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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Matrik Jurnal Eksplora Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) KOMPUTEK Infotech: Journal of Technology Information Bulletin of Computer Science Research J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal Software Engineering and Computer Science (IJSECS) Acta Pedagogia Asiana The Indonesian Journal of Computer Science
Kamayani, Mia
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Mechanical Engineering Other

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Social Media Sentiment Analysis of Twitter Regarding People's Housing Savings (TAPERA) Using Naïve Bayes Dewy, Avry Liyanah; Kamayani, Mia
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4126

Abstract

The advancement of technology has transformed how people interact and express opinions on social media platforms. This research examines Twitter conversations regarding Indonesia's government-initiated Housing Savings Program (TAPERA) through sentiment analysis. The study employed Naïve Bayes classification methodology, with data acquisition conducted via Google Colab platform utilizing the tweet-harvest library. The collection process yielded 1,800 tweets matching predetermined search parameters. Data underwent rigorous preprocessing, including text cleaning and manual sentiment annotation to establish reliable training datasets. Examination of 720 test tweets revealed 473 (65.69%) expressed negative sentiment while 247 (34.31%) conveyed positive sentiment toward the program. The implemented Naïve Bayes model achieved 84.17% accuracy, with negative class precision at 88.71% and recall at 88.60%, while positive class precision reached 78.54% with 76.08% recall. Results indicate the Naïve Bayes approach effectively categorizes public sentiment regarding the TAPERA program, offering valuable feedback for stakeholders responsible for program assessment and enhancement.
Evaluation of Computer Lab at XYZ Institution using BAI & DSS Domains of COBIT 2019 Febriawan, Dimas; Kamayani, Mia; Imanda, Rahmi
Jurnal Ilmiah Matrik Vol. 27 No. 2 (2025): Jurnal Ilmiah Matrik
Publisher : Direktorat Riset dan Pengabdian Pada Masyarakat (DRPM) Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33557/wsbeqv24

Abstract

This research aims to measure the IT governance implementation in the computer laboratory at XYZ Institution using COBIT 2019 framework. Based on the scope and the problems that were identified, BAI and DSS aspects are the domains chosen to measure the implementation of the IT governance. The methods for this research are focus group discussion and field assessment. The BAI and DSS domains consist of 16 objectives, which are then divided into 104 practices and then divided further into 535 activities. These 535 activities are the processes that we have to determine for each capability level. After determining the capability levels for each process, we summarized the values and then evaluated the average values for each objective. These average values are the values that we used to determine the capability levels for each objective. We presented the result of our self assessment using a radar diagram. XYZ Institution is still in the starting phase of having good IT governance. This condition is reflected by the achievement of each objective’s capability levels ranging from 1 to 2. In addition to this condition, there is only one objective that meets the Institution’s capability level target of 3.
Deteksi hate speech pada kolom komentar TikTok dengan menggunakan SVM Ariska, Amelia; Kamayani, Mia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3982

Abstract

The TikTok application provides numerous features, including the comment section for users to interact with each other. Users can exchange their opinions openly through the comment section. However, as the interaction or exchange of opinions among users increases, the use of hate speech, consciously or unconsciously, remains prevalent. Hate speech refers to actions by an individual or group that can incite criminal acts, thereby harming others. This study aims to identify the use of hate speech in TikTok comment sections using the SVM algorithm and to compare two libraries used in the labeling process to observe the performance of the SVM algorithm model. The labeling process employs a lexicon-based approach. The dictionaries used in this study are the Inset lexicon and VaderSentiment. The SVM algorithm is used as the model to test the evaluation results. The results obtained using the Inset lexicon labeling show an accuracy of 82%, while the second labeling method using VaderSentiment yields an accuracy of 96.21%.
Co-Authors Arif, Zainul Ariska, Amelia Attyyatullatifah, Iqlimah Ayyubi, Reza Al Azhar, Nur Chalik Azzahra, Nabila Dewy, Avry Liyanah Eka Novianti, Eka Febriawan, Dimas Firman Noor Hasan Komarudin, Ahmad Mahendra, Ristian Pratiwi, Anissya Agsani Rahman Malik, Luqman Abdur Rahmi Imanda Siduningrum, Estu Sulistiawati, Risa Utami, Arneitta Dwicahya Yunita, Rani