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All Journal JSAI (Journal Scientific and Applied Informatics) Journal Collabits
Zamzami, Muhammad Aryaka
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Computer Science & IT Engineering

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Pengembangan Aplikasi Taskify Untuk Manajemen Tugas Menggunakan Framework Laravel Zamzami, Muhammad Aryaka; Kurnia Kito, Ramadani; Suratno, Igo Prayoga; Maritza, Kaka Irsyad; Salamah, Umniy
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 3 (2024): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i3.7560

Abstract

Task management is one of the important aspects that support success in learning and working activities. The number of tasks and responsibilities often creates challenges, such as forgetting schedules or errors in recording tasks manually. To overcome these problems, a technology-based solution is needed, one of which is a web-based task management application that can help users organize and monitor tasks in a more structured manner. This research adopts a software development approach based on the Agile method, which allows the process of iteration and development that is adaptive to user needs. The “Taskify” application was developed using the Laravel framework to support backend functionality and MySQL as a database. Application testing is carried out using the Blackbox Testing method to ensure all features run according to user needs. The app also features a user-friendly and responsive interface, providing an optimal user experience..
Comparative Analysis of Public Sentiment Towards Sri Mulyani and Purbaya as Finance Ministers on the X Platform Using the Indobertweet Model Zamzami, Muhammad Aryaka; Maesaroh, Siti; Managas, Dendy Jonas
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37962

Abstract

The development of social media has positioned platform X (Twitter) as a primary source for expressing public opinion toward government figures and policies. This study aims to analyze public sentiment toward two Indonesian public figures, Sri Mulyani Indrawati and Purbaya Yudhi Sadewa, by utilizing the transformer-based IndoBERTweet model. The data were collected from January 1, 2025, to November 1, 2025. A total of 11,000 tweets related to Sri Mulyani were collected; however, only 2,500 tweets were used for data processing and model training, with a maximum limit of 1,000 tweets per month. Meanwhile, 650 tweets were obtained for Purbaya Yudhi Sadewa. This research employs a supervised learning approach with labeled data consisting of positive, negative, and neutral sentiment classes. Minimal preprocessing was applied, considering that IndoBERTweet is specifically designed to handle the characteristics of social media text. The model was trained for five epochs and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the IndoBERTweet model can classify sentiment effectively, particularly on the Sri Mulyani dataset, which contains a larger volume of data and achieves an accuracy of over 82%. In contrast, the model’s performance on the Purbaya Yudhi Sadewa dataset shows a lower accuracy of 71%, influenced by the limited amount of data. This study confirms that the quantity and distribution of data significantly affect the performance of transformer-based sentiment analysis models. Based on the sentiment classification results, public sentiment toward Sri Mulyani Indrawati tends to be dominated by negative and neutral sentiments, while sentiment toward Purbaya Yudhi Sadewa shows a distribution dominated by neutral and positive sentiments.
Co-Authors Kurnia Kito, Ramadani Managas, Dendy Jonas Maritza, Kaka Irsyad Salamah, Umniy Siti Maesaroh Suratno, Igo Prayoga