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All Journal JSI: Jurnal Sistem Informasi (E-Journal) Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Sistem Komputer dan Informatika (JSON) JoMMiT : Jurnal Multi Media dan IT Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia
Damayanti, Ariesta
Universitas Teknologi Digital Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Environmental Science Library & Information Science Social Sciences Other

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Journal : Jurnal Sistem Komputer dan Informatika (JSON)

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Penentuan Tingkat Kerawanan Penyebaran Leptospirosis Menggunakan Inferensi Fuzzy Tsukamoto Ariesta Damayanti
Jurnal Sistem Komputer dan Informatika (JSON) Vol 1, No 1 (2019): September 2019
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.598 KB) | DOI: 10.30865/json.v1i1.1388

Abstract

Cases of leptospirosis in Indonesia mainly occur in areas that often experience floods and areas where the majority of its citizens work as farmers. Special Region of Yogyakarta (DIY) was the province with the most leptospirosis cases in Indonesia in 2011. In 2010-2011 an extraordinary event (KLB) of leptospirosis occurred in Bantul district and in 2014 the number of leptospirosis cases in Bantul district increased by 76 cases.. Based on Kementerian Kesehatan report, data shows that there has been an outbreak of leptospirosis in Bantul , so in addition to epidemiological data necessary case information is also needed to determine the geographic case risk factors and mitigation efforts.In the processing of digital maps for GIS , often found important objects that are not appropriate in its processing can not even be excluded because of uncertainty owned. Applications are made in this study was built and designed by the architectural Tsukamoto fuzzy inference method for handling uncertainty. The results of the application is the visualization of the spread of the disease leptospirosis vulnerability maps based determinants that also involves uncertainty factors that will be resolved with the Tsukamoto fuzzy inference method for use as detection and prevention against the spread of disease leptospirosis in the future
Analisis Sentimen Tindakan Pemerintah Indonesia Dalam Penanganan Covid-19 Menggunakan Metode Support Vector Machine Ariesta Damayanti; Helda Ludya Safitri; Rudy Cahyadi
Jurnal Sistem Komputer dan Informatika (JSON) Vol 4, No 2 (2022): Desember 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i2.5341

Abstract

Corona Virus Disease 2019 (Covid-19) which has hit the world including Indonesia since the beginning of 2020 is an outbreak that has become a serious threat to world health. The Indonesian government is taking various actions to deal with this problem, while the public, with the existence of social media, has provided many responses to these government policies. Twitter is one of the social media that is widely used by the public to convey comments in the form of responses, suggestions, to criticism of the government regarding the handling of Covid-19. The comments that appear should be used by the government as part of the reference in evaluating a policy or action taken in handling Covid-19. So that one way that can be used to deal with this is one of the methods that exist in the domain of text mining, namely sentiment analysis. This research was conducted by analyzing sentiment using the Support Vector Machine (SVM) method with the Kernel Radial Basis Function (RBF). Tweets will be classified into positive, negative and neutral sentiments, so that the percentage of each opinion category can be known. This study uses data of 600 tweets obtained from the results of scraping using a Twitter scraper. The result of this study is that the training accuracy rate is 77% in classifying positive, negative, and neutral sentiments. From the results of the data classification, it was found that most of the tweets consisted of negative sentiments.
Co-Authors Asyahri Hadi Nasyuha Erna Hudianti Pujiarini Hartono, Rudi Helda Ludya Safitri Ike - Festiana Irwan Setiawan Komarudin, Agus Muhammad Fahrur Rifai Nugroho, Muhammad Agung Rudy Cahyadi Sri Redjeki Syahara, Pujiatus Syamsu Windarti Syamsu Windarti