Article Per Year (5 Year)
p-Index From 2021 - 2026
2.092
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika Tech-E JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) Zero : Jurnal Sains, Matematika, dan Terapan bit-Tech Jurnal Pendidikan dan Teknologi Indonesia Jurnal Impresi Indonesia Journal Of Artificial Intelligence And Software Engineering Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Journal Innovations Computer Science INOVTEK Polbeng - Seri Informatika Jurnal TAM (Technology Acceptance Model)
Sri Redjeki
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

Published : 27 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : tech-e

 1 
Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan redjeki, sri
Tech-E Vol 1 No 1 (2017): Tech-E
Publisher : BSTI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results
Comparison of Seven Machine Learning Algorithms in the Classification of Public Opinion Sri Redjeki; Setyawan Widyarto
Tech-E Vol. 5 No. 2 (2022): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v5i1.1046

Abstract

Sentiment analysis is one way that is widely used to identify the beginning of public opinion in various fields of life which are associated with very massive and a lot of information through social media. This study aims to compare several algorithms in machine learning to see the best ability in sentiment classification. The research dataset uses a dataset of public opinion related to tourism in Indonesia. The number of datasets used is 10,228 twitter data that have been cleaned and labelled. The machine learning algorithm used is Logistic Regression, KNN, AdaBoost, Decision Tree, SVM, Random Forest and Gaussian. The seven algorithms for sentiment classification from the Twitter public opinion each produce a Gaussian accuracy of 0.52; SVM 0.78; KNN 0.98; Logistic Regression, Random Forest, Decision Tree, AdaBoost of 0.99. This study shows that the selection of the right machine learning algorithm will have a very good impact on the classification of public opinion through social media
Neural Network Modeling for Family Welfare Classification Sri Redjeki
Tech-E Vol 1 No 2 (2018): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (824.581 KB) | DOI: 10.31253/te.v1i2.62

Abstract

Welfare in general can be defined as the level of a person's ability to meet their basic needs in the form of clothing, food, boards, education, and health. Welfare can be assessed in terms of family welfare. This study aims to perform analysis of artificial neural network modeling backpropagation method. The model will compare the optimization algorithm of artificial neural network results. The data used are 251 data of pre prosperous family in Banguntapan District, Bantul Regency. There are 16 input variables with 14 variables from BPS and 2 additional variables. There is one variable that has constant data so that this variable is not used in artificial neural network model analysis. There is a hidden layer with a number of dynamic neurons. Output layer there are 4 neurons which is the family welfare category. Data is processed using Matlab and SPSS. The system results show that the best accuracy for training is 68% of the Scale Conjugate Gradient algorithm while for best test results it is 68.8% of the Gradient Descent algorithm.
Analisis Performance Fuzzy Tsukamoto Dalam Klasifikasi Bantuan Kemiskinan Sri Redjeki
Tech-E Vol 1 No 1 (2017): Tech-E
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (887.066 KB) | DOI: 10.31253/te.v1i1.20

Abstract

The Central Bureau of Statistics (BPS) showed that the poverty rate in Indonesia in September 2014 still high at about 27.7 million people, or about 10.96%. As a basis for policy countermeasures, understand the problem of poverty often demands the effort of defining, measuring, and identifying the root causes of poverty. This study wanted to use one of the methods that exist in fuzzy logic to classify beneficiaries of poverty that exist in Bantul. Fuzzy Inference System used in this study using Tsukamoto with 8 rule established by a group of poor criteria and types of poverty relief. There are three groups of criteria of poverty derived from 11 criteria of poverty in Bantul. While the types of assistance that are used are Raskin, BLT and KUR. The system is built using PHP. To see the performance Tsukamoto method in this study used 50 data poor people in Sub Districs Banguntapan. From the test results turned out to obtained an accuracy of 52%, meaning that there were 26 correct data according to the original data. It is necessary to modify the rules and membership functions to improve system accuracy results
Co-Authors Alfonso Situmorang Almagribi, Ahmad Bilal Anas Taufan Asyahri Hadi Nasyuha Ayu Larasati Bambang Purnomosidi Dwi Putranto Basanto Joshi Damayanti, Ariesta Dara Kusumawati Dedi Rosadi Dini Fakta Sari Dodon Turianto Nugrahadi Edi Faizal Erna Hudianti Pujiarini Gerry Firmansyah Iskandar, Edi Khabib Mustofa M. Guntara Muhammad Guntara Muhammad Reza Faisal, Muhammad Reza Muslih, Muhamad Naufal, Nabil Nurohman, Muhamad Pius Anggoro Rahayu, Fenny Winda Rizky Maulana Robby Cokro Buwono Satriani, Bayu Setyawan Widyarto Sri Redjeki Thomas Edison Tarigan Yagus Cahyadi