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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Applied Data Sciences Pengabdian: Jurnal Abdimas
Yulanda Yulanda
Universitas Hang Tuah Pekanbaru
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering

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Enhancing Educational Practices through Simple Online Learning Applications for Vocational Teachers at Abdurrab Pekanbaru Rika Melyanti; Yulanda Yulanda; Hendry Fonda
Pengabdian: Jurnal Abdimas Vol. 3 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/abdimas.v3i2.1388

Abstract

ABSTRACTBackground. The rapid advancement of technology in education necessitates the adoption of digital tools to facilitate effective teaching and learning processes. Purpose. The primary objective of this community service activity is to empower vocational teachers with an easy-to-use online learning platform that enhances their instructional methods and improves student engagement.Method. The application was developed using user-centered design principles to ensure it meets the specific needs of the teachers. Training sessions were conducted to familiarize the teachers with the application, followed by a pilot phase to gather feedback and make necessary adjustments. Results. The implementation of the online learning application resulted in increased teacher satisfaction, improved student participation, and a more interactive learning environment. Conclusion. The project demonstrates that simple, user-friendly digital tools can significantly enhance educational practices in vocational schools. Future community service activities should explore the long-term impacts of such applications on teaching efficacy and student outcomes.
An Explainable Multimodal Hybrid Deep Learning Framework for Real-Time IoT-Based Fish Freshness Classification Rian Ordila; Miratu Megasari; Yulanda Yulanda; Yuda Irawan; Refni Wahyuni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7844

Abstract

Reliable assessment of fish freshness supports product quality, food safety, and consumer acceptance throughout fisheries supply chains. Sensory inspection remains common, but reliance on individual judgment can produce inconsistent assessments and limit monitoring efficiency. Here, a multimodal framework combines Computer Vision, IoT sensing, and Deep Learning to monitor Nile tilapia freshness in realtime. Its visual branch uses YOLO26 to locate fish eyes and EfficientNet B3 with CBAM to classify the extracted regions. A BiLSTM network learns temporal patterns from MQ136 and MQ137 measurements of H₂S and NH₃ associated with spoilage. An MLP then combines the class probabilities from both branches to assign the final freshness category. In the detection experiment, YOLO26 recorded 0.930 precision and 0.953 mAP@0.5. Visual classification with EfficientNet B3 plus CBAM reached 92% accuracy, whereas sensor classification with BiLSTM reached 93%, with AUC values as high as 0.99. Combining the modalities yielded 97% accuracy and a macro average F1 score of 0.97, exceeding the performance of either branch alone. Grad CAM maps located the main visual responses within eye regions relevant to freshness deterioration. These results support the framework's interpretability and its potential use for intelligent fish freshness assessment under near realtime monitoring conditions.
Multimodal Deep Learning and IoT Sensor Fusion for Real-Time Beef Freshness Detection Bambang Kurniawan; Refni Wahyuni; Yulanda Yulanda; Yuda Irawan; Muhammad Habib Yuhandri
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.977

Abstract

Beef freshness quality is one of the important indicators in ensuring food safety and suitability. However, conventional methods such as manual visual inspection and laboratory testing cannot be widely applied in real-time and mass scale. To overcome these challenges, this study proposes a meat freshness detection system based on a multimodal approach that combines visual imagery and gas sensor data in a single IoT-based framework. This system is designed by utilizing the YOLOv11 architecture that has been optimized using the Adam optimizer. The dataset consisted of 540 original beef images, expanded into 1,296 images after augmentation. The model is trained on these augmented images and is able to achieve detection performance with a mAP@0.5 value of 99.4% and mAP@0.5:0.95 of 95.7%. As a further improvement, the cropped image features from the YOLOv11 model are processed through a combination of the ViT model and CNN to classify the level of meat freshness into three classes: Fresh, Medium, and Rotten with an accuracy of 99%. On the other hand, chemical data was obtained from the MQ136 and MQ137 gas sensors to detect H₂S and NH₃ levels which are indicators of meat spoilage. Data from visual and chemical data were then combined through a multimodal fusion method and classified using the Random Forest algorithm, producing a final prediction of Fit for Consumption, Need to Check, and Not Fit for Consumption. This multimodal model achieved a classification accuracy of 98% with a ROC-AUC score approaching 1.00 across all classes. While the proposed system achieved very high accuracy, further validation across diverse real-world environments is recommended to establish its generalizability.
Optimization of Machine Learning Models for Risk Prediction of DHF Spread to Support Management Strategies in Urban Areas Yesica Devis; Muhamadiah Muhamadiah; Yulanda Yulanda; Yuda Irawan; Refni Wahyuni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.898

Abstract

Dengue fever is an endemic disease that poses a serious threat to public health in tropical regions such as Indonesia. Efforts to control this disease require a data-based approach that is able to accurately predict the level of risk so that interventions can be targeted. This study aims to develop a predictive model of DHF risk using ensemble stacking method optimized with Optuna algorithm and integrated into an interactive dashboard based on Streamlit. The dataset used includes environmental, climate, and socio-demographic indicators from 2015 to 2024 with a total of 1,440 data entries. The preprocessing process includes normalization with Standard Scaler, feature selection using LASSO, and label data balancing with the SMOTE method. Model validation was performed using 10-Fold Cross Validation to ensure model generalization to new data. The stacking model is built with three basic algorithms, namely SVM, KNN, and Random Forest, which are combined using Logistic Regression as a meta-learner. The evaluation results show that the model is able to achieve an average accuracy of 97.57%, with high precision, recall, and f1-score values in all three prediction classes (low, medium, high). The ROC-AUC for each class also showed near-perfect performance. The implementation of the model in the Streamlit dashboard allows non-technical users such as health center or health office staff to perform regional risk prediction and obtain data-driven intervention recommendations automatically. This research not only contributes to the development of predictive technology, but also strengthens evidence-based health promotion practices in urban areas. Further research is recommended to integrate IoT-based real-time data and expand the scope of application areas.
Co-Authors Devis, Yesica Hendry Fonda Kurniawan, Bambang Megasari, Miratu Melyanti, Rika Muhamadiah Muhamadiah Muhammad Habib Yuhandri Refni Wahyuni Rian Ordila Yuda Irawan