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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JAIA - Journal of Artificial Intelligence and Applications

Fransiskus Zoromi

Unknown Affiliation

Author-ID : 1993367

Computer Science & IT Engineering

Published : 4 Documents Claim Missing Document

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Articles

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

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

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

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.

Product Classification Based on Categories and Customer Interests on the Shopee Marketplace Using the Naïve Bayes Method Muhammad Oase Ansharullah; Wirta Agustin; Lusiana; Junadhi; Susi Erlinda; Fransiskus Zoromi
JAIA - Journal of Artificial Intelligence and Applications Vol. 2 No. 2 (2022): JAIA - Journal of Artificial Intelligence and Applications
Publisher : STMIK Amik Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33372/jaia.v2i2.888

Marketplace is an electronic product marketing platform that brings together many sellers and buyers to transact with each other. The large variety of products sold on Shopee is one of the reasons this application is in great demand by all walks of life. However, the weakness of the large variety of products sold in a marketplace causes buyers who have no potential to buy these products. To overcome this problem, it is necessary to do a classification to determine which products are most in demand by customers. Product categories consist of: Clothing, Beauty Products, Daily Goods, Electronics, and Accessories. The classification method used is Naïve Bayes and the software used is WEKA. The next data collection is done by distributing questionnaires to the existing customers on social media namely, Whatsapp and Instagram, the distribution of the questionnaire is conducted through Google form. There are 90 questionnaires that will be distributed in this study. Some of the indicators asked in the questionnaire namely, do you like shopping online? And what marketplaces are commonly used. These results will be the training data. Interest categories are divided into 4 categories, namely: Very interested, Interested, Not interested, Very not interested. The results obtained in this study are clothing products (72 respondents) are products that are in great demand, daily goods products (7 respondents) are products of interest, beauty and electronic products (5 respondents) are products that are not in demand, and accessories (1 respondents ) is a product that is not very attractive to customers on the Shopee marketplace

Co-Authors Anam, M Khairul Hadi Asnal, Hadi Junadhi Junadhi Lusiana Muhammad Oase Ansharullah Rahmaddeni Rahmaddeni SUSI ERLINDA Wirta Agustin Yansyah Saputra Wijaya

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