Article Per Year (5 Year)
p-Index From 2019 - 2024
0.882
P-Index
This Author published in this journals
All Journal Jurnal Informatika International Journal of Advances in Intelligent Informatics Refleksi Pembelajaran Inovatif Science in Information Technology Letters
Tuti Purwaningsih
Universitas Islam Indonesia
Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Science%20in%20Information%20Technology%20Letters

 1 
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.
Mapping crime determinants in Central Java: an in-depth exploration through local spatial association and regression analysis Nanda Lailatul Humairoh; Tuti Purwaningsih; Shoffan Saifullah; Felix Andika Dwiyanto; Ilyos Rabbimov
Science in Information Technology Letters Vol 3, No 1 (2022): May 2022
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v3i1.1212

Abstract

Economic development often brings prosperity to communities, but it can also be accompanied by growing disparities that, when unaddressed, lead to increased crime rates. Central Java, an Indonesian province, has been grappling with a persistent high crime rate, necessitating an in-depth examination of the factors underlying this phenomenon. In this study, we employ a rigorous research methodology, incorporating data sources from the Central Java Central Statistics Agency (BPS) and utilizing key independent variables, including population, unemployment, poverty, Age-Dependency Ratio (APS), and Relative Location Quotient (RLS). Through the application of advanced spatial analysis techniques such as the Local Indicator of Spatial Association (LISA) and the Spatial Autoregressive Model (SAR), this research offers a nuanced exploration of the spatial relationships and regression analysis of these variables. Notably, the study presents a tree map highlighting crime distribution in Central Java's districts and cities. The findings reveal that these five variables exhibit a 75.48% accuracy in predicting crime in Central Java. Through this comprehensive analysis, our research aims to provide valuable insights for policymakers, law enforcement, and the community at large, enabling informed strategies for crime reduction and the promotion of a safer, more prosperous Central Java
Co-Authors Alya Cintami Anusua Ghosh Badaruddin Nurhab Baharudin Machmud Baiq Siska Febriani Astuti Chumairoh Chumairoh Dhanukhresna Hangga Yudhawan Felix Andika Dwiyanto Ilyos Rabbimov Meiga Isyatan Mardiyah Ming Foey Teng Muhamad Irpan Nurhab Nanda Lailatul Humairoh Shoffan Saifullah