Journal of Deep Learning, Computer Vision and Digital Image Processing

Development of Android-Based Smart Learning Media for the Operating Systems Course Using the ADDIE Model

Kurnia Wahyu Prima — 2026-06-09

Purpose – The rapid advancement of digital technology has encouraged higher education institutions to integrate innovative learning media to enhance the quality of the teaching and learning process. However, learning activities in Operating Systems courses are still predominantly conducted using conventional methods, causing students to experience difficulties in understanding abstract concepts such as process management, memory management, CPU scheduling, and file systems. This study aims to develop Android-Based Smart Learning media for the Operating Systems course and to determine the feasibility level of the developed media as an interactive learning tool.
Method – This study employed a Research and Development (R&D) approach using the ADDIE model, which consists of five stages: Analysis, Design, Development, Implementation, and Evaluation. The developed product was validated by subject-matter experts and media experts before being implemented with students of the Informatics and Computer Engineering Education Program who were enrolled in the Operating Systems course. Data were collected through validation sheets and student response questionnaires using a five-point Likert scale and were analyzed using descriptive quantitative methods.
Results – The findings indicate that the Android-Based Smart Learning media was successfully developed by integrating learning materials, instructional videos, interactive quizzes, and automated feedback features into a single Android application. The material expert validation yielded a score of 89.00%, while the media expert validation achieved a score of 90.00%, both categorized as highly feasible. Furthermore, student responses obtained an average percentage of 90.27%, classified as very good. Therefore, the developed media was considered suitable for supporting the learning process in the Operating Systems course.
Research Implications – This study was limited to a single study program and Android devices; therefore, the generalizability of the findings remains limited.
Originality – This research integrates the concepts of mobile learning and smart learning into a single interactive learning medium specifically designed to support Operating Systems education in higher education institutions.

An Intelligent IoT-Based Waste Bin System Utilizing Nearest Neighbor Algorithms for Optimized Waste Collection Routes

Dinan Yulianto — 2026-06-18

Purpose – Despite advances in IoT-enabled waste monitoring, existing solutions generally fail to integrate real-time bin status information with adaptive route optimization, resulting in inefficient collection operations. This study aims to design and implement an integrated system that leverages real-time waste data to facilitate intelligent, data-driven route optimization for improved waste collection operations.
Methods –This study presents an ESP32-based smart waste system using reed switch event-driven control and deep-sleep mode for energy efficiency. Waste levels were estimated using the arithmetic mean fusion of four VL53L0X sensors. A cloud-based MQTT-over-TLS architecture enables secure real-time communication, whereas a priority-based nearest-neighbor routing algorithm is evaluated across 150 nodes.
Findings – The results demonstrate that the proposed system provides accurate waste-level estimation with a mean error of 1.98%, significantly reduces energy consumption by 90.9% through deep-sleep operation, and supports near-real-time communication with an average latency of 4.66 s. Moreover, the priority-based route optimization strategy decreased the travel distance by 42.7%, ensured the immediate servicing of all full-status bins, and maintained operational feasibility within a fleet capacity of 2,700 L.
Research implications – The evaluation results demonstrate the feasibility of integrating real-time monitoring and adaptive route optimization for smart waste management. Future research should extend the validation to large-scale real-world deployments and incorporate road network-based routing models to enhance operational realism and optimization accuracy.
Originality – This study proposes an integrated smart waste platform that combines energy-efficient event-driven sensing, dynamic priority-based nearest-neighbor routing, and hardware-assisted digital twin validation for scalable and cost-effective waste management evaluation.

RT-DETR-Based Computer Vision System for Real-Time Detection and Classification of Oil Palm Fruit Maturity Levels in Plantations

Nur Hafiqah Rambe — 2026-06-18

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.

Comparative Analysis of IndoBERT and BiLSTM For Public Sentiment Classification Toward The Indonesian National Police on Youtube

Hardeva Satria Hazz — 2026-06-18

Purpose – This study aimed to compare the performance of IndoBERT and Bidirectional Long Short-Term Memory (BiLSTM) in classifying public sentiment toward the Indonesian National Police (INP) based on YouTube comments. This study sought to identify a robust sentiment classification model to support text-based public perception monitoring, particularly under a highly imbalanced sentiment distribution.
Method – YouTube comments were collected using the YouTube Data API. A total of 8,268 raw comments were obtained, and 7,197 comments were retained as the final dataset after preprocessing, automatic pseudo-labeling, and confidence filtering using a 0.5 threshold. To address concerns regarding threshold selection, an additional sensitivity analysis was conducted using confidence thresholds of 0.65 and 0.75. The experiment applied a dual-track preprocessing pipeline, cost-sensitive learning through class-weighted loss, bootstrap confidence interval analysis, and BiLSTM preprocessing ablation.
Findings – The results show that IndoBERT achieved stronger performance than BiLSTM. IndoBERT obtained an accuracy of 92.92% and a Macro-F1 Score of 0.8548, whereas BiLSTM achieved an accuracy of 76.11% and a Macro-F1 Score of 0.6124. Bootstrap analysis showed a Macro-F1 difference of 0.2424, with a 95% confidence interval of 0.1870 to 0.2959, indicating that IndoBERT’s advantage was statistically significant. Sensitivity analysis also confirmed that IndoBERT consistently outperformed BiLSTM across all the tested thresholds.
Research Implications – The findings indicate that IndoBERT is more suitable for Indonesian sentiment classification in public perception monitoring than other models. However, because the dataset labels were generated using a BERT-based classifier, the evaluation may contain architectural circularity that favors the IndoBERT model. Future studies should use human-annotated gold-standard data and broader cross-platform validations.
Originality – This study provides a comparative evaluation of transformer-based and recurrent models using sensitivity analysis, bootstrap testing, cost-sensitive learning, and pre-processing ablation under imbalanced sentiment conditions.

Design and Implementation of an AI Agent-Based Workflow Automation System for Scheduling and Information Dissemination in Oil Palm Plantations

Septianur Eka Amri — 2026-06-25

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.

Comparative Analysis of Facial Feature Extraction in RGB and Near-Infrared Images Using YOLOv11 for Edge-Deployed Driver Monitoring

Ahmadil Barokah — 2026-06-25

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.