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Journal : Jurnal Informatika

Analysis of factors affecting the area of forest and land fires in Indonesia uses spatial regression Geoda and SaTScan Tuti Purwaningsih; Alya Cintami
Jurnal Informatika Vol 12, No 2: July 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1331.898 KB) | DOI: 10.26555/jifo.v12i2.a12340

Abstract

This study discusses the factors that influence the extent of forest and land fires in Indonesia that relate several other factors such as rain, fire events, and wind speed which were the events during 2015. Forest fires are one of the environmental and forest problems that is a local and global concern. Countermeasures have been carried out for a long time but are relatively low. By looking for the best regression model with a significance level of 0.05 or 95% using the Spatial Autoregressive Model (SAR) method, the coefficient of determination of 25.00% is obtained which can be obtained by the research regression model and leaves 75.00% needed by other variables that are variables changed
Developing support vector regression model to forcast stock prices of mining companies in Indonesia Dhanukhresna Hangga Yudhawan; Tuti Purwaningsih
Jurnal Informatika Vol 14, No 2 (2020): May 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v14i2.a17283

Abstract

The modern era as it is now the world of stock investment is in great demand by investors, both long-term and short-term stock investments. Stock investment provides many benefits for investors. To get large profits, investors need to do an analysis in stock investments to predict the price of the shares to be purchased. Very volatile stock price movements make it difficult for investors to predict stock prices. The main hope of investors is to benefit from each price that changes from time to time or can be referred to as time series data. Data mining is a process of extracting large information from a data by collecting, using data, historical patterns of data relationships, and relationships in large data sets. Support vector regression has advantages in making accurate stock price predictions and can overcome the problem of overfitting by itself. PTBA, and ITMG are the leading coal mining companies in Indonesia, so many people want to invest in the company. ADRO, PTBA, and ITMG stock price prediction analysis using support vector regression algorithm has good predictive accuracy values, including. PTBA stock price have an R-square value of 97.9% in the RBF kernel and linear with MAPE respectively of 2,465 and 2,480. And for ITMG stock price it has an R-square accuracy of 94.3% in the RBF kernel and linear with MAPE respectively 5.874 and 5.875. These results indicate that the SVR method is best used for forecasting stock prices.
Deep learning application using neural network classification for cyberspace dataset with backpropagation algorithm and log-linear models Baiq Siska Febriani Astuti; Tuti Purwaningsih
Jurnal Informatika Vol 12, No 1: January 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.249 KB) | DOI: 10.26555/jifo.v12i1.a8566

Abstract

This study aims to classify bloggers in the Kohkiloye and Boyer Ahmad Province in Iran where causes of users tend cyberspace on there. The database was got from UCI Machine Learning Repository. There are 100th object and 6th variables. All of the variables were Professional Bloggers, Political and Social Space (LPSS), Local Media Turnover (LMT), Political Caprice, Topics, and Degree. This study has using Artificial Neural Network with backpropagation algorithm and Log-linear models for classify Bloggers (Cyber Space). We classify blogger to two groups: professional bloggers and seasonal (temporary) bloggers. The result of this study is Neural network with backpropagation algorithm has been shown to be useful tool for prediction, especially for this case. From this study, we can see on the result that miss-classification with backpropagation algorithm less than using Log-Linear Models