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All Journal Jurnal Teknologi Informatika dan Komputer Economic Reviews Journal Jurnal Informatika Dan Tekonologi Komputer
Satrio Agung Prakoso
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Analysis of Public Sentiment Towards the Use of AI in Monitoring Waste via the SEMAR Monitoring Web and Its Impact on Flood Management in Semarang City Priskila Dwi Nilam Sari; Yohana Tri Widayati; Satrio Agung Prakoso
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2838

Abstract

Waste management and flood mitigation are key challenges in Semarang City. The Semarang City Government, through the Department of Communication and Informatics (Diskominfo), implemented the AI-based Pantau Semar web system to automatically detect waste accumulation and water puddles via a CCTV network. This study examines public sentiment toward the system, particularly from social media comments, and develops a sentiment classification model using IndoBERT. A quantitative approach with Natural Language Processing (NLP)-based sentiment analysis was applied. A total of 430 public comments from social media were classified into positive, negative, and neutral sentiments, and analyzed using a fine-tuned IndoBERT model. Results show that negative sentiment dominates (54%), followed by positive (30%) and neutral (16%). The model achieved 81% accuracy, with the highest F1-score in the negative class (0.89). These findings indicate that the public remains critical of the system’s performance, especially regarding waste accumulation and flooding, while also highlighting AI’s potential in environmental management and public opinion detection. The results provide a basis for developing more adaptive monitoring systems and improving government communication strategies to better address community needs.
Implementasi Sistem Informasi Media Promosi Pada Semesta Resto Berbasis Website Rudi Efendi, Muhammad; Yohana Tri Widayati; Satrio Agung Prakoso
Economic Reviews Journal Vol. 4 No. 4 (2025): Economic Reviews Journal
Publisher : Masyarakat Ekonomi Syariah Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/mrj.v4i4.903

Abstract

This study focuses on the design and implementation of a web-based promotional media information system for Semesta Resto, a culinary business. The aim is to enhance the restaurant's operational efficiency and promotional effectiveness through a responsive website that offers interactive menus, online reservations, and real-time promotional updates. The research highlights the growing importance of digital media in the culinary industry, where consumer behavior is shifting towards online platforms. The study employs a qualitative descriptive approach, utilizing a case study methodology, and applies the Prototyping method in the development of the system. The findings demonstrate that the proposed system is expected to improve customer engagement, streamline operations, and increase revenue through better promotional strategies and easier customer interaction.
Analysis of Public Sentiment Towards the Use of AI in Monitoring Waste via the SEMAR Monitoring Web and Its Impact on Flood Management in Semarang City Priskila Dwi Nilam Sari; Yohana Tri Widayati; Satrio Agung Prakoso
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2838

Abstract

Waste management and flood mitigation are key challenges in Semarang City. The Semarang City Government, through the Department of Communication and Informatics (Diskominfo), implemented the AI-based Pantau Semar web system to automatically detect waste accumulation and water puddles via a CCTV network. This study examines public sentiment toward the system, particularly from social media comments, and develops a sentiment classification model using IndoBERT. A quantitative approach with Natural Language Processing (NLP)-based sentiment analysis was applied. A total of 430 public comments from social media were classified into positive, negative, and neutral sentiments, and analyzed using a fine-tuned IndoBERT model. Results show that negative sentiment dominates (54%), followed by positive (30%) and neutral (16%). The model achieved 81% accuracy, with the highest F1-score in the negative class (0.89). These findings indicate that the public remains critical of the system’s performance, especially regarding waste accumulation and flooding, while also highlighting AI’s potential in environmental management and public opinion detection. The results provide a basis for developing more adaptive monitoring systems and improving government communication strategies to better address community needs.
Implementasi Metode Seleksi Fitur Untuk Penentuan Indikator pada Klasifikasi Kompetensi Pemetaan Kuadran 9 di Pemprov Jateng Yudha Widi Harjanto; Harries Arizonia Ismail; Satrio Agung Prakoso
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 3 (2025): November : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i3.8366

Abstract

Quadrant 9 (nine) is a model of employee competency mapping which will be used as a strategy for developing talented individuals and individuals who have weaknesses. In addition, quadrant 9 (nine) is also used in succession planning by identifying anyone who has the potential to become a leader in the organization. This study aims to obtain a data mining algorithm with a more accurate selection feature to find indicators that affect the classification of quadrant 9 competencies. The Forward Selection feature selection algorithm based on Naive Bayes is proven to be accurate and effective in determining the most influential attributes, namely IQ/Thinking Capacity, Self-Management, Social Communication, Service Orientation, Decision Making, Managing Others with 79.10% accuracy results and is included in the "Good Kappa" category.
Co-Authors Harries Arizonia Ismail Priskila Dwi Nilam Sari Rudi Efendi, Muhammad Yohana Tri Widayati Yudha Widi Harjanto