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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Teknika Journal of Information Systems and Technology Research Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Computers and Digital Business Bulletin of Network Engineer and Informatics (BUFNETS) Journal of Technology Informatics and Engineering Journal of Deep Learning, Computer Vision and Digital Image Processing Jurnal Pengabdian Masyarakat Bangsa Journal of Digital Technology and Computer Science
Andi Prayogi
Department Information Systems And Technology, Institut Teknologi Sawit Indonesia, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering

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Journal : journal of deep learning computer vision and digital image processing

 1 
Design and Implementation of an AI Agent-Based Workflow Automation System for Scheduling and Information Dissemination in Oil Palm Plantations Septianur Eka Amri; Andi Prayogi; Ratu Mutiara Siregar
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1370

Abstract

Purpose – Oil palm plantation operations in Indonesia require coordination across multiple divisions; however, meeting scheduling and information dissemination are often managed through separate, manual processes. This may cause communication delays, scheduling conflicts, and inconsistent information deliveries. This study aims to design and implement an AI-based workflow automation system that integrates meeting scheduling and information dissemination into a centralized platform to support the automated coordination and information management across organizational units.Methods – This study employed the Design Science Research (DSR) approach, covering problem identification, literature review, system design, implementation, testing and evaluation. The proposed system integrates Gemini AI, Natural Language Processing (NLP), Telegram Bot, Zoom API, Google Calendar API, and Google Sheets to automate meeting scheduling, information dissemination, and document management.Findings – The implemented system successfully automated meeting scheduling, calendar synchronization, information dissemination, and documentation management within an integrated platform. Functional testing confirmed that the core features operated as intended in the scenarios evaluated. The system also supports information classification based on public and private access.Research implications – The system was evaluated through functional testing in a simulated oil palm plantation context and depends on third-party API services, which may limit its generalizability. User acceptance, organizational effectiveness, and efficiency were not evaluated. Nevertheless, the proposed architecture can be adapted to other organizational settings that require automated coordination and centralized information management.Originality – This study proposes an AI-based workflow automation architecture that integrates communication and productivity services to support the end-to-end automation of meeting scheduling and information dissemination in oil palm plantation operations.
Design and Implementation of a Dual-LLM Prescriptive ESG Reporting System in the Indonesian Palm Oil Industry Niko Firzi Anansyah; Ratu Mutiara Siregar; Andi Prayogi; Muhammad Akbar Syahbana Pane
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1404

Abstract

Purpose – This study aims to develop an automated Environmental, Social, and Governance (ESG) reporting information system based on Large Language Model (LLM) for the palm oil industry to overcome low efficiency, data inconsistencies, and analysis limitations inherent in manual reporting processes.Methods – This applied research employs the Design Science Research (DSR) paradigm, encompassing needs analysis, system design, implementation, and black-box testing. The system was developed using the Laravel MVC framework and integrated a Dual-LLM API failover architecture (Groq Llama 3 as primary and Gemini as backup). The case study was conducted at PT Surya Mata Ie.Findings – The developed system successfully automated ESG indicator extraction and prescriptive narrative generation. It utilizes a Strict Weighting Rule, a programmatic safeguard capping the ESG score at 50.0 (as a proof-of-concept testing constraint) if Ganoderma infection exceeds a 20% threshold (supported by agronomic research). During prototype evaluation, this rule intercepted an overly optimistic raw LLM score of 60.0 and corrected it to 50.0. This demonstrates the system's capability to function as a risk-control mechanism, mitigating potential hallucination-driven score inflation and supporting mathematically accountable outputs.Research implications – The implementation of this system significantly accelerates reporting workflows and serves as an early warning instrument for environmental risks, thereby enhancing real-time managerial decision-making and corporate transparency in complying with global sustainability standards.Originality – This study pioneers the integration of a Dual-LLM failover mechanism within a Laravel framework tailored for the palm oil sector. It introduces a novel programmatic constraint approach in JSON object parsing to maintain strict mathematical accountability in AI-generated ESG drafts.
YOLOv8-Based IoT System for Oil Palm Harvest Readiness Identification through Loose Fruit Detection with Real-Time Web Monitoring Wira Jhohan Simatupang; Andi Prayogi; Muhammad Akbar Syahbana Pane
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1447

Abstract

Purpose - This study develops and evaluates an Internet of Things (IoT)-based monitoring system for automatically identifying oil palm harvest readiness through loose fruit detection using the YOLOv8 algorithm. The system addresses the subjectivity, labor intensity, and inefficiency of conventional manual observation in large-scale plantations.Methods - An experimental design was applied using 2,000 images of oil palm loose fruits collected from the Indonesian Institute of Palm Oil Technology, North Sumatra. Images were captured using an ESP32-CAM, annotated through Roboflow, and used to train the YOLOv8x model for 120 epochs at a resolution of 640 × 640 pixels.Findings - The prototype performed image acquisition, loose fruit detection, and dashboard-based monitoring in near real time. On the independent testing dataset, the model achieved a precision of 0.933, recall of 0.954, F1-score of 0.944, mAP@0.5 of 0.943, and mAP@0.5:0.95 of 0.495. These results demonstrate the system’s feasibility as a prototype for supporting harvest-readiness monitoring, although broader field validation is still required.Research Implications - The dataset was obtained from a single plantation site and may not represent highly variable environmental conditions. Future studies should use larger and more diverse datasets and assess cloud-based deployment for improved scalability.Originality - This study integrates ESP32-CAM, YOLOv8x, IoT communication, and web-based monitoring into a prototype architecture for data-driven oil palm harvest-readiness assessment through loose fruit detection.
Development of an Integrated Web-Based Palm Oil Production Monitoring Dashboard Using Laravel at PT Paluta Inti Sawit Aditya Pratama; Raden Aris Sugianto; Andi Prayogi
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1470

Abstract

Purpose – Data management at PT Paluta Inti Sawit (PT PIS) currently faces efficiency constraints due to fragmented operational data for Fresh Fruit Bunches (FFB), Crude Palm Oil (CPO), and Kernel (PK) stored in scattered spreadsheets. This study aims to design and build an integrated web-based dashboard to centralize monitoring and automate production reporting.Methods – The research employs an applied system development and functional validation design following the Waterfall System Development Life Cycle (SDLC). The system was developed using the Laravel framework and MySQL database, with ApexCharts for near-real-time interactive visualization after data ingestion.Findings – The developed dashboard successfully integrates multi-departmental production data into a single source of truth. Key features include an automated Excel/CSV parser with conflict resolution (skip/overwrite), a two-level role-based verification system (Staff and Manager), and near-real-time KPI tracking for Oil Extraction Rate (OER) and Kernel Extraction Rate (KER) upon data approval. Research implications – This system accelerates the daily reporting cycle and supports data integrity through a digital audit trail, assisting rapid, data-driven managerial decisions.Originality – Unlike previous studies that focus on mobile-only reporting, this system provides a mill-level integrated prototype for synchronized FFB reception and processing yield analytics, minimizing manual recapitulation errors.
Development of an ESP32-Based Motor-Balancing Prototype for Fresh Fruit Bunch Transportation Using Crawler and Loader Wheel Modes Ridho Agustiawan Rangkuti; Andi Prayogi; Raden Aris Sugianto
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 2 June 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i2.1477

Abstract

Purpose – This study aimed to develop and evaluate an ESP32-based motor-balancing prototype for Fresh Fruit Bunch (FFB) transportation using crawler and loader wheel modes to address inefficiencies in manual FFB transportation on uneven plantation terrains.Methods – A prototype development approach was employed, consisting of problem identification, system requirement analysis, mechanical and electronic design, component testing, prototype assembly, and performance evaluation. The prototype integrated an ESP32 microcontroller, HC-12 wireless communication modules, an L298N motor driver, DC motors, a power supply system, and a substitute load container. Performance evaluation included basic movement testing, HC-12 wireless communication testing, terrain adaptability testing on simulated muddy and rocky surfaces, and load-carrying testing using substitute loads ranging from 500 to 2000 g.Findings – The developed prototype successfully executed forward, backward, left turn, right turn, and stop commands with a 100% success rate during repeated laboratory testing. Stable wireless communication was maintained up to 20 m using HC-12 modules, while the crawler and loader wheel configurations demonstrated stable mobility on simulated muddy and rocky terrains. The prototype successfully transported substitute loads of up to 1500 g, whereas a 2000 g load exceeded the available motor torque.Research implications – The prototype was evaluated only under laboratory conditions using simulated terrains and substitute loads. Therefore, further validation under actual plantation environments and with real Fresh Fruit Bunches is required before practical deployment.Originality – This study presents a laboratory-scale ESP32-based motor-balancing transportation prototype that integrates HC-12 wireless communication and interchangeable crawler-loader wheel modes into a single platform specifically designed for Fresh Fruit Bunch transportation in oil palm plantations.
Co-Authors Abud Jabidi Aditya Pratama Ali Ikhwan Ananda Apri Anata Bayu Danuarta Bintang Permata Hati Simanjuntak Febriani Putri Wulandari Ferdy Hardiansyah Hasanal Fachri Satia Simbolon Ifan Gultom Khairul Saleh M. Ilham Saputra Maisarah Maudy Hellena Harlyn Moustafa H. Aly Muhammad Akbar Muhammad Akbar Syahbana Pane Muhammad Akbar Syahbana Pane Muhammad Akbar Syahbana Pane Muhammad Hatta Ridho Mulyara, Budi Navea, Roy Francis Niko Firzi Anansyah Nurliana Pamitta Maulina Sijabat Phaklen Ehkan Raden Aris Sugianto Raden Aris Sugianto Rahmad Dian Rasyid Abdullah Habib Syaban Ratu Mutiara Siregar Ratu Mutiara Siregar Ready Perdana Ridho Agustiawan Rangkuti Ritna Wahyuni Rizky Delianngi Septianur Eka Amri Simbolon, Hasanal Fachri Satia Sri Lestari Rahayu Syem Vicra Lumban Gaol Wira Jhohan Simatupang