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All Journal International Journal of Robotics and Control Systems Jurnal Abdi Masyarakat Indonesia
Chrismawan, Stephen Prasetya
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Humanities Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Public Health

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Manajemen Set Data Temperatur Packing Sealer di Pabrik Liquid Unilever Menggunakan Microsoft Power BI Kurniawan, Yogiek Indra; Chrismawan, Stephen Prasetya; Sunan, Huzaely Latief; Aditama, Maulana Rizki; Laksono, FX Anjar Tri
Jurnal Abdi Masyarakat Indonesia Vol 5 No 5 (2025): JAMSI - September 2025
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.2093

Abstract

Pabrik Liquid Unilever merupakan salah satu lini produksi yang memprioritaskan kualitas kemasan produk, khususnya jenis pouch, guna menghindari defect seperti kebocoran akibat temperatur penyegelan yang tidak optimal. Dalam upaya meningkatkan pemantauan kualitas kemasan, dilakukan Pengabdian Masyarakat yang bertujuan mengelola dan memvisualisasikan data temperatur mesin sealer menggunakan Microsoft Power BI. Data diperoleh dari sensor temperatur pada 21 mesin pengemasan dengan frekuensi pencatatan tinggi dan disimpan dalam file CSV secara berkala. Tahapan proyek meliputi pembersihan data (handling error, null, dan duplikasi), transformasi, pemodelan relasional, hingga pembuatan dashboard interaktif. Dashboard yang dikembangkan dilengkapi fitur filter berdasarkan tanggal, jenis mesin pouch, dan sensor temperatur (temp) sehingga memudahkan pengguna dalam menganalisis tren temperatur dan mengidentifikasi anomali yang dapat menyebabkan cacat kemasan. Evaluasi oleh mitra menunjukkan bahwa dashboard telah memenuhi kebutuhan pemantauan, meskipun performa pemuatan data masih perlu dioptimalkan. Proyek ini memberikan kontribusi nyata dalam pengambilan keputusan berbasis data di lini produksi serta dapat dikembangkan lebih lanjut melalui integrasi otomatisasi data dan fitur tambahan sesuai kebutuhan industri.
Accelerating Convergence in Data Offloading Solutions: A Greedy-Assisted Genetic Algorithm Approach Zulfa, Mulki Indana; Chrismawan, Stephen Prasetya; Hartoyo, Adhwa Moyafi; Nursakti, Wafdan Musa; Ahmed, Waleed Ali
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1652

Abstract

Data offloading, a technique that distributes data across the network, is crucial for alleviating congestion and enhancing system performance. One challenge in this process is optimizing web caching, which can be modeled as a dynamic knapsack problem in edge networks. This study introduces a Greedy-Assisted Genetic Algorithm (GA-Greedy) to tackle this challenge, accelerating convergence and improving solution quality. The greedy heuristic is integrated into the GA at two stages: during initialization to create a superior starting population, and at the end of each iteration to refine solutions generated through genetic operations. The GA-Greedy’s effectiveness was evaluated using the IRcache dataset, focusing on hit ratio—an indicator of successful cache accesses that reduces network load and speeds up data retrieval. Results show that GA-Greedy outperforms traditional GA and standalone greedy algorithms, especially with smaller cache sizes. For instance, with a 3K cache size, the half-greedy GA achieved a hit ratio of 0.55, compared to 0.2 for the pure GA and 0.1 for the greedy algorithm. Similarly, the full-greedy GA reached a hit ratio of 0.45. By enhancing convergence and guiding the search, GA-Greedy enables more efficient data distribution in edge networks, reducing latency and improving user experience.
Co-Authors Aditama, Maulana Rizki Ahmed, Waleed Ali Hartoyo, Adhwa Moyafi Kurniawan, Yogiek Indra Laksono, FX Anjar Tri Mulki Indana Zulfa, Mulki Indana Nursakti, Wafdan Musa Sunan, Huzaely Latief