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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Algoritme Jurnal Mahasiswa Teknik Informatika
Labib Jundillah, Muhammad
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Computer Science & IT Engineering

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Comparison of WASPAS and VIKOR methods to determine non-cash food assistance recipients Ramadiani, Ramadiani; Luthfi Fahrozi, Muhammad; Labib Jundillah, Muhammad; Azainil, Azainil
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1430-1442

Abstract

Non-cash food assistance or bantuan pangan non-tunai (BPNT) is a government program of the Republic of Indonesia by distributes food assistance in non-cash to beneficiary families. The process of distributing BPNT still needs to be done with the data and criteria set, because the existing BPNT distribution is considered not right on target. We need a method that can help provide an objective decision. One method that can be used in making decisions is the weighted aggregated sum product assessment (WASPAS) and Vlsekriterijumsko Koompromisno Rangiranje (VIKOR) methods. The results of the calculations from the two methods will then be chosen which is the best, by conducting sensitivity tests and accuracy tests. This study uses 100 sample data and 16 criteria. The sensitivity test results are 9.780678997% for the WASPAS method and -0.0759182% for the VIKOR method, while the results of the accuracy test show that both methods have the same level of accuracy, which is 80%. Based on the comparison of the sensitivity test and accuracy test of the two methods, the WASPAS method is considered more accurate in determining the recipients of the BPNT program because the WASPAS method has a higher sensitivity test value than the VIKOR method.
Personalisasi Jalur Pembelajaran Mahasiswa Sistem Informasi dengan Recurrent Neural Network Caesar Ananta, Firzian; Irsyad, Akhmad; Labib Jundillah, Muhammad
Algoritme Jurnal Mahasiswa Teknik Informatika Vol 6 No 2 (2026): April 2026 || Algoritme Jurnal Mahasiswa Teknik Informatika
Publisher : Program Studi Teknik Informatika Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/algoritme.v6i2.13631

Abstract

Personalized learning faces challenges when Information Systems students must choose a study path among many specialization options, while existing systems often fail to map student interests accurately. Static preference data are commonly treated as independent features, which prevents models from capturing relationships between interest scores. This study proposes a solution using a Simple Recurrent Neural Network that represents seven interest scores as a single sequence to capture positional context across features. A dataset of 318 respondents was used for training, and SMOTE was applied to address label imbalance. The model was compared with a Dense Neural Network to evaluate the impact of the sequential representation. SimpleRNN achieved an accuracy of 90.10 percent at 100 epochs, outperforming the DNN result of 80.20 percent. Evaluation using the confusion matrix along with precision, recall, and F1-score showed that SimpleRNN offers more stable classification, especially for interest categories with similar characteristics. These results indicate that applying a sequential approach to static data improves interest classification performance and supports more accurate personalized learning path recommendations.
Co-Authors Azainil Azainil Caesar Ananta, Firzian Irsyad, Akhmad Luthfi Fahrozi, Muhammad Ramadiani -