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Journal of Deep Learning, Computer Vision and Digital Image Processing
Published by Sakura Digital Nusantara
ISSN : 29868920     EISSN : 29868939     DOI : https://doi.org/10.61255/decoding
Core Subject : Science,
Computer Science & IT
The Journal of Deep Learning, Computer Vision and Digital Image Processing (DECODING), covers all topics of artificial intelligence and soft computing and their applications, including but not limited to: • Neural networks • Reasoning and evolution • Intelligent search • Intelligent planning • Intelligence applications • Computer vision and speech understanding • Multimedia and cognitive informatics • Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning • Technology and computing (like particle swarm optimization); intelligent system architectures • Knowledge representation • Bioinformatics • Natural language processing • Automated reasoning • Logic programming • Machine learning • Visual/linguistic perception • Evolutionary and swarm algorithms • Derivative-free optimisation algorithms • Fuzzy sets and logic • Rough sets • Simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc) • Multi-agent systems • Data and web mining • Emotional intelligence • Hybridisation of intelligent models/algorithms • Parallel and distributed realisation ofintelligent algorithms/systems • Application in pattern recognition, image understanding, control, robotics and bioinformatics • Application in system design, system identification, prediction, scheduling and game playing • Application in VLSI algorithms and mobile communication/computing systems
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 52 Documents
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A Web-Based Sentiment Analysis System for IMDb Movie Reviews Using TF-IDF and Multinomial Naïve Bayes Syahrur Ramadhan; Sadr Lufti Mufreni
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.v4i2.1385

Abstract

Purpose – The rapid growth of online movie platforms has produced large volumes of user reviews that contain valuable audience opinions. However, manual review analysis is inefficient. This study aims to develop a web-based sentiment analysis application using TF-IDF feature representation and the Multinomial Naïve Bayes algorithm to classify movie reviews into positive and negative sentiments.Methods – The model was trained and evaluated using the IMDb 50K Movie Reviews dataset with an 80:20 train–test split. An additional 600 reviews from six different movies were used to demonstrate application-level implementation. Text preprocessing included cleaning, lowercase normalization, tokenization, stopword filtering, and lemmatization using Natural Language Processing techniques. The processed texts were transformed into TF-IDF vectors and classified using Multinomial Naïve Bayes with the default smoothing parameter (α = 1.0). The trained model was deployed in a Flask-based web application for interactive sentiment prediction.Findings – The model achieved an accuracy of 84.93%, with precision, recall, and F1-score showing relatively balanced performance across positive and negative classes. The web application successfully classified movie reviews and displayed sentiment distributions through an interactive interface.Research implications – The findings indicate that lightweight machine learning methods can support practical web-based sentiment analysis with low computational demands. However, performance may decline when processing sarcasm, irony, or implicit contextual meaning.Originality – This study combines benchmark evaluation with web-based validation using 600 additional real-world movie reviews, demonstrating practical applicability beyond dataset-level testing.
Comparative Analysis of Facial Feature Extraction in RGB and Near-Infrared Images Using YOLOv11 for Edge-Deployed Driver Monitoring Ahmadil Barokah; Dewi Permata Sari; Agum Try Wardhana
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.v4i2.1397

Abstract

Purpose – This study proposes a robust edge-computed Driver Monitoring System (DMS) using the YOLOv11 architecture to detect driver fatigue across daytime RGB and nighttime near-infrared (NIR) environments.Methods – A lightweight YOLOv11 model was trained on an augmented multi-spectral dataset of 2,289 images containing two critical fatigue markers: drowsy_eye and open_mouth. For real-time deployment on a resource-constrained Raspberry Pi 4B, the model was compiled into an optimized ONNX format with a 240 × 320 pixel input matrix. A Temporal State Machine using strict logical conjunction (AND logic) was integrated to process sequential frame updates and reduce false-positive alerts caused by micro-blinking.Findings – Under live multi-spectral stationary cabin hardware evaluation, the integrated prototype achieved real-time inference of 22.9–72.4 FPS in daytime RGB conditions and 20.7–28.7 FPS in nighttime NIR conditions. In total darkness, NIR feature extraction remained stable, with empirical confidence ranges of 0.70–0.82 for drowsy_eye and 0.93–0.94 for open_mouth. The state machine successfully confirmed microsleep events lasting more than two seconds and triggered synchronized voice alerts with a randomized LED array as a chaotic counter-fatigue sensory stimulus.Research implications – The system demonstrates the feasibility of deploying advanced AI-based DMS models on low-power, standalone, cloudless edge hardware for automotive safety applications.Originality – This study presents a multi-illumination RGB–NIR comparative evaluation of an ONNX-optimized YOLOv11 model integrated with an active randomized LED counter-fatigue intervention loop.
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.
Laravel Dashboard for Immature Oil Palm (TBM III) Monitoring Using XYZ Tiles and Large Language Models Maghfirah; Ritna Wahyuni; 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.v4i2.1449

Abstract

Purpose – This research addresses the strategic urgency of digitalizing plantation monitoring to achieve precision agriculture at PTPN IV Regional I. The monitoring of Immature Plants (TBM) III currently relies on fragmented manual spreadsheets, leading to data redundancy and delayed analysis.Methods – A web-based data visualization dashboard was developed using the Laravel framework, integrating Geographic Information Systems (GIS) with Static Raster Tiling (XYZ Tiles) to optimize high-resolution map rendering. The system incorporates Large Language Model (LLM) API integration (Gemini 1.5 Flash and Llama 3) for prescriptive analytics, transforming biometric growth data into automated maintenance recommendations through prompt engineering.Findings – Results indicate that the system achieves significant workflow simplification by transforming the fragmented, multi-stage manual reporting pipeline into an automated, single-step data ingestion process, successfully reducing administrative touchpoints. The Static Raster Tiling (XYZ Tiles) technique successfully reduced high-resolution orthophoto rendering latency from over 12,000 ms to an average of 180 ms. Validation using Fleiss' Kappa statistics yielded a score of 0.8105, categorized as "Almost Perfect Agreement," confirming that the AI-generated recommendations are highly consistent with expert agronomic standards. Research implications – This system provides a comprehensive managerial evaluation tool, bridging the gap between raw field data and strategic decision-making in oil palm management.Originality – The integration of spatial optimization and prescriptive AI analytics offers a novel approach compared to existing descriptive-only monitoring platforms.
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.
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.
Automatic Detection of Toxic Content in Short Videos Using Deep Learning-Based Text and Audio Feature Integration Heri Agus Supriyanto; Ryan Ari Setyawan; Jemmy Edwin Bororing
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.v4i2.1540

Abstract

Purpose – This study develops and evaluates a multimodal classification model combining audio and text features via configurable weighted late fusion to detect toxic content in keyword-retrieved Indonesian short videos containing profanity-related contexts, addressing text-only detection limitations.Methods – The pipeline utilized FFmpeg for audio extraction, MFCC for audio features, and Google Speech Recognition for text. Three fusion configurations (Audio-Text: 40%–60%, 50%–50%, and 60%–40%) combining a DNN and BiLSTM were evaluated on 1,484 manually labeled Indonesian short videos.Findings – The Audio 60%–Text 40% configuration achieved the numerically highest test accuracy of 93.94% with a 95% confidence interval of 90.62%–96.13%, using a decision threshold of 0.60 selected from the validation set. The model obtained an F1-score of 0.95 for the toxic class. Compared with unimodal baselines, all fusion models achieved higher accuracy, indicating the benefit of integrating audio and text features.Research implications – The findings suggest that multimodal audio-text fusion can improve Indonesian short-form video toxicity detection compared with audio-only or text-only models. However, the differences among the three fusion weighting schemes were not statistically significant based on McNemar’s test, so the audio-dominant configuration should be interpreted as the numerically best configuration in this dataset rather than as a universally superior setting.Originality – This study systematically compares unimodal baselines and configurable audio-text late-fusion weighting strategies for Indonesian short-form video toxicity detection. The study also applies validation-based threshold selection, confidence intervals, and pairwise McNemar testing to provide a more reliable evaluation of multimodal model performance.
Performance and Security Analysis of Academic Information System Integration with Cloud Computing Technology Muhammad Ridho Ardiansyah; Mahmud; Ibnu Aqil
Journal of Deep Learning, Computer Vision, and Digital Image Processing Volume 4 Issue 1 March 2026
Publisher : CV. Sakura Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61255/decoding.v4i1.978

Abstract

Purpose – The implementation of cloud computing technology for integrating Academic Information Systems (SIAKAD) offers solutions to challenges regarding centralized infrastructure, limited scalability, and data security issues faced by higher education institutions. This study aims to analyze and evaluate the performance and data security aspects of academic information systems integrated via cloud computing platforms.Methods – Employing both quantitative and qualitative approaches, the performance evaluation focuses on access speed, scalability, and operational efficiency. Meanwhile, the security analysis examines data protection mechanisms both at rest and in transit as well as compliance with cloud security standards.Findings – The results indicate that cloud-based integration significantly enhances access speed, service availability, and the flexibility of academic data management.Research implications – However, the findings also identify that data security and privacy governance remain critical challenges, necessitating the implementation of rigorous access controls such as encryption and identity management—to mitigate cyber threats.Originality – In conclusion, the adoption of cloud technology yields substantial efficiency improvements in academic services, provided it is accompanied by a comprehensive and structured cybersecurity strategy.
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.

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