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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Helmawati, Nita
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Optimizing the Profile Matching Algorithm using the Analytical Hierarchy Process in the Selection of Teaching Assistants Helmawati, Nita; Norhikmah, Norhikmah
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3172

Abstract

The selection of the best practicum assistants is traditionally done through a conventional method, which involves voting by active students attending lab classes. However, upon evaluation, it was found that the results were not accurate. Some cases revealed that assistants were chosen based solely on popularity or recognition among the students, possibly influenced by physical appearance or public speaking skills in front of the class, while other important aspects were not considered. This situation could lead to social jealousy. The problem lies in the difficulty of combining evaluation criteria and determining the relative weights for each criterion in the process of selecting the best practicum assistants at the college. Additionally, there is a lack of objectivity in decision-making during the selection process, resulting in an unstructured and immature decision-making process. Therefore, this research aims to enhance the process of selecting the best practicum assistants at the college through optimizing the profile matching algorithm using the Analytic Hierarchy Process (AHP) method. AHP's role involves checking the weights and making paired comparisons to evaluate each criterion and determine the criterion weights. AHP is also utilized to ensure consistency in determining the weights. On the other hand, the role of profile matching is to provide accurate rankings or comparisons based on the suitability scores between the profiles of potential assistants and the reference profile. The combination of these two algorithms is expected to result in a more accurate selection of practicum assistants by effectively measuring the decision criteria weights. Therefore, the difficulty of combining evaluation criteria and determining the relative weights for each criterion can be minimized. Furthermore, optimizing the profile matching algorithm will enable a more objective decision-making process for selecting the best practicum assistants through more accurate rankings or comparisons based on the suitability scores with the reference profile. Based on this optimization, the collaboration of the two algorithms can achieve comparison results with an accuracy rate of 90%.
Deep Learning-Based Soybean Leaf Disease Classification Using DenseNet121, Xception, and MobileNetV2 Helmawati, Nita; Buana, Yopy Tri; Darmawan, Eko Rahmad; Kusrini, Kusrini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6271

Abstract

This study is driven by the challenge of soybean leaf diseases, which significantly reduce agricultural productivity and pose a threat to food security. To address this issue, we developed a deep learning–based classification model for soybean leaf disease detection, employing three prominent architectures: DenseNet121, Xception, and MobileNetV2. The dataset comprised 770 images representing six disease categories and one healthy category, which was expanded to 5,880 images using data augmentation techniques. The dataset was evaluated under three experimental scenarios with splits of 70% training, 10% validation, and 20% testing. Experimental results demonstrated that the DenseNet121 model, optimized with AdamW, achieved the highest accuracy at 90.14%, outperforming MobileNetV2 (85.48%) and Xception (65.37%). Moreover, DenseNet121 exhibited the most consistent performance in classifying the diverse categories of soybean leaf diseases.
Co-Authors Buana, Yopy Tri Darmawan, Eko Rahmad Kusrini, Kusrini Norhikmah Norhikmah