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All Journal Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains IT JOURNAL RESEARCH AND DEVELOPMENT INOVTEK Polbeng - Seri Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar Jurnal Teknologi Informatika dan Komputer Jurnal Computer Science and Information Technology (CoSciTech) Jurnal PkM (Pengabdian kepada Masyarakat) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi BATOBO: Jurnal Pengabdian Kepada Masyarakat CSRID
Hadiwandra, T Yudi
Universitas Riau
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Journal : CSRID

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Implementasi Deep Learning Untuk Identifikasi Jenis Biji Kopi Menggunakan Metode Convolutional Neural Network Pratama, Munawwar Anugrah; Hadiwandra, T. Yudi
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.387-398

Abstract

Indonesia is one of the largest coffee producers in the world, with various types of coffee beans such as Arabica, Robusta, and Liberica. Each type of coffee bean has unique characteristics that influence the taste, aroma, and overall quality of the coffee. However, many people are still unable to visually distinguish between these types of beans. This research aims to develop a Deep Learning-based system using the Convolutional Neural Network (CNN) method with the Xception architecture to identify coffee bean types from images. The dataset was obtained from direct image collection and online sources, then processed through preprocessing and data augmentation stages. The model training process was conducted using transfer learning techniques to improve classification performance. The resulting model is capable of classifying coffee bean images into three main categories with an accuracy 81.63%. The system is implemented as a web interface using Flask, allowing users to upload images of coffee beans and obtain classification results via a website. This study demonstrates that the CNN method with Xception architecture is effective for visual recognition of coffee bean types and can be a solution to help the general public in identifying different coffee bean varieties. This study aims to develop a deep learning–based system using the Convolutional Neural Network (CNN) method with the Xception architecture to identify coffee bean types from images. A total of 600 images of Arabica, Robusta, and Liberica beans were collected from primary and online sources, and then divided into training (80%), validation (10%), and testing (10%) sets. The dataset was processed through image preprocessing and augmentation techniques such as rotation, flipping, zooming, and brightness adjustment to improve model generalization. The training was performed using a transfer learning approach, followed by fine-tuning several deeper layers to enhance feature extraction. Evaluation was conducted using a confusion matrix and F1-score to validate class-wise performance. The model achieved an accuracy of 81.63% using the testing dataset. In practical implementation through a Flask-based website, the system achieved above 90% accuracy for several input angles, indicating strong recognition ability under controlled image conditions. This work demonstrates that the CNN Xception model is effective for visual identification of coffee bean types and can be applied as a practical solution to assist the general public, farmers, and coffee industry practitioners. Future enhancement may include expanding bean classes, optimizing architecture, and real-world testing.
Pemanfaatan Teknologi OCR dan JWT Authentication dalam Pengembangan Aplikasi Pencatatan Aset Multi-Platform Berbasis Flutter Agus Syuhada; T. Yudi Hadiwandra; Rayhan Al Farassy
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 2 (2026): Juni 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.2.2026.333-350

Abstract

Conventional asset management in the IT Division of PT Pertamina Hulu Rokan (PHR) causes data redundancy, manual errors, and slow validation. This study aims to build a multi-platform asset management application to improve recording effectiveness. The system integrates Optical Character Recognition (OCR) for faster asset label identification and Role-Based Access Control (RBAC) for security. Quality testing utilized the ISO/IEC 25010 standard and experimental testing. Results show the application successfully bridges operational needs via a mobile platform (Android) for Field Officers and a desktop platform (Windows) for Admins. ISO/IEC 25010 testing confirmed all main functionalities work validly. Experimental testing proved the application significantly reduces data redundancy and human errors. In conclusion, this multi-platform architecture effectively creates a centralized, accurate, flexible, and real-time asset management ecosystem, outperforming conventional methods.
Sistem Rangkuman Data (Data Summarization System) Berbasis Website dengan Mengintegrasikan Large Language Models (LLM) dan Spreadsheet Data Menggunakan Model Context Protocol (MCP) Rayhan Al Farassy; T. Yudi Hadiwandra; Agus Syuhada
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 2 (2026): Juni 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.2.2026.300-319

Abstract

Asset management in information technology at the IT Division of PT Pertamina Hulu Rokan currently faces scalability challenges due to reliance on conventional manual spreadsheet-based processes in managing approximately 30,000 asset data. This condition triggers the risk of human error, data redundancy, and time inefficiency in preparing asset condition recapitulation reports. This study aims to design a website-based Data Summary System that integrates Large Language Models (LLM) technology and spreadsheet data through the use of the Model Context Protocol (MCP) as a form of secure and scalable integration standard. System development was also carried out using a two-iteration prototype method, with Microsoft Azure infrastructure, the FastAPI framework (Python) on the backend side, and React.js on the frontend side. System quality testing was carried out based on the ISO/IEC 25010 standard which covers the eight main characteristics. The test results showed 100% achievement in the functional suitability aspect and the highest rating (Rating A) in the reliability, maintainability, and security aspects using SonarQube analysis. In terms of performance, the system demonstrated high responsiveness with a GTMetrix score of 100% and a usability level of 80.9 (Good category) on the System Usability Scale (SUS). The effectiveness of the system was tested through a One-Group Pretest Posttest experimental design on 8 IT employee respondents. The analysis results showed a good increase in productivity with a Normalized Gain value of 0.91 (High category) and an effectiveness percentage reaching 91.48% (Effective category). Therefore, this Data Summary System has proven successful in minimizing repetitive work steps and increasing accuracy and speed in managing IT assets at PT Pertamina Hulu Rokan.
Co-Authors Adila, Mutiara Dafa Agus Syuhada Alfarisy, Salman Andhi, Rahmat Rizal Arianda, Habil Putra As'Ari, Azi dwi saputra Feri Candra Fiantino, Dzaky Mahendri, Diffa Rahmanda Putra Mhd. Ripaldi Mutiara Dafa Adila Noveri Lysbetti M Oktory, Hafidh Dwika Pratama, Munawwar Anugrah Rahman, Salsabilah Aulia Rahyul Amri Rayhan Al Farassy Salhazan Nasution Salman Alfarisy Sasongko, Hariyo Shinta Fesri Wandira Wandira, Shinta Fesri Wasilah Wasilah