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Anjar Wanto
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JOMLAI: Journal of Machine Learning and Artificial Intelligence
Published by Yayasan Literasi Sains Indonesia
ISSN : 28289102     EISSN : 28289099     DOI : 10.55123/jomlai
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Focus and Scope JOMLAI: Journal of Machine Learning and Artificial Intelligence is a scientific journal related to machine learning and artificial intelligence that contains scientific writings on pure research and applied research in the field of machine learning and artificial intelligence as well as an overview of the development of theories, methods, and related applied sciences. Topics cover the following areas (but are not limited to): Software engineering Hardware Engineering Information Security System Engineering Expert system Decision Support System Data Mining Artificial Intelligence System Computer network Computer Engineering Image processing Genetic Algorithm Information Systems Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Other relevant study topics Noted: Articles have primary citations and have never been published online or printed before
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 2 Documents
 1 
Search results for , issue "Vol. 2 No. 3 (2023): September" : 2 Documents clear
The Implementation of Rough Set Algorithm to Classify Student Comfort Level Using Rosetta Siregar, Muhammad Rahmansyah; Sugiandi, Jeni; Pahriza, Alpiki; Sitorus, Salomo Marudut Pandapotan
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 3 (2023): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i3.2884

Abstract

Student comfort in the campus environment is an integral aspect in creating optimal learning conditions. Students who feel comfortable are more likely to be involved in academic and social activities. Several students were identified as frequently not attending class, and their interest in learning appeared to be lacking. This creates serious challenges in creating an optimal learning environment and meeting student needs. The research classifies student comfort levels and also provides a basis for developing more targeted campus policies. The data collection method uses a questionnaire method. The data processing method uses the Rough Set algorithm. Data processing uses Rosetta software. Based on the analysis carried out from 154 rules, the number of occurrences of the rest level attribute was 94 times, the class environment attribute was 110 times, the assignment difficulty level attribute was 114 times, the lecturer's teaching method attribute was 98 times, the campus facilities attribute was 136 times. So it can be seen that the campus facility attribute is the most influential because it has the highest number of occurrences. The next influential attribute after facilities is the level of difficulty of assignments, class environment, lecturer's teaching method and level of rest and reduce statistics show that campus facilities are a condition attribute that is very influential in student comfort levels, namely with an occurrence of 90,9%.
Overview of Infant Nutrition Status Classification with Rough Set Method Napitupulu, Jessica Evonella; Trianda, Dimas; Nababan , Refly Natalius
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 3 (2023): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i3.2893

Abstract

Infant growth and development is an important issue that can be known through nutritional status assessment. A measure of the fulfillment of nutrition in children that can be predicted based on their weight. In assessing the nutritional status of infants, there are concerns in the community about nutritional problems that are good to know, many babies are malnourished and also want to know which children whose nutrition is really ideal]. Rough Set Algorithm can be used as a mathematical tool to overcome uncertainty and imprecise information. This study aims to classify the percentage of nutritional status of infants, using Microsoft Excel and Rosetta version 2.0.0.0 for research and data analysis. The research produced 20 rules in the form of rule patterns as a reference for classifying the nutritional status of infants as poor, less, normal and more. Based on the rules generated, it is concluded that the most influential condition attributes in classifying the nutritional status of infants are gender, age, weight, height and gender, weight, height.

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