JOMLAI: Journal of Machine Learning and Artificial Intelligence
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
Articles
77 Documents
<![CDATA[Calligraphy Text Types Recognition Using Learning Vector Quantization ]]>
<![CDATA[Mhd Furqan]]>;
<![CDATA[Abdul Halim Hasugian]]>;
<![CDATA[Ziqra Addilah]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1653
<![CDATA[Calligraphy is the art of beautiful writing. The term calligraphy comes from simplified English (calligraphy) taken from the Latin word "kalios" which means beautiful, and "graph" which means writing or script. The art of writing Arabic letters is called the science of khat, known as the science of Arabic calligraphy or Islamic calligraphy. There are many types and varieties of Islamic calligraphy. Each has a different form and function. There are seven types of calligraphy that are popular and known by lovers of calligraphy art in Indonesia, such as, Khat Naskhi, Tsuluts, Farisi, Riq'ah, Diwani, Diwani Jali, and Kufi. The method commonly used to identify what type of calligraphy is made is by looking directly at the shape and characteristics of the calligraphy itself (calligraphy experts). Here the author tries to create a computerized calligraphy type recognition system using the Learning Vector Quantization method. Where this method is a method that works with each unit of output representing a class. So with this system, we can recognize the type of calligraphy text computerized. The accuracy value obtained in the results of calligraphy image recognition is 75%.]]>
<![CDATA[Clustering Production of Plantation Crops by Province Using the K-Means Method ]]>
<![CDATA[Azhari Abdillah Simangunsong]]>;
<![CDATA[Indra Gunawan]]>;
<![CDATA[Zulaini Masruro Nasution]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1661
<![CDATA[The purpose of this research is to classify the results of plantation crop production each year based on provinces in Indonesia, so that it can be known which provinces produce the most plantation crop production and which produce less. In this study using the K-Means Algorithm Data Mining technique. The data source for this research was collected based on plantation data obtained from the Indonesian Central Bureau of Statistics (BPS). The data used is data from 2018-2020 which consists of 34 provinces. The results of this study are groupings which are divided into 3 Clusters, namely low Clusters, medium Clusters, and high Clusters. Based on the results of calculations using the K-Means Algorithm, 6 items (Provinces) were obtained for high Clusters, 2 Provinces for medium Clusters and 27 Provinces for low Clusters. The conclusion that can be obtained is that the grouping of plantation crop production in Indonesia can be solved by applying the K-Means algorithm.]]>
<![CDATA[Data Classification of Marriage Readiness in Young Adults Using the Naïve Bayes Algorithm ]]>
<![CDATA[Rahmi Fauziah]]>;
<![CDATA[Heru Satria Tambunan]]>;
<![CDATA[Susiani Susiani]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1665
<![CDATA[Readiness to get married usually must be owned by every individual who wants to run a married life in order to become a harmonious family. However, not all young adults prepare for marriage such as financially, emotionally, roles and others. So the classification is carried out to determine the readiness for marriage with ready and not ready classes. Classification is part of data mining that performs the process of building a model based on existing training data, then using the model for classification on new data. The research data used were taken from 103 young adult, male and female. The algorithm used is Naïve Bayes. The conclusion of this research is testing as much as 5 testing data that is processed in RapidMiner 5.3. get test results with an accuracy of 74,33%, namely 3 data that are not ready and 2 data that are ready. So that the research process can be done quickly and efficiently.]]>
<![CDATA[Application of Data Mining in Drug Prevention Classification Using the Naïve Bayes Algorithm in BNN Pematangsiantar City ]]>
<![CDATA[Rosta Dermawan Situmorang]]>;
<![CDATA[Sumarno Sumarno]]>;
<![CDATA[Nani Hidayati]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1667
<![CDATA[The problem of drugs in Indonesia is still something urgent and complex. In the last decade this problem has become widespread. It is proven by the significant increase in the number of drug abusers or addicts, along with the increasing disclosure of drug crime cases, which are increasingly diverse in pattern and the more massive the syndicate network is. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values from a given dataset. In the classification process to find out the results of prevention activities with urine test activities, which are indicated and not indicated, the authors want to know the overall results with the Naïve Bayes classification technique in order to make it easier to get the overall results of the percentage of patients indicated and not indicated in terms of preventing drug use. Based on the results of the study obtained 2 classifications, namely indicated and not indicated.]]>
<![CDATA[Application of Data Mining on Patterns of Sales of Goods in Minimarkets Using the Apriori Algorithm ]]>
<![CDATA[Siti Hadija]]>;
<![CDATA[Eka Irawan]]>;
<![CDATA[Irfan Sudahri Damanik]]>;
<![CDATA[Jaya Tata Hardinata]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1668
<![CDATA[Minimarket is a shop that sells goods for daily needs. Each minimarket generates a lot of sales data every day. Sales transaction data can only be stored without further analysis. Based on this description, research was conducted to assist minimarket managers in making it easy to solve sales pattern problems at minimarkets using the Apriori algorithm. The Apriori algorithm is an algorithm that searches for item set frequencies using the association rule technique. The final result of using data mining using the Apriori association method is proven to be able to find out the results of the analysis that appear simultaneously based on sales data at the Mawar Simp.Tangsi Balimbingan Minimarket with a minimum amount of support of 30% and 80% confidence resulting in 8 association rules that are formed.]]>
<![CDATA[Artificial Neural Network Predicts Motorcycle Sales Level Using Back-propagation Method ]]>
<![CDATA[Reza Pratama]]>;
<![CDATA[Poningsih Poningsih]]>;
<![CDATA[Anjar Wanto]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1670
<![CDATA[Motorcycles are everyone's choice as a means of transportation because they are affordable and can be used for a long time. The high level of motorcycle sales made CV Apollo Motor dealers experience difficulties in procuring motorcycle variants to be sold. The large number of motorcycle variants in one manufacturer makes sales different for each of these variants; there are variants with high and low sales. Therefore predictions about this matter are essential as information material for the company. Input data was obtained from CV Apollo Siantar from 2018 to 2022 as a sales prediction target consisting of 10 data based on Honda motorcycles. Each data has seven variables and one target. This data will later be transformed into data between 0 to 1 before training and testing are carried out using the Back-propagation algorithm artificial neural network. This study uses the back-propagation algorithm. Based on the analysis results, the best architectural model is 7-3-5-1 because it has the highest level of accuracy compared to other models, which is 100%. MSE Testing of 0.08501.]]>
<![CDATA[Application of Data Mining Classification to Store Customer Satisfaction Bombay Textiles ]]>
<![CDATA[Siti Sundari]]>;
<![CDATA[Agus Perdana Windarto]]>;
<![CDATA[Yuegilion Pranayama Purba]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1672
<![CDATA[This study aims to obtain a model of rules in classifying the level of customer satisfaction at Bombay Textile Stores. By knowing the level of customer satisfaction, shop owners can improve service if it is not good and further improve service if the level of satisfaction is good. This study measures the level of customer satisfaction at the Bombay Textile Store. The method used in this study is the C4.5 Algorithm, where the data source used is a questionnaire/questionnaire technique given to Bombay Textile Store customers. The variables used include Service, Quality of Goods, Price, Facilities, and Promotion. The results obtained 11 rules for the classification of customer satisfaction levels with 5 rules satisfied status and 6 rules dissatisfied status. The C4.5 algorithm can be used in the case of customer satisfaction levels with an accuracy rate of 96.67%. From the results of the analysis, it is hoped that it can be applied so that it can be used as a decision to improve service to customers.]]>
<![CDATA[Application of the FP-Growth Algorithm in Analyzing Patterns and Layout of Foodstuffs ]]>
<![CDATA[Ayu Padillah]]>;
<![CDATA[Heru Satria Tambunan]]>;
<![CDATA[Rizki Alfadillah Nasution]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1673
<![CDATA[The purpose of this study was to determine the pattern and layout of the appropriate goods in Gono Stores using the FP-Growth Algorithm. Gono Store is a store that is engaged in the sale of food ingredients located in Nagori Dolok Kataran, Kec. Dolok Batu Nanggar. The arrangement of the layout of the goods greatly affects the volume of sales. However, in setting the layout at the Gono Store, there are some problems, namely the lack of knowledge of the shop owner in setting the layout . The FP-Growth algorithm is one of the alternative data mining algorithms that can be used to determine groups of data that often appear (Frequent item sets) in a set of data.The source of the research data used is by conducting observations and interviews at Gono Stores. From the overall results of the sales data 10 rules are formed with the minimum value limit of Support = 0.3and Confidence = 0,8. Its hoped that the result of this study will provide benefits in the form of information that can help shop owners in analyzing the pattern and layout of foodstuffs.]]>
<![CDATA[Naïve Bayes Algorithm For Predicting Sales at the Pematang Siantar VJCakes Store ]]>
<![CDATA[Juwita Juwita]]>;
<![CDATA[M. Safii]]>;
<![CDATA[Bahrudi Efendi Damanik]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1674
<![CDATA[Along with the development of the era, competition in the world of business and technology is overgrowing, so business people are competing to develop their business by utilizing existing technology to develop their business, and also so that their business always survives in the rapid business competition. Sales of cake products are expected to continue to increase profits, one of which is by providing products according to market demand so that there are no losses. So far, companies often experience losses because they do not have a system that can predict sales. This writing is done to implement and prove that the Naïve Bayes Algorithm can be used to predict sales of cakes at the VJCakes Pematangsiantar store. The research data is cake sales data consisting of 10 types of cakes with various sizes, tastes, and shapes, which were obtained from the VJCakes Pematangsiantar Store from June 2021 – March 2022. The results of the calculations that have been carried out show that the calculation process is manual and assisted with Rapid software. Miner is the same, which means that the calculation can be said to be successful by producing a probability table of each variable and an accuracy rate of 83.44% of the testing data that has been carried out, and knowing this can be informed to VJCakes to make better decisions in the future.]]>
<![CDATA[Application of Multiple Regression in Estimating the Amount of Population Growth in Siantar District ]]>
<![CDATA[Ayu Wulandari]]>;
<![CDATA[Agus Perdana Windarto]]>;
<![CDATA[Hendry Qurniawan]]>
<![CDATA[ JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 1 No. 4 (2022): December ]]>
Publisher : <![CDATA[Yayasan Literasi Sains Indonesia ]]>
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DOI: 10.55123/jomlai.v1i4.1677
<![CDATA[The purpose of this study is to solve a case or problem that makes decision makers experience various obstacles in estimating the amount of population growth each year by using multiple linear regression algorithms as a solution to solving cases. The data used in this study was obtained directly from the official website of the Central Statistics Agency (BPS) Simalungun in the form of a softcopy book file entitled "Siantar District in Figures 2020" via the url http://simalungun.bps.go.id. with population data from the Siantar District from 2016-2020, there are 17 villages. The data that has been obtained is then processed using Data Mining estimates of the Multiple Linear Regression algorithm and research testing is carried out using the help of Rapid Miner 9.10 Software. By doing this research, research results are obtained that can provide information or input to the government through related agencies to anticipate the number of population growth in Siantar District every year.]]>
