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All Journal Indonesian Journal of Applied Technology and Innovation Science
Rizki Andreas
Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparative Analysis of the Combination of AHP-SAW and AHP-WP in Making Decisions on Hiring New Employees Rizki Andreas; Margareta Amalia MP; Sri Maharani Sinaga; Teguh Brahmana; Dian Kusmawati
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 2 No. 1 (2025): IJATIS February 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v2i1.1777

Abstract

This paper's web-based employee recruitment the goal is to help Human Resource Development (HRD) managers automatically calculate criterion weights and alternative weights, refinement of potential employees  and a faster selection process. Recommendation system applications use Combination of Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP). The AHP method determines the importance of each professional criterion is at the moment. SAW, on the other hand, determines the position or priority of a potential employee, calculated from alternative options. In the AHP method, criteria influence the outcome of a decision. The resulting calculations are examined using the specified priority weights to see which criteria are most important. The weight value for the CI criterion was 0.0603, and the CR value was 0.0538. However, a sensitivity analysis of criterion priorities is required to examine the extent to which small effects on criterion weights change the ranking of alternatives. Based on the ranking results using AHP-WP, Fajar ranked first with a preference value of 0.1037289. You can also see how important the selection criteria are to the ranking results.
Application of Deep Learning for the Classification of Brain Tumor Magnetic Resonance Imaging Images Rizki Andreas; Mustakim Mustakim; Ma. Angelica M. Qin; Safril Siregar
IJATIS: Indonesian Journal of Applied Technology and Innovation Science Vol. 3 No. 2 (2026): IJATIS August 2026
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/ijatis.v3i2.3089

Abstract

Accurate and timely classification of brain tumors from Magnetic Resonance Imaging (MRI) is essential for supporting clinical diagnosis and treatment planning. This study presents a comparative evaluation of transfer learning-based Convolutional Neural Network (CNN) architectures, including ResNet-50, DenseNet121, and EfficientNet-B2, for binary brain tumor MRI classification (Glioma and Meningioma). The dataset was augmented to improve model generalization, and the data were split into training and test sets using an 80:20 hold-out split. The models were trained using Adam and RMSProp optimizers with different learning rates, and their performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrate that data augmentation significantly enhances classification performance across all evaluated architectures. Among the tested models, ResNet-50 with the RMSProp optimizer and a learning rate of 0.001 achieved the best performance, yielding the lowest training and validation losses and perfect classification results on the test set, with accuracy, precision, recall, and F1-score approaching 100%. DenseNet121 and EfficientNet-B2 also achieved excellent performance but were slightly inferior to ResNet-50. These findings indicate that transfer learning with ResNet-50 and RMSProp provides an effective and reliable solution for automated brain tumor MRI image classification
Co-Authors Dian Kusmawati Ma. Angelica M. Qin Margareta Amalia MP Mustakim Mustakim Safril Siregar Sri Maharani Sinaga Teguh Brahmana