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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : -
Core Subject : Science,
Computer Science & IT
Journal of Soft Computing Exploration is a journal that publishes manuscripts of scientific research papers related to soft computing. The scope of research can be from the theory and scientific applications as well as the novelty of related knowledge insights. Soft Computing: Artificial Intelligence Applied Algebra Neuro Computing Fuzzy Logic Rough Sets Probabilistic Techniques Machine Learning Metaheuristics And Many Other Soft-Computing Approaches Area Of Applications: Data Mining Text Mining Pattern Recognition Image Processing Medical Science Mechanical Engineering Electronic And Electrical Engineering Supply Chain Management, Resource Management, Strategic Planning Scheduling Transportation Operational Research Robotics
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 142 Documents
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Decision support system for selecting outstanding students using simple addictive weighting (SAW) and rank order centroid (ROC) methods Sholikha, Hidayatin; Ardiansyah, Hery; Bianto, Mufti Ari
Journal of Soft Computing Exploration Vol. 6 No. 3 (2025): September 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i3.574

Abstract

Selecting outstanding students is essential in fostering appreciation and motivation within the school environment. Nevertheless, many educational institutions continue to use manual assessment methods, which are often subjective and inefficient. This research focuses on the development of a web-based decision support system designed to assist in the selection process at a in Indonesia. The system integrates the Simple Additive Weighting (SAW) technique to generate student rankings based on preference scores, while the Rank Order Centroid (ROC) method is applied to assign weight values to the evaluation criteria, including academic performance, attendance, behavior, and extracurricular involvement. Data for this study were collected through interviews, direct observation, and student records. The application was developed using PHP for the backend, MySQL for database handling, and Bootstrap for the user interface design. The system’s functionality was verified using black box testing, which confirmed that all features operated correctly. Additionally, the system was evaluated against the manual selection process conducted by the school, and the results showed an accuracy level of 80% in matching student rankings. This system proves to be a practical and structured solution for enhancing the transparency and objectivity of student achievement evaluations.
Corn sales analysis using linear regression and svm methods to improve production planning Saputra, Ahmad Hakiki; Priyanto, Dadang; Hammad, Rifqi
Journal of Soft Computing Exploration Vol. 6 No. 3 (2025): September 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i3.591

Abstract

This research aimed to analyze and predict corn sales at UD Muara Kasih to improve production planning accuracy. The study used historical corn sales data collected over a specific period, covering 42 data entries from January 2021 to December 2024. The dataset included variables such as sales date, quantity sold, selling price per ton, total sales value, weather conditions, market demand (in tons), and the number of laborers. Prior to model training, the data underwent comprehensive preprocessing involving data cleaning, feature extraction, and normalization to ensure its quality and readiness for analysis. Two predictive models were applied: Linear Regression and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Simulation data for 2024 and 2025 were generated based on the monthly averages derived from the historical dataset. The results showed that the Linear Regression model produced more stable predictions with a lower Root Mean Squared Error (RMSE) of 255.84 compared to the SVM model’s RMSE of 256.42. While the SVM model showed greater responsiveness to seasonal variations, the Linear Regression model was identified as the most suitable for the given dataset. The predictive models developed in this study are expected to support UD Muara Kasih in making more accurate and informed production decisions in the future.

Page 15 of 15 | Total Record : 142

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