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All Journal Jurnal Teknologi Informasi dan Pendidikan The Indonesian Journal of Computer Science
Rahmatika, Hayati
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Revealing The Advantages of the Best Cooking Oil Brand: A Case Study of Weighted Aggregated Sum Assessment (WASPAS) Method in Decision Support System Hendriyani, Yeka; Mariani, Mariani; Rahmatika, Hayati; Emelsy, Nalurry
Jurnal Teknologi Informasi dan Pendidikan Vol. 16 No. 2 (2023): Jurnal Teknologi Informasi dan Pendidikan
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jtip.v16i2.760

Abstract

The purpose of this research is to implement the Weighted Aggregated Sum Product Assessment (WASPAS) method in determining the best cooking oil brand. The research method used is the Weighted Aggregated Sum Product Assessment (WASPAS) method with the stages of determining criteria, weights, and alternatives, creating a decision matrix, calculating the normalization value of the matrix and WASPAS weights in decision making Calculating the Qi value of normalization and weights makes ranking from the highest Qi value. From the research it is known that alternative A4 with the description Bimoli has the highest Qi value of 0.9162, so it is concluded that alternative A4 or Bimoli was chosen as the best cooking oil among the eight alternatives or other cooking oil brands. The result of this study is that the WASPAS method is able to recommend the selection of the best cooking oil for consumers.
Decision Support System for Determining the Best Online Learning Application Using the Weighted Product Method (WPM) Yeka Hendriyani; Mariani; Rahmatika, Hayati; Akbar Ilahi; Hardeyenti, Dessy
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3352

Abstract

This article discusses the use of the Weighted Product (WP) Method in a Decision Support System to determine the best online learning application. The goal is to help users choose a learning app that suits their needs and preferences. Relevant criteria, such as user interface, learning content, interactive features, difficulty level, and subscription price, are identified and assigned relative weights. This study uses data from various online learning apps to train the WP model and rank the apps based on the highest scores. The experimental results show that the WP method successfully identifies the best online learning apps with high accuracy, allowing users to have an effective and efficient learning experience according to their individual goals. Thus, this SDM can be an effective guide for users in making informed decisions to meet their education and learning needs
Co-Authors Akbar Ilahi Emelsy, Nalurry Hardeyenti, Dessy Mariani Mariani Mariani Yeka Hendriyani