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All Journal International Journal of Electrical and Computer Engineering TELKOMNIKA (Telecommunication Computing Electronics and Control) Buletin Ilmiah Sarjana Teknik Elektro
Yamin Thwe
Rajamangala University of Technology Thanyaburi
Computer Science & IT Electrical & Electronics Engineering

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Accurate fashion and accessories detection for mobile application based on deep learning Yamin Thwe; Nipat Jongsawat; Anucha Tungkasthan
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.pp4347-4356

Abstract

Detection and classification have an essential role in the world of e-commerce applications. The recommendation method that is commonly used is based on information text attached to a product. This results in several recommendation errors caused by invalid text information. In this study, we propose the development of a fashion category (FC-YOLOv4) model in providing category recommendations to sellers based on fashion accessory images. The resulting model was then compared to YOLOv3 and YOLOv4 on mobile devices. The dataset we use is a collection of 13,689, which consists of five fashion categories and five accessories' categories. Accuracy and speed analysis were performed by looking at mean average precision (mAP) values, intersection over union (IoU), model size, loading time, average RAM usage, and maximum RAM usage. From the experimental results, an increase in mAP was obtained by 99.84% and an IoU of 88.49 when compared to YOLOv3 and YOLOv4. Based on these results, it can be seen that the models we propose can accurately identify fashion and accessories categories. The main advantage of this paper lies in i) providing a model with a high level of accuracy and ii) the experimental results presented on a smartphone.
Neuromarketing case study: recognition of sweet and sour taste in beverage products based on EEG signal features Yuri Pamungkas; Riva Satya Radiansyah; Padma Nyoman Crisnapati; Yamin Thwe
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26626

Abstract

Consumers’ acceptance of a food product hinges on its taste. Culinary practitioners typically conduct organoleptic tests to evaluate a food/beverage’s taste. Organoleptic tests have a subjective nature, making a clear description difficult. In this study, we suggest implementing a brain signal-based electroencephalogram (EEG) taste assessment system to evaluate consumer responses to the tastes of a drink, specifically sour and sweet. The system distinguishes flavors based on EEG data. These classifiers, including recurrent neural network (RNN), long-short term memory (LSTM), and gated recurrent unit (GRU), are utilized for the classification process. Total 35 participants’ EEG data were recorded for this study. Temporal (T3 and T4) and centro parietal (CP1 and CP2) channels are used for recording. EEG signal processing involves filtering, artefact elimination, and band decomposition into delta, theta, alpha, beta, and gamma frequencies. In the time domain of clean EEG data, mean absolute value, standard deviation, and variance are used for signal feature extraction. Several classifiers (RNN, LSTM, and GRU) will be fed with the signal feature values as input. An accuracy of 88.62% was achieved using LSTM in the classification. The RNN and GRU models achieved classification accuracies of 88.56% and 87.15% respectively.
A Review of EEG Applications in Neuromarketing: Methods, Insights, and Future Directions Yuri Pamungkas; Yamin Thwe; Abdul Karim; Uda Hashim
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14375

Abstract

EEG is increasingly applied in neuromarketing as it provides direct insights into consumer cognition and emotion beyond traditional self-report measures. However, challenges such as small samples, low ecological validity, and methodological limitations hinder its broader real-world application. The research contribution is a comprehensive synthesis of 40 empirical studies that examine EEG applications in neuromarketing, highlighting methodological approaches, analytical techniques, key insights, and persistent gaps that define the current state of the field. This review applied a structured comparative method by extracting and analyzing details from published EEG-based neuromarketing studies, including sample characteristics, device specifications, stimuli types, analytical techniques, and outcomes. The data were organized into a review table and further examined for patterns, strengths, limitations, and emerging opportunities. The results reveal that EEG can reliably classify consumer preferences when paired with deep learning models, while EEG indices such as neural synchrony and frontal alpha asymmetry predict advertising effectiveness and purchase intention. Emotional and attentional processes were consistently reflected in ERP components, and multimodal integration with physiological and behavioral data improved predictive validity. Nonetheless, most studies relied on small, homogeneous samples and static laboratory stimuli, limiting generalizability. In conclusion, EEG holds strong potential for advancing neuromarketing research and practice, yet future work must address scalability, cross-cultural validation, and ecological realism to fully harness its promise.
Co-Authors Abdul Karim Anucha Tungkasthan Nipat Jongsawat Padma Nyoman Crisnapati Riva Satya Radiansyah Uda Hashim Yuri Pamungkas