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All Journal Indonesian Journal of Applied Technology and Innovation Science
Qhairani Frilla F. Safiesza
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Using Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (F-AHP) Methods in Criteria and Alternative Perspectives for Ranking Qhairani Frilla F. Safiesza; Laras Mayangda Sari; M. Yogi; Alvin Andiran Sunarya; Muhammad Naufal Farras; Muhammad Fikri Evizal
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 2 (2024): IJATIS August 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i2.1137

Abstract

The application of Analytic Hierarchy Process (AHP) and Fuzzy - Analytic Hierarchy Process (F-AHP) is used as the main method in conducting the ranking process in the case of choosing a major in the Department of Information System UIN Suska Riau. The perspectives of both methods are applied in two levels of hierarchy, namely on criteria and alternatives. In this experiment, AHP was used to rank the criteria, while F-AHP was used for the alternatives. The results of the experiment show that both have a CR value smaller than 0.1. The rankings obtained in order on the criteria side are RP, PD, MMK, Kindergarten and MAP. On the alternative side, the CRM course is followed by SCM, SIC, DS, ITQ, and ITG. This assessment is based on the calculation of the pairwise comparison matrix of some of the best objects from 10 experiments conducted. The conclusion that can be given is that both methods can be implemented in the case study being worked on and all have a good consistency ratio.
Performance Evaluation of Machine Learning Algorithms in Predicting Global Warming: A Comparative Study of Random Forest, K-Nearest Neighbors and Support Vector Machine Anisa Putri; Refri Martiansah; Qhairani Frilla F. Safiesza; Muhammad Fahri Abduh
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 1 No. 2 (2024): IJATIS August 2024
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v1i2.1194

Abstract

Global Warming is a global warming phenomenon that has a significant impact on human health and the environment. This research aims to apply Machine Learning algorithms, namely the Random Forest algorithm, K-Nearest Neighbors (K-NN), and Support Vector Machines (SVM) in predicting global warming. First, global warming data downloaded from Kaggle via dataset is used as research material. Then, a global warming prediction model is built using this algorithm and then evaluated using criteria such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean squared error (RMSE), R2, and Confusion Matrix. Finally, based on the evaluation results, research confirms that the K-NN algorithm shows the best performance, with the highest R2 value and low prediction error compared to other algorithms, such as Random Forest which shows the lowest performance. In terms of classification, K-NN achieved the highest accuracy (96.55%) and excellent performance in the confusion matrix and classification report. Overall, the findings of this study emphasize the dominance of K-NN in this context, thereby providing a strong basis for selecting models for predicting global warming phenomena.
Co-Authors Alvin Andiran Sunarya Anisa Putri Laras Mayangda Sari M. Yogi Muhammad Fahri Abduh Muhammad Fikri Evizal Muhammad Naufal Farras Refri Martiansah