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SISTEM MONITORING IOT BOBOT MUATAN DAN LIVE TRACKING PADA KENDARAAN PENGANGKUT TBS BERBASIS MQTT-NEXTJS Muhammad Hatta Ridho; Andi Prayogi; Ratu Mutiara Siregar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8264

The development of Internet of Things (IoT) technology provides opportunities for to improve the efficiency of Fresh Fruit Bunches (FFB) transportation in oil palm plantations. Common challenges include difficulties in monitoring vehicle locations in real time and recording load weights manually. This study aims to design and develop a load weight monitoring and live tracking system for an FFB transportation vehicle prototype using an ESP32 microcontroller, an HX711 Load Cell sensor, a Neo-6M GPS module, the Message Queuing Telemetry Transport (MQTT) protocol, and a NextJS-based monitoring dashboard. The research methodology included hardware design, software development, system integration, and functional testing. The Load Cell sensor measured the load weight, while the GPS module obtained real-time vehicle coordinates. The ESP32 processed the collected data and transmitted them via MQTT to a broker and database for visualization on the dashboard. The test results showed that the system successfully monitored load weight and tracked the prototype vehicle in real time, achieving a 100% data transmission success rate under normal network conditions. The developed system provides effective, accurate, and integrated transportation monitoring, improving the efficiency of FFB transportation management in oil palm plantations.

Design and Development of a Web-Based Information System for Palm Oil Derivative Product Education Febriani Putri Wulandari; Ritna Wahyuni; Andi Prayogi; M. Ilham Saputra
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.839

Purpose – Public literacy regarding palm oil derivative products remains limited, while structured and accessible educational resources are still scarce. This study aimed to design and develop a web-based information system that provides organized educational content on palm oil derivative products and supports public access to information on downstream palm oil industries. Methods – This study employed a library research approach using secondary data from scientific literature, government publications, and official references related to palm oil derivative products. System requirements were analyzed and modeled using Unified Modeling Language (UML). The system was developed using HTML, CSS, PHP, MySQL, the Laravel Framework, and Laragon. Functional evaluation was conducted using Black Box Testing on 14 core system features. Findings – The study produced a web-based educational information system containing categorized content on oleofood, oleochemical, and bioenergy products, supported by article, video, search, and administrative content-management features. Functional testing showed that all 14 test scenarios were successfully completed, resulting in a functional success rate of 100%. Research implications – The findings indicate that the system is functionally reliable for delivering structured educational information on palm oil derivative products. However, the evaluation was limited to functional testing and did not assess usability, user satisfaction, or educational effectiveness. Originality – This study contributes a specialized web-based educational platform that integrates categorized palm oil derivative product information, multimedia resources, and centralized content management within a single system.

Classification of Oil Palm Fresh Fruit Bunch Ripeness Levels Using the YOLOv11n Algorithm Ananda Apri Anata; Ratu Mutiara Siregar; Andi Prayogi
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.894

Purpose – Manual assessment of oil palm fresh fruit bunch (FFB) ripeness remains subjective and may reduce harvest quality consistency. This study aims to evaluate YOLOv11n for six-class FFB ripeness detection using instance-level object detection metrics. Methods – A Roboflow dataset of 17,437 augmented images was split into training, validation, and held-out test subsets across six classes: Empty Bunch, Less Ripe, Abnormal FFB, Ripe FFB, Unripe FFB, and Overripe. A qualitative consistency check was conducted by one harvest foreman. Findings – Evaluation on 1,756 held-out test images containing 6,372 FFB instances achieved precision of 0.968, recall of 0.980, F1-score of 0.974, mAP50 of 0.988, and mAP50-95 of 0.901. Overripe was the weakest class, with mAP50-95 of 0.846. Research implications – The results indicate that YOLOv11n has strong potential to support automated FFB ripeness grading. However, broader field validation, multi-annotator agreement analysis, and testing under more diverse plantation conditions are still required. Originality – This study contributes a six-class YOLOv11n-based FFB ripeness detection evaluation using held-out test-set instance-level metrics and strict localization assessment through mAP50-95.

PALMARA: A Location-Aware Digital Intermediation Platform for Transparent Smallholder Palm Oil Distribution Bayu Danuarta; Andi Prayogi; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.961

Purpose – Crude Palm Oil (CPO) distribution using tanker trucks faces challenges including delayed reporting, limited visibility, and potential cargo losses due to leakage or human error. This study develops an IoT-based dashboard for real-time monitoring and threshold-triggered leakage alerting during CPO distribution. Methods – A prototype was developed and tested in a controlled laboratory-scale environment. The system integrated an ESP32 microcontroller with a YF-S201 flow sensor, HC-SR04 ultrasonic level sensor, and NEO-6M GPS module. Sensor data were transmitted via MQTT, visualized on a web dashboard, and stored in a Supabase cloud database for historical tracking and operational review. Findings – Testing showed average error rates of 2.48% for the flow sensor and 2.67% for the ultrasonic level sensor. The GPS module captured location data across all test points. The system supported four simulated distribution phases: Loading, Transportation, Unloading, and Completed. During Transportation, a leakage alert was generated when outward flow was detected, confirming that the rule-based alert mechanism operated according to predefined logic. Research Implications – The system offers a prototype framework for improving transparency, traceability, and operational monitoring in CPO logistics. However, field validation using industrial-grade sensors and full-scale tanker truck deployment is required. Originality – This study integrates flow monitoring, tank level measurement, GPS tracking, cloud storage, and rule-based leakage alerting in a single IoT dashboard for CPO tank truck distribution.

IoT-Based Dashboard System for Real-Time Monitoring and Rule-Based Leakage Alerting in CPO Tank Truck Distribution Abud Jabidi; Andi Prayogi; Muhammad Akbar Syahbana Pane
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.973

Purpose – Crude Palm Oil (CPO) distribution using tanker trucks faces challenges including delayed reporting, limited visibility, and potential cargo losses due to leakage or human error. This study develops an IoT-based dashboard for real-time monitoring and threshold-triggered leakage alerting during CPO distribution. Methods – A prototype was developed and tested in a controlled laboratory-scale environment. The system integrated an ESP32 microcontroller with a YF-S201 flow sensor, HC-SR04 ultrasonic level sensor, and NEO-6M GPS module. Sensor data were transmitted via MQTT, visualized on a web dashboard, and stored in a Supabase cloud database for historical tracking and operational review. Findings – Testing showed average error rates of 2.48% for the flow sensor and 2.67% for the ultrasonic level sensor. The GPS module captured location data across all test points. The system supported four simulated distribution phases: Loading, Transportation, Unloading, and Completed. During Transportation, a leakage alert was generated when outward flow was detected, confirming that the rule-based alert mechanism operated according to predefined logic. Research Implications – The system offers a prototype framework for improving transparency, traceability, and operational monitoring in CPO logistics. However, field validation using industrial-grade sensors and full-scale tanker truck deployment is required. Originality – This study integrates flow monitoring, tank level measurement, GPS tracking, cloud storage, and rule-based leakage alerting in a single IoT dashboard for CPO tank truck distribution.

Crude Palm Oil Moisture Reduction: Design and Implementation of an Arduino UNO-Based Automated Heating Control System Syem Vicra Lumban Gaol; Andi Prayogi; Raden Aris Sugianto
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.987

Purpose – Crude Palm Oil (CPO) quality is strongly influenced by moisture content because excessive water can accelerate hydrolysis, increase free fatty acid formation, promote oxidation, and reduce storage stability. This study aimed to develop and functionally evaluate a low-cost Arduino-based automated heating control prototype to support laboratory-scale CPO moisture-reduction experiments. Methods – This study employed a Research and Development method with a quantitative experimental orientation. The prototype integrated Arduino UNO as the main controller, DHT22 for ambient humidity monitoring, MAX6675 with a Type-K thermocouple for direct CPO temperature measurement, I2C 16×2 LCD for data display, a two-channel 5V relay for heater switching, a 350 W water heater, and LED-buzzer indicators. Functional testing was conducted through observation of sensor readability, LCD display, relay switching, heater response, and integrated system operation. Findings – The results showed that all main components operated according to their intended functions. The system could read humidity and CPO temperature, display real-time data, activate and deactivate the heater through relay control, and provide visual and audible indicators. Research Implications – The prototype provides a functional baseline for automated CPO heating control. However, it does not directly measure actual CPO moisture content because DHT22 only reads ambient humidity. Originality – This study offers a low-cost Arduino-based prototype for supporting CPO heating-control experimentation.

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

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

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

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

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.

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