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Classification Of Population Data On Status In The Family Based On Last Education And Work Using The Clustering Method (Case Study: Sei Prison Village Office) Fauziah Ningsih; Yani Maulita; Marto Sihombing
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.264

Abstract

Population data is structured individual or individual data through population registration, civil registration and population census activities. It is important to know population data because in making policies and planning regional or state development, population data is needed to describe the condition of an area. Population data include births, deaths, transfers or migration, population composition, population density and so on. This grouping is done so that population data that is already in the archives will be input into an application that will be designed to make it easier for parties who need data without having to look at the data that is still manual. The problems that exist are such as the increase in the number of residents in a city, village or even a district which is increasing while the population that has been recorded still does not have a job, such as status in the family, namely the head of the family is still there who does not work in terms of recent education can still be considered to get a job that matches the last type of education. From the research process conducted on 20 data, 3 groups were obtained, Cluster 1 contained 16 data, Cluster 2 contained 1 data, and Cluster 3 contained 3 data. And the most group obtained is cluster 1, there is education last high school, has a type of work that has not worked and status in the family of the head of the family.
Diagnosis of Parasitic Diseases in Animals Cat Using Bayes Theorem Method Salsabila Khairunisa; Yani Maulita; Magdalena Simanjuntak
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.265

Abstract

Cats are one of the most popular pets in the world, including Indonesian people who like to keep cats as pets, and even become a hobby for cat lovers. Diseases that often attack cats are caused by parasites, namely worms and fleas. Parasites that attack cats are grouped into two, namely ectoparasites and endoparasites. expert system which is a computer program , which is able to store knowledge and rules like an expert . With the help of an expert system, someone who is lay or not an expert in a particular field will be able to answer questions, solve problems, and make decisions that are usually made by an expert . The Bayes Theorem method can be applied to diagnose parasitic diseases in cats based on input symptoms chosen by the users, the system can perform analysis based on predetermined rules or knowledge base. Based on the probability value of each symptom and disease that has been made, the system can diagnose parasitic diseases in cats with different accuracy results, the highest value or percentage which is the result of the diagnosis of the parasitic disease. From the results of trials conducted by the expert system for diagnosing parasitic diseases in cats using the Bayes Theorem method, the highest value was obtained, namely the type of parasitic disease Flea Disease (P03) with a percentage of 38.66%.
Grouping Patient Data Based On Work And Place Of Residence On Perceived Complaints Jhody Alkhalis Sembiring; Yani Maulita; Suci Ramadani
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.268

Abstract

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.
The Effect of Social Media on Student Learning Motivation Using the Apriori Method Chairmayni Pratiwi Tiwi; Yani Maulita; Imeldawaty Gultom
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.273

Abstract

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.
Expert System To Diagnose Stem Border In Sugarcane Plant With Certainty Factor Method Diah Wardhani Wardhani; Yani Maulita; Siswan Syahputra
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.291

Abstract

Sugarcane (Saccharum Officanarum L) is an annual plantation crop, which has its own characteristics, because it contains sugar in its stems. Sugarcane belongs to the grass family (graminae) like rice, reeds, corn, bamboo and others. One of the problems that exist in sugarcane plantations at PT Perkebunan Nusantara II is stem borer pests. Losses due to borer attacks can be in the form of a decrease in sugarcane weight, yield and quality of the sap obtained. pests on sugarcane plants cause a decrease in sugar production of about 10%. There are three types of stem borer pests found in PT Perkebunan Nusantara II, namely shoot borer pests, striped stem borer pests, and giant stem borer pests. Meanwhile, biological control is by spreading natural predators of pests which will suppress the population of widespread distribution and breeding of pests which are commonly called parasitoids. Parasitoids will become predators of their respective hosts, so appropriate diagnostic therapy for stem borer pests is needed so that the spread of stem borer pests does not become more widespread and the parasitoids to be spread operate effectively. By using the Certainty Factor method, you can find out the types of pests that are attacking sugarcane plants. Based on manual calculations, the dipole yield was 79% on one of the selected pest types based on the symptoms induced in the sugarcane plant.
Use of Case Based Reasoning (CBR) Methods to Diagnosis Diseases in Pregnancy Nurul Elsa Fadilah; Yani Maulita; Husnul Khair
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.292

Abstract

Diseases in pregnant women are diseases that many people need to pay attention to, because diseases that occur in pregnant women will not only endanger one life, but more than that. Hypertension currently occupies the 2nd position as a type of disease that threatens the lives of many pregnant women. The application of Case Based Reasoning (CBR) in diagnosing diseases that occur during pregnancy is motivated by the difficulty of consulting an obstetrician due to costs, time or even the limited number of doctors in a hospital. The use of CBR aims to solve new problems by adapting solutions to problems that occurred before. The expert system itself is one of the solutions to solve problems faced by users in the health sector, this system can minimize costs incurred to consult about diseases in pregnancy to specialist doctors. "Use of Case Base Reasoning (CBR) to Diagnose Diseases in Pregnancy" is expected to help the general public, especially pregnant women, make a simple diagnosis of symptoms and diseases in pregnancy.
Application Of The Profile Matching Method In The Selection Of New Students For Batak Karo Bridal Makeup Skills In The PKK Program Suma Dia Syahwani; Yani Maulita; Mili Alfhi Syari
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.293

Abstract

Makeup is the art of using makeup materials to change the natural face shape into an artistic face. Work Skills Education (PKK) itself is an education and training service program oriented towards the development of work skills provided to students in order to have competence in certain skills that are in accordance with job opportunities. Profile matching is the process of comparing the actual data value of a profile to be assessed with the expected profile value so that it can be known the difference in competence or the distance between one value and another. This research aims to facilitate the selection of new prospective students in the PKK program at the Pelawi Salon Binjai Course and Training Institute (LKP) and optimize admin work time in the process of selecting new students. By applying this method, it aims to see the eligibility of prospective students according to predetermined criteria so that the opportunity to take part in the PKK program can be received by the right person. There are 5 sample data with 6 criteria in this research, the final result in this study is 3.5 being the highest value and 2.99 being the lowest value.
Identifikasi Tingkat Kepuasan Pengunjung Wisata Menggunakan Metode Naive Bayes Fisyanda Yusmalizar; Yani Maulita; Suci Ramadani
Bulletin of Multi-Disciplinary Science and Applied Technology Vol 2 No 5 (2023): Agustus 2023
Publisher : Forum Kerja Sama Penddikan Tinggi (FKPT)

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Abstract

One way to implement development in all these fields is through development in the tourism sector, namely by developing and utilizing existing tourism resources and potential. To support government programs in developing tourist objects, especially in Bukit Lawang, it is necessary to pay attention to the level of satisfaction of tourist visitors visiting Bukit Lawang. To measure the level of satisfaction with tourist visitors, of course, parameters or assessment criteria will be used starting from road access to tourist attractions to cottage facilities. Measuring the level of satisfaction is usually done manually, namely by distributing or conducting surveys of visitors. If only questions are asked without any stored data, of course the Tourism Office will have difficulty measuring or evaluating the level of tourist visitor satisfaction, because it does not have sufficient data. For this reason, it is necessary to build a system that can assist the Langkat Regency Tourism Office in identifying the level of visitor satisfaction at Bukit Lawang. With this system, of course the Department will find it easier to evaluate and identify the level of visitor satisfaction quickly and precisely. So that all deficiencies to visitor satisfaction can be resolved quickly. To measure the level of satisfaction required a method that can measure the level of satisfaction precisely, one of the methods used is Naive Bayes. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by summing the frequencies and combinations of values from a given dataset. Based on the results of trials that have been carried out in this study, results are obtained based on the 20 data used with the results of 17 TRUE data and 3 FALSE data, with an accuracy value of 65%.
Penentuan Calon Penerima Program Keluarga Harapan (PKH) Dengan Metode Analytical Hierarchy Process (AHP) Agi Kakana Bangun; Yani Maulita; Suci Ramadani
Bulletin of Multi-Disciplinary Science and Applied Technology Vol 2 No 5 (2023): Agustus 2023
Publisher : Forum Kerja Sama Penddikan Tinggi (FKPT)

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Abstract

Poverty is a situation where there is a lack of ordinary things to have such as food, clothing, shelter and drinking water. Poverty sometimes also means a lack of access to education and employment that can overcome the problem of poverty and gain the respect one deserves as a citizen. Poverty itself in developing countries is quite a complicated problem even though several developing countries have succeeded in implementing development in terms of production and national income. One of the government's social assistance-based programs is the Family Hope Program (PKH). To overcome the above problems, it is necessary to build a system that can make it easier for Kuala District, Langkat Regency to make decisions about recipients of PKH assistance. To support accuracy in building a decision-making system, a method is needed that is capable of making the right decisions based on predetermined criteria. In this research, the method used is AHP. Analytic Hierarchy Process (AHP) is a structured method for managing and analyzing complex decisions using mathematical and psychological concepts. The AHP method helps in determining the priority weight of each parameter that is taken into consideration in making decisions. Based on the value of importance of criteria and sub-criteria from 10 alternative data, the highest value was obtained, namely alternative recipient 8 with a system analysis value of 0.2105 with the income criterion of the head of the family being IDR. 2,657,000, Head of family's education is high school, well water source, home toilet, boarded house building, parent's house ownership status, number of dependents 3 people, number of dependents Education of children 3 people, owns a motorbike
Grouping Mortgage Data By Job Using The Clustering Method Kusmananda Lubis; Yani Maulita; Marto Sihombing
Nusantara Journal of Multidisciplinary Science Vol. 1 No. 3 (2023): NJMS - Oktober 2023
Publisher : PT. Inovasi Teknologi Komputer

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Abstract

This research discusses the application of a cluster-based data grouping method (clustering) to group Home Ownership Credit (KPR) data based on the type of work of the borrowers. The aim of this research is to identify possible patterns in mortgage data and group them into groups that have similar job characteristics. In this study, the cluster method is used to classify mortgage data based on the job attributes of the borrowers. The data collected includes job information and several other related attributes. The clustering process is carried out by applying certain algorithms to group data into different groups. The results of this study are expected to provide insight into the relationship between the type of work and the characteristics of mortgage borrowers. With a better understanding of these patterns, financial institutions and related agencies can make more informed decisions in managing mortgage products, credit risk, and developing more effective marketing strategies. This data grouping method can contribute to improving the efficiency of data analysis and decision making in the financial sector.
Co-Authors , Achmad Fauzi ., Novriyenni Achmad Fauzi Acmad Fauzi Agi Kakana Bangun Ahmad Fauzi Ahmad Kurniawan Prahadi Alanis Humairoh Alfinaty, Nurma Alta Mirah Ambarita, Indah Andika, Rio Andri Kristiawan Arisma Yulistiani Arisya, Feby Arliana, Lina Aula, Nurhasanah Aulia, Damai Aulia Br Karo Ayu Rahayu Febria Ayu Rahayu Febria Buaton, Relita Budi Serasi Ginting Budi Serasi Ginting Budi Serasi Ginting Chairmayni Pratiwi Tiwi Citra Ayu Wasih Dea Syafitri Dhea Armaya Diah Wardhani Wardhani Dicky Ananda Azhari Dieo Alfiky Ananda Dila Aulia Putri Dimas Prayogi Dina Ervianna Simarmata Dita Sahputri Elfira Iriani Esti Sundari Eva Sasmita Farid Reza Malau Farid Reza Malau Farida Hanum Fauzi, Achmad Fauziah Ningsih Fisyanda Yusmalizar Fresti Anjeli Gea, Wisda Wati Gultom, Imeldawaty Hafizh, Faisal Hermansyah Sembiring Hermansyah Sembiring I Gusti Prahmana I Gusti Prahmana Ika Indah Rahayu Intan Sari Irfan Yusuf Jecika Azzahra Jhody Alkhalis Sembiring Kadim, Lina Arliana Nur Katen Lumbanbatu Katen Lumbanbatu Khair, Husnul Kristina Annatasia Br Sitepu Kusmananda Lubis Lala Arika Leni Tri Ramadhayanti Lina Arliana Liyanti Armaya Sari Magdalena Simanjuntak Magdalena Simanjuntak Magdalena Simanjuntak Magdalena Simanjuntak magdalena simanjuntak Malau, Farid Reza Manik, Laurensia Agustin Mariza Marto Sihombing Maskanda Rizky Maulidina, Nadia Mhd Arif Permata Mili Alfhi Syari Muhammad Prabowo Hartanta Sitepu Muhammad Rivaldi Prastowo Muhammad Yusri Nadilla Ayudia Pasa Naftali, Juliana Nisrina Naufalia Santoso Novriyenni - Novriyenni, Novriyenni Nurhayati Nursakinah Nurul Elsa Fadilah Pakpahan , Victor Maruli Pakpahan, Victor Maruli Pardede, Akim Manaor Hara Pasaribu, Tioria Piper Warni Gea Prahmana, I Gusti Puteri Diyana Putri Ladya Elvanny Putri Lestari Rafli Pramudia Rahayu, Rizka Putri Rahmat Ramadhan Ramadana, Noval Ramadani, Suci Rayuni, Rayuni Retni Noviyanti Siregar Rindi Asti Ananda Rizki Irwansyah Rizky Ramadhan Rusmin Saragih, Rusmin Salsabila Khairunisa Sari, Elisa Puspita Saripurna, Darjat Selviyani, Selviyani Sembiring, Hermansyah Sembiring, Wildan Yuanda Malik Setia Adiyasa Lubis Setia Ningsih Shella Nadya Shely Eninta BR PA Sihombing, Anton Silvia, Sindy Simanjuntak, Magdalena Sinurat, Sylvia Natalia Siregar, Retni Noviyanti Siswan Syahputra Siswan Syahputra Suci Rahmadani Suci Ramadani Suci Ramadani Suci Ramadani suci ramadani Suci Ramadani Suci Ramadani Suci Ramadani Sujayanti Br.Giniting, Novia Suma Dia Syahwani Sundari, Yeni Sundari, Yeni Suria Alamsyah Suria Alamsyah Putra Surya Alamsyah Putra Syahputra , Siswan Syahputra, Suria Alam Syahputri, Heni Syari, Milli Alfhi Tarigan, Kiki Dea Ananda Tria Damayanti Wildan Yuanda Malik Sembiring wildan yuanda malik sembiring Winda Sari Yuyun Arnia Zema Zema Zema Zema