Journal of Artificial Intelligence and Engineering Applications (JAIEA)
The Journal of Artificial Intelligence and Engineering Applications (JAIEA) is a peer-reviewed journal. The JAIEA welcomes papers on broad aspects of Artificial Intelligence and Engineering which is an always hot topic to study, but not limited to, cognition and AI applications, engineering applications, mechatronic engineering, medical engineering, chemical engineering, civil engineering, industrial engineering, energy engineering, manufacturing engineering, mechanical engineering, applied sciences, AI and Human Sciences, AI and education, AI and robotics, automated reasoning and inference, case-based reasoning, computer vision, constraint processing, heuristic search, machine learning, multi-agent systems, and natural language processing. Publications in this journal produce reports that can solve problems based on intelligence, which can be proven to be more effective.
Articles
430 Documents
<![CDATA[Grouping Patient Data Based On Work And Place Of Residence On Perceived Complaints ]]>
<![CDATA[Sembiring, Jhody Alkhalis]]>;
<![CDATA[Maulita, Yani]]>;
<![CDATA[Ramadani, Suci]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.268
<![CDATA[Every day the Sawit Seberang Health Center serves many patients with various kinds of disease complaints from various areas in Sawit Seberang District. The number of patients can even reach tens of people in one day resulting in a large number of patient visit data. Limited information regarding the spread of diseases that are often suffered by patients in several areas at the Sawit Seberang Health Center has resulted in less optimal policy action, anticipation of treatment and prevention of disease in the community. To find information about grouping patient data based on work and place of residence for perceived complaints, a large or large data mining technique is needed, namely data mining techniques using the clustering method. The purpose of this study is to process and cluster patient data based on work, place of residence and complaints that are felt using the Clustering method, to analyze the results of applying data mining using K-Means Clustering in grouping patient data based on work, place of residence and complaints that are felt and find out the results of the settlement grouping patient data based on work and place of residence on perceived complaints using clustering and data mining methods.]]>
<![CDATA[Grouping Number of Library Members For Determining the Location of Socialization Using Clustering Method ]]>
<![CDATA[Dwi Pratiwi, Sella]]>;
<![CDATA[Fauzi, Achmad]]>;
<![CDATA[Prahmana, I Gusti]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.270
<![CDATA[The high use of smartphones at this time led to a decline in public interest in reading books in the library directly. Especially students and students. This is certainly a problem for the Langkat Regency Archives and Libraries Office. Socialization is needed to increase efforts to read interest in the community. The right socialization location must have several criteria so that the socialization carried out is right on target. The existence of a database for each member of the library will facilitate the location selection process. Data mining techniques can classify the number of library members based on the results of large data analysis into information in the form of patterns. The clustering method is a method in data mining that can analyze data with the aim of grouping data based on the same characteristics. The K-Means algorithm is a simple algorithm for classifying a large number of objects with certain attributes into clusters which are usually used in data mining.]]>
<![CDATA[Comparison Of K-Nearest Neighbor And CNN Classification Methods In Diabetic Data Sets ]]>
<![CDATA[Nisa R, Ajeng Arina]]>;
<![CDATA[Pardede, A M H]]>;
<![CDATA[Sihombing, Marto]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.272
<![CDATA[The number of diabetics worldwide is projected to increase by 204 million (48%), from 425 million in 2017 to 629 million in 2045. Indonesia ranks sixth out of ten countries with the most number of diabetics in the world or 10 million people. The majority of people with diabetes are between 20 and 64 years old, or 327 million people, compared to 123 million people between 65 and 99 years old. The incidence of diabetes increases by about 4.8% at the age of 55-64 years, and women (1.7%) suffer from diabetes more than men (1.4%). Therefore, the authors will create a program to determine the patient's diabetes. One approach is to use machine learning as a data mining classification technique. The author will do a classification comparison with the two methods, namely the KNN and CNN methods to provide the best results of the two methods for testing. So that the accuracy of the data from the diagnosis and photo images of the disease can be known to provide early treatment before the severity of the disease.]]>
<![CDATA[The Effect of Social Media on Student Learning Motivation Using the Apriori Method ]]>
<![CDATA[Tiwi, Chairmayni Pratiwi]]>;
<![CDATA[Maulita, Yani]]>;
<![CDATA[Gultom, Imeldawaty]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.273
<![CDATA[The success of student learning can be determined by their motivation. Students who have high learning motivation tend to have high achievement as well, otherwise their learning motivation is low, their learning achievement will also be low. student learning motivation in the subject is very low. Some students prefer to play social media rather than pay attention to the material explained by the teacher during class hours. Therefore, this study aims to explore the influence of social media on students' learning motivation. This research uses data mining method with Apriori algorithm to identify patterns related to social media usage and students' learning motivation. The Apriori algorithm is one of many algorithms in data mining that is used for frequent itemsets and association rules in databases on transactional data that are generated by identifying each item that exists, and combining larger sets of items provided that the items appear frequently enough in the database. Based on the research that has been done, the author can draw the conclusion that using the Rapid Miner 7.1 application tools in applying the apriori algorithm produces the same rules as manual calculations using 300 data on the learning motivation of Abdi Negara Binjai SMKS students and the system can generate association rules using 300 student learning motivation data with a minimum support of 12% and a minimum confidence of 75% and produce 5 association rules 3 itemsets to determine the learning motivation of Abdi Negara Binjai SMKS students. One of the rules that has the highest confidence value is, if YT and J2 then M1. Which means that every student who uses YOUTUBE Social Media with a length of use is 3-4 HOURS then INCREASES STUDY MOTIVATION. Then the less the ΙΈ (frequent) value is set, the more data that can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.]]>
<![CDATA[Sentiment Analysis Using Text Mining Techniques On Social Media Using the Support Vector Machine Method Case Study Seagames 2023 Football Final ]]>
<![CDATA[Rifa'i, Muhammad]]>;
<![CDATA[Buaton, Relita]]>;
<![CDATA[Prahmana, I Gusti]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.274
<![CDATA[This thesis aims to analyze sentiment on text data from social media related to the 2023 SEA Games, especially in the final match of the soccer sport. The method used is the Text Mining Technique with the SVM (Support Vector Machine) algorithm to classify user sentiment as positive or negative regarding the match. Text data is retrieved from various social media platforms during and after the match. The results of the sentiment analysis are expected to provide insight into the public's view of the sporting event. This research can contribute to the understanding of public sentiment towards the 2023 SEA Games final football match through the analysis of text data from social media.]]>
<![CDATA[Clustering Disease on Settlements Inhabitant In place seedy With Use Clustering Method ]]>
<![CDATA[Sitepu, Ruine Buana Br]]>;
<![CDATA[Achmad Fauzi]]>;
<![CDATA[Saragih, Rusmin]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.275
<![CDATA[Residents living in slum areas often face serious problems related to public health, where the prevalence of disease tends to be high and its spread is difficult to control. The impact of the formation of slums for the community is that safety is threatened, health deteriorates, and social conditions worsen, causing many diseases for people living in slums. Therefore, this study aims to identify patterns and clusters of diseases that exist in residential areas in slums Binjai city using clustering method. The K-Means Algorithm clustering method was chosen because it is able to group data based on similar characteristics, so that it can help identify diseases in a more focused and efficient manner, using the MATLAB application is also very appropriate in this problem so that it can produce output from data mining that can be used in decision making. future decisions. By utilizing the data mining process using the clustering method, clustering can be a problem of grouping diseases in slum settlements. Based on the results of trials with 20 sample data conducted with MATLAB obtained in cluster 1 DHF cases with high slums, Cluster 2 cases of vomiting with moderate slums and cluster 3 cases of diarrhea with moderate slums. The results of this study are expected to provide in-depth insight into disease patterns and clusters in residential areas in slums.]]>
<![CDATA[Grouping Data On Infrastructure Development In Langkat District Using The Clustering Method (Case Study: PUPR, Langkat Regency) ]]>
<![CDATA[Diva Alifya]]>;
<![CDATA[Buaton, Relita]]>;
<![CDATA[Ramadani, Suci]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.278
<![CDATA[A building is a man-made structure consisting of walls and a roof permanently erected in a place. Buildings can also be called houses and buildings, namely all facilities, infrastructure or infrastructure in culture as well as human life in building their civilization. Public Works and Public Housing (PUPR) play an important role in increasing the development of national infrastructure in Indonesia so that PUPR can assist in clustering research in infrastructure development in Langkat Regency which is very large every year by grouping the data based on activity names, company names, sub-districts development, and look at the last four years.To classify existing development infrastructure in Langkat Regency with the previous system used by the PUPR Service which is still running by recording in a ledger and hindering reporting performance in grouping PUPR service infrastructure development in road construction, bridge construction and others. So that the existence of grouping using the clustering method helps the PUPR service in clustering infrastructure development data in Langkat Regency to be more effective and efficient.The clustering method is one of the methods that can be applied in classifying infrastructure development data taken from the analysis of Langkat Regency PUPR data regarding developments that have taken place in several sub-districts in Langkat Regency. This clustering method has been widely used by previous studies to group data]]>
<![CDATA[Application of Data Mining in Analyzing the Effect of Parents' Employment and Education Level on Student Behavior Using the A PRIORI Method (Case Study: SDN 024769 Binjai) ]]>
<![CDATA[Mayaza, Suha Baby]]>;
<![CDATA[Buaton, Relita]]>;
<![CDATA[Ramadani, Suci]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.279
<![CDATA[Behavior is a person's reaction to a stimulus that comes from the external environment. Parents are one of the main factors in the formation of children's behavior. This study aims to find out the effect of parents' work and education on student behavior. By using RapidMiner in testing 234 SDN 024769 Binjai student data, using the Apriori method and setting a minimum support value of 8% and 70% confidence, 1207 rules were obtained in the entire set and 2 rules in 9 itemsets. And the best rule with the highest value is obtained, if the father's job is self-employed, the mother's job is self-employed, the father's last education is high school, the mother's last education is high school, the time the father spends working is more than 8 hours per day, the time the mother spends working is more than 8 hours per day, the time the father spends on family is every day, and the time the mother spends on family is every day then the student has good behavior at school, with a support value of 8.5% and a certainty value of 95.2%.]]>
<![CDATA[Expert System To Determine Psychological Disorders In Chronic Kidney Failure (CKD) Patients Undergoing Hemodialysis Therapy Using Certainty Factor Method ]]>
<![CDATA[SELVY ANGGRAINI, SELVY]]>;
<![CDATA[Saragih, Rusmin]]>;
<![CDATA[Khair, Husnul]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.280
<![CDATA[Chronic Kidney Failure (CKD) is damage to the kidneys both in structure and/or function that lasts for 3 months or more. Hemodialysis is a prolonged therapy that can significantly impact the physical and psychological well-being of patients with chronic kidney disease. This therapy has a big effect on sufferers. The psychological impact that appears can affect the success of therapy so it is important to recognize these symptoms and provide appropriate treatment to overcome them. Based on research at Delia General Hospital, patients who will undergo Hemodialysis therapy must come to the hospital to receive comprehensive therapy by a doctor. Long patient queues when undergoing therapy can make patients tired and remember the patient's condition in order to get information and therapy. Handling of these problems can be overcome by building a system that can determine psychological disorders in patients. Expert systems are computer-based systems that use knowledge, facts and reasoning techniques in solving problems that usually can only be solved by an expert in a particular field. Certainty Factor (CF) is a method capable of defining the degree of certainty of a rule or fact in describing an expert's belief in the problem at hand. With an expert system, it can help identify and determine early on psychological disorders in patients. From the results of trials conducted by expert systems to determine psychological disorders in patients with kidney failure using the Certainty Factor method, the highest value is depression with a percentage of 94.59%.]]>
<![CDATA[Application of the ANFIS Method to Predict Satisfaction with Facilities and Infrastructure ]]>
<![CDATA[Turnip, Mardi]]>;
<![CDATA[Priambodo, Ganang Reza]]>;
<![CDATA[Sihaloho, Theresia Delima]]>;
<![CDATA[Ndruru, Jonathan Haris P.]]>;
<![CDATA[Sigalingging, Josepta]]>;
<![CDATA[Salsabillah]]>;
<![CDATA[Panjaitan, Haposan Daniel]]>
<![CDATA[ Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023 ]]>
Publisher : <![CDATA[Yayasan Kita Menulis ]]>
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DOI: 10.59934/jaiea.v3i1.283
<![CDATA[Facilities and infrastructure are all movable or immovable objects or objects that are used to support every aspect of human life. Students, lecturers and office workers at least spend about half of their active hours at work. Therefore it is very important to pay attention to the high level of comfort, security, completeness in a building. There fore we need a way to predict satisfaction with facilities and infrastructure. To provide solutions to existing problems, the authors create applications that can predict the satisfaction of facilities and infrastructure. In this article, a satisfaction prediction approach based on a data-driven technique, representing system behavior using the Takagi-Sugeno model is developed. The Adaptive Neuro Fuzzy Inference System method is used to build a predictive model. The research was conducted by interview, observation and literature study. Data were taken from 92 respondents consisting of lecturers, students, and staff/employees in the research area. The test results using this method showed satisfactory results, indicating a success rate with an accuracy of 97.2%.]]>
