and Electrical Engineering
Hindra Syahputra
Universitas Pembangunan Panca Budi
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Predicting The Effectiveness of Online Exam Platforms With LMS to Improve Online Learning Using The Cobit 5 Method Zulham Sitorus; Hindra Syahputra; Bambang Sugito; Ibrahim
Journal of Information Technology, computer science and Electrical Engineering Vol. 1 No. 2 (2024): June-September 2024
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v1i2.19

Abstract

Distance learning today is not just about getting online content and creating interactive and collaborative learning experiences. The aim of this research is to analyze the extent to which the online test platform is effective using Cobit Method 5. The respondents in this study were 50 students in computer systems and 10 lecturers in computer systems. The research methods used are governance audits: planning, field inspection, reporting, and follow-up. With data analysis using SPSS 20. The results of the study refer to the calculation; the level of ability (performance) obtained is a figure of 4.02, which belongs to the established level, which indicates that the process has been implemented using a defined process and has the ability to achieve the desired process result (result). While the expected level of capacity obtains a number of 4.35, which is included in the predictable level, indicating that processes have been started and operated with defined limits ensures that the performance of the process helps the organization's goals and process objectives.
Analysis of Artificial Neural Network and Support Vector Machine Algorithms in Predicting General or Vocational School Choices for Panca Budi Middle School Students in Medan Hindra Syahputra; Muhammad Iqbal; Khairul
Bahasa Indonesia Vol 18 No 06 (2026): Instal : Jurnal Komputer
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalinstall.v18i06.534

Abstract

Determining the educational trajectory following junior high school graduation represents a pivotal decision shaped by students' academic competence, personal interests, and inherent personality inclinations. In practice, this selection process is frequently carried out in a subjective manner, which risks producing a disconnect between students' genuine potential and their eventual educational placement. The present research seeks to examine and compare the predictive performance of the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms in forecasting students' preference for either senior high school (SMA) or vocational high school (SMK) among learners at SMP Panca Budi Medan. A total of 220 student records were employed as the dataset, incorporating academic performance data alongside RIASEC personality scores as the predictor variables. All data processing was executed within the WEKA application environment utilizing 10-fold cross validation as the evaluation scheme. The ANN model was constructed through the Multilayer Perceptron approach, while SVM relied on the Sequential Minimal Optimization (SMO) technique. Experimental findings revealed that both classifiers attained an identical accuracy rate of 85.45%; however, the ANN model demonstrated a superior ROC Area value of 0.925 relative to the SVM's 0.849, signifying that ANN possesses stronger discriminative capability in distinguishing SMA from SMK selections. The study confirms that integrating academic metrics with RIASEC scores provides a viable foundation for constructing a machine learning-driven school-choice prediction system that is both more objective and better attuned to individual student profiles.
Co-Authors Bambang Sugito Ibrahim Khairul Muhammad Iqbal Sitorus, Zulham