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All Journal JOIV : International Journal on Informatics Visualization Journal of Informatics Development
Dedes, Khen
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Computer Science & IT

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Integration of AHP and TOPSIS Methods in Decision Making Models to Identify High Achieving Students Hermansyah, Masud; Mujiono, Mujiono; Fatimatuzzahra, Fatimatuzzahra; Dedes, Khen; Firdausi, M Faiz
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1547

Abstract

Selection of outstanding students is an essential process in education to ensure that students with high achievements receive appropriate recognition and guidance. However, this process often suffers from subjectivity and the absence of a structured decision-making system. This study aims to develop an objective and accountable decision support model by integrating the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is used to determine the weight of each selection criterion, while the TOPSIS method is used to rank students based on their proximity to the ideal solution. The study involved 10 student candidates and assessed them based on 6 criteria, including academic performance, discipline, extracurricular activities, and religious values. The results show that the integrated AHP-TOPSIS model successfully identifies students with the highest preference values as the most outstanding, while those with lower values are recommended for further coaching. The model demonstrates its effectiveness in supporting accurate, data-driven student selection at MIMA 37 Sunan Kalijogo Ambulu.
Neural Machine Translation of Spanish-English Food Recipes Using LSTM Dedes, Khen; Putra Utama, Agung Bella; Wibawa, Aji Prasetya; Afandi, Arif Nur; Handayani, Anik Nur; Hernandez, Leonel
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.804

Abstract

Nowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes. Based on that, this study developed a translation formula using a neural machine translation (NMT). NMT is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders. Our experiment led to novel insights and practical advice for building and extending NMT with the applied long short-term memory (LSTM) method to 47 bilingual food recipes between Spanish-English and English-Spanish. LSTM is one of the best machine learning methods for translating languages because it can retain memories for an extended period concurrently, grasp complicated connections between data, and provides highly useful information in deciding translation outcomes. The evaluation for this neural machine translation is to use BLEU. The comparing results show that the translation of recipes from Spanish-English has a better BLEU value of 0.998426 than English-Spanish with a data-sharing of 70%:30% during epoch 1000. Researchers can convert the country's popular cuisine recipes into another language for further research, allowing it to become more widely recognized abroad.
Co-Authors A.N. Afandi Aji Prasetya Wibawa Anik Nur Handayani Fatimatuzzahra Fatimatuzzahra Hermansyah, Masud Leonel Hernandez, Leonel M Faiz Firdausi Mujiono Mujiono Putra Utama, Agung Bella