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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.

Development of an Android-Based QR Code Information System for Offline-First Palm Oil Harvest Recording in Plantations Bintang Permata Hati Simanjuntak; Raden Aris Sugianto; Andi Prayogi
Journal of Digital Technology and Computer Science Vol. 3 No. 3 (2026): August 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i3.791

Purpose – Manual palm oil harvest recording at remote plantations often causes transcription errors, duplicate records, delayed reporting, and difficulties in implementing fully online data collection because of unstable Internet connectivity. This study aimed to design and develop an Android-based palm oil harvest recording information system using Quick Response (QR) Code identification and an offline-first data architecture. Methods – This study employed a Research and Development approach using the Rapid Application Development model. Data were collected through field observations, interviews with five foremen and one administrative staff member, documentation reviews, and functional system testing. The application was developed using Android Studio and Flutter, with SQLite for local offline storage and Firebase Cloud Firestore for online data synchronization. Findings – The developed system enables QR Code-based worker identification, harvest data input, offline data storage, automatic synchronization, digital signature validation, activity-log monitoring, and report export. Black-box testing of seven functional scenarios showed that all tested features operated correctly in both offline and online conditions. Research implications – The findings indicate that an offline-first mobile architecture can support harvest recording in plantation areas with limited connectivity and low-cost devices. However, the system was tested in one plantation environment; therefore, broader implementation requires further multisite evaluation. Originality – This study contributes by integrating QR Code identification, SQLite-based offline persistence, Firebase synchronization, and Android-based field reporting into a single harvest recording system for remote palm oil plantation operations.

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