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Richki Hardi
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Multica Science and Technology
Published by Universitas Mulia
ISSN : -     EISSN : 27762386     DOI : https://doi.org/10.47002/mst.v1i1
Core Subject : Science,
Computer Science & IT Energy Library & Information Science Physics
Focus and Scope The journal covers all aspects of science and technology, that is: Science: Bioscience & Biotechnology; Chemistry; Food Technology; Applied Biosciences and Bioengineering; Environmental; Health Science; Mathematics; Statistics; Applied Physics; Biology; Pharmaceutical Science; etc. Technology: Artificial Intelligence; Computer Science; Computer Network; Data Mining; Web; Language Programming; E-Learning & Multimedia; Information System; Internet & Mobile Computing; Database; Data Warehouse; Big Data; Machine Learning; Operating System; Algorithm; Computer Architecture; Computer Security; Embedded system; Cloud Computing; Internet of Thing; Robotics; Computer Hardware; Geographical Information System; Virtual Reality; Augmented Reality; Multimedia; Computer Vision; Computer Graphics; Pattern & Speech Recognition; Image processing; ICT interaction with society; ICT application in social science; ICT as a social research tool; ICT in education
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 3 Documents
 1 
Search results for , issue "Vol 4 No 2 (2024): Multica Science and Technology" : 3 Documents clear
Implementation of Ward AHC for Material Clustering Based on Mechanical Parameters Yusuf, Edy; Bakhtiar; Syukriah; Burhanuddin; Riyadhul Fajri
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.977

Abstract

This study aims to implement the Ward Agglomerative Hierarchical Clustering (Ward AHC) algorithm to classify materials based on mechanical parameters, including tensile strength (Su), yield strength (Sy), elastic modulus (E), shear modulus (G), Poisson's ratio (μ), and density (ρ). The clustering results reveal that the data is divided into three main groups with the following distributions: Cluster 1 (321 data points), Cluster 2 (403 data points), and Cluster 3 (828 data points). Each cluster exhibits unique characteristics: Cluster 1 is dominated by materials with low Su and Sy values, moderate E and G values, and light ρ. Cluster 2 features materials with very high E values, while Su, Sy, and G values vary. Cluster 3 is characterized by moderate Su values, low Sy values, high E and G values, and light ρ. An evaluation using the Silhouette Score yielded a value of 0.492, indicating that the clustering quality is reasonably good, though there is evidence that some data points may lie near the boundaries between clusters.
Prediction of Shrimp Sales Using the ARIMA (AutoRegressive Integrated Moving Average) Method at UD Udang Makmur Peureulak Veri Ilhadi; Muliana Muliana; Zulfia , Anni; Ulya, Athiyatul; Sahputra , Ilham
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.978

Abstract

UD. Udang Makmur is a shrimp farming business that often faces challenges in accurately predicting sales stock due to reliance on manual forecasting methods. This study aims to develop a web-based sales prediction application utilizing the AutoRegressive Integrated Moving Average (ARIMA) method. The application uses daily sales data from January to December 2023 for analysis. The results indicate that the ARIMA (2,1,1) model delivers accurate predictions, achieving a Mean Squared Error (MSE) of 0.264295. Forecasts for the next 24 periods demonstrate a stable projection, with predicted values converging around 2.5 and a narrow 95% confidence interval. These findings highlight the model's reliability and low uncertainty for the forecasted time frame. The application was successfully tested using the Black-Box method, confirming its functionality and effectiveness in supporting sales predictions.
Decision Support System for Land Suitability Assessment of Horticultural Crops of Legume Commodities Using AHP-VIKOR Ilham Sahputra; Rizky Putra Phonna; Natasya Natasya; Annisa Karima; T. Sukma Achriadi Sukiman
Multica Science and Technology Vol 4 No 2 (2024): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v4i2.979

Abstract

This Decision Support System (DSS) is designed to evaluate land suitability for horticultural crops, specifically legumes, using a combination of Analytical Hierarchy Process (AHP) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methods. The system aids farmers in determining the appropriate crops based on the available land conditions. The research includes problem identification, literature review, data collection, and system design. The implementation of the AHP-VIKOR methods has proven effective and accurate in providing horticultural crop recommendations. This system adds value to modern and efficient agricultural land management. The research results show that the AHP-VIKOR methods successfully applied in determining the suitability of land for legumes in the areas of Bireun, Bukit Rata, Sawang, and Pesisir Pelabuhan Kreung Geukuh with satisfactory outcomes. Therefore, the AHP-VIKOR methods are considered optimal for weighting criteria and ranking alternatives in selecting land for legume crops

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