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All Journal Dinamika Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Komtika (Komputasi dan Informatika) JURNAL PENDIDIKAN TAMBUSAI Informasi Interaktif Respati Jurnal Ekonomi dan Teknik Informatika International Journal Artificial Intelligent and Informatics Techno Jurnal Indonesia Sosial Teknologi Jurnal Komtika (Komputasi dan Informatika)
Sudarmawan, Sudarmawan
UNiversitas AMIKOM Yogyakarta
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Sentimen Analisis Terhadap Aplikasi pada Google Playstore Menggunakan Algoritma Naïve Bayes dan Algoritma Genetika Rahman, Arif; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5188

Abstract

Sentiment analysis is a science to extract text to get someone's emotions for that. The benefits of sentiment analysis have many benefits, one of which is to see whether or not customers have a good response to the product and this can be an input for the development of the product's business in the future. The weakness of previous studies in research sentiment analysis is that the authors conduct research to improve the results of previous studies using the naïve Bayes algorithm that is optimized with a genetic algorithm. From the results of the research that has been done, the average value in this study is on average better than previous studies, no applications have been identified as underfitting or overfitting and finally the naïve Bayes algorithm that has been optimized by the genetic algorithm can be a classification solution for sentiment analysis.
Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes Setiawan, Hendrik; Utami, Ema; Sudarmawan, Sudarmawan
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 1 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i1.5189

Abstract

The World Health Organization (WHO) COVID-19 is an infectious disease caused by the Coronavirus which originally came from an outbreak in the city of Wuhan, China in December 2019 which later became a pandemic that occurred in many countries around the world. This disease has caused the government to give a regional lockdown status to give students the status of "at home" for students to enforce online or online lectures, this has caused various sentiments given by students in responding to online lectures via social media twitter. For sentiment analysis, the researcher applies the nave Bayes algorithm and support vector machine (SVM) with the performance results obtained on the Bayes algorithm with an accuracy of 81.20%, time 9.00 seconds, recall 79.60% and precision 79.40% while for the SVM algorithm get an accuracy value of 85%, time 31.60 seconds, recall 84% and precision 83.60%, the performance results are obtained in the 1st iteration for nave Bayes and the 423th iteration for the SVM algorithm
Implementasi Pengembangan Sistem Model Water Fall Untuk Data Warehouse Akademik Sofan Tohir, Arik Sofan Tohir; Kusrini, Kusrini; Sudarmawan, Sudarmawan
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 1 No 2 (2017): Vol. 1 No. 2 Agustus 2017
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (234.158 KB) | DOI: 10.29407/intensif.v1i2.837

Abstract

Data warehouse is a concept and a technology to store transactional data from several sources that have been through the process of filtering and selection of data. By using the Ectract, Transform and Load (ETL) process in the data warehouse, OLTP data is processed to produce good data and ready for use for the analysis process. For the design of this warehouse data will be built by using the Nine Step Method from Kimbal, so that the resulting warhouse data can be as expected. For the development of life flow system (SDLC) with waterfall model. By using the wate fall model will be built a prototype to implement the data warehouse design results.
Co-Authors Abdillah, M A Abidarin Rosidi Adiwisanghagni, Mohammad Agung Mulyono Agung, Kris Ahmad Zarkasih Amrullah, Ahmad Afief Arief Setyanto Arif Rahman Arik Sofan Tohir Bambang Soedijono W A, Bambang Soedijono Eko Purwanto Ema Utami Fatkhurohman, Ari Hendrik Setiawan, Hendrik Hermanto (STMIK AMIKOM Yogyakarta) JOKO PRAYITNO Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini La Ariandi, Hadin Listyanto, Ahmad Wildan Mohammad Suyanto Napianto, Riduwan Nugroho, Muhammad Agung Nurmalasari, Maulidya Dwi Putra, Afrizal Yudano Perdana Rumini - Sukasna, Sukasna Suryadin, Imam Tri Tri Yusnanto