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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 82 Documents
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Foundation Scholars Decision Support System with Simple Additive Weighting Sundari Retno Andani; Sumarlin, Sumarlin
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2067

Abstract

Scholarships are one of the solutions in dealing with dropping out of school to increase the quantity of human resources in Indonesia. There are various types of scholarships given to outstanding students, one of which is a foundation scholarship. Foundation scholarships are scholarships whose funding comes from higher education foundations intended for outstanding and economically disadvantaged students. A decision support system (SPK) is needed in determining foundation scholarship recipients. With SPK, the results obtained are more objective based on the criteria used, accurate, effective and efficient. The SPK method used in this study is the Simple Additive Weighting (SAW) method. The SAW method performs a search with a weighted sum of performance ratings for each alternative on all criteria. The criteria used in this study are the Grade Point Average (GPA), parents' income and the number of dependents. Calculations using the SAW method will produce weight values, criteria, alternatives and ranking results of scholarship recipients as material for decision making.
Utilization of the Profile Matching Method for Recommendations for the Appointment of Honorary Teachers to Become Permanent Teachers Febriyanto, R Tri Hadi; Wanto, Anjar; Damanik, Bahrudi Efendi
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2357

Abstract

This study aims to utilize the Profile Matching method in the recommendation process for hiring honorary teachers to become permanent teachers at Taman Siswa Bah Jambi Private High School. Honorary teachers are an important part of the education system in Indonesia, and recommendations for their appointment as permanent teachers require the right approach to ensure fair and efficient selection. In this study, an analysis of the Profile Matching method was carried out in assessing the feasibility and integrity of honorary teachers. Data collection was carried out by collecting information on academic achievement, teaching experience, and other qualifications of honorary teachers at Taman Siswa Bah Jambi Private High School. The results of the study show that Profile Matching provides recommendations that are quite relevant and can be considered in the appointment of honorary teachers to become permanent teachers. The Profile Matching method tends to place more emphasis on suitability of teaching qualifications and experience. This research is expected to provide valuable information for related parties in choosing the appropriate method for recommendations for hiring honorary teachers to become permanent teachers at Taman Siswa Bah Jambi Private High School.
Implementation of the Mamdani Fuzzy Method in Handling Room Availability in 2022 at Hotel Inna Parapat Nugroho, Muhammad Rizky Tri; Winanjaya, Riki; Susiani, Susiani
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2368

Abstract

This study aims to implement the Fuzzy Mamdani Method in handling room availability in 2022 at Hotel Inna Parapat. The Fuzzy Mamdani method is a mathematical approach to dealing with uncertainty and ambiguity in decision-making. This study collected and analyzed data regarding the number of hotel rooms, occupancy rates, and room demand during 2022 at Hotel Inna Parapat. Then, the Fuzzy Mamdani model was developed to predict room availability based on predetermined variables. The study results show that implementing the Fuzzy Mamdani Method can provide a more accurate prediction regarding room availability at Hotel Inna Parapat. The Mamdani Fuzzy Model can handle uncertainty and ambiguity in the data and provides membership values that provide a more precise picture of the actual situation. With the Fuzzy Mamdani model, Hotel Inna Parapat management can be more effective in optimizing room utilization, improving customer service, and anticipating high room demand in 2022. Implementing the Fuzzy Mamdani Method is important to handling room availability in 2022 at Inna Hotel Parapat. This approach is expected to help hotels and the hospitality industry make smarter, data-based decisions to deal with changing demands and dynamic business situations.
Prediction of Palm Oil Seed Stock Production Results with the Back-propagation Algorithm Damayanti, Tri Febri; Wanto, Anjar; Tambunan, Heru Satria
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2391

Abstract

Palm oil is the largest plantation export commodity in Indonesia because Indonesia has a soil structure that is suitable for planting oil palms. As is the case with the production of oil palm seed stock, of course, it does not always increase, and the production of oil palm seed stock will undoubtedly decrease. Therefore, an algorithm is needed to predict it so that the company can find out the future development of oil palm seed stock production using the Back-propagation algorithm. The Back-propagation Algorithm is used to predict the yield of oil palm seed stock production using data from the Marihat unit Oil Palm Research Center (PPKS) in 2019-2022. The Back-propagation Algorithm is an algorithm that reduces the error rate by adjusting the weights based on the desired output and target, as well as Testing the Back-propagation algorithm using Matlab. Based on the test results of the five architectural models used, one best architectural model was obtained, namely 2-14-1, using the Back-propagation method, which produced an MSE value of 0.0551030 with a Training time of 08:00 seconds with a test accuracy of 75%. Based on the research results obtained, it is expected to be input, suggestions, and efforts, especially for the Marihat Unit PPKS company, increase the stock of oil palm production seeds in each period to increase company profits more optimally.
Decision Support System in Determining the Level of Success of Teaching Teachers Using the AHP Method Abdillah, Mhd Khoiri; Hartama, Dedy; Nasution, Rizki Alfadillah
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2399

Abstract

This study aims to implement a Decision Support System (SPK) that uses the Analytic Hierarchy Process (AHP) method in assessing and determining the level of success of teachers teaching at SMA Taman Siswa, Bah Jambi Branch. This study uses five assessment criteria, namely the number of attendance, lesson plans, teaching period, learning media, and discipline, as a basis for evaluating teacher performance. In this study, the AHP method was used to calculate the relative weight of each criterion and sub-criteria involved in assessing teacher performance. The data collected from various teachers was evaluated using the AHP method, and the results provide an overview of the level of success of each teacher in carrying out the teaching and learning process. The results showed that among the many existing teachers, Siti Palastri managed to score 0.81002, indicating good performance in the teaching and learning process. The application of this system is expected to assist schools in making decisions regarding teacher performance evaluation in a more objective and structured manner. In addition, this system can provide guidance and recommendations that are more effective in identifying areas for improvement by teachers who have not achieved the expected level of success. Thus, improving the quality of teaching can be achieved through corrective measures that are right on target.
Application of the Naïve Bayes Algorithm in Predicting Sales Prices for Snacks Gulo, Maleakhi; Sumarno, Sumarno; Anggraini, Fitri
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2444

Abstract

This study aims to apply the Naïve Bayes Algorithm in predicting the selling price of snacks at Toko Timbul II. The data used in this study were obtained from January to December 2018-2021, covering various variables relevant to snack sales. The data collected is divided into two parts, namely Data Training and Data Testing. The Training Data consists of 20 alternatives, which are used to train the prediction model of the Naïve Bayes Algorithm. While Data Testing consists of 16 alternatives, which are used to test the extent of the model's ability to predict the selling price of snacks. Testing was carried out using the Rapid Miner application. The test results show that the implemented model achieves an accuracy rate of 100% in predicting the selling price of snacks. These results indicate that the Naïve Bayes Algorithm has great potential in predicting the selling price of snacks at Timbul II Stores. These findings can provide valuable insights for store managers and snack food industry stakeholders, as well as encourage the use of predictive analytical methods in similar contexts. It is hoped that the results of this study can contribute to optimizing sales strategies and making more informed decisions in the future.
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%.
Analysis of Customer Satisfaction Levels in Purchasing Plastic Flower Crafts in Pematangsiantar using the Rought Set method Putri, Adelia; Lubis, Aditya Rifki; Anarki, Lintang Lantang Jagad
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.2888

Abstract

Customer satisfaction is a key indicator in assessing the quality of service and a product, reflecting the extent to which customer expectations are met after purchasing or interacting with a product.This study applies the Rought Set algorithm to analyze customer satisfaction based on condition attributes such as Flower Type, Color, Size, Price and Quality, with the decision attribute being the Satisfaction Level.The research data was conducted by means of a field survey by distributing questionnaires to a sample of customers in Pematangsiantar.The data classification process was carried out with the Rosetta application. The research resulted in 13 reductions with 116 rules connecting the condition attributes and the level of satisfaction.The results show that the Rought Set algorithm can perform the classification quite well.The conclusion of this study is to use the Rought Set algorithm in classifying the level of satisfaction in purchasing plastic flower crafts for customers in Pematangsiantar.This research is important with the aim of developing the level of customer satisfaction in purchasing plastic flower crafts in Pematangsiantar such as product quality, and the Rought Set algorithm is useful in analyzing the level of customer satisfaction.
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.
Implementation of the Multiple Linear Regression Method to Predict Student Achievement Based on Social Status and Discipline Saragih, Reynaldo; Gunawan, Indra; Parlina, Iin
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 2 (2023): June
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i2.3128

Abstract

This research aims to implement the multiple linear regression method as an analytical tool to predict the academic performance level of students at SMA Kartika 1-4. The primary focus of the analysis will be placed on four critical predictor variables, namely parental income, discipline, attendance, and academic achievement. The multiple linear regression method is chosen because it can provide a robust statistical foundation for understanding the complex relationships between these variables and academic performance. Through the collection of data related to students' socio-economic status and their level of discipline, this research will build a multiple linear regression model to predict the level of student performance. The results of this research are expected to provide a more comprehensive understanding of the factors influencing students' performance in the environment of SMA Kartika 1-4. In-depth analysis of the relationships between parental income, discipline, attendance, and academic achievement can offer valuable contextual insights. This research is anticipated to provide a basis for the development of strategies or policies at the school level to improve student performance by paying specific attention to these aspects.

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