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All Journal Indonesian Journal of Business Analytics (IJBA) Jurnal Pelita Pengabdian SAINTEK
Asep Arwan Sulaeman
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering

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Analisis Sentimen Masyarakat Mengenai Kandidat Calon Presiden 2024 Dari Media Sosial Twitter Menggunakan Metode Naive Bayes Dan Feature Selection Particle Swarm Optimization Asep Arwan Sulaeman; Endrik
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

The 2024 Indonesian presidential election represents the realization of people’s sovereignty in selecting leaders who are aspirational, qualified, and responsible for public welfare. Public opinion plays a crucial role in shaping the popularity of each candidate, especially through social media platforms such as Twitter. The large volume of opinions shared online provides valuable data for analyzing public sentiment toward the top three presidential candidates in 2024. This study aims to analyze public sentiment regarding Anies Baswedan, Ganjar Pranowo, and Prabowo Subianto using the Naïve Bayes method combined with Feature Selection Particle Swarm Optimization (PSO) to improve classification performance. The evaluation metrics used in this research are Accuracy, Precision, and Recall to measure the effectiveness of the sentiment classification model. The results show varying performance across the three datasets. For Anies Baswedan, the model achieved an accuracy of 63.02%, recall of 65.13%, and precision of 64.61%. For Ganjar Pranowo, the highest performance was obtained with an accuracy of 87.14%, recall of 87.46%, and precision of 85.43%. Meanwhile, Prabowo Subianto’s dataset resulted in an accuracy of 83.17%, recall of 83.17%, and precision of 84.17%. Overall, the method demonstrated the best performance on Ganjar Pranowo’s dataset.
Sistem Informasi Inventory Sparepart Maintenance Berbasis Web Menggunakan Agile Software Development Pada PT. Unilever Indonesia Asep Arwan Sulaeman; Indri Lestari
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

The rapid advancement of information technology has encouraged digital transformation in industrial sectors, including the Maintenance Department at PT Unilever Indonesia. The department faces recurring challenges in managing machine maintenance, particularly during equipment failures and spare part replacements, due to the absence of a centralized and integrated system. Inefficient inventory management has affected operational effectiveness and asset control. This study aims to develop a web-based spare part inventory and maintenance information system using the Agile Software Development method. The system includes modules for asset data management, location tracking, asset status monitoring, and maintenance scheduling and documentation. Blackbox Testing was conducted to evaluate system functionality and ensure it meets internal user requirements. Implementation and user evaluation results show that the system improves work efficiency by up to 40%, reduces data entry errors, and accelerates spare part requests and maintenance scheduling. Additionally, it provides structured and traceable historical data to support data-driven decision-making. Overall, the system is expected to strengthen asset control, enhance maintenance accuracy, and support digital transformation within the company.
Co-Authors Adi Rusdi Widya Agus Suwarno, Agus Antika Zahrotul Kamalia Dian Aditya Edy Widodo Endrik Indri Lestari Nawangsih, Ismasari