Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.166
Sri Rahmayani, S. Sumarno, Zulia Almaida Siregar
During the Covid-19 pandemic, the government provided assistance distributed through each sub-district throughout the province of Indonesia, one of which was the Pahlawan Village in the East Siantar District Pematangsiantar City. So far, the assistance provided by Kelurahan Pahlawan is still done manually, so errors in data collection and distribution of aid may occur. To overcome this problem, a study was carried out by applying the K-Means algorithm to determine the eligibility cluster of Covid-19 beneficiaries, which was carried out by collecting population data according to predetermined attributes. Then the population data will be clustered using the K-Means algorithm and tested using the Rapid Miner application. The clustering results obtained are that cluster 0 consists of 26 data and that cluster 1 consists of 24 data. The recipients of Covid-19 social assistance using the K-Means algorithm show that those entitled to receive the gift are the elderly (elderly). Based on this, it can be concluded that the K-Means Algorithm can be applied to produce more practical information in determining who is entitled to receive assistance
{"title":"Analysis of K-Means Algorithm for Clustering of Covid-19 Social Assistance Recipients","authors":"Sri Rahmayani, S. Sumarno, Zulia Almaida Siregar","doi":"10.55123/jomlai.v1i1.166","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.166","url":null,"abstract":"During the Covid-19 pandemic, the government provided assistance distributed through each sub-district throughout the province of Indonesia, one of which was the Pahlawan Village in the East Siantar District Pematangsiantar City. So far, the assistance provided by Kelurahan Pahlawan is still done manually, so errors in data collection and distribution of aid may occur. To overcome this problem, a study was carried out by applying the K-Means algorithm to determine the eligibility cluster of Covid-19 beneficiaries, which was carried out by collecting population data according to predetermined attributes. Then the population data will be clustered using the K-Means algorithm and tested using the Rapid Miner application. The clustering results obtained are that cluster 0 consists of 26 data and that cluster 1 consists of 24 data. The recipients of Covid-19 social assistance using the K-Means algorithm show that those entitled to receive the gift are the elderly (elderly). Based on this, it can be concluded that the K-Means Algorithm can be applied to produce more practical information in determining who is entitled to receive assistance","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81493375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.176
Nawaf Naofal, Muhammad Rifqi Daffa Ulhaq, C. Prianto
Az-zahra Furniture Store is a furniture store which is located at Jalan Raya Pasar Cipunagara, Subang, West Java. In its business process, as an effort to expand the reach of consumers and move towards digitization, a website-based e-commerce system called e-fazastore is designed which is expected to help az-zahra furniture store in running business processes. With this e-commerce system, it will assist in several activities such as selling and managing furniture data, managing customer order data and facilitating transactions between the two parties, namely the seller and their customers, as well as making it easier to find out the available inventory. The system is built using the Laravel framework with an MVC (Model, View, Controller) architectural design system, which is a design method that divides the program structure into three main parts, namely data (Model), system view (View) and how to operate a data flow (Model). controller) in the system
Az-zahra家具店是一家家具店，位于西爪哇苏邦的Jalan Raya Pasar Cipunagara。在业务流程中，为了扩大消费者的覆盖范围，向数字化迈进，设计了一个基于网站的电子商务系统e-fazastore，希望能够帮助az-zahra家具店进行业务流程的运行。有了这个电子商务系统，它将协助一些活动，例如销售和管理家具数据，管理客户订单数据，促进双方(即卖家和他们的客户)之间的交易，以及更容易找到可用库存。本系统采用Laravel框架构建，采用MVC (Model, View, Controller)架构设计系统，是一种将程序结构分为数据(Model)、系统视图(View)和如何操作数据流(Model)三大部分的设计方法。控制器)在系统中
{"title":"Development of E-Commerce Information System at Az-Zahra Shop Using Laravel Framework","authors":"Nawaf Naofal, Muhammad Rifqi Daffa Ulhaq, C. Prianto","doi":"10.55123/jomlai.v1i1.176","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.176","url":null,"abstract":"Az-zahra Furniture Store is a furniture store which is located at Jalan Raya Pasar Cipunagara, Subang, West Java. In its business process, as an effort to expand the reach of consumers and move towards digitization, a website-based e-commerce system called e-fazastore is designed which is expected to help az-zahra furniture store in running business processes. With this e-commerce system, it will assist in several activities such as selling and managing furniture data, managing customer order data and facilitating transactions between the two parties, namely the seller and their customers, as well as making it easier to find out the available inventory. The system is built using the Laravel framework with an MVC (Model, View, Controller) architectural design system, which is a design method that divides the program structure into three main parts, namely data (Model), system view (View) and how to operate a data flow (Model). controller) in the system","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90328551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.142
Elvri Rahayu, I. Parlina, Zulia Almaida Siregar
CV. Kerinci Motor is a company engaged in the transportation and automotive sector, especially in the sale of motorcycles. The uncertainty in the number of motorcycle sales at this company has hampered the company's development, because the company cannot take definite policies regarding the sales that occur. Therefore, it is necessary to estimate the sales of motorcycles at this company in the future, so that the management can estimate consumer demand in the future. So that the company is able to serve and provide consumer demand. The estimation algorithm that will be used in this research is Multiple Linear Regression which is one of the data mining methods. This method was chosen because it is able to make an estimate by utilizing data regarding sales. The results of the estimated (estimated) sales of manual motorcycles in 2021 by January are 56,941 or 57 motorcycles in the manual category. This means that there is an increase in the number of manual motorbikes by 5 motorbikes, while the results until May 2021 amounted to 65,710 motorbikes. So it can be concluded that sales of motorcycles at CV. Kerinci Motor have increased sales in the next 5 months.
{"title":"Application of Multiple Linear Regression Algorithm for Motorcycle Sales Estimation","authors":"Elvri Rahayu, I. Parlina, Zulia Almaida Siregar","doi":"10.55123/jomlai.v1i1.142","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.142","url":null,"abstract":"CV. Kerinci Motor is a company engaged in the transportation and automotive sector, especially in the sale of motorcycles. The uncertainty in the number of motorcycle sales at this company has hampered the company's development, because the company cannot take definite policies regarding the sales that occur. Therefore, it is necessary to estimate the sales of motorcycles at this company in the future, so that the management can estimate consumer demand in the future. So that the company is able to serve and provide consumer demand. The estimation algorithm that will be used in this research is Multiple Linear Regression which is one of the data mining methods. This method was chosen because it is able to make an estimate by utilizing data regarding sales. The results of the estimated (estimated) sales of manual motorcycles in 2021 by January are 56,941 or 57 motorcycles in the manual category. This means that there is an increase in the number of manual motorbikes by 5 motorbikes, while the results until May 2021 amounted to 65,710 motorbikes. So it can be concluded that sales of motorcycles at CV. Kerinci Motor have increased sales in the next 5 months.","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85678163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.160
Sri Nuraini, I. Gunawan, Widodo Saputra
Palm oil is still a prima donna commodity in the plantation sector and as a major foreign exchange earner to date. Research and development of this commodity is very important to maintain Indonesia's position as the largest palm oil producing country in the world. The purpose of this study was to analyze what internal and external factors are the strengths, weaknesses, opportunities and threats for marketing oil palm sprouts in PPKS Marihat. To analyze what are the priority strategies to be implemented for the marketing of sprouts at PPKS Marihat. The research method used is the K-Medoids clustering algorithm by selecting the sprout data in order to determine the best quality of sprouts. Based on the results of research using the K-Medoids algorithm with manual calculations and testing, the same results were obtained, namely cluster 1 with very good sprouts category had 7 members, cluster 2 with good sprouts category had 12 members and cluster 3 with poor sprouts category had 7 members. . Testing data on Rapid Miner using the K-Medoids algorithm can display 3 classes with an accuracy percentage of 100%. So it can be concluded that the K-Medoids algorithm can be used for clustering oil palm sprouts at PPKS Marihat.
{"title":"Utilization of K-Medoids Algorithm for Klustering of Oil Palm Sprouts","authors":"Sri Nuraini, I. Gunawan, Widodo Saputra","doi":"10.55123/jomlai.v1i1.160","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.160","url":null,"abstract":"Palm oil is still a prima donna commodity in the plantation sector and as a major foreign exchange earner to date. Research and development of this commodity is very important to maintain Indonesia's position as the largest palm oil producing country in the world. The purpose of this study was to analyze what internal and external factors are the strengths, weaknesses, opportunities and threats for marketing oil palm sprouts in PPKS Marihat. To analyze what are the priority strategies to be implemented for the marketing of sprouts at PPKS Marihat. The research method used is the K-Medoids clustering algorithm by selecting the sprout data in order to determine the best quality of sprouts. Based on the results of research using the K-Medoids algorithm with manual calculations and testing, the same results were obtained, namely cluster 1 with very good sprouts category had 7 members, cluster 2 with good sprouts category had 12 members and cluster 3 with poor sprouts category had 7 members. . Testing data on Rapid Miner using the K-Medoids algorithm can display 3 classes with an accuracy percentage of 100%. So it can be concluded that the K-Medoids algorithm can be used for clustering oil palm sprouts at PPKS Marihat.","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"144 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86199713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.165
Weni Ratna Sari Oktapia Ningse, S. Sumarno, Zulaini Masuro Nasution
The purpose of the research is to assist the school in selecting student data as recipients of the PIP (Smart Indonesia Program) to be more objective and practical and to assist in increasing the accuracy of the targeting of the recipients of the PIP funds. In this study using Data Mining techniques using the C4.5 algorithm. The source of the research data used was obtained from observations and interviews with the MIS Al-Khoirot Tambun Nabolon Pematang Siantar school. The research variables used were parents' occupations, parents' income, KKS (Prosperous Family Card) holders, SKTM holders (Poor Certificate). In this study, the alternative used as a sample is the data of MIS Al-Khoirot students. The results of this study found that the most dominant attribute was the SKTM holder with a gain of 0.833764907, besides that this study produced 8 (eight) rules with an accuracy rate of 98.00%. Based on this, it can be concluded that the C4.5 algorithm can be used for the classification of the Determination of Smart Indonesia Program Recipients at MIS Al-Khoirot
{"title":"C4.5 Algorithm Classification for Determining Smart Indonesia Program Recipients at MIS Al-Khoirot","authors":"Weni Ratna Sari Oktapia Ningse, S. Sumarno, Zulaini Masuro Nasution","doi":"10.55123/jomlai.v1i1.165","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.165","url":null,"abstract":"The purpose of the research is to assist the school in selecting student data as recipients of the PIP (Smart Indonesia Program) to be more objective and practical and to assist in increasing the accuracy of the targeting of the recipients of the PIP funds. In this study using Data Mining techniques using the C4.5 algorithm. The source of the research data used was obtained from observations and interviews with the MIS Al-Khoirot Tambun Nabolon Pematang Siantar school. The research variables used were parents' occupations, parents' income, KKS (Prosperous Family Card) holders, SKTM holders (Poor Certificate). In this study, the alternative used as a sample is the data of MIS Al-Khoirot students. The results of this study found that the most dominant attribute was the SKTM holder with a gain of 0.833764907, besides that this study produced 8 (eight) rules with an accuracy rate of 98.00%. Based on this, it can be concluded that the C4.5 algorithm can be used for the classification of the Determination of Smart Indonesia Program Recipients at MIS Al-Khoirot","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77568350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.162
Mahmuda Lailiya, N. L. W. S. R. Ginantra, G. Mahendra
The activities of the budget adjustments in the manufacture of the allocation of procurement of goods still have not been done optimally. This leads to lack of control over spending budget. The purpose of this research is to make the Information Systems Budget Adjustments Purchase Website Based on PT. Taspen (Persero) Kantor Cabang Denpasar, which is the solution of the weakness of the existing system. This study aims to produce a system that will simplify and accelerate the employees of PT. Taspen (Persero) Denpasar in adjusting the budget the purchase of equipment and supplies so as to produce the management of the orderly, effective, and efficient. The stages in achieving this goal based on the methods of the waterfall includes Flowmap, Context Diagram, Data Flow Diagram, Entity Relationship Diagram, and database design using software package xampp and MySQL. Testing methods carried out using black box testing. The results obtained in the form of the establishment of a system that supports the process of inputting the data of the budget, the calculation of the adjustment of the budget, and reporting the data required as an accountability report
{"title":"Website-Based Budget Adjustment Information System at PT. Taspen (Persero) Denpasar Branch Office","authors":"Mahmuda Lailiya, N. L. W. S. R. Ginantra, G. Mahendra","doi":"10.55123/jomlai.v1i1.162","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.162","url":null,"abstract":"The activities of the budget adjustments in the manufacture of the allocation of procurement of goods still have not been done optimally. This leads to lack of control over spending budget. The purpose of this research is to make the Information Systems Budget Adjustments Purchase Website Based on PT. Taspen (Persero) Kantor Cabang Denpasar, which is the solution of the weakness of the existing system. This study aims to produce a system that will simplify and accelerate the employees of PT. Taspen (Persero) Denpasar in adjusting the budget the purchase of equipment and supplies so as to produce the management of the orderly, effective, and efficient. The stages in achieving this goal based on the methods of the waterfall includes Flowmap, Context Diagram, Data Flow Diagram, Entity Relationship Diagram, and database design using software package xampp and MySQL. Testing methods carried out using black box testing. The results obtained in the form of the establishment of a system that supports the process of inputting the data of the budget, the calculation of the adjustment of the budget, and reporting the data required as an accountability report","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82978516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.177
Sri Rahayuni, Indra Gunawan, Ika Okta Kirana
Along with the increasing growth of technology and the development of science, business competition is also getting faster and therefore we are required to always develop the business in order to always survive in the competition. Family Gypsum is a store whose sales system is the same as a supermarket, namely the buyer will take the goods to be purchased himself. From this, data on sales, purchases of goods, and unexpected expenses are not structured properly so that the data only functions as an archive. In this research, data mining is applied using the K-Means calculation process which provides a standard process for using data mining in various fields to be used in clustering because the results of this method are easy to understand and interpret. The results obtained from the K-Means method that has been implemented into Rapid Miner have the same value, which produces 3 clusters, namely clusters that do not sell, clusters that sell, and clusters that sell very well. With red clusters with 2 items, the clusters selling green with 28 items, the clusters selling with blue with 30 items. The results of this study can be entered into the Family Gypsum store Jl. H. Ulakma Sinaga, Red Rambung who is getting more attention on each sale based on the cluster that has been done
随着技术的日益增长和科学的发展，商业竞争也越来越快，因此我们需要不断发展业务，以便在竞争中始终生存。家庭石膏是一种销售系统与超市相同的商店，即购买者将自己拿着要购买的商品。因此，关于销售、商品购买和意外费用的数据结构不合理，因此数据只能用作存档。在本研究中，使用K-Means计算过程来应用数据挖掘，由于该方法的结果易于理解和解释，因此为将数据挖掘应用于聚类的各个领域提供了一个标准过程。在Rapid Miner中实现的K-Means方法得到的结果具有相同的值，它产生3个聚类，即不卖的聚类，卖的聚类和卖得很好的聚类。红色的有2件物品，绿色的有28件物品，蓝色的有30件物品。本研究结果可发表于《家庭石膏库》。H. Ulakma Sinaga, Red Rambung，他在每笔销售中都获得了更多的关注，这是基于已经完成的集群
{"title":"Material Sales Clustering Using the K-Means Method","authors":"Sri Rahayuni, Indra Gunawan, Ika Okta Kirana","doi":"10.55123/jomlai.v1i1.177","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.177","url":null,"abstract":"Along with the increasing growth of technology and the development of science, business competition is also getting faster and therefore we are required to always develop the business in order to always survive in the competition. Family Gypsum is a store whose sales system is the same as a supermarket, namely the buyer will take the goods to be purchased himself. From this, data on sales, purchases of goods, and unexpected expenses are not structured properly so that the data only functions as an archive. In this research, data mining is applied using the K-Means calculation process which provides a standard process for using data mining in various fields to be used in clustering because the results of this method are easy to understand and interpret. The results obtained from the K-Means method that has been implemented into Rapid Miner have the same value, which produces 3 clusters, namely clusters that do not sell, clusters that sell, and clusters that sell very well. With red clusters with 2 items, the clusters selling green with 28 items, the clusters selling with blue with 30 items. The results of this study can be entered into the Family Gypsum store Jl. H. Ulakma Sinaga, Red Rambung who is getting more attention on each sale based on the cluster that has been done","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85752949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.163
Widya Juli Mawaddah, I. Gunawan, Ika Purnama Sari
Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. In this study, we will discuss oil palm yields at PPKS Marihat, one of the Oil Palm Research Center branches located in Simalungun Regency, Medan, North Sumatra. Know how it grows. The Clustering algorithm is used in K-Means. Using this method, the data will be grouped into 3 (three) Clusters, where the application of the K-Means Clustering process uses the Rapid Miner tools. The data used is data on oil palm harvests at PPKS Marihat in 2020, consisting of 100 data items. The results obtained are crop yields with an excellent value of 66 items, harvest data with a good deal of 32 items, and harvest data with a reasonably good value of 2 items, based on net total and gross amount for each region. Based on this, it can be concluded that the K-Means Algorithm can be used to Cluster oil palm yields at PPKS Marihat
{"title":"Implementation of Data Mining Algorithm for Clustering of Palm Oil Harvested Data","authors":"Widya Juli Mawaddah, I. Gunawan, Ika Purnama Sari","doi":"10.55123/jomlai.v1i1.163","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.163","url":null,"abstract":"Palm oil is one of the plantation commodities that has a strategic role in Indonesia's economic development. In this study, we will discuss oil palm yields at PPKS Marihat, one of the Oil Palm Research Center branches located in Simalungun Regency, Medan, North Sumatra. Know how it grows. The Clustering algorithm is used in K-Means. Using this method, the data will be grouped into 3 (three) Clusters, where the application of the K-Means Clustering process uses the Rapid Miner tools. The data used is data on oil palm harvests at PPKS Marihat in 2020, consisting of 100 data items. The results obtained are crop yields with an excellent value of 66 items, harvest data with a good deal of 32 items, and harvest data with a reasonably good value of 2 items, based on net total and gross amount for each region. Based on this, it can be concluded that the K-Means Algorithm can be used to Cluster oil palm yields at PPKS Marihat","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74028158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.143
Widia Ayu Lestari Sinaga, S. Sumarno, Ika Purnama Sari
Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.
{"title":"The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District","authors":"Widia Ayu Lestari Sinaga, S. Sumarno, Ika Purnama Sari","doi":"10.55123/jomlai.v1i1.143","DOIUrl":"https://doi.org/10.55123/jomlai.v1i1.143","url":null,"abstract":"Population growth in an area is important for development and is a benchmark for an area to develop. The way to predict population growth is to use Data Mining. Data mining is able to analyze data into information. This study will discuss the amount of population growth in the District of Gunung Malela. The estimation technique that will be used is Multiple Linear Regression. This method was chosen because it can make an estimate/prediction by utilizing old data regarding population growth so that it can produce a pattern of relationships. This Multiple Linear Regression method aims to make the best predictions. The research data used is the population in the Gunung Malela sub-district in 2016-2020. Based on the research that has been done using the Multiple Linear Regression method, the results of the population growth are 40078 residents. This means that there is an additional population of 469 people in Gunung Malela District. The results of this study can be input to the Gunung Malela Sub-District Office to anticipate the rate of population growth and it can be concluded based on this study that the Multiple Linear Regression method can be used to estimate the population.","PeriodicalId":14854,"journal":{"name":"JOMLAI: Journal of Machine Learning and Artificial Intelligence","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72644213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-18DOI: 10.55123/jomlai.v1i1.161
M. F. Rozi, Dedy Hartama, Ika Purnama Sari, Rafiqa Dewi, Zulia Almaida Siregar
In carrying out promotions, it is also necessary to pay for the manufacture of brochures, banners and other promotional media to provide information to prospective students and attract prospective students to register. Determining the location of the promotion is one of the success factors in promotional activities. In this study, the Artificial Neural Network will be used to predict the location of the promotion. Backpropagation is one of the best artificial neural network methods used for prediction, this method is widely used by researchers in predicting a problem. The data analysis tool used is Matlab or what we call the (Matrix Laboratory) which is a program to analyze and compute numerical data, and Matlab is also an advanced mathematical programming language, which was formed on the premise of using the properties and forms of matrices. From the results of the algorithm used, it is expected to get good accuracy results with some architectural experiments later. So that this research can be an indicator to optimize promotions in the following year in order to attract prospective students to register for AMIK and STIKOM Tunas Bangsa Pematangsiantar
在进行促销活动时，还需要付费制作宣传册、横幅等宣传媒体，向准学生提供信息，吸引准学生报名。确定促销地点是促销活动成功的因素之一。在本研究中，将使用人工神经网络来预测促销的位置。反向传播是人工神经网络预测中最好的方法之一，被研究人员广泛应用于预测问题。所使用的数据分析工具是Matlab或我们所说的(Matrix Laboratory)，它是一种分析和计算数值数据的程序，Matlab也是一种先进的数学编程语言，它是在利用矩阵的性质和形式的前提下形成的。从所使用的算法的结果来看，期望在以后的一些建筑实验中得到较好的精度结果。因此，这项研究可以作为一个指标，以优化促销在接下来的一年，以吸引未来的学生注册AMIK和STIKOM Tunas Bangsa Pematangsiantar
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