Pub Date : 2024-02-20DOI: 10.33480/inti.v18i2.5325
Fachri Amsury
The library produces a lot of book loan transaction data every day, but the data has not been maximally utilized due to the limited knowledge of the data, therefore the librarian cannot provide the right book recommendations for readers. The research aims to analyze book loan data by applying the Knowledge Discovery in Database (KDD) method. The research stages are observation and interviews, data selection and data preprocessing, data transformation. Data processing using the apriori algorithm association rule mining approach to provide an overview in seeing the pattern of book loan transactions. This is to provide book recommendations that match the reading interests of library members, so that it can become a reference in the layout of books on the shelf according to the results of the rules formed. The book loan transaction data used is the September period of 2023, the implementation uses the rapidminer application to find association rules. The results obtained as many as 77 rule recommendations with the highest support value of 10.7%, the highest confidence value of 100% and the highest lift value of 14. The rule formed is that if a library member borrows a book by Dale Carneige, the chances that the library member will also borrow a book by George Orwel are 100%. The results obtained can be a reference for the library to provide book recommendations to readers, maintain the availability of book stock and arrange the placement of these books on adjacent shelves.
{"title":"PENERAPAN METODE ASOSIASI PADA ANALISA POLA PEMINJAMAN BUKU PERPUSTAKAAN","authors":"Fachri Amsury","doi":"10.33480/inti.v18i2.5325","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5325","url":null,"abstract":"The library produces a lot of book loan transaction data every day, but the data has not been maximally utilized due to the limited knowledge of the data, therefore the librarian cannot provide the right book recommendations for readers. The research aims to analyze book loan data by applying the Knowledge Discovery in Database (KDD) method. The research stages are observation and interviews, data selection and data preprocessing, data transformation. Data processing using the apriori algorithm association rule mining approach to provide an overview in seeing the pattern of book loan transactions. This is to provide book recommendations that match the reading interests of library members, so that it can become a reference in the layout of books on the shelf according to the results of the rules formed. The book loan transaction data used is the September period of 2023, the implementation uses the rapidminer application to find association rules. The results obtained as many as 77 rule recommendations with the highest support value of 10.7%, the highest confidence value of 100% and the highest lift value of 14. The rule formed is that if a library member borrows a book by Dale Carneige, the chances that the library member will also borrow a book by George Orwel are 100%. The results obtained can be a reference for the library to provide book recommendations to readers, maintain the availability of book stock and arrange the placement of these books on adjacent shelves.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"726 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140446427","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 : 2024-02-20DOI: 10.33480/inti.v18i2.4995
Sri Harjunawati, Taufik Baidawi, Ida Hendarsih
To win business competition and maintain market share, companies are required to be able to adapt to market conditions and use appropriate marketing strategies. Customer Relationship Management (CRM) is a marketing strategy to create and maintain good relationships with customers thereby reducing the possibility of customers moving to competitors. The aim of the research is to analyze and develop web-based CRM in the Marketing Information System at Toko Agung, East Jakarta. The research method used in this research is the Software Development Method using the waterfall model. The result is a web-based CRM system application that can manage customer data, product orders, payments and delivery of goods to customers. The implementation of this web-based digital CRM application is expected to make it easier for customers to select the desired product, place an order, make payments and receive goods without having to come to Toko Agung. For busy customers, this system is more efficient and effective. It is hoped that this convenience will have an impact on repeat purchases and will increase sales volume, which can then increase company profits. The Customer Relationship Management (CRM) application can simplify, speed up and optimize the quality of customer service, especially at Toko Agung, East Jakarta.
为了在商业竞争中获胜并保持市场份额，企业必须能够适应市场条件并采用适当的营销策略。客户关系管理（CRM）是一种营销策略，旨在建立和维护与客户的良好关系，从而降低客户转向竞争对手的可能性。本研究旨在分析和开发东雅加达 Toko Agung 营销信息系统中基于网络的客户关系管理。本研究采用的研究方法是瀑布模型软件开发法。研究结果是一个基于网络的客户关系管理系统应用程序，它可以管理客户数据、产品订单、付款和向客户交付货物。这一基于网络的数字客户关系管理应用系统的实施预计将使客户更轻松地选择所需产品、下订单、付款和收货，而无需亲临 Toko Agung。对于繁忙的客户来说，这一系统更加高效和有效。我们希望这种便利性将对重复购买产生影响，并增加销售量，从而提高公司利润。客户关系管理 (CRM) 应用程序可以简化、加快和优化客户服务质量，特别是在东雅加达的 Toko Agung。
{"title":"PENERAPAN MODEL WATERFALL DALAM PERANCANGAN APLIKASI DIGITAL CUSTOMER RELATIONSHIP MANAGEMENT PRODUK FASHION","authors":"Sri Harjunawati, Taufik Baidawi, Ida Hendarsih","doi":"10.33480/inti.v18i2.4995","DOIUrl":"https://doi.org/10.33480/inti.v18i2.4995","url":null,"abstract":"To win business competition and maintain market share, companies are required to be able to adapt to market conditions and use appropriate marketing strategies. Customer Relationship Management (CRM) is a marketing strategy to create and maintain good relationships with customers thereby reducing the possibility of customers moving to competitors. The aim of the research is to analyze and develop web-based CRM in the Marketing Information System at Toko Agung, East Jakarta. The research method used in this research is the Software Development Method using the waterfall model. The result is a web-based CRM system application that can manage customer data, product orders, payments and delivery of goods to customers. The implementation of this web-based digital CRM application is expected to make it easier for customers to select the desired product, place an order, make payments and receive goods without having to come to Toko Agung. For busy customers, this system is more efficient and effective. It is hoped that this convenience will have an impact on repeat purchases and will increase sales volume, which can then increase company profits. The Customer Relationship Management (CRM) application can simplify, speed up and optimize the quality of customer service, especially at Toko Agung, East Jakarta.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"278 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447339","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 : 2024-02-15DOI: 10.33480/inti.v18i2.5269
E. Fitri, Siti Nurhasanah Nugraha
Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.
{"title":"OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN","authors":"E. Fitri, Siti Nurhasanah Nugraha","doi":"10.33480/inti.v18i2.5269","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5269","url":null,"abstract":"Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"253 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139834624","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 : 2024-02-15DOI: 10.33480/inti.v18i2.5269
E. Fitri, Siti Nurhasanah Nugraha
Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.
{"title":"OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN","authors":"E. Fitri, Siti Nurhasanah Nugraha","doi":"10.33480/inti.v18i2.5269","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5269","url":null,"abstract":"Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"64 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139775077","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 : 2024-02-13DOI: 10.33480/inti.v18i2.5034
Maharrandi Dwiki Saputra, Kusmayanti Solecha
Currently, property developer agencies are progressing rapidly, the supporting elements of home ownership are also facilitated by the presence of home ownership loans. In the process of selling land property, it still uses conventional methods so that customers must be present in person to find and order land plots. The sales process is still manual, causing errors in reporting and making it difficult for employees to find documents. The purpose of this research is to design a website-based land plot sales program that can facilitate the process of providing online land availability information, the land plot sales process which is equipped with a payment scheme calculator feature and a track booking feature. This research uses the Rapid application Development (RAD) model with the aim that the application can be efficient in processing time, and uses the black-box testing method in its testing. The result of this research is a land plot sales website with a payment scheme calculator feature and a track booking feature, so that the sales process becomes efficient, reporting data becomes accurate and data search becomes easier.
{"title":"PENERAPAN METODE APPLICATION DEVELOPMENT DALAM PERANCANGAN PROGRAM PENJUALAN TANAH KAVLING BERBASIS WEBSITE","authors":"Maharrandi Dwiki Saputra, Kusmayanti Solecha","doi":"10.33480/inti.v18i2.5034","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5034","url":null,"abstract":"Currently, property developer agencies are progressing rapidly, the supporting elements of home ownership are also facilitated by the presence of home ownership loans. In the process of selling land property, it still uses conventional methods so that customers must be present in person to find and order land plots. The sales process is still manual, causing errors in reporting and making it difficult for employees to find documents. The purpose of this research is to design a website-based land plot sales program that can facilitate the process of providing online land availability information, the land plot sales process which is equipped with a payment scheme calculator feature and a track booking feature. This research uses the Rapid application Development (RAD) model with the aim that the application can be efficient in processing time, and uses the black-box testing method in its testing. The result of this research is a land plot sales website with a payment scheme calculator feature and a track booking feature, so that the sales process becomes efficient, reporting data becomes accurate and data search becomes easier.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"129 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780812","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 : 2024-02-13DOI: 10.33480/inti.v18i2.4963
Sri Watmah, Dwizah Riana, Rachmawati Darma Astuti
The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.
2020 年 3 月初在印度尼西亚爆发的 CORONA 病毒引发了动荡，尤其是在商业领域。这种影响导致一些中小型企业倒闭，因此需要正确的营销策略来维持和提高客户忠诚度。本研究的目的是通过比较基于 RFM 的 K-Means 算法和 K-Medoids 算法的属性，根据 PT Megadaya Maju Selaras 的客户特征对其进行细分。使用的数据集来自 PT Megadaya Maju Selaras 客户的购买交易数据。本研究的实验使用了 CRISP-DM 模型。结果显示，K-Means 算法的戴维斯-博尔丁指数（DBI）值小于 K-Medoids，这意味着 K-Means 方法是适合本研究的方法。使用 K-Means 方法后，总体数据显示最优 k 位于群组 4，DBI 值为 0.286；大流行前的数据显示最优 k 值位于群组 2，DBI 值为 0.299；大流行后的数据显示最优 k 位于群组 5，DBI 值为 0.278。总体数据分为 4 个部分，即超级明星、典型客户、偶然客户和休眠客户。大流行之前的数据分为 2 个部分，即典型客户和超级明星。大流行后的数据则分为 5 个部分，即典型客户、偶发客户、黄金客户、休眠客户和超级明星。通过这项研究，PT Megadaya Maju Selaras 可以为每个客户群提供合适的服务。
{"title":"PENERAPAN K-MEANS DAN K-MEDOIDS BERBASIS RFM PADA SEGMENTASI PELANGGAN DI MASA PANDEMI COVID-19","authors":"Sri Watmah, Dwizah Riana, Rachmawati Darma Astuti","doi":"10.33480/inti.v18i2.4963","DOIUrl":"https://doi.org/10.33480/inti.v18i2.4963","url":null,"abstract":"The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"136 43","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139780632","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 : 2024-02-13DOI: 10.33480/inti.v18i2.4963
Sri Watmah, Dwizah Riana, Rachmawati Darma Astuti
The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.
2020 年 3 月初在印度尼西亚爆发的 CORONA 病毒引发了动荡，尤其是在商业领域。这种影响导致一些中小型企业倒闭，因此需要正确的营销策略来维持和提高客户忠诚度。本研究的目的是通过比较基于 RFM 的 K-Means 算法和 K-Medoids 算法的属性，根据 PT Megadaya Maju Selaras 的客户特征对其进行细分。使用的数据集来自 PT Megadaya Maju Selaras 客户的购买交易数据。本研究的实验使用了 CRISP-DM 模型。结果显示，K-Means 算法的戴维斯-博尔丁指数（DBI）值小于 K-Medoids，这意味着 K-Means 方法是适合本研究的方法。使用 K-Means 方法后，总体数据显示最优 k 位于群组 4，DBI 值为 0.286；大流行前的数据显示最优 k 值位于群组 2，DBI 值为 0.299；大流行后的数据显示最优 k 位于群组 5，DBI 值为 0.278。总体数据分为 4 个部分，即超级明星、典型客户、偶然客户和休眠客户。大流行之前的数据分为 2 个部分，即典型客户和超级明星。大流行后的数据则分为 5 个部分，即典型客户、偶发客户、黄金客户、休眠客户和超级明星。通过这项研究，PT Megadaya Maju Selaras 可以为每个客户群提供合适的服务。
{"title":"PENERAPAN K-MEANS DAN K-MEDOIDS BERBASIS RFM PADA SEGMENTASI PELANGGAN DI MASA PANDEMI COVID-19","authors":"Sri Watmah, Dwizah Riana, Rachmawati Darma Astuti","doi":"10.33480/inti.v18i2.4963","DOIUrl":"https://doi.org/10.33480/inti.v18i2.4963","url":null,"abstract":"The outbreak of the CORONA virus in Indonesia in early March 2020 has created unrest, especially in the business world. The impact caused some small and medium-sized businesses to go out of business, so the right marketing strategy is needed to maintain and increase customer loyalty. The purpose of this research is to segment PT Megadaya Maju Selaras' customers based on their characteristics by comparing the RFM-based K-Means and K-Medoids algorithms as attributes in the research. The dataset used comes from the purchase transaction data of PT Megadaya Maju Selaras customers. Experiments in this study used the CRISP-DM model. The results showed that the K-Means algorithm has a smaller Davies Bouldin Index (DBI) value than K-Medoids, meaning that the K-Means method is the right method for this research. With the K-Means method, the overall data shows the optimal k in cluster 4 with a DBI value of 0.286, the data before the pandemic shows the optimal k value in cluster 2 with a DBI value of 0.299, after the pandemic shows the optimal k in cluster 5 with a DBI value of 0.278. The overall data is divided into 4 segments, namely superstar, typical customer, occational customer and dormant customer. Data before the pandemic is divided into 2 segments, namely typical customers and superstars. Meanwhile, after the pandemic is divided into 5 segments, namely typical customer, occational customer, golden customer, dormant customer and superstar. With this research, PT Megadaya Maju Selaras can provide the right service for each customer group.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"30 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840590","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 : 2024-02-13DOI: 10.33480/inti.v18i2.5034
Maharrandi Dwiki Saputra, Kusmayanti Solecha
Currently, property developer agencies are progressing rapidly, the supporting elements of home ownership are also facilitated by the presence of home ownership loans. In the process of selling land property, it still uses conventional methods so that customers must be present in person to find and order land plots. The sales process is still manual, causing errors in reporting and making it difficult for employees to find documents. The purpose of this research is to design a website-based land plot sales program that can facilitate the process of providing online land availability information, the land plot sales process which is equipped with a payment scheme calculator feature and a track booking feature. This research uses the Rapid application Development (RAD) model with the aim that the application can be efficient in processing time, and uses the black-box testing method in its testing. The result of this research is a land plot sales website with a payment scheme calculator feature and a track booking feature, so that the sales process becomes efficient, reporting data becomes accurate and data search becomes easier.
{"title":"PENERAPAN METODE APPLICATION DEVELOPMENT DALAM PERANCANGAN PROGRAM PENJUALAN TANAH KAVLING BERBASIS WEBSITE","authors":"Maharrandi Dwiki Saputra, Kusmayanti Solecha","doi":"10.33480/inti.v18i2.5034","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5034","url":null,"abstract":"Currently, property developer agencies are progressing rapidly, the supporting elements of home ownership are also facilitated by the presence of home ownership loans. In the process of selling land property, it still uses conventional methods so that customers must be present in person to find and order land plots. The sales process is still manual, causing errors in reporting and making it difficult for employees to find documents. The purpose of this research is to design a website-based land plot sales program that can facilitate the process of providing online land availability information, the land plot sales process which is equipped with a payment scheme calculator feature and a track booking feature. This research uses the Rapid application Development (RAD) model with the aim that the application can be efficient in processing time, and uses the black-box testing method in its testing. The result of this research is a land plot sales website with a payment scheme calculator feature and a track booking feature, so that the sales process becomes efficient, reporting data becomes accurate and data search becomes easier.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"193 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139840536","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 : 2024-02-07DOI: 10.33480/inti.v18i2.5125
S. Nurlela, M. Ilham, S. Supriatna
Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536> F table 2.81 and the significance value is 0.0000011 <0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.
{"title":"ANALISIS KEPUASAN PENGGUNA WEBSITE ORLANSOFT MENGGUNAKAN METODE WEBQUAL 4.0","authors":"S. Nurlela, M. Ilham, S. Supriatna","doi":"10.33480/inti.v18i2.5125","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5125","url":null,"abstract":"Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536> F table 2.81 and the significance value is 0.0000011 <0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"14 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139795397","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 : 2024-02-07DOI: 10.33480/inti.v18i2.5125
S. Nurlela, M. Ilham, S. Supriatna
Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536> F table 2.81 and the significance value is 0.0000011 <0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.
{"title":"ANALISIS KEPUASAN PENGGUNA WEBSITE ORLANSOFT MENGGUNAKAN METODE WEBQUAL 4.0","authors":"S. Nurlela, M. Ilham, S. Supriatna","doi":"10.33480/inti.v18i2.5125","DOIUrl":"https://doi.org/10.33480/inti.v18i2.5125","url":null,"abstract":"Orlansoft website is an ERP (Enterprise Resource Planning) solution that unifies business operations into a single system that integrates and optimizes business processes and provides real-time critical information for all entities and office locations from a single source. PT Multifortuna Sinardelta, in its business processes, uses the Orlansoft website. The quality of the website greatly affects the level of user satisfaction itself. The higher the quality of a website, the more users will access the website. So far, there is no appropriate method and way to measure user quality of the Orlansoft website. This research examines the extent of user satisfaction in using website services. The Webqual 4.0 method has been successfully applied to similar research with website quality measurement and helps to understand the factors that affect user satisfaction, with three measurement categories including usability, information quality and service interaction quality. From the test results, the calculated F value = 11.536 with a significance of 0.0000011. In this study, the calculated F value is 11.536> F table 2.81 and the significance value is 0.0000011 <0.01, thus it can be concluded that variables X1 (usability quality), X2 (information quality), and X3 (service interaction quality) have a significant and positive effect on variable Y (user satisfaction). This is evidenced by the results of the analysis which gives positive results for each variable on the dependent variable.","PeriodicalId":197142,"journal":{"name":"INTI Nusa Mandiri","volume":"78 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139855449","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：