Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.
{"title":"PREDIKSI CURAH HUJAN DI KABUPATEN BADUNG, BALI MENGGUNAKAN METODE LONG SHORT-TERM MEMORY","authors":"Brando Dharma Saputra, Lely Hiryanto, Teny Handhayani","doi":"10.24912/jiksi.v11i2.26002","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26002","url":null,"abstract":"Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82613644","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26037
R. Chandra, Ery Dewayani
Technology that is now growing rapidly is widely used by the community for various activities, such as administrative activities. Of the many administrative activities that exist, the library is one of them that requires information technology in data management. The library is a building of an institution or agency that has the task of providing, compiling, and managing various collections of books. SMK Negeri 1 Tanjungpinang is one of the schools that still processes library data manually, which still does not utilize existing information technology. Data collection that is still done manually will cause problems, where data collection is slow and inefficient. Therefore, the design of this library information system will assist the administrative activities of SMK Negeri 1 Tanjungpinang by having a library data management website. This is useful for librarians in facilitating all data management processes, including making membership cards, the process of making reports, making and collecting data on borrowing and returning books. Making a web-based library information system at SMK Negeri 1 Tanjungpinang using the laravel framework with mysql database.
目前正在迅速发展的技术被社区广泛用于各种活动，例如管理活动。在现有的许多管理活动中，图书馆是需要信息技术进行数据管理的活动之一。图书馆是一个机构或机构的建筑，其任务是提供、编辑和管理各种藏书。SMK Negeri 1 Tanjungpinang是仍然手工处理图书馆数据的学校之一，仍然没有利用现有的信息技术。仍然手动进行的数据收集将导致问题，其中数据收集缓慢且效率低下。因此，这个图书馆信息系统的设计将通过一个图书馆数据管理网站来协助SMK Negeri 1 Tanjungpinang的行政活动。这对图书馆人员方便所有数据管理过程非常有用，包括制作会员卡、制作报告、制作和收集借书和还书的数据。使用laravel框架和mysql数据库，在丹中槟榔村SMK Negeri 1建立了一个基于web的图书馆信息系统。
{"title":"Rancangan Sistem Informasi Perpustakaan Berbasis Web Pada SMK Negeri 1 Tanjungpinang","authors":"R. Chandra, Ery Dewayani","doi":"10.24912/jiksi.v11i2.26037","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26037","url":null,"abstract":"Technology that is now growing rapidly is widely used by the community for various activities, such as administrative activities. Of the many administrative activities that exist, the library is one of them that requires information technology in data management. The library is a building of an institution or agency that has the task of providing, compiling, and managing various collections of books. SMK Negeri 1 Tanjungpinang is one of the schools that still processes library data manually, which still does not utilize existing information technology. Data collection that is still done manually will cause problems, where data collection is slow and inefficient. Therefore, the design of this library information system will assist the administrative activities of SMK Negeri 1 Tanjungpinang by having a library data management website. This is useful for librarians in facilitating all data management processes, including making membership cards, the process of making reports, making and collecting data on borrowing and returning books. Making a web-based library information system at SMK Negeri 1 Tanjungpinang using the laravel framework with mysql database.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89850559","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}
Humans have needs that must be met, one of which is the need for food, but food prices often change. Factors that affect price changes occur because the amount of demand is high while the supply is small. Making predictions about price changes will be very helpful to get an idea of the pattern of price changes. Therefore making predictions from price patterns is useful for providing information to the public. Predictions regarding price changes can be made using many methods. Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) are several methods that can be used to predict time series data, these two methods can provide an overview of the predictions made. The results of the study show that both algorithms have good results in terms of the the evaluation value. The evaluation results showed no significant difference between the two algorithms. The evaluation value of the rice commodity showed that ELM tended to be better with MAE values of 6,721, MAPE 0.061%, MSE 115,281, RMSE 10,737 and CV 3,699%, while LSTM with MAE 31,707, MAPE 0.286%, MSE 1927.633, RMSE 43.905 and CV 3.655%. However, for other commodities, LSTM can produce a better evaluation value.
{"title":"PERBANDINGAN LSTM DAN ELM DALAM MEMPREDIKSI HARGA PANGAN KOTA TASIKMALAYA","authors":"Andry Winata, Manatap Dolok Lauro, Teny Handhayani","doi":"10.24912/jiksi.v11i2.26015","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26015","url":null,"abstract":"Humans have needs that must be met, one of which is the need for food, but food prices often change. Factors that affect price changes occur because the amount of demand is high while the supply is small. Making predictions about price changes will be very helpful to get an idea of the pattern of price changes. Therefore making predictions from price patterns is useful for providing information to the public. Predictions regarding price changes can be made using many methods. Long Short-Term Memory (LSTM) and Extreme Learning Machine (ELM) are several methods that can be used to predict time series data, these two methods can provide an overview of the predictions made. The results of the study show that both algorithms have good results in terms of the the evaluation value. The evaluation results showed no significant difference between the two algorithms. The evaluation value of the rice commodity showed that ELM tended to be better with MAE values of 6,721, MAPE 0.061%, MSE 115,281, RMSE 10,737 and CV 3,699%, while LSTM with MAE 31,707, MAPE 0.286%, MSE 1927.633, RMSE 43.905 and CV 3.655%. However, for other commodities, LSTM can produce a better evaluation value.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"282 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86739738","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26016
None Calvin, None Hugeng, Tri Sutrisno
A decision support system (DSS) for selecting coffee shops is a technological solution that can help coffee enthusiasts find coffee shops that suit their preferences. The purpose of this research is to design a decision support system to facilitate the selection of coffee shops. The methodology used in this research is the waterfall method which consists of needs analysis, design, implementation, and system testing. Needs analysis is carried out by conducting surveys and interviews with respondents who are coffee consumers. The results of the needs analysis will be used as the basis for system design. The system design is carried out using the ERD and DFD models, as well as the selection of the right programming language and database. Implementation is done by developing the system according to the design that has been made. Finally, system testing is carried out to ensure that the system can run properly and according to user requirements. It is hoped that the results of this study can provide benefits for coffee fans in choosing coffee shops that suit their preferences.
{"title":"PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMUDAHKAN PEMILIHAN KEDAI KOPI","authors":"None Calvin, None Hugeng, Tri Sutrisno","doi":"10.24912/jiksi.v11i2.26016","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26016","url":null,"abstract":"A decision support system (DSS) for selecting coffee shops is a technological solution that can help coffee enthusiasts find coffee shops that suit their preferences. The purpose of this research is to design a decision support system to facilitate the selection of coffee shops. The methodology used in this research is the waterfall method which consists of needs analysis, design, implementation, and system testing. Needs analysis is carried out by conducting surveys and interviews with respondents who are coffee consumers. The results of the needs analysis will be used as the basis for system design. The system design is carried out using the ERD and DFD models, as well as the selection of the right programming language and database. Implementation is done by developing the system according to the design that has been made. Finally, system testing is carried out to ensure that the system can run properly and according to user requirements. It is hoped that the results of this study can provide benefits for coffee fans in choosing coffee shops that suit their preferences.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135570379","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26034
M. Pertiwi, Tony, Manatap Dolok Lauro
Skripsi dashboard to monitor incoming and outgoing stock, sales volume, and analyzing the best-selling products of the month at PT Becek Grup Indonesia. The dashboard is well designed to provide accurate and credible information about stock items, so that PT Becek Grup Indonesia can make the right and optimal decisions. The methodology used in this study was interview, observation, and data analysis through the prototyping method. The results of the research show that stocks go in and out and identify a product accurately and provide effective information. In addition, this dashboard has a visualization designed using Power BI so that users can easily use it and can be accessed via mobile devices. Then the visualized data is expected to improve service quality at PT Becek Grup Indonesia and provide significant benefits for warehouse management. Then the data that has been visualized can be used to make it easier for users to find out the details and information they want to know
Skripsi仪表板监控进货和出货库存，销售量，并分析当月PT Becek group Indonesia的最畅销产品。仪表板设计得很好，可以提供关于库存项目的准确和可靠的信息，以便PT Becek group Indonesia可以做出正确和最佳的决策。本研究采用访谈法、观察法和原型法进行数据分析。研究结果表明，库存的进出能够准确识别产品，并提供有效的信息。此外，该仪表板具有使用Power BI设计的可视化，以便用户可以轻松使用它，并可以通过移动设备访问它。然后，可视化数据有望提高PT Becek group Indonesia的服务质量，并为仓库管理提供显著的好处。然后可以使用可视化的数据，使用户更容易找到他们想要知道的细节和信息
{"title":"VISUALISASI DATA STOK BARANG PADA PT BECEK GRUP INDONESIA","authors":"M. Pertiwi, Tony, Manatap Dolok Lauro","doi":"10.24912/jiksi.v11i2.26034","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26034","url":null,"abstract":"Skripsi dashboard to monitor incoming and outgoing stock, sales volume, and analyzing the best-selling products of the month at PT Becek Grup Indonesia. The dashboard is well designed to provide accurate and credible information about stock items, so that PT Becek Grup Indonesia can make the right and optimal decisions. The methodology used in this study was interview, observation, and data analysis through the prototyping method. The results of the research show that stocks go in and out and identify a product accurately and provide effective information. In addition, this dashboard has a visualization designed using Power BI so that users can easily use it and can be accessed via mobile devices. Then the visualized data is expected to improve service quality at PT Becek Grup Indonesia and provide significant benefits for warehouse management. Then the data that has been visualized can be used to make it easier for users to find out the details and information they want to know","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76783657","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26017
Ricky Giovanni Putra Tanjaya, Dedi Trisnawarman
PT Wanapotensi Nusa is a company engaged in the processing of logs. Recording of production carried out by PT Wanapoten Nusa when this journal was made was still using the manual method, making it difficult for the company to analyze and also make decisions on production data. Against the background of these problems, a production information system was proposed to be able to store and display data. production so that it is expected to help companies to be able to facilitate access to production data. To make it easier for companies to make decisions about production data in this journal, a production recommendation method will also be discussed using the Economic Production Quantity (EPQ) which is expected to provide input in the form of production suggestions for the next month.
{"title":"SISTEM INFORMASI PRODUKSI PADA PT. WANAPOTENSI NUSA BERBASIS WEB","authors":"Ricky Giovanni Putra Tanjaya, Dedi Trisnawarman","doi":"10.24912/jiksi.v11i2.26017","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26017","url":null,"abstract":"PT Wanapotensi Nusa is a company engaged in the processing of logs. Recording of production carried out by PT Wanapoten Nusa when this journal was made was still using the manual method, making it difficult for the company to analyze and also make decisions on production data. Against the background of these problems, a production information system was proposed to be able to store and display data. production so that it is expected to help companies to be able to facilitate access to production data. To make it easier for companies to make decisions about production data in this journal, a production recommendation method will also be discussed using the Economic Production Quantity (EPQ) which is expected to provide input in the form of production suggestions for the next month.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91118659","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}
Long Short-Term Memory is the development of an artificial neural network that has the ability to overcome the vanishing gradient problem, and makes it possible to remember long-term information, and understand temporal patterns in time series data, so that LSTM has good performance in predicting food prices [1]. In Indonesia, especially in Surabaya, food prices are often unstable. Fluctuations in food prices can be caused by many factors such as weather, growing season and production. Under these conditions, this research was conducted to predict future food prices. The purpose of this study is to apply the LSTM method in predicting food prices so that it can provide maximum results and can be used by the community in making good decisions. In this study the dataset used included 5 types of food, namely rice, beef, chicken eggs, granulated sugar, and cooking oil. The dataset was obtained from the website of the National Strategic Food Price Information Center (PIHPS Nasional, https://www.bi.go.id/hargapangan). Predictive results are evaluated with RMSE and MAE. RMSE and MAE values of 5 types of food, namely rice 32 and 27, beef 229 and 125, chicken eggs 319 and 213, cooking oil 424 and 215, granulated sugar 30 and 18.
{"title":"PREDIKSI HARGA PANGAN DI PASAR TRADISIONAL KOTA SURABAYA DENGAN METODE LSTM","authors":"Teddy Ericko, Manatap Dolok Lauro, Teny Handhayani","doi":"10.24912/jiksi.v11i2.26012","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26012","url":null,"abstract":"Long Short-Term Memory is the development of an artificial neural network that has the ability to overcome the vanishing gradient problem, and makes it possible to remember long-term information, and understand temporal patterns in time series data, so that LSTM has good performance in predicting food prices [1]. In Indonesia, especially in Surabaya, food prices are often unstable. Fluctuations in food prices can be caused by many factors such as weather, growing season and production. Under these conditions, this research was conducted to predict future food prices. The purpose of this study is to apply the LSTM method in predicting food prices so that it can provide maximum results and can be used by the community in making good decisions. In this study the dataset used included 5 types of food, namely rice, beef, chicken eggs, granulated sugar, and cooking oil. The dataset was obtained from the website of the National Strategic Food Price Information Center (PIHPS Nasional, https://www.bi.go.id/hargapangan). Predictive results are evaluated with RMSE and MAE. RMSE and MAE values of 5 types of food, namely rice 32 and 27, beef 229 and 125, chicken eggs 319 and 213, cooking oil 424 and 215, granulated sugar 30 and 18.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73545985","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26023
Veronika, Dedi Trisnawarman
Tourism is an important sector in building the Indonesian economy. Jakarta as the nation's capital has a variety of tourist destinations that can be an attraction for tourists to visit the city. Due to diverse destinations, tourists find it difficult to choose destinations to visit according their preferences. Therefore, a decision support system is needed in recommending tourist destinations in terms of the average 3 stars hotel price, the number of nearby restaurants, the distance from airport to tourism and destinations rating. This study aims to develop a decision support system based on Fuzzy Tahani in recommending Jakarta tourist destinations. The decision support system development method uses the stages of Rapid Application Development,, which consist of requirement analysis, design, implementation and testing. The results of this study are designing a decision support system that applies the Fuzzy Tahani algorithm method with an interface in the form of a web page that helps tourists get recommendations based on the criteria entered. For the testing there are two tests, fuzzy algorithm testing by comparing fuzzy algorithm calculations manually with calculations from the system and Black Box testing to test the suitability of the functionality of the decision support system.
{"title":"PENERAPAN FUZZY TAHANI PADA REKOMENDASI OBJEK WISATA DI JAKARTA","authors":"Veronika, Dedi Trisnawarman","doi":"10.24912/jiksi.v11i2.26023","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26023","url":null,"abstract":"Tourism is an important sector in building the Indonesian economy. Jakarta as the nation's capital has a variety of tourist destinations that can be an attraction for tourists to visit the city. Due to diverse destinations, tourists find it difficult to choose destinations to visit according their preferences. Therefore, a decision support system is needed in recommending tourist destinations in terms of the average 3 stars hotel price, the number of nearby restaurants, the distance from airport to tourism and destinations rating. This study aims to develop a decision support system based on Fuzzy Tahani in recommending Jakarta tourist destinations. The decision support system development method uses the stages of Rapid Application Development,, which consist of requirement analysis, design, implementation and testing. The results of this study are designing a decision support system that applies the Fuzzy Tahani algorithm method with an interface in the form of a web page that helps tourists get recommendations based on the criteria entered. For the testing there are two tests, fuzzy algorithm testing by comparing fuzzy algorithm calculations manually with calculations from the system and Black Box testing to test the suitability of the functionality of the decision support system.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"137 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79734393","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26003
Sudono Widjaja, Bagus Mulyawan, Novario Jaya Perdana
In 2019, pandemic hits the world and the world was forced to change their way to do normal activities, including schools to do school activities from home. Elementary School children don’t yet know how to use devices properly therefore their parents have to help and guide them for them to understand the tasks given by school. The Information system for monitoring students expected able to help teachers and parents to coordinate and evaluate their student’s data. This system is made using two platforms, namely web-based whose main feature is for teacher to enter the students data, and mobile-based to view data that has been enetered from the web.
{"title":"SISTEM INFORMASI MONITORING MURID OLEH GURU DENGAN ORANGTUA BERBASIS WEB DAN MOBILE","authors":"Sudono Widjaja, Bagus Mulyawan, Novario Jaya Perdana","doi":"10.24912/jiksi.v11i2.26003","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26003","url":null,"abstract":"In 2019, pandemic hits the world and the world was forced to change their way to do normal activities, including schools to do school activities from home. Elementary School children don’t yet know how to use devices properly therefore their parents have to help and guide them for them to understand the tasks given by school. The Information system for monitoring students expected able to help teachers and parents to coordinate and evaluate their student’s data. This system is made using two platforms, namely web-based whose main feature is for teacher to enter the students data, and mobile-based to view data that has been enetered from the web.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74505639","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 : 2023-08-23DOI: 10.24912/jiksi.v11i2.26026
Kristopher Halim, Dyah Erny Herwindiati, T. Sutrisno
To predict the weather requires a lot of weather data variables, many influencing factors such as the large amount of data. Because this makes prediction accuracy and speed less accurate. So with that made a way to make predictive model research using several data mining techniques. The data to be used in this study were obtained from timeanddate.com, timeanddate.com is a site that provides data and information about daily weather conditions. The data used ranges from December 2021 to August 2022. The research aims to obtain weather classification patterns using the data mining classification algorithm, namely the C4.5 algorithm. The results of testing the C4.5 algorithm using the php website and program get an accuracy of 82%.
{"title":"PENERAPAN METODE DECISION TREE UNTUK PRAKIRAAN CUACA KOTA BEKASI","authors":"Kristopher Halim, Dyah Erny Herwindiati, T. Sutrisno","doi":"10.24912/jiksi.v11i2.26026","DOIUrl":"https://doi.org/10.24912/jiksi.v11i2.26026","url":null,"abstract":"To predict the weather requires a lot of weather data variables, many influencing factors such as the large amount of data. Because this makes prediction accuracy and speed less accurate. So with that made a way to make predictive model research using several data mining techniques. The data to be used in this study were obtained from timeanddate.com, timeanddate.com is a site that provides data and information about daily weather conditions. The data used ranges from December 2021 to August 2022. The research aims to obtain weather classification patterns using the data mining classification algorithm, namely the C4.5 algorithm. The results of testing the C4.5 algorithm using the php website and program get an accuracy of 82%.","PeriodicalId":34309,"journal":{"name":"Jurnal Sisfokom","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81386356","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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