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All Journal Jurnal Ilmu Komputer dan Informasi Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Computer System and Informatics (JoSYC)
Hasmawati Hasmawati
Telkom University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Journal : Indonesian Journal on Computing (Indo-JC)

 1 
Modifikasi Headstega berdasarkan Penyisipan Karakter Hasmawati Hasmawati; Ari Moesriami Barmawi
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 1 (2017): Maret, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.1.145

Abstract

AbstractHead steganography or Headstega is one of noiseless steganography paradigm, or Nostega.This method utilizes the email header as a media of  message concealment. There are several problems that can be enhanced in Headstega, i.e. low embedding capacity and high level of suspicion. Modified Headstega based on Character Hiding uses a combination of consonant vowel to embed the secret messages into email address. The messages embedding process using four consonant vowel combination that represented one character in Indonesian language.  From the experiments conducted, the results obtained that the Modified Headstega has a better performance than the Original Headstega in term of embedding capacity and also in suspicion level. Keyword : Steganography, Nostega, Headstega, Character Hiding
Sentiment Analysis of University Social Media Using Support Vector Machine and Logistic Regression Methods Fazainsyah Azka Wicaksono; Ade Romadhony; Hasmawati
Indonesia Journal on Computing (Indo-JC) Vol. 7 No. 2 (2022): August, 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2022.7.2.638

Abstract

Social media has become one of the most powerful platforms for information sharing. Colleges and universities now have official social media profiles to convey information about the campus and boost its branding and popularity. Instagram is a popular social networking website among college students. It is important for a university to comprehend its performance from the community's perspective, whether positive, negative, or indifferent toward the university. One solution is to examine the university's social media sentiment to establish the public's perception of the university. In this study, we will conduct a sentiment analysis on university social media based on public opinion or comments for each post on the university's Instagram to identify whether the comments are “Positive,” “Negative,” or “Neutral.” To classify posts on university Instagram, we use two methods: Support Vector Machine and Logistic Regression. The results suggest combining the Support Vector Machine approach with the TF-IDF feature yields the best F1-Score performance. In contrast, Logistic Regression with the FastText feature produces the worst performance of all models and feature extraction employed.
Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates Primanda Sayarizki; Hasmawati; Hani Nurrahmi
Indonesia Journal on Computing (Indo-JC) Vol. 9 No. 2 (2024): August, 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2024.9.2.934

Abstract

In this modern era, Indonesian society widely utilizes social media, particularly Twitter, as a means to express their opinions. Every day, various opinions of Indonesian citizens are disseminated on this platform, including their views on prospective presidential candidates for the year 2024. Analyzing public opinions regarding prospective presidential candidates in 2024 is crucial to understanding the sentiment of the people toward these candidates. Such sentiment analysis can be conducted using deep learning techniques such as IndoBERT to acquire knowledge regarding the classification of sentiments as positive, neutral, or negative. IndoBERT is employed to generate vector representations that encapsulate the meaning of tokens, words, phrases, or texts. These representation vectors can then be input into a classification model to perform sentiment analysis. The sentiment classification model undergoes testing with a diverse set of tweets in the test dataset, which represent a wide range of public opinions. The evaluation results indicate an overall accuracy rate of 80%, with precision rates of 62% for negative sentiment, 81% for neutral sentiment, and 85% for positive sentiment. Additionally, the recall rates for each sentiment are 64% for negative, 81% for neutral, and 84% for positive, with corresponding F1-scores of 63%, 81%, and 85%, respectively.
Co-Authors Ade Romadhony Ari Moesriami Barmawi Arya Prima Al Aufar Donni Richasdy Fazainsyah Azka Wicaksono Hani Nurrahmi Muhammad Farrel Muhammad Iqbal Muhammad Zaky Aonillah Nazar Azmi Primanda Sayarizki