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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) ILKOM Jurnal Ilmiah Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Journal of Information Systems and Technology Research Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Computers and Digital Business Wahana Internet of Things and Artificial Intelligence Journal SERVIRE: Jurnal Penelitian dan Pengabdian Jurnal Agro Fabrica Journal of Technology Informatics and Engineering Journal of Deep Learning, Computer Vision and Digital Image Processing The Indonesian Journal of Computer Science Jurnal Edukasi Citra Olahraga Bhakti: Jurnal Pengabdian dan Pemberdayaan Masyarakat Journal of Digital Technology and Computer Science
Ratu Mutiara Siregar
Institut Teknologi Sawit Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Design and Development of a Web-Based Attendance System Using QR Code ID Cards and Geofencing Validation Aslina Gulo; Ritna Wahyuni; Ratu Mutiara Siregar
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.v3i2.901

Abstract

Purpose – Manual attendance in plantation operations suffers from high administrative vulnerability and data fraud, creating an efficiency gap for remote work units. This study aims to develop a low-cost, web-based employee attendance information system utilizing static QR Code ID cards for Palm Oil Plant. Methods – The system was engineered using a plan-driven Waterfall lifecycle model, encompassing UML design, Laravel 10 framework implementation, a MySQL database layer, and rigorous verification via 22 granular Black Box Testing operational scenarios. Findings – The prototype successfully deployed multi-layered authentication features, returning a 100% "Compliant" status during testing. Single-user performance benchmarks recorded highly responsive empirical latencies: 320 ms for QR matrix string extraction, 145 ms for geofence processing, and 42 ms for MySQL database commits. Research Implications – The study's scope was limited by single-user benchmark testing and a lack of live database API integration into corporate SAP payroll systems. Practically, this architecture supports the technical feasibility of open-source web applications in shifting infrastructure weights away from enterprises operating in severe industrial dust environments. Originality – The value lies in the operational synthesis of an open-source framework, Haversine-driven geofencing, and visual capture customized for low-connectivity plantation workers. Future steps must focus on transitioning toward offline-first Progressive Web Apps (PWAs) and integrating lightweight machine learning models (such as FaceNet or MediaPipe) to transition from passive visual documentation to automated biometric verification.
AI-Assisted Barcode Inventory Management for Plantation Warehouse Operations in a Desktop Environment Bintang Florentina Aruan; Ritna Wahyuni; Ratu Mutiara Siregar
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.v3i2.933

Abstract

Purpose – This study aims to Development of a Desktop-Based Warehouse Inventory Management Application with Barcode Technology Integration Methods – The study used a Research and Development approach with the Waterfall development model. Data were collected through literature study, observation, and interviews, while system functions were validated using Black Box Testing. Findings – The developed application supports item data management, category, unit, warehouse location, incoming goods, outgoing goods, stock adjustment, inventory reporting, minimum stock alerts, and an Inventory AI Assistant as a supporting database-search feature. The testing results indicate that the main functions run according to the planned scenarios. Research implications – The system can serve as an initial solution to support more orderly and traceable inventory administration in a plantation warehouse environment. Originality – This study positions barcode as an item-identification mechanism in a local desktop application and includes an Inventory AI Assistant that is limited to database-based information retrieval.  
RT-DETR-Based Computer Vision System for Real-Time Detection and Classification of Oil Palm Fruit Maturity Levels in Plantations Nur Hafiqah Rambe; Ratu Mutiara Siregar; 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.v4i2.1281

Abstract

Purpose – This study aimed to develop an automated oil palm fruit maturity level detection system using the real-time detection transformer (RT-DETR) algorithm to overcome the limitations of conventional visual inspection methods, which are often subjective and inconsistent. This study evaluated the effectiveness of the RT-DETR in detecting and classifying oil palm fruit maturity levels to support quality control processes in plantation operations.Method – A computer vision-based approach was implemented using the RT-DETR-L object detection model. The dataset consisted of 14,620 annotated oil palm fruit images categorized into four maturity levels: unripe, underripe, ripe, and overripe. The research process included data collection, image annotation, preprocessing, model training, and evaluation of the model. The model performance was assessed using precision, recall, mean Average Precision (mAP@50), and inference speed metrics.Findings – The experimental results show that the RT-DETR-L model achieved a precision of 93.2%, 95.6%, and mAP@50 of 96.9%, respectively. The model successfully detected and classified oil palm fruit maturity levels across all categories with high accuracy. Furthermore, the model achieved an inference time of 25–28 ms per image and a processing speed of 10–14 FPS on an NVIDIA RTX 3050 4GB GPU, demonstrating its capability for real-time applications.Research Implications – The findings indicate that RT-DETR-L can improve the efficiency, consistency, and accuracy of oil palm fruit sorting and quality control processes. However, this study was limited to the available datasets and testing scenarios used. Future research should evaluate the model under diverse environmental conditions, lighting variations, and field deployment settings to improve its generalizability and robustness.Originality – Unlike previous studies that primarily employed CNN-based detectors or focused on binary maturity classification, this study investigated the application of a transformer-based RT-DETR-L architecture for detecting four oil palm fruit maturity categories. The results demonstrate that RT-DETR-L can provide high detection accuracy while maintaining real-time performance in smart agriculture applications.
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.
Performance Evaluation and Comparative Analysis of Small Solar Panels for IoT Battery Charging Using ESP32 Microcontroller Platform Saddam Husein Siregar; IzuKhairi Misrawi Rohali; Mohamad Rifa Algifari Mulia Sembiring; Sumaryanto; 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.v4i2.1525

Abstract

Purpose – Small photovoltaic panels are widely used to support low-cost Internet of Things (IoT) sensor nodes; however, many locally available panels provide only nominal voltage labels without complete electrical specifications. This study evaluates the charging performance of three small solar panel configurations for a single 18650 lithium-ion battery used in an IoT-based monitoring node.Methods – The experimental system used Lolin D32 boards based on ESP32, TP4056 charging modules, and identical battery connections. Node 1 used a large 9 V solar panel connected to a DC-DC step-down module calibrated to 5 V before entering the TP4056 input. Node 2 used a medium 6 V panel, while Node 3 used a mini solar panel directly connected to the charging module. Battery voltage was recorded every five minutes, converted into apparent state of charge (SoC), and uploaded to Google Sheets. The main field test was conducted from 10:00 to 15:00 on 8 June 2026.Findings – Node 1 produced the highest apparent charging performance, with a charging rate of 12.8 percentage points per hour and a total SoC increase of 63.8 percentage points. In contrast, Node 2 and Node 3 each achieved only 0.5 percentage points per hour during the same test period. Research implications – The findings indicate that nominal voltage alone is insufficient for selecting solar panels for IoT battery charging. Panel area, current capability, charger compatibility, firmware measurement sequence, and direct field validation must be considered.Originality – This study provides practical field-based evidence for selecting small photovoltaic panels in low-cost IoT battery charging applications.
Perbandingan Support Vector Regression dan Long Short-Term Memory untuk Prediksi Harga Crude Palm Oil Berjangka Khoirun Nisa Harahap; Ratu Mutiara Siregar; Raden Aris Sugianto
Journal of Computers and Digital Business Vol. 5 No. 3 (2026): Articles in Press
Publisher : PT. Delitekno Media Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i3.996

Abstract

Indonesia sebagai produsen Crude Palm Oil (CPO) terbesar dunia memerlukan sistem prediksi harga yang akurat untuk mendukung pengambilan keputusan strategis. Penelitian ini membandingkan kinerja algoritma Support Vector Regression (SVR) dan Long Short-Term Memory (LSTM) dalam memprediksi harga CPO berjangka Bursa Malaysia menggunakan 1.426 data harian dari Investing.com periode 2 Januari 2020 hingga 30 Desember 2025. Data diproses melalui pembersihan, normalisasi Min-Max, dan pembentukan sekuens dengan lookback 30, lalu dibagi secara kronologis dengan rasio 80:20. SVR menggunakan kernel Radial Basis Function (RBF) dengan penyetelan parameter melalui GridSearchCV, sedangkan LSTM dibangun dengan arsitektur dua lapis dan Early Stopping. Evaluasi dengan RMSE dan MAPE menunjukkan kedua model mencapai akurasi sangat baik dengan MAPE di bawah 2% pada data uji. SVR memperoleh MAPE 1,29% dan RMSE 76,17 MYR/ton, sedangkan LSTM memperoleh MAPE 1,32% dan RMSE 75,22 MYR/ton. Perbedaan antar model sangat kecil sehingga tidak dapat diklaim signifikan secara statistik tanpa uji formal. Temuan ini mengindikasikan bahwa SVR dan LSTM memiliki kapabilitas setara untuk prediksi harga CPO jangka pendek berbasis data historis, dengan keterbatasan yang sama dalam menghadapi guncangan eksternal yang tidak terepresentasi pada variabel input.
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.
Co-Authors Abdussalam Al Akbar Alex Rizky Saputra Ali Ikhwan Alimuddin Yasin Alisarjuni Padang Ananda Apri Anata Andi Prayogi Andi Prayogi Andi Prayogi Annisa Annisa Ardila Aslina Gulo Asnita Yani Awal, Elsa Elvira Balqis Nurmauli Damanik Bayu Ath Thariq Syams Bintang Florentina Aruan Busrizal Faisal Chengly Jaxon Boahlander Siahaan Elfitra Erin Triani Sipayung Fadilah, Sandi Fajar Maulana . Ferdy Hardiansyah Friska Anggraini Barus Guntoro, Guntoro Hiskia Sitanggang Ifan Gultom IzuKhairi Misrawi Rohali Kan, Phak Len Al Eh Khairul Saleh Khoirun Nisa Harahap Kisah Tiara Sihombing Liga Mayola Mahyuddin K. M Nasution Maisarah Maisarah Maisarah, Maisarah Makhrani Sari Ginting Maudy Hellena Harlyn Mohamad Rifa Algifari Mulia Sembiring Mohammad Farodis Azhari Moustafa H. Aly Muhammad Akbar Syahbana Pane Muhammad Akbar Syahbana Pane Muhammad Akbar Syahbana Pane Muhammad Hatta Ridho Muhammad Ikbal Aritonang Muhammad Yusuf Dibisono Mulyara, Budi Nabil Azzaidan Nasution Navea, Roy Francis Niko Firzi Anansyah Nur Hafiqah Rambe Nurliana Phaklen Ehkan Prayogi, Andi Raden Aris Sugianto Rahmad Dian Rahmad Dian Rahmad Dian Rahmamnsyah, Eka Patma Ready Perdana Ritna Wahyuni Rizky Delianngi Saddam Husein Siregar Sari, Yessi Ratna Sepriano Sepriano Septianur Eka Amri Shaira Fitdina Amelia Silky Safira Simbolon, Hasanal Fachri Satia Sri Wahyuna Saragih Sumaryanto Syerlina Sari Tuty Ningsih WARDATUL HUSNA IRHAM Wisnu Ananta Kusuma Yasir, Amru Zia