Pub Date : 2024-02-04DOI: 10.58776/ijitcsa.v2i1.144
Donny Fernando, Dentik Karyaningsih
The business activities of a company require technology that can drive operational efficiency. Marketplaces have become one of the effective marketing channels today for promoting the company's products and services. To reach a broader market, having more than one store on a marketplace is a choice that many companies currently opt for. However, managing multiple stores on marketplaces is not an efficient management approach. This research is an applied study conducted in one startup company (profitto.co.id) that sells developing furniture components, aiming to provide the implementation of an API-based system integration solution to manage multiple stores on Tokopedia and utilizes the Scrum methodology for software development.
公司的业务活动需要能够提高运营效率的技术。如今，市场已成为推广公司产品和服务的有效营销渠道之一。为了进入更广阔的市场，在市场上开设一家以上的商店是目前许多公司的选择。然而，管理市场上的多个商店并不是一种有效的管理方法。本研究是在一家销售开发家具组件的初创公司（profititto.co.id）中进行的应用研究，旨在提供一个基于 API 的系统集成解决方案，以管理 Tokopedia 上的多个商店，并利用 Scrum 方法进行软件开发。
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Pub Date : 2024-02-04DOI: 10.58776/ijitcsa.v2i1.144
Donny Fernando, Dentik Karyaningsih
The business activities of a company require technology that can drive operational efficiency. Marketplaces have become one of the effective marketing channels today for promoting the company's products and services. To reach a broader market, having more than one store on a marketplace is a choice that many companies currently opt for. However, managing multiple stores on marketplaces is not an efficient management approach. This research is an applied study conducted in one startup company (profitto.co.id) that sells developing furniture components, aiming to provide the implementation of an API-based system integration solution to manage multiple stores on Tokopedia and utilizes the Scrum methodology for software development.
公司的业务活动需要能够提高运营效率的技术。如今，市场已成为推广公司产品和服务的有效营销渠道之一。为了进入更广阔的市场，在市场上开设一家以上的商店是目前许多公司的选择。然而，管理市场上的多个商店并不是一种有效的管理方法。本研究是在一家销售开发家具组件的初创公司（profititto.co.id）中进行的应用研究，旨在提供一个基于 API 的系统集成解决方案，以管理 Tokopedia 上的多个商店，并利用 Scrum 方法进行软件开发。
{"title":"Design and Build API-Based Multistore Marketplace Using Agile Methodology","authors":"Donny Fernando, Dentik Karyaningsih","doi":"10.58776/ijitcsa.v2i1.144","DOIUrl":"https://doi.org/10.58776/ijitcsa.v2i1.144","url":null,"abstract":"The business activities of a company require technology that can drive operational efficiency. Marketplaces have become one of the effective marketing channels today for promoting the company's products and services. To reach a broader market, having more than one store on a marketplace is a choice that many companies currently opt for. However, managing multiple stores on marketplaces is not an efficient management approach. This research is an applied study conducted in one startup company (profitto.co.id) that sells developing furniture components, aiming to provide the implementation of an API-based system integration solution to manage multiple stores on Tokopedia and utilizes the Scrum methodology for software development.","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139806728","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 : 2023-09-10DOI: 10.58776/ijitcsa.v1i3.47
Susan Brandy
In today's data-driven business landscape, Data Analytics (DA) has emerged as a vital tool for organizations to extract insights from their existing data, enabling informed decision-making. While large enterprises have wholeheartedly embraced DA as a strategic asset for operational enhancement, SMEs have been comparatively slower in adopting these transformative solutions. To remain competitive and surpass their rivals, SMEs must recognize the significance of harnessing their data assets effectively to drive decision-making processes. This research aims to delve into the challenges hindering the adoption of DA among SMEs, particularly focusing on issues such as inadequate information infrastructure and limited awareness of the benefits that DA can offer. Furthermore, this study investigates the implementation of data analytics as a practical solution to address these challenges, providing a comprehensive analysis of both the advantages and disadvantages associated with DA adoption in the SME context. By shedding light on the untapped potential of data analytics, this research aims to empower SMEs and equip them with the necessary tools to thrive in today's digitally-driven era of business.
{"title":"Overcoming Challenges and Unlocking the Potential: Empowering Small and Medium Enterprises (SMEs) with Data Analytics Solutions","authors":"Susan Brandy","doi":"10.58776/ijitcsa.v1i3.47","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i3.47","url":null,"abstract":"In today's data-driven business landscape, Data Analytics (DA) has emerged as a vital tool for organizations to extract insights from their existing data, enabling informed decision-making. While large enterprises have wholeheartedly embraced DA as a strategic asset for operational enhancement, SMEs have been comparatively slower in adopting these transformative solutions. To remain competitive and surpass their rivals, SMEs must recognize the significance of harnessing their data assets effectively to drive decision-making processes. This research aims to delve into the challenges hindering the adoption of DA among SMEs, particularly focusing on issues such as inadequate information infrastructure and limited awareness of the benefits that DA can offer. Furthermore, this study investigates the implementation of data analytics as a practical solution to address these challenges, providing a comprehensive analysis of both the advantages and disadvantages associated with DA adoption in the SME context. By shedding light on the untapped potential of data analytics, this research aims to empower SMEs and equip them with the necessary tools to thrive in today's digitally-driven era of business.
","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071636","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 : 2023-09-10DOI: 10.58776/ijitcsa.v1i3.42
None Ozzi ardhiyanto, None Muhammad Salam Asyidqi, None Ajif Yunizar Pratama Yusuf, S.Si, M.Eng, None Dr. Tb. Ai Munandar, S.Kom., MT
Clustering infant nutrition based on weight, height, and age is a data analysis method used to group infant nutritional status based on these characteristics. The research on clustering infant nutrition aims to analyze whether there are still many infants in the area with insufficient or excessive nutrition, and to identify groups of infants requiring special attention regarding their nutritional intake. In the analysis of infant nutrition clustering, data on weight, height, and age of infants are collected and then grouped based on similarities in body height and weight at certain ages. The method used in this research is hierarchical clustering, which can help in grouping the data. Clustering analysis can help understand how infants' feeding patterns vary based on their weight, height, and age. The results of research on clustering infant nutrition based on weight, height, and age can provide valuable insights for nutrition experts, pediatricians, and community health workers in developing appropriate intervention programs to improve infant feeding patterns and meet their nutritional needs. Additionally, the results of clustering infant nutrition can also be used to identify groups of infants requiring special attention regarding their nutritional needs, thus minimizing the risk of malnutrition and unhealthy growth in infants.
{"title":"Clustering of Child Nutrition Status using Hierarchical Agglomerative Clustering Algorithm in Bekasi City","authors":"None Ozzi ardhiyanto, None Muhammad Salam Asyidqi, None Ajif Yunizar Pratama Yusuf, S.Si, M.Eng, None Dr. Tb. Ai Munandar, S.Kom., MT","doi":"10.58776/ijitcsa.v1i3.42","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i3.42","url":null,"abstract":"Clustering infant nutrition based on weight, height, and age is a data analysis method used to group infant nutritional status based on these characteristics. The research on clustering infant nutrition aims to analyze whether there are still many infants in the area with insufficient or excessive nutrition, and to identify groups of infants requiring special attention regarding their nutritional intake. In the analysis of infant nutrition clustering, data on weight, height, and age of infants are collected and then grouped based on similarities in body height and weight at certain ages. The method used in this research is hierarchical clustering, which can help in grouping the data. Clustering analysis can help understand how infants' feeding patterns vary based on their weight, height, and age. The results of research on clustering infant nutrition based on weight, height, and age can provide valuable insights for nutrition experts, pediatricians, and community health workers in developing appropriate intervention programs to improve infant feeding patterns and meet their nutritional needs. Additionally, the results of clustering infant nutrition can also be used to identify groups of infants requiring special attention regarding their nutritional needs, thus minimizing the risk of malnutrition and unhealthy growth in infants.","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072586","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 : 2023-09-10DOI: 10.58776/ijitcsa.v1i3.52
Syaban Tirta Maulana
This study extends the exploration of ordering apps in the context of coffee shop owners, specifically focusing on the utilization of popular apps like Grabfood and Foodpanda. With the increasing number of coffee shops adopting ordering apps, there arises a clear necessity for a coffee-focused app that can effectively address the unique demands of establishments. The objective of this study is to conduct a comprehensive review of a mobile app specifically designed to streamline the process of ordering coffee in advance, with a paramount emphasis on ensuring its reliability. By developing an app that caters to the specific needs of coffee shops, both owners and customers can benefit greatly. The app will serve as a dedicated platform, connecting coffee enthusiasts with quality coffee shops, while offering a seamless and convenient ordering experience. By providing a high-quality ordering system that encompasses the full range of customization options for beverages, the developed app is expected to significantly enhance the customer experience and ultimately boost sales for the coffee establishments listed on the platform. With a focus on reliability, the app will enable coffee shop owners to efficiently manage orders, minimize errors, and improve overall operational efficiency. Moreover, by fostering a user-friendly interface and intuitive design, the app will engage customers and encourage them to explore new coffee shops, further promoting the growth of the coffee industry. This study will contribute to the existing body of knowledge by highlighting the importance of tailored ordering apps for coffee shops and providing insights into the development and implementation of such apps. The findings will be valuable not only for coffee shop owners seeking to enhance their business operations but also for app developers looking to cater to the specific needs of the coffee industry. Ultimately, the study aims to bridge the gap between technology and the coffee business, fostering innovation and growth in the ever-evolving digital landscape.
{"title":"Development and Analysis of a Unified Mobile App for Coffee Shop Operations and Ordering Experience: A Proposal Review","authors":"Syaban Tirta Maulana","doi":"10.58776/ijitcsa.v1i3.52","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i3.52","url":null,"abstract":"This study extends the exploration of ordering apps in the context of coffee shop owners, specifically focusing on the utilization of popular apps like Grabfood and Foodpanda. With the increasing number of coffee shops adopting ordering apps, there arises a clear necessity for a coffee-focused app that can effectively address the unique demands of establishments. The objective of this study is to conduct a comprehensive review of a mobile app specifically designed to streamline the process of ordering coffee in advance, with a paramount emphasis on ensuring its reliability. By developing an app that caters to the specific needs of coffee shops, both owners and customers can benefit greatly. The app will serve as a dedicated platform, connecting coffee enthusiasts with quality coffee shops, while offering a seamless and convenient ordering experience. By providing a high-quality ordering system that encompasses the full range of customization options for beverages, the developed app is expected to significantly enhance the customer experience and ultimately boost sales for the coffee establishments listed on the platform. With a focus on reliability, the app will enable coffee shop owners to efficiently manage orders, minimize errors, and improve overall operational efficiency. Moreover, by fostering a user-friendly interface and intuitive design, the app will engage customers and encourage them to explore new coffee shops, further promoting the growth of the coffee industry. This study will contribute to the existing body of knowledge by highlighting the importance of tailored ordering apps for coffee shops and providing insights into the development and implementation of such apps. The findings will be valuable not only for coffee shop owners seeking to enhance their business operations but also for app developers looking to cater to the specific needs of the coffee industry. Ultimately, the study aims to bridge the gap between technology and the coffee business, fostering innovation and growth in the ever-evolving digital landscape.","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136072651","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 : 2023-09-10DOI: 10.58776/ijitcsa.v1i3.50
Huyanah Baghati
This paper highlights the primary business issues faced by CeilBrakes Furniture Retail Company, namely inconsistent sales across different continents. While Europe demonstrates strong sales, North America, Australia, Asia, and Africa exhibit varying levels of mediocre and low sales in terms of the quantity of products ordered. Additionally, determining appropriate discount percentages for each continent poses a challenge. To address the issue of inconsistent sales, the company aims to enhance advertising efforts, improve customer relationships, and analyze trends in continents with low sales (excluding Europe). For determining continent-specific discounts, a solution involves identifying continents with low sales and offering higher discounts accordingly. In the case of Australia, which experiences low sales, CeilBrakes should provide more significant discounts. Despite Asia and Africa already having high discounts, their sales remain low. This situation calls for considering factors beyond retail price reductions, such as cultural influences. To facilitate easier viewing and analysis, a dashboard has been developed to visualize the aforementioned issues. The dashboard enables the identification of trends and patterns, empowering the company to make informed decisions in addressing the observed problems.
{"title":"Addressing Inconsistent Sales and Determining Continent-Specific Discounts: A Case Study of CeilBrakes Furniture Retail Company","authors":"Huyanah Baghati","doi":"10.58776/ijitcsa.v1i3.50","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i3.50","url":null,"abstract":"This paper highlights the primary business issues faced by CeilBrakes Furniture Retail Company, namely inconsistent sales across different continents. While Europe demonstrates strong sales, North America, Australia, Asia, and Africa exhibit varying levels of mediocre and low sales in terms of the quantity of products ordered. Additionally, determining appropriate discount percentages for each continent poses a challenge. To address the issue of inconsistent sales, the company aims to enhance advertising efforts, improve customer relationships, and analyze trends in continents with low sales (excluding Europe). For determining continent-specific discounts, a solution involves identifying continents with low sales and offering higher discounts accordingly. In the case of Australia, which experiences low sales, CeilBrakes should provide more significant discounts. Despite Asia and Africa already having high discounts, their sales remain low. This situation calls for considering factors beyond retail price reductions, such as cultural influences. To facilitate easier viewing and analysis, a dashboard has been developed to visualize the aforementioned issues. The dashboard enables the identification of trends and patterns, empowering the company to make informed decisions in addressing the observed problems.
 
","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071892","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 : 2023-09-10DOI: 10.58776/ijitcsa.v1i3.92
Matthew Pratama
PT Ajidarma Delta Medika is a company engaged in the sale of medical devices in the city of Bekasi. This company markets a variety of medical device products. Judging from the large number of consumer requests for medical device products based on sales data for the last 3 years, predictions are needed for the best-selling product sales, in order to facilitate the company in planning the supply of stock. To find out the best-selling medical device product sales, data prediction techniques are used with the Linear Regression algorithm. By using the Linear Regression algorithm, the results are obtained to predict the best-selling sales of several products sold at PT Ajidarma Delta Medika. This research produces an accuracy value with the MAPE formula for predicting the best-selling product sales of 14.2%. This shows that the linear regression method is good at predicting sales of medical devices in the following year.
{"title":"Utilizing Linear Regression for Predicting Sales of Top-Performing Products","authors":"Matthew Pratama","doi":"10.58776/ijitcsa.v1i3.92","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i3.92","url":null,"abstract":"PT Ajidarma Delta Medika is a company engaged in the sale of medical devices in the city of Bekasi. This company markets a variety of medical device products. Judging from the large number of consumer requests for medical device products based on sales data for the last 3 years, predictions are needed for the best-selling product sales, in order to facilitate the company in planning the supply of stock. To find out the best-selling medical device product sales, data prediction techniques are used with the Linear Regression algorithm. By using the Linear Regression algorithm, the results are obtained to predict the best-selling sales of several products sold at PT Ajidarma Delta Medika. This research produces an accuracy value with the MAPE formula for predicting the best-selling product sales of 14.2%. This shows that the linear regression method is good at predicting sales of medical devices in the following year.","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136071175","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 : 2023-09-10DOI: 10.58776/ijitcsa.v1i3.40
None Amalia Nur Soliha, None Tb Ai Munandar, None Muhammad Yasir
The development of the financial system is characterized by the emergence of digital banking service applications that are widely circulated and can be accessed for free. With so many applications, users often feel confused in choosing which applications are safe to use. Before downloading an application on the Google Play Store, users will usually look at ratings and reviews first. However, the title of the best application cannot be pinned if only seen from the rating and number of downloads. This research was conducted to analyze sentiment on user reviews of digital banking service applications on the Google Play Store using the NBC (Naïve Bayes Classifier) method. Research using the NBC algorithm produced an accuracy value of 81% on the classification of Allo Bank reviews and 78% on the classification of Line Bank reviews
金融系统发展的特点是数字银行服务应用程序的出现，这些应用程序广泛流通，可以免费访问。应用程序如此之多，用户在选择哪些应用程序可以安全使用时常常感到困惑。在Google Play Store下载应用之前，用户通常会先查看应用的评分和评论。然而，如果只从评分和下载量来看，最佳应用的头衔是无法确定的。本研究使用NBC (Naïve贝叶斯分类器)方法分析了Google Play商店中数字银行服务应用程序的用户评论情绪。使用NBC算法的研究在Allo Bank评论分类上的准确率为81%，在Line Bank评论分类上的准确率为78%
{"title":"Sentiment Analysis of the Use of Digital Banking Service Applications On Google Play Store Reviews Using Naïve Bayes Method","authors":"None Amalia Nur Soliha, None Tb Ai Munandar, None Muhammad Yasir","doi":"10.58776/ijitcsa.v1i3.40","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i3.40","url":null,"abstract":"The development of the financial system is characterized by the emergence of digital banking service applications that are widely circulated and can be accessed for free. With so many applications, users often feel confused in choosing which applications are safe to use. Before downloading an application on the Google Play Store, users will usually look at ratings and reviews first. However, the title of the best application cannot be pinned if only seen from the rating and number of downloads. This research was conducted to analyze sentiment on user reviews of digital banking service applications on the Google Play Store using the NBC (Naïve Bayes Classifier) method. Research using the NBC algorithm produced an accuracy value of 81% on the classification of Allo Bank reviews and 78% on the classification of Line Bank reviews","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136073202","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 : 2023-06-01DOI: 10.58776/ijitcsa.v1i2.34
Jhon Kristian Vieri, Tb Ai Munandar, Dwi Budi Srisulistiowati, Dwipa Handayani, Achmad No’eman, Tyastuti Sri Lestari
Customers are the main goal of all business fields, without customers the company will not be able to continue or compete in the business field it is in, even though the company has brilliant products, if it does not have an increase in the number of customers the business will not be able to develop or even go bankrupt. Therefore, it is necessary to make observations and make applications that are able to predict customers who will subscribe so that companies can predict customers who will subscribe correctly without having to wait for confirmation from customers whose possibilities are still unknown. This can be very useful for any company because companies no longer need to look for random customers where it only takes time to find customers. PT. Telekomunikasi Indonesia with its product (Indihome) which is struggling to compete in the business world in the telecommunications and internet sector. Therefore research and development of this application are carried out so that PT. Indonesian telecommunications can get its customers quickly without having to spend a lot of money and effort. Making this application uses a classification method from machine learning technology based on customer historical data. The classification method has many strong algorithms for predicting variables that have more than 1 label. Some of the algorithms used are Logistic Regression, Random Forest Classifier, Support Vector Machine and Decision Tree which are provided by modules in the python programming language, namely SkLearn. The four algorithms will be tested with data balanced using the Oversampling method from the Smote algorithm to get optimal results in automatically predicting customers.
{"title":"Comparative Study of Classification Algorithms for Customer Decisions on Telecommunication Products Using Supervised Learning","authors":"Jhon Kristian Vieri, Tb Ai Munandar, Dwi Budi Srisulistiowati, Dwipa Handayani, Achmad No’eman, Tyastuti Sri Lestari","doi":"10.58776/ijitcsa.v1i2.34","DOIUrl":"https://doi.org/10.58776/ijitcsa.v1i2.34","url":null,"abstract":"Customers are the main goal of all business fields, without customers the company will not be able to continue or compete in the business field it is in, even though the company has brilliant products, if it does not have an increase in the number of customers the business will not be able to develop or even go bankrupt. Therefore, it is necessary to make observations and make applications that are able to predict customers who will subscribe so that companies can predict customers who will subscribe correctly without having to wait for confirmation from customers whose possibilities are still unknown. This can be very useful for any company because companies no longer need to look for random customers where it only takes time to find customers. PT. Telekomunikasi Indonesia with its product (Indihome) which is struggling to compete in the business world in the telecommunications and internet sector. Therefore research and development of this application are carried out so that PT. Indonesian telecommunications can get its customers quickly without having to spend a lot of money and effort. Making this application uses a classification method from machine learning technology based on customer historical data. The classification method has many strong algorithms for predicting variables that have more than 1 label. Some of the algorithms used are Logistic Regression, Random Forest Classifier, Support Vector Machine and Decision Tree which are provided by modules in the python programming language, namely SkLearn. The four algorithms will be tested with data balanced using the Oversampling method from the Smote algorithm to get optimal results in automatically predicting customers.","PeriodicalId":500526,"journal":{"name":"International Journal of Information Technology and Computer Science Applications","volume":"594 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136248771","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}
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