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All Journal PRAJA: Jurnal Ilmiah Pemerintahan Teknika JSIP: Jurnal Studi Ilmu Pemerintahan
Serlina, Andi
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Arts Computer Science & IT Electrical & Electronics Engineering Engineering Social Sciences

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Building Smart Tourism: Exploring the Potential of Tourism Apps for Branding Serlina, Andi; Lutfi, Muhammad; Baharuddin, Baharuddin
Jurnal Studi Ilmu Pemerintahan Vol. 5 No. 1 (2024): JSIP: Jurnal Studi Ilmu Pemerintahan
Publisher : Department of Government Studies, Universitas Muhammadiyah Buton.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35326/jsip.v5i1.4030

Abstract

This research comprehensively studies the impact of the "Ayo ke Sinjai" app on smart branding in Sinjai Regency. Using a qualitative research approach, this study conducted an in-depth analysis through literature reviews, online media portals, and rarely explored journals. The resulting findings demonstrate the tremendous tourism potential inherent in Sinjai Regency. As a strategic move to showcase this potential, the Sinjai Regency government launched the "Ayo ke Sinjai" application to ease tourists' access to information regarding various exciting tourist attractions. Despite the promising potential of this application, its implementation faces challenges, especially in terms of user accessibility due to persistent technical errors. This interferes with the smooth dissemination of vital information to potential tourists, thereby reducing the expected benefits of the app. This study highlights the many tourism riches waiting to be explored in Sinjai Regency and underscores the importance of resolving technical issues for the "Ayo ke Sinjai" app. A functional and easy-to-use platform is essential to drive smart branding effectively so that tourists can easily explore and appreciate the various tourist attractions in the area.
ANALISIS BIBLIOMETRIK: PENINGKATAN KAPASITAS PEMERINTAH DESA DALAM PENERAPAN E-GOVERNMENT DI INDONESIA Lutfi, Muhammad; Serlina, Andi; Nursaifullah, Nursaifullah; Hikmatun, Septi; Rasmalasani, Kiki
PRAJA: Jurnal Ilmiah Pemerintahan Vol 13 No 1 (2025): Februari 2025
Publisher : FISIP Universitas Muhammadiyah Sidenreng Rappang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55678/prj.v13i1.1864

Abstract

Abstract This research analyzes the capacity building of village governments in Indonesia in implementing e-government. Through an in-depth literature review, this research identifies three main challenges: limitations in infrastructure, human resources, and finances. The results of the bibliometric analysis indicate an increase in research interest in this topic in recent years, highlighting the lack of studies conducted. The research results indicate that there is still a gap in understanding the factors influencing the success of e-government implementation at the village level. This research suggests the need for a comprehensive approach to address these challenges, including increased investment in information technology infrastructure, the development of structured training programs for village officials, and the provision of sustainable financial support.
Comparative Analysis of Naïve Bayes Algorithm Performance in English and Indonesian Text Sentiment Classification on Duolingo Application in Playstore Serlina, Andi; Rahim, Abdul; Arbansyah
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1207

Abstract

Text classification is an important topic in Natural Language Processing (NLP), especially when conducting research on user reviews on language learning apps such as Duolingo. This study compares the effectiveness of the Naïve Bayes algorithm in identifying sentiment in English and Indonesian reviews on the Duolingo app on Playstore. The approach includes data collection, text preparation (case folding, tokenization, stopword removal, and stemming), and Naïve Bayes algorithm evaluation for each dataset. Model performance was evaluated using accuracy, precision, recall, and F1-score. The Naïve Bayes method obtained 84% accuracy on the English dataset with a 90:10 data split and 67% accuracy on the Indonesian dataset with the same split ratio. The difference in the results obtained is due to several variables, including the use of informal language, slang, and more complicated word variants in Indonesian, which make proper classification more difficult for the model to achieve.
Co-Authors Abdul Rahim Arbansyah Baharuddin Baharuddin Hikmatun, Septi Muhammad Lutfi Nursaifullah, Nursaifullah Rasmalasani, Kiki