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All Journal Economic Reviews Journal Jurnal Ilmiah Multidisiplin Indonesia
MacLaurin Hutagalung
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Agriculture, Biological Sciences & Forestry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Physics Social Sciences

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Pemanfaatan Agentic AI Model Chatgpt dari Openai untuk Meningkatkan Efektivitas Pengambilan Keputusan Operasional di CV. Central Telematika Sempurna Robertus Robby Julianto Simonaji; Maclaurin Hutagalung
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.v4i4.857

Abstract

The digital transformation era compels organizations to adopt intelligent technologies to enhance operational efficiency and decision-making effectiveness. One such emerging technology is Agentic AI, particularly ChatGPT by OpenAI, which operates autonomously and adapts to dynamic work contexts. This study aims to explore how the implementation of Agentic AI supports operational decision-making at CV. Central Telematika Sempurna, assess its impact on work efficiency, evaluate the leadership’s role in the adoption process, and uncover employee perceptions regarding its use. Employing a qualitative approach with an exploratory case study design, data were collected through in-depth interviews with 5 informants across various divisions, supplemented by non-participant observation and internal documentation. Thematic analysis was conducted using NVivo software, involving open coding, categorization, and interpretative synthesis. The findings are expected to offer deep insights into the contribution of Agentic AI in enhancing operational decision-making and to support strategic technology management in digitally transforming organizations.
Development of Low-Power IOT Devices with Edge Machine Learning on ESP32-S3-Cam for Early Detection of Rice Diseases: Supporting Agricultural Efficiency Maclaurin Hutagalung; Yoyok Gamaliel; Nella Puspita Manullang; T.A Nugroho; Dina Angela
Jurnal Ilmiah Multidisiplin Indonesia (JIM-ID) Vol. 5 No. 03 (2026): Jurnal Ilmiah Multidisplin Indonesia (JIM-ID), March 2026
Publisher : Sean Institute

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Abstract

  This study aims to develop an early detection system for rice plant diseases using a machine learning (ML) approach based on edge computing with ESP32-S3 Cam devices and the Edge Impulse platform. This system is expected to provide an efficient and cost-effective solution for detecting rice diseases in agricultural areas with limited internet and electricity access. In this study, CNN and MobileNetV2 models were used to classify rice leaf diseases, including brown spot, tungro, and blight, achieving 92.73% accuracy on the test dataset. This system is designed with an offline-first principle, allowing the device to operate locally by optimising power and memory usage. The model, which is optimised through quantisation and transfer learning, is small in size, only about 587 KB, and can be operated on devices with limited resources. In addition, this system can send notifications via Telegram and Google Sheets when connectivity is available. Field test results show that the system performs well across various environmental conditions, including low light and high humidity, with a detection accuracy of 90-95%. With innovations in lightweight ML models and edge computing, this study contributes to improving agricultural efficiency in Indonesia, especially in addressing the challenges posed by climate change that affect rice production. This research also provides insights for the further development of smart farming systems integrated with IoT technology for real-time disease detection.
Co-Authors Dina Angela Nella Puspita Manullang Robertus Robby Julianto Simonaji T.A Nugroho Widyanti Hidayat Yoyok Gamaliel