Jurnal Sisfokom最新文献_第10页

PREDIKSI CURAH HUJAN DI KABUPATEN BADUNG, BALI MENGGUNAKAN METODE LONG SHORT-TERM MEMORY 对巴东地区降雨的预测使用了短期记忆的方法

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26002

Brando Dharma Saputra, Lely Hiryanto, Teny Handhayani

{"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}

引用次数: 0

Rancangan Sistem Informasi Perpustakaan Berbasis Web Pada SMK Negeri 1 Tanjungpinang

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26037

R. Chandra, Ery Dewayani

引用次数: 0

PERBANDINGAN LSTM DAN ELM DALAM MEMPREDIKSI HARGA PANGAN KOTA TASIKMALAYA LSTM和ELM之间的比较可以预测塔斯克马来亚市的食品价格

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26015

Andry Winata, Manatap Dolok Lauro, Teny Handhayani

{"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}

引用次数: 0

PERANCANGAN SISTEM PENDUKUNG KEPUTUSAN UNTUK MEMUDAHKAN PEMILIHAN KEDAI KOPI 设计一个决策支持系统，为选择咖啡店提供便利

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26016

None Calvin, None Hugeng, Tri Sutrisno

引用次数: 0

VISUALISASI DATA STOK BARANG PADA PT BECEK GRUP INDONESIA 数据可视化库存关于PT BECEK集团印度尼西亚

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26034

M. Pertiwi, Tony, Manatap Dolok Lauro

引用次数: 0

SISTEM INFORMASI PRODUKSI PADA PT. WANAPOTENSI NUSA BERBASIS WEB 基于WEB的PT. wanaposterous NUSA的生产信息系统

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26017

Ricky Giovanni Putra Tanjaya, Dedi Trisnawarman

引用次数: 0

PREDIKSI HARGA PANGAN DI PASAR TRADISIONAL KOTA SURABAYA DENGAN METODE LSTM 用LSTM方法预测泗水传统市场的食品价格

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26012

Teddy Ericko, Manatap Dolok Lauro, Teny Handhayani

{"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}

引用次数: 0

PENERAPAN FUZZY TAHANI PADA REKOMENDASI OBJEK WISATA DI JAKARTA 将TAHANI应用于雅加达旅游景点推荐

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26023

Veronika, Dedi Trisnawarman

{"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}

引用次数: 0

SISTEM INFORMASI MONITORING MURID OLEH GURU DENGAN ORANGTUA BERBASIS WEB DAN MOBILE 由家长网络和移动的教师监控学生信息系统

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26003

Sudono Widjaja, Bagus Mulyawan, Novario Jaya Perdana

引用次数: 0

PENERAPAN METODE DECISION TREE UNTUK PRAKIRAAN CUACA KOTA BEKASI 处置树木方法的应用，为贝卡西的天气预报

Jurnal Sisfokom Pub Date : 2023-08-23 DOI: 10.24912/jiksi.v11i2.26026

Kristopher Halim, Dyah Erny Herwindiati, T. Sutrisno

引用次数: 0