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All Journal Jurnal Ilmiah Sistem Informasi Jurnal Bina Informatika dan Komputer (BINER)
Wibowo, Yuniar Satrio
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Computer Science & IT

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SISTEM INFORMASI LAYANAN CONTENT CREATOR PADA BANDHAWA PROJECT BERBASIS WEB Wibowo, Yuniar Satrio; Chamid, Ahmad Abdul; Murti, Alif Catur
Bina Informatika dan Komputer (BINER) Vol 3, No 1 (2025): Jurnal Bina Informatika dan Komputer (BINER)
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/biner.v3i1.12201

Abstract

ABSTRACT The development of service has turned into online media, to make it easier for customers and employees to managew work. In the case of the Bandhawa Project which includes digital marketing design services, sales are no tvery effective. The aim of the research is to build a service system entitled Grapich Design Service Sales Information System for Web-Based Bandhawa Project Vendors. Build web applications to claim business progress, including transaction facility services for customers. Therefore it can be concluded that the development of service sales information systems can facilitate the implementation of transactions by customers. Keywords : Information Systems, Graphic Design, Sales
Visi Robotika Berbasis Sosial: Memanfaatkan Data Komputer dan Media Sosial untuk Robotika Industri Adaptif Adjie Bangsawan; Farid, Ahmad; Wijayanto, Maulana; Tsani, Nabilla Mutiara; Wibowo, Yuniar Satrio
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 2 (2025): Mei : Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pt4vff36

Abstract

The rapid evolution of industrial robotics has been significantly influenced by the integration of computational data and social media, enabling robots to become more adaptive and responsive in collaborative work environments. This study investigates the role of social media and computational data in enhancing the adaptability of industrial robotics through machine learning techniques. By integrating sentiment analysis from social media with sensor data from industrial robots, this study examines how real-time data functions can improve robot decision-making and human-robot collaboration. Experimental results show a 23% increase in operational efficiency, an 89% accuracy rate in social interaction classification, and a 15% reduction in prediction errors. Furthermore, 62% of public sentiment toward adaptive robots is positive, highlighting the growing acceptance despite concerns over the impact of automation on jobs. These findings suggest that leveraging social media and computational data can significantly enhance the adaptability of robots, leading to a more efficient and socially conscious industrial ecosystem.
Co-Authors Adjie Bangsawan Ahmad Abdul Chamid ahmad farid Alif Catur Murti, Alif Catur Tsani, Nabilla Mutiara Wijayanto, Maulana