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p-Index From 2021 - 2026
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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Ervin Gubin Moung
Universiti Malaysia Sabah
Computer Science & IT Electrical & Electronics Engineering

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A review of hyperspectral imaging-based plastic waste detection state-of-the-arts Owen Tamin; Ervin Gubin Moung; Jamal Ahmad Dargham; Farashazillah Yahya; Sigeru Omatu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3407-3419

Abstract

Plastic waste issues emerged from the build-up of plastics that negatively impacts the environment. As a result, plastic waste detection is proposed in many research studies to tackle the problems. Therefore, this paper aims to review hyperspectral imaging techniques and machine learning in plastic waste detection. Hyperspectral imaging techniques are found to be effective in detecting plastic waste and microplastics as they were able to capture plastic reflectance spectral by using the near-infrared sensor. However, the review also shows that hyperspectral imaging techniques were less efficient in capturing the electromagnetic spectrum of black plastics due to carbon-black absorption properties. Carbon-black strongly absorbs light in the ultraviolet and infrared spectral range of the electromagnetic spectrum, therefore not detected by the near-infrared sensor. This paper also reviews how machine learning can alternatively detect and sort all types of waste, including plastics. Multiple studies show that the machine learning model achieved good accuracy in detecting all types of plastics based on the waste dataset. Finally, it can be seen that the spectral information of plastic can be used as feature extraction for machine learning models for better plastic detection. It is hoped that this study will contribute to more systematic research on the same topic.
A review of solar drying technology for agricultural produce Mohd Khairulanwar Rizalman; Ervin Gubin Moung; Jamal Ahmad Dargham; Zuhair Jamain; Nurul’azah Mohd Yaakub; Ali Farzamnia
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1407-1419

Abstract

Agriculture contributes to large export earnings for many countries and provides food all over the world. However, most agricultural products need some post-harvest processing, such as drying, to extend their shelf life while still maintaining their respective nutrient quality. One popular post-harvest processing method is drying using solar energy. It is a type of renewable energy that is abundant and free. Conventional dryers use grid electricity and can be expensive to operate. Consequently, there is a growing need for cost-effective solar-powered agricultural dryers that is reasonable for smaller-scale farmers. Although current solar dryers are still not on par with modern electricity-powered dryers, solar dryers have lower running costs and are sustainable and able to generate electricity. They can also be used practically anywhere with abundant solar energy. As numerous solar drying technologies have been proposed over the past decade, it is necessary to assess the current state of solar drying technology in the agricultural sector to identify current advancements and potential research gaps. In this paper, a review of existing solar dryers mechanism and the state of the art of solar drying technology research for agricultural products is presented.
Effective dashboards for urban water security monitoring and evaluation Zada Qusyairin Mohd Zainuddin; Farashazillah Yahya; Ervin Gubin Moung; Bashirah Mohd Fazli; Mohammad Fikry Abdullah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i4.pp4291-4305

Abstract

This paper reviews the factors affecting effective dashboards for urban water security monitoring and evaluation. Urban water security is a constantly evolving field influenced by several factors, including changes in climate, ecosystems, socio-economic status, and human beings. Although urban water security has been discussed in some parts of the literature, there has been minimal literature review that focused on the factors of urban water security and the effective dashboards for monitoring and evaluation. Using systematic literature review (SLR) and preferred reporting items for systematic reviews and meta-analysis (PRISMA), this paper reviewed 143 articles. The result shows growth in the environmental informatics landscape since the last ten years when the first article on the urban water management dashboard was published. The visual design was the most frequently discussed factor for dashboards, followed by user customization. It also shows that this topic can go deeper to integrate both factors and design an effective environmental dashboard. The discussion identified three potential opportunities for future research in water security and informatics: i) exploring other dimensions of effective dashboards, ii) considering more research on the environmental dashboard, and iii) investigating the real-life application of dashboards in urban water security.
Object detection for waste management: a comparative review of models, challenges, and future directions Owen Tamin; Ervin Gubin Moung; Ali Farzamnia
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.10696

Abstract

Despite growing interest in automated waste detection, existing surveys either focus on a narrow set of models or lack systematic comparisons across object detection paradigms. This review addresses that gap by examining recent advances in deep learning for waste management, spanning two-stage detectors (Faster region-based convolutional neural network (Faster R-CNN) and Mask region-based convolutional neural network (Mask R-CNN)), single-shot frameworks (you only look once version 1 (YOLO)v1 to YOLOv11), and emerging Transformer-based models (ViT-WM and AL-DETR). Faster R-CNN achieved category-level accuracy of 91.68% and overall accuracy of 89.68%, while Mask R-CNN reported AP values between 26.2% and 34.5% across varied datasets. YOLO models demonstrated strong real-time capability, with YOLOv5 reaching a mAP@0.5 of 92.96% and YOLOv8 achieving 97.63% accuracy with precision and recall above 93%. Transformer-based approaches are especially promising: ViT-WM achieved 98.17% accuracy, the highest among reviewed models, and AL-DETR reported a mAP of 58.9% while integrating active learning (AL) strategies to reduce reliance on extensive labeled data. These results emphasize YOLO’s efficiency for real-time waste sorting and the potential of Transformer architectures for handling complex, cluttered environments. Remaining challenges include dataset variability, computational demand, and limited standardized benchmarks. Future research should prioritize developing comprehensive datasets, optimizing Transformers for real-time use, and leveraging AL to enhance generalizability with reduced annotation effort.
Multitask deep learning for sentiment analysis with sarcasm detection in bilingual code-mixed social media content Mohd Suhairi Md Suhaimin; Adi Wibowo; Ervin Gubin Moung; Patricia Anthony; Mohd Hanafi Ahmad Hijazi
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10935

Abstract

Sentiment analysis in social media often hindered by sarcasm, which can reverse text meaning, and bilingual code-mixing, which adds complexity in non-English primary context. Existing approaches extract separate features for each language and translate them into a single language, resulting in the loss of contextual meaning and omission of crucial features. This paper proposes a multitask learning model for sentiment analysis with sarcasm detection tailored to bilingual code-mixed social media content. A hybrid feature engineering technique is integrated into a multitask deep learning architecture designed to capture the nuances of sentiment and sarcasm while addressing the complexities of processing bilingual code-mixed content. The hybrid technique combines domain-knowledge-based natural language processing (NLP) with a deep learning-based embedding approach. It includes rule-based preprocessing, normalization, spellchecking, feature extraction and selection, and feature representation. The engineered features are integrated into a multitask deep learning network using bidirectional long short-term memory (Bi-LSTM) combined with gated recurrent units (GRU). Using a public dataset that contains bilingual code-mixed social media content related to public security, our proposed model achieved a higher F1score compared to two baseline models that employ single task and multitask approaches.
Co-Authors Adi Wibowo Ali Farzamnia Ali Farzamnia Bashirah Mohd Fazli Farashazillah Yahya Jamal Ahmad Dargham Mohammad Fikry Abdullah Mohd Hanafi Ahmad Hijazi Mohd Khairulanwar Rizalman Mohd Suhairi Md Suhaimin Nurul’azah Mohd Yaakub Owen Tamin Patricia Anthony Sigeru Omatu Zada Qusyairin Mohd Zainuddin Zuhair Jamain