Pub Date : 2024-02-02DOI: 10.30787/restia.v2i1.1364
Sadri Talib, Sakinah Sudin, Muhammad Dzikrullah Suratin
Leaves are a very important plant component because they play an important role in differentiating plant species, including clove plants. Currently, the identification of clove species, namely Afo, Siputih, and Zanzibar, relies on manual observation of the characteristics of the fruit and flowers, which can take a long time, especially considering the long fruiting period of the clove plant. To answer this problem, the authors conducted a study to classify the three types of clove leaves based on the characteristics and texture of the Gray gray-level co-occurrence Matrix (GLCM), which includes four parameters: Contrast, Correlation, Energy, and Homogeneity. The Support Vector Machine (SVM) classification algorithm processes extracted feature values and accurately class leaves. This study achieves the highest accuracy of 56.67% on an image size of 250x250 pixels and 48.33% on an image size of 150x150 pixels using 150 training data and 60 test data. These results indicate the potential of automatic leaf classification in efficiently identifying clove plant species. Keywords : Clove, Leaf, Processing, Texture, SVM
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Pub Date : 2024-02-02DOI: 10.30787/restia.v2i1.1364
Sadri Talib, Sakinah Sudin, Muhammad Dzikrullah Suratin
Leaves are a very important plant component because they play an important role in differentiating plant species, including clove plants. Currently, the identification of clove species, namely Afo, Siputih, and Zanzibar, relies on manual observation of the characteristics of the fruit and flowers, which can take a long time, especially considering the long fruiting period of the clove plant. To answer this problem, the authors conducted a study to classify the three types of clove leaves based on the characteristics and texture of the Gray gray-level co-occurrence Matrix (GLCM), which includes four parameters: Contrast, Correlation, Energy, and Homogeneity. The Support Vector Machine (SVM) classification algorithm processes extracted feature values and accurately class leaves. This study achieves the highest accuracy of 56.67% on an image size of 250x250 pixels and 48.33% on an image size of 150x150 pixels using 150 training data and 60 test data. These results indicate the potential of automatic leaf classification in efficiently identifying clove plant species. Keywords : Clove, Leaf, Processing, Texture, SVM
{"title":"PENERAPAN METODE SUPPORT VECTOR MACHINE (SVM) PADA KLASIFIKASI JENIS CENGKEH BERDASARKAN FITUR TEKSTUR DAUN","authors":"Sadri Talib, Sakinah Sudin, Muhammad Dzikrullah Suratin","doi":"10.30787/restia.v2i1.1364","DOIUrl":"https://doi.org/10.30787/restia.v2i1.1364","url":null,"abstract":"Leaves are a very important plant component because they play an important role in differentiating plant species, including clove plants. Currently, the identification of clove species, namely Afo, Siputih, and Zanzibar, relies on manual observation of the characteristics of the fruit and flowers, which can take a long time, especially considering the long fruiting period of the clove plant. To answer this problem, the authors conducted a study to classify the three types of clove leaves based on the characteristics and texture of the Gray gray-level co-occurrence Matrix (GLCM), which includes four parameters: Contrast, Correlation, Energy, and Homogeneity. \u0000The Support Vector Machine (SVM) classification algorithm processes extracted feature values and accurately class leaves. This study achieves the highest accuracy of 56.67% on an image size of 250x250 pixels and 48.33% on an image size of 150x150 pixels using 150 training data and 60 test data. These results indicate the potential of automatic leaf classification in efficiently identifying clove plant species. \u0000Keywords : Clove, Leaf, Processing, Texture, SVM \u0000 ","PeriodicalId":517273,"journal":{"name":"Jurnal Riset Sistem dan Teknologi Informasi","volume":"35 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893421","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}
Minimarkets are shops that sell daily necessities. This mini market is located on Jalan Station Kauman, Krikilan Hamlet, Dawungan Village, Masaran District, Sragen Regency, Central Java Province. In developing inter-company leaders, it is difficult to make decisions about the location of new branches, because there are many criteria such as: strategic location, distance and population to facilitate decision making. The purpose of this study is that researchers assist company leaders in choosing the best new minimarket branch locations using the SAW algorithm method. This method was chosen because it is able to carry out the process of ranking and weighting the best alternatives by applying many criteria. The technique used in this research is observation (observation), interview (interview), and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. This application is made using the PHP programming language and the database uses MySQL. The final result is a report on the best location data. System testing is done by testing the functionality and testing the validity of the obtained results are 100% valid.
小型市场是出售日常必需品的商店。该小型市场位于中爪哇省 Sragen 县 Masaran 区 Dawungan 村 Krikilan 小镇 Jalan Station Kauman。在发展公司间领导的过程中，很难对新分支机构的选址做出决策，因为有许多标准，如：战略位置、距离和人口，以方便决策。本研究的目的是，研究人员利用 SAW 算法方法，协助公司领导选择最佳的新微型市场分店位置。之所以选择这种方法，是因为它能够通过应用多种标准对最佳备选方案进行排序和加权。本研究采用的技术包括观察（观察）、访谈（访谈）和文献研究。在设计本系统时，使用了上下文图、HIPO、DAD、表间关系和数据库设计。该应用程序使用 PHP 编程语言，数据库使用 MySQL。最终结果是一份关于最佳位置数据的报告。系统测试是通过测试功能和测试所得结果的有效性来完成的，测试结果 100%有效。
{"title":"SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI CABANG MINIMARKET TERBAIK MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING BERBASIS WEB","authors":"Aisyah Mutia Dawis, Muqorobin Muqorobin, Moch Bagoes Pakarti","doi":"10.30787/restia.v2i1.1207","DOIUrl":"https://doi.org/10.30787/restia.v2i1.1207","url":null,"abstract":"Minimarkets are shops that sell daily necessities. This mini market is located on Jalan Station Kauman, Krikilan Hamlet, Dawungan Village, Masaran District, Sragen Regency, Central Java Province. In developing inter-company leaders, it is difficult to make decisions about the location of new branches, because there are many criteria such as: strategic location, distance and population to facilitate decision making. The purpose of this study is that researchers assist company leaders in choosing the best new minimarket branch locations using the SAW algorithm method. This method was chosen because it is able to carry out the process of ranking and weighting the best alternatives by applying many criteria. The technique used in this research is observation (observation), interview (interview), and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. This application is made using the PHP programming language and the database uses MySQL. The final result is a report on the best location data. System testing is done by testing the functionality and testing the validity of the obtained results are 100% valid.","PeriodicalId":517273,"journal":{"name":"Jurnal Riset Sistem dan Teknologi Informasi","volume":"5 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139893523","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 : 2024-02-02DOI: 10.30787/restia.v2i1.1327
Kamarudin
Tourism is an activity that is liked by many people, even tourism is one of the important needs, especially regarding socio-economic activities which are seen as having good prospects in the future. In South Kalimantan, especially the city of Banjarmasin, there are many good tourist attractions such as the historical mosque of Sultan Suriansyah, Siring Park, Banjarmaisn City, and culinary tours of Arab villages. Of the tourist attractions that have been mentioned, tourists are still confused in determining tourist attractions because there are many places and a lack of information about tourist attractions in South Kalimantan. From this description, a Decision Support System (DSS) or Decision Support System (DSS) was created to determine tourist attractions in South Kalimantan called the Get Tour application. In addition to displaying information about tourist attractions, this application also displays information on tourist attractions in the form of a map. The results of calculations in the application are in accordance with the formula and expected results based on several criteria, namely distance, parking area, the first special criteria, the second special criteria and the third special criteria using the Simple Additive Weighting (SAW) method.
旅游是很多人都喜欢的一项活动，甚至旅游业也是人们的重要需求之一，尤其是在社会经济活动方面，因为这些活动被认为在未来具有良好的前景。在南加里曼丹，尤其是班加罗尔马辛市，有许多很好的旅游景点，如历史悠久的苏里扬苏丹清真寺、西林公园、班加罗尔马辛市和阿拉伯村庄美食之旅。在上述旅游景点中，由于南加里曼丹旅游景点众多且信息匮乏，游客在确定旅游景点时仍然感到困惑。根据这一描述，我们创建了一个决策支持系统（DSS）或决策支持系统（DSS）来确定南加里曼丹的旅游景点，称为 Get Tour 应用程序。除了显示旅游景点信息外，该应用程序还以地图的形式显示旅游景点信息。该应用程序的计算结果符合基于几项标准的公式和预期结果，即距离、停车场面积、第一项特殊标准、第二项特殊标准和使用简单加权法（SAW）的第三项特殊标准。
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Pub Date : 2024-02-02DOI: 10.30787/restia.v2i1.1327
Kamarudin
Tourism is an activity that is liked by many people, even tourism is one of the important needs, especially regarding socio-economic activities which are seen as having good prospects in the future. In South Kalimantan, especially the city of Banjarmasin, there are many good tourist attractions such as the historical mosque of Sultan Suriansyah, Siring Park, Banjarmaisn City, and culinary tours of Arab villages. Of the tourist attractions that have been mentioned, tourists are still confused in determining tourist attractions because there are many places and a lack of information about tourist attractions in South Kalimantan. From this description, a Decision Support System (DSS) or Decision Support System (DSS) was created to determine tourist attractions in South Kalimantan called the Get Tour application. In addition to displaying information about tourist attractions, this application also displays information on tourist attractions in the form of a map. The results of calculations in the application are in accordance with the formula and expected results based on several criteria, namely distance, parking area, the first special criteria, the second special criteria and the third special criteria using the Simple Additive Weighting (SAW) method.
旅游是很多人都喜欢的一项活动，甚至旅游业也是人们的重要需求之一，尤其是在社会经济活动方面，因为这些活动被认为在未来具有良好的前景。在南加里曼丹，尤其是班加罗尔马辛市，有许多很好的旅游景点，如历史悠久的苏里扬苏丹清真寺、西林公园、班加罗尔马辛市和阿拉伯村庄美食之旅。在上述旅游景点中，由于南加里曼丹旅游景点众多且信息匮乏，游客在确定旅游景点时仍然感到困惑。根据这一描述，我们创建了一个决策支持系统（DSS）或决策支持系统（DSS）来确定南加里曼丹的旅游景点，称为 Get Tour 应用程序。除了显示旅游景点信息外，该应用程序还以地图的形式显示旅游景点信息。该应用程序的计算结果符合基于几项标准的公式和预期结果，即距离、停车场面积、第一项特殊标准、第二项特殊标准和使用简单加权法（SAW）的第三项特殊标准。
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Minimarkets are shops that sell daily necessities. This mini market is located on Jalan Station Kauman, Krikilan Hamlet, Dawungan Village, Masaran District, Sragen Regency, Central Java Province. In developing inter-company leaders, it is difficult to make decisions about the location of new branches, because there are many criteria such as: strategic location, distance and population to facilitate decision making. The purpose of this study is that researchers assist company leaders in choosing the best new minimarket branch locations using the SAW algorithm method. This method was chosen because it is able to carry out the process of ranking and weighting the best alternatives by applying many criteria. The technique used in this research is observation (observation), interview (interview), and literature study. In the design of this system is made with Context Diagram, HIPO, DAD, relations between tables and database design. This application is made using the PHP programming language and the database uses MySQL. The final result is a report on the best location data. System testing is done by testing the functionality and testing the validity of the obtained results are 100% valid.
小型市场是出售日常必需品的商店。该小型市场位于中爪哇省 Sragen 县 Masaran 区 Dawungan 村 Krikilan 小镇 Jalan Station Kauman。在发展公司间领导的过程中，很难对新分支机构的选址做出决策，因为有许多标准，如：战略位置、距离和人口，以方便决策。本研究的目的是，研究人员利用 SAW 算法方法，协助公司领导选择最佳的新微型市场分店位置。之所以选择这种方法，是因为它能够通过应用多种标准对最佳备选方案进行排序和加权。本研究采用的技术包括观察（观察）、访谈（访谈）和文献研究。在设计本系统时，使用了上下文图、HIPO、DAD、表间关系和数据库设计。该应用程序使用 PHP 编程语言，数据库使用 MySQL。最终结果是一份关于最佳位置数据的报告。系统测试是通过测试功能和测试所得结果的有效性来完成的，测试结果 100%有效。
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