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All Journal Journal of Community Development Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Fredimus Kasang
Universitas Sarjanawiyata Tamansiswa Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Nursing Public Health Social Sciences

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Sosialisasi Cyber Ethics Dalam Membangun Budaya Literasi Digital di SMK Bina Harapan Titik Rahmawati; Eka Yulia Sari; Agung Priyanto; Anjasmara Tanjung Sakti; Fredimus Kasang
Journal of Community Development Vol. 5 No. 2 (2024): December
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i2.255

Abstract

The current development of information technology has led to digital interactions which are often known as cyberspace. Cyberspace is a notional environment where communication via computer networks occurs, in this case via the Internet, which can be in the form of text, images, audio, video, animation or anything in digital form. The virtual world now has almost the same intensity as the real world. With recent technological advances making it possible to have more intensity than the real world in the future. This has created new problems in social life, especially among teenagers who are the biggest users of cyberspace. Many bad things happen in teenagers' interactions in cyberspace, such as cyber bullying, or accessing content that is not suitable for teenagers to consume. This certainly cannot be allowed. To be able to overcome this problem, the outreach team carried out cyber ethics outreach activities in building a digital literacy culture. The aim of this service activity is to help increase understanding of cyber ethics among SMK Bina Harapan students. The number of participants in the socialization was 63 students consisting of class X and class XI of SMK Bina Harapan. From this activity, an evaluation was carried out in the form of a post-test using a multiple choice questionnaire via Google Form. The questions are written in essay form consisting of two answers Yes or No. The results of the Google Form questionnaire show that 90% of Bina Harapan Vocational School students understand the cyber ethics socialization material. Thus, it can be concluded that after conducting socialization, it can help increase students' understanding of Bina Harapan Vocational School about the importance of cyber ethics in building a digital literacy culture, so that their digital literacy activities become safer and healthier.
Analisis Sentimen Komentar Tik Tok terhadap Program Makan Bergizi Gratis (MBG) Menggunakan Algoritma Support Vector Machine (SVM) Fredimus Kasang; Julia Kurniasih; Dina Yuliana
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 5 No. 2 (2026): Jurnal Teknik Mesin, Industri, Elektro dan Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v5i2.6210

Abstract

The Free Nutritious Meal Program (MBG) is a government initiative designed to improve community nutrition, particularly among students, toddlers, pregnant women, and breastfeeding mothers. Public responses to this policy have been widely expressed through social media platforms, including TikTok, where comments are often brief, informal, and unstructured. This study aims to classify public sentiment toward the MBG program into positive, negative, and neutral categories using the Support Vector Machine (SVM) algorithm and to evaluate the model’s performance. Data were collected through TikTok comment crawling techniques, resulting in 2,777 comments, of which 2,602 were retained after data cleaning. The preprocessing stages included text cleaning, case folding, normalization, tokenization, stopword removal, and stemming using the Sastrawi library. The dataset was divided into training and testing data with an 80:20 ratio, followed by TF-IDF feature extraction and SVM-based classification. Model performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results indicate that the SVM algorithm effectively classified public sentiment regarding the MBG program and successfully identified sentiment distribution patterns. These findings provide valuable insights into public perceptions of the program and contribute to the development of machine learning-based sentiment analysis for public policy evaluation in Indonesia.
Co-Authors Agung Priyanto Anjasmara Tanjung Sakti Dina Yuliana Eka Yulia Sari Julia Kurniasih Titik Rahmawati