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All Journal Riau Jurnal Teknik Informatika Journal of Informatics and Vocational Education
Arvi Pramudyantoro
Universitas Muhammadiyah Bangka Belitung
Computer Science & IT Education Social Sciences

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Generation of Batik Cual Bangka Belitung Motif Variations Using Stable Diffusion Models Rakha Piadika; Zikri Wahyuzi; Arvi Pramudyantoro
Journal of Informatics and Vocational Education Vol. 9 No. 1 (2026): Journal of Informatics and Vocational Education - March
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i1.3215

Abstract

Batik Cual Bangka Belitung is a visual cultural heritage characterized by repetitive motifs, fine line details, and high ornament complexity, presenting significant challenges for digital exploration without compromising its visual identity. This study aims to generate variations of Batik Cual motifs using a prompt-guided image-to-image (img2img) approach based on the Stable Diffusion model with a single-reference image. Furthermore, the research analyzes the influence of the strength parameter on the delicate balance between structural similarity and generative visual variation. The dataset consists of 13 independently collected Batik Cual motifs, pre-processed through RGB conversion and standardized to a resolution of 512×512 pixels. Controlled experiments were conducted using varied strength values of 0.4, 0.6, and 0.8, while maintaining other parameters constant. Quantitative evaluation utilized the Structural Similarity Index Measure (SSIM) to assess structural integrity and CLIP similarity to measure semantic alignment between the prompt and output image. The results indicate that increasing strength consistently decreases SSIM values, signifying greater structural deviation from the reference image, whereas CLIP similarity remains relatively stable across configurations. Quantitatively, a strength of 0.4 offers the optimal combination of structural similarity and semantic suitability. However, qualitative assessments reveal that for certain motifs, a strength of 0.6 produces a more balanced variation between pattern innovation and motif character preservation. These findings confirm a measurable trade-off between identity preservation and generative exploration, demonstrating the potential of Stable Diffusion as a controlled method for developing Batik Cual digital assets.
Pendeteksi Penyakit Daun Kentang Menggunakan Algoritma Convolutional Neural Network (CNN) Arvi Pramudyantoro; Muhamad Kurniawan; Hendi Hendra Bayu
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4740

Abstract

Potato leaf disease is one of the main problems in potato cultivation because it can reduce plant quality, decrease crop yield, and cause economic losses for farmers. Manual disease detection still has limitations because it depends on farmers’ experience and is prone to errors, especially when disease symptoms have similar visual characteristics. This study aims to apply the Convolutional Neural Network (CNN) algorithm to predict potato leaf diseases based on digital images. The dataset used in this study was obtained from Kaggle and consisted of 1,500 potato leaf images divided into three classes: healthy leaves, early blight, and late blight. The research stages included dataset collection, data splitting into training, testing, and validation data, CNN modeling using Jupyter Notebook, model training with 50 epochs, model evaluation using a Confusion Matrix, and model implementation into a web-based system using Flask. The test results show that the CNN model was able to classify potato leaf diseases with an accuracy of 97%. These results indicate that CNN is effective in recognizing visual patterns in potato leaf images, such as color changes, spots, and leaf damage. This study is expected to serve as a basis for developing an early detection system for potato leaf diseases that is faster, more accurate, and easier for farmers to use.
Early Fire Detection Using IoT and Deep Learning Syandhu Dea Fermanda; Zikri Wahyuzi; Arvi Pramudyantoro
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3515

Abstract

Fire incidents in strategic facilities such as weapon storage rooms can cause severe damage, threaten personnel safety, and disrupt operational readiness. Conventional fire detection systems generally rely on smoke or temperature sensors, which often respond only after hazardous conditions reach a certain threshold. Therefore, this study proposes an early fire prevention system based on Internet of Things (IoT) and deep learning using CCTV cameras. The system was developed using the System Development Life Cycle (SDLC) with the Waterfall model. The object detection model employed YOLOv8 and was trained on a laptop before being deployed to a Raspberry Pi 5 as the real-time processing unit. The implemented hardware consisted of a Raspberry Pi 5, a Logitech webcam, monitor, keyboard, and mouse. Testing was conducted in a room measuring 6 m × 4 m × 3.5 m. The developed system successfully detected four object classes, namely fire, smoke, cigarette, and person. The implemented logic mechanism classified fire detection as a fire incident, while simultaneous cigarette and smoke detection was categorised as smoking activity with potential fire risk. In addition, the system successfully sent automatic warning notifications through Telegram, enabling faster response without continuous manual monitoring. The results indicate that combining YOLOv8, Raspberry Pi 5, and IoT communication can provide an effective, practical, and low-cost intelligent fire prevention solution for indoor strategic facilities.
Face Recognition-Based Attendance System for Village Officials Using YOLOv8n, ArcFace, and Web-Based Microservice Architecture Wenni Siswanti; Zikri Wahyuzi; Arvi Pramudyantoro
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3516

Abstract

The attendance system at Bencah Village has been conducted manually, creating vulnerability to data manipulation, recording errors, and lack of transparency in managing village official attendance data. This study aimed to design and develop a face recognition-based attendance system for Bencah Village officials using deep learning technology, implemented as a web-based platform. The system was developed using the Waterfall Software Development Life Cycle (SDLC) model with a layered microservice architecture, integrating face detection using the lightweight YOLOv8n model, face feature extraction using InsightFace buffalo_l with the ArcFace approach generating 512-dimensional embedding vectors, and identity matching using cosine similarity with a threshold of 0.5. The backend was built with Laravel 12 and Python Flask, containerized using Docker and communicating via REST API. Functional testing using the black-box testing method on 25 main system functions confirmed that all functions performed as expected. Face recognition performance evaluation on 30 test data yielded a Recognition Accuracy of 93.33%, a False Acceptance Rate (FAR) of 0%, and a False Rejection Rate (FRR) of 10%. The FAR of 0% confirmed the system successfully prevented unauthorized identity acceptance, which is critical for attendance data integrity. These results demonstrate the system is feasible as a biometric attendance solution for village government environments.
Co-Authors Hendi Hendra Bayu Muhamad Kurniawan Rakha Piadika Syandhu Dea Fermanda Wenni Siswanti Zikri Wahyuzi