Afwan Anggara
Information System, Universitas Teknologi Yogyakarta, Indonesia

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Application of Multi Agent System for Information Extraction Needs Blood on Twitter using Naive Bayes Classifier Afwan Anggara; Widya Setia Findari
International Journal of Applied Business and Information Systems Vol. 2 No. 1 (2018)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (603.896 KB) | DOI: 10.31763/ijabis.v2i1.122

Abstract

Poincaré Plot Method for Physiological Analysis of the Gadget Use Effect on Children Stress Level Umar Zaky; Afwan Anggara; Muhammad Zakariyah; Ilham Fathullah
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.809

Abstract

Stress in children can affect the way they think, act, and feel. The habit of using gadgets has several advantages and disadvantages, but there has been no in-depth study of the effect of using gadgets on stress levels in children. This study aims to determine the representation of the physiological condition of using gadgets on stress levels in children. A total of 18 electrocardiogram data were extracted with poincaré plot features. This research has found that there is no difference in the level of stress in children between before and after using gadgets in terms of autonomic nervous activity (Sig. > 0.05). However, there is an increase in sympathetic activity that occurs in children even though they have finished using gadgets. Such conditions certainly need to get more attention, especially related to the duration of gadget use and accessible content.
Analysis of Netizen Comments Sentiment on Public Official Statements on Instagram Social Media Accounts Afwan Anggara; Suyud Widiono; Ahmad Tri Hidayat; Sutarman Sutarman
International Journal of Advances in Data and Information Systems Vol. 3 No. 2 (2022): October 2022 - International Journal of Advances in Data and Information System
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/ijadis.v3i2.1244

Abstract

Statements issued by public officials will be pros and cons in the community, there are those who respond positively, negatively or respond neutrally. Likewise on Instagram social media, every statement written on Instagram will get various responses written by netizens in the comment’s column posted. Netizen is an acronym for internet citizens, namely people who are actively using the internet. Due to the large number of comments, it is difficult to see whether the public response is a positive, negative or neutral comment when responding to statements from public officials. Whether the statements issued by public officials through Instagram have a positive, negative or neutral impact, so that if they can be grouped into labels, it can be seen how much public opinion is against these public figures. On social media accounts, not all comments written by netizens have the same writing structure, so we need a mechanism that is able to help analyze comments from netizens by classifying them into positive, negative or neutral response classes. By applying POS Tagging to determine opinion sentences or not and also the Naïve Bayes Classifier method and the tf-idf feature to be able to classify comments into several classes of positive, negative or neutral comments. The classification testing stage uses the cross validation method to test the accuracy of the naive bayes classification algorithm and the tf-idf feature.