Tuti Purwaningsih
Universitas Islam Indonesia

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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
Influence of overweight and obesity on the diabetes in the world on adult people using spatial regression Tuti Purwaningsih; Baharudin Machmud
International Journal of Advances in Intelligent Informatics Vol 2, No 3 (2016): November 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v2i3.72

Abstract

This research discussed about the case of diabetes, overweight, and obesity which aimed to determine the factors that most affect the number of adult people with Diabetes from Obesity and Overweight in the world and looking for the best spatial model to make predictions in the next period. This research based on data WHO in 2015 from The 2016 Global Nutrition Report. At 5% level of significance for 2015, factor that influence diabetes is obesity and the most excellent spatial model used in the analysis is Spatial Error Model (SEM) that use Weight Level Order 1 and has R2 value 81.82%.
Spatial data modeling in disposable income per capita in china using nationwide spatial autoregressive (SAR) Tuti Purwaningsih; Anusua Ghosh; Chumairoh Chumairoh
International Journal of Advances in Intelligent Informatics Vol 3, No 2 (2017): July 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i2.93

Abstract

China as a country became the economic center of the world. However, with a population of 1.3 billion, China's per capita income is still at number 80 in the world. In the world, considering the imbalance between town and country with 100 million people still living in poverty. Thus, to address this imbalance, it is necessary to study the condition in depth, because income per capita is often used as a benchmark to measure the prosperity of a country. With greater and equitable income per capita, the country will be judged increasingly affluent. Two factors, mainly industry and tourism, play an important role in the economic progress in China. These are include Per capita Disposable Income Nationwide (yuan), Total Value of Exports of operating units (1,000 USD), Registered Unemployed Person in Urban Area (10000 person), Foreign Exchange Earning from International tourism(in millions USD) and Number of Overseas Visitor Arrivals (million person/time). Thus, it is necessary to investigate the influence of these factors to increase per capita income. Since the economic development of a region usually affect the surrounding area, this study aims to include spatial effects, using Spatial Autoregressive (SAR) Model. The results suggest that the per capita income affected by the Tourism factor is about 58.65% (R-squared).
Circular(2)-linear regression analysis with iteration order manipulation Muhamad Irpan Nurhab; Badaruddin Nurhab; Tuti Purwaningsih; Ming Foey Teng
International Journal of Advances in Intelligent Informatics Vol 3, No 2 (2017): July 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v3i2.90

Abstract

Data in the form of time cycle or point position to the angle of possibility is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data. This paper aims to examine the comparison of Linear Regression Analysis with Circular Regression Analysis. The writing method used is literature review using simulation data. Data simulation and analysis is done with the help of R program. The results showed that circular data is better analyzed by Circular Regression Analysis rather than Classical Linear Regression Analysis. The use of classical linear statistic method is not recommended due to the direction and the angle influence the position between one data with other data.
Developing deep learning architecture for image classification using convolutional neural network (CNN) algorithm in forest and field images Meiga Isyatan Mardiyah; Tuti Purwaningsih
Science in Information Technology Letters Vol 1, No 2: November 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i2.160

Abstract

Indonesia is an agricultural country with a variety of natural resources such as agriculture and plantations. Agriculture and plantations in Indonesia are diverse, such as rice fields that can produce rice, soybeans, corn, tubers, and others. Meanwhile, plantations in Indonesia are like forests with timber products, bamboo, eucalyptus oil, rattan, and others. However, rice fields, which are examples of agriculture, and forests that are examples of plantations, have the same characteristics. It is not easy to distinguish when viewed using aerial photographs or photographs taken from a certain height. For recognizing with certainty the shape of rice fields and forests when viewed using aerial photographs, it is necessary to establish a model that can accurately recognize the shape of rice fields and forest forms. A model is to utilize computational science to take information from digital images to recognize objects automatically. One method of deep learning that is currently developing is a Convolutional Neural Network (CNN). The CNN method enters (input data) in the form of an image or image. This method has a particular layer called the convulsive layer wherein an input image layer (input image) will produce a pattern of several parts of the image, which will be easier to classify later. The convolution layer has the function of learning images to be more efficient to be implemented. Therefore, researchers want to utilize this CNN method to classify forests and rice fields to distinguish the characteristics of forests and rice fields. Based on the classification results obtained by testing the accuracy of 90%. It can be concluded that the CNN method can classify images of forests and rice fields correctly.