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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JAIA - Journal of Artificial Intelligence and Applications
Fransiskus Zoromi
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Computer Science & IT Engineering

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Penerapan Metode Multi Atribut Utility Theory Dalam Sistem Seleksi Penerimaan Dosen Di Stmik-Amik-Riau. Hadi Asnal; Fransiskus Zoromi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 5 No 1 (2020): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.992 KB) | DOI: 10.36341/rabit.v5i1.1065

Abstract

STMIK Amik Amik Riau is an institution of higher education that focuses on computer science, in the learning process required by lecturers with the best qualifications, to get lecturers with the best qualifications needed for an objective selection process, and a system that can be built to be used to involve this selection process, a system built using the multi-attribute utility theory method, the multi-attribute utility theory method will be used to process predetermined criteria, and this criterion will be processed into a recommendation. Multi attribute attribute theory is used to compare quantitative values ​​that combine measurements from existing values. Multi-attribute utility theory is also used to convert several criteria into numerical values. The author agrees that this system provides solutions to existing problems and provides recommendations to leaders in determining decisions.
Chatbot Designing Information Service for New Student Registration Based on AIML and Machine Learning Yansyah Wijaya; Rahmaddeni; Fransiskus Zoromi
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 1 (2020): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (863.021 KB) | DOI: 10.33372/jaia.v1i1.638

Abstract

One of the efforts made by universities to serve prospective students is by providing consulting services and information that is usually carried out directly at the booth provided, through phone service or live chat support available on the college website. Increased visitors will result in waiting times due to limited availability of officers, which results in decreased satisfaction of prospective new students, moreover this service is only available during campus operating hours. One alternative solution to overcome this problem is to use Chatbot, able to answer questions raised by prospective new students which can be categorized as Frequently Asked Questions abbreviated as FAQ. Chatbot technology can be developed with a variety of AI (Artificial Intelligence) techniques. One of them is the AIML (Artificial Intelligence Markup Language) technique. One of the main drawbacks of AIML is that there is no reasoning ability so a learning system that is focused on supervised learning is needed. In the chatbot that will be built the learning process uses a selective neural conversational model or commonly called the Deep Semantic Similarity Model (DSSM) developed by Microsoft. Meanwhile, the measurement of chatbot performance will be done using Confusion Matrix which is a method of evaluating the performance of the algorithm from Machine Learning (ML). The results of the study stated that the chatbot system that was built was able to answer questions posed by prospective students properly and correctly while the questions were available in the chatbot knowledge base.
4 Star Complementary Food Menu Recommendation System Using the Mobile-Based Fuzzy Multiple Attribute Decision Making (FMADM) Method Rahmaddeni; Fransiskus Zoromi; Yansyah Saputra Wijaya; M. Khairul Anam
JAIA - Journal of Artificial Intelligence and Applications Vol. 1 No. 2 (2021): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1269.823 KB) | DOI: 10.33372/jaia.v1i2.793

Abstract

Toddler in the age category of six to twenty-four months should be ready to be given complementary food. In order to fulfill the nutritional needs for the toddler's growth, complementary foods must be sufficient for the kid according to their age while still paying attention to the continuity of breastfeeding. One thing that must be considered in choosing complementary foods is the Recommended Dietary Allowances (RDA) which is categorized by age, weight, and food texture, which is adjusted to the toddler age category. In terms of fulfilling all aspects of choosing complementary foods, this study proposes the design of a 4-star daily menu recommendation system for toddlers which refers to the intake of daily calorie needs for toddlers, namely carbohydrates, animal protein, vegetable protein, and vitamins/minerals using the FMADM method (Fuzzy Multiple Attribute Decision Making). The FMADM method used is the Electre method. In this study, the authors succeeded in building the desired recommendation system using the Electre method which produces a daily menu based on the number of mealtimes, based on the age and weight of toddlers by observing the user's tendency to the texture and composition of food and its nutritional content in the recommendation system that is built, so that can be accessed via mobile devices owned by the user.
Co-Authors Anam, M Khairul Hadi Asnal, Hadi Rahmaddeni Rahmaddeni Yansyah Saputra Wijaya