Article Per Year (5 Year)
p-Index From 2020 - 2025
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This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Hanif Baharin
Universiti Kebangsaan Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Digitizing food experience: Food taste perception on digital image and true form using hashtags Afdallyna Fathiyah Harun; Norhafiza Ruslan; Wan Adilah Wan Adnan; Saiful Izwan Suliman; Juhaida Ismail; Hanif Baharin
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (550.133 KB) | DOI: 10.11591/eei.v9i5.2252

Abstract

Food experience is now omnipresent with the increased use of social media such as Instagram. Users often share food images or video which is often accompanied with #hashtags. Readers of the post are required to tap into their visual cognition and perception of what it represents. Little exploration has been done to understand if image can simulate food taste and how similar is the perception after user tasted the food. We were motivated to understand the difference of food taste perception on digital image and its true form by studying user hashtags. We applied the case study approach where we focused on a Malaysian dessert to compare user perception of the cakes’ dimension which are appearance, flavour, texture and hedonic. Using Instagram, users were requested to create hashtags that depict their taste perception of the cakes before and after tasting the cake. The hashtags were then analysed using content analysis where we found that the perception on digital image and true form had a degree of difference where many of the initial perception were inaccurate. This implies that visual images may not be able to facilitate accurate food taste perception and would need further technology interactivity to achieve the objective.
An exploratory study in conceptualizing user view on digital taste using design thinking Afdallyna Fathiyah Harun; Juhaida Ismail; Ho Yun Shiang; Nor Laila Md Noor; Hanif Baharin; Saiful Izwan Suliman
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 1: January 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i1.pp379-388

Abstract

Experiencing food involves accumulation of multiple senses, experienced through visuals, auditory and palatability. There has been significant growth in digitizing taste using electrical and thermal components to stimulate taste sensations where users need to lick devices or place a metal peripheral on their tongue. Such unnatural interaction appears undesirable motivating us to explore if taste experience could be stimulated by just viewing food visuals. This places considerable emphasis on understanding user association to food visuals and perceived food taste. Inability to understand their needs before-hand may result into a system with poor potentiality to trigger taste experience and not excite them to try the food they view on a digital platform. Using Design Thinking as an approach, we were able to identify user perceptions and expectations from food visuals as well as their desires on how images could trigger their interest to try the food. This paper presents the findings from the excursions and how this affected our strategy for the next phase of our research.
K-means variations analysis for translation of English Tafseer Al-Quran text Mohammed A. Ahmed; Hanif Baharin; Puteri Nor Ellyza Nohuddin
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.pp3255-3265

Abstract

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption.
Co-Authors Afdallyna Fathiyah Harun Afdallyna Fathiyah Harun Ho Yun Shiang Juhaida Ismail Juhaida Ismail Mohammed A. Ahmed Nor Laila Md Noor Norhafiza Ruslan Puteri Nor Ellyza Nohuddin Saiful Izwan Suliman Saiful Izwan Suliman Wan Adilah Wan Adnan