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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) 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 Journal of Digital Technology and Computer Science
Andi Prayogi
Department Information Systems And Technology, Institut Teknologi Sawit Indonesia, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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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

Abstract

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 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.
Perbandingan Model Spasial Kesesuaian Lahan Kelapa Sawit di Pulau Sumatera Menggunakan Algoritma Machine Learning Ferdy Hardiansyah; Ratu Mutiara Siregar; Muhammad Akbar Syahbana Pane; Andi Prayogi
Journal of Computers and Digital Business Vol. 5 No. 2 (2026)
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i2.987

Abstract

Pulau Sumatera merupakan salah satu wilayah utama pengembangan kelapa sawit di Indonesia dengan karakteristik biofisik yang kompleks. Pemanfaatan lahan yang tidak mempertimbangkan kesesuaian biofisik berpotensi menurunkan produktivitas dan meningkatkan degradasi lingkungan. Penelitian ini bertujuan mengintegrasikan pendekatan berbasis aturan FAO dengan metode machine learning untuk memodelkan kesesuaian lahan kelapa sawit secara lebih interpretatif. Algoritma Decision Tree digunakan untuk mempelajari pola klasifikasi dari kriteria FAO dan dibandingkan dengan K-Nearest Neighbor (KNN). Variabel penelitian meliputi kemiringan lereng, curah hujan, suhu udara, pH tanah, tekstur tanah, kedalaman tanah, dan tutupan lahan. Dataset diperoleh dari ekstraksi data raster ke format tabular dengan pembagian data latih dan uji sebesar 80:20. Hasil penelitian menunjukkan kelas S2 mendominasi wilayah penelitian sebesar 61,06%, diikuti S3 sebesar 18,46%, S1 sebesar 14,26%, dan N sebesar 6,22%. Evaluasi cross-validation menunjukkan akurasi Decision Tree sebesar 88,94% dan KNN sebesar 87,18%. Decision Tree memiliki performa lebih stabil dan mudah diinterpretasikan. Penelitian ini menunjukkan integrasi FAO dan machine learning dapat mendukung perencanaan penggunaan lahan yang lebih objektif, transparan, dan berkelanjutan.
Palm Oil Quality Based on Free Fatty Acid Using SVM Andi Prayogi; Moustafa H. Aly; Ali Ikhwan; Muhammad Akbar Syahbana Pane; Ratu Mutiara Siregar
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24797

Abstract

Background: Palm oil is one of the key commodities in both the food and non-food industries, with its quality largely influenced by the level of Free Fatty Acid (FFA). Obejctive: High FFA content can reduce the stability and market value of the oil. Classify palm oil quality based on FFA levels using the Support Vector Machine (SVM) algorithm. Methods: FFA levels were measured across multiple samples with varying usage frequencies (0, 5, 7, and 9 cycles) using the alkalimetric titration method. The measured data was categorized as "Suitable" if FFA ≤ 0.3% and "Unsuitable" if it exceeded this threshold. The developed SVM model was trained using 70% of the data and tested with the remaining 30%. Results: Evaluation results indicate that the model achieved an accuracy of 95%, a precision of 92%, and a recall of 94%, demonstrating SVM's effectiveness in classifying data. Additionally, hyperplane visualization using PCA provided a clearer distinction between oil categories based on FFA levels. Conclusion: This study highlights that SVM can serve as an effective alternative for FFA-based palm oil quality classification. The implementation of this model is expected to enhance efficiency in the palm oil industry, particularly.
Performance Evaluation of YOLOv9, YOLOv10, and YOLOv11 for Real-Time Early Detection of Ganoderma Boninense in Oil Palm Rizky Delianngi; Ratu Mutiara Siregar; Nurliana; Muhammad Akbar Syahbana Pane; Phaklen Ehkan; Andi Prayogi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7479

Abstract

Early detection of Ganoderma boninense infection is essential to reduce yield losses in oil palm plantations. This study aims to evaluate the performance of three recent YOLO architectures, namely YOLOv9, YOLOv10, and YOLOv11, for real-time detection of early infection symptoms under natural field conditions. A dataset of 2,000 annotated RGB images was used with a 70:20:10 split for training, validation, and testing. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP50 and mAP50–95), and inference speed. The results show that YOLOv9 achieved the highest detection accuracy with an mAP50 of 0.989 and F1-score of 0.968. Meanwhile, YOLOv11 demonstrated the best computational efficiency with an inference speed of 35 FPS and processing time of 28.5 ms per frame. These findings indicate a trade-off between accuracy and speed, where YOLOv9 is suitable for accuracy-oriented applications, while YOLOv11 is more appropriate for real-time deployment in precision agriculture.
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 Aditya Pratama Ali Ikhwan Febriani Putri Wulandari Ferdy Hardiansyah Hasanal Fachri Satia Simbolon Ifan Gultom Khairul Saleh M. Ilham Saputra Maisarah Moustafa H. Aly Muhammad Akbar 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 Rahmad Dian Rasyid Abdullah Habib Syaban Ratu Mutiara Siregar Ready Perdana Ridho Agustiawan Rangkuti Rizky Delianngi Simbolon, Hasanal Fachri Satia Wira Jhohan Simatupang