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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
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Journal : journal of digital technology and computer science

 1 
Crude Palm Oil (CPO) Production Prediction Information System Using A Linear Regression Algorithm Erin Triani Sipayung; 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.v3i2.679

Abstract

Purpose – Crude Palm Oil (CPO) production fluctuates with Fresh Fruit Bunches (FFB) supply and operational conditions, making production planning difficult for palm oil mills. This study develops a CPO production prediction information system that integrates a simple linear regression model into a Progressive Web Application (PWA) to provide an accessible decision-support tool. Methods – The model was developed using simple linear regression based on 39 monthly production records. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²), and implemented in a PWA-based system using PHP and MySQL. Findings – The regression model produced the equation Y = -101.869 + 0.238X. The model achieved R² = 0.8716, MAE = 279.80 tons, and RMSE = 335.72 tons. With average monthly CPO production of 2,000–3,000 tons, the MAE represents an approximate error rate of 10–14%, indicating moderate predictive performance. Research implications – The findings are useful for preliminary production planning, but generalization is limited by the use of one predictor, 39 observations, one palm oil mill, and the absence of k-fold cross-validation. Originality – This study contributes by combining an interpretable linear regression model with a PWA-based system for real-time CPO prediction and visualization.
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

Abstract

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.
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.  
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