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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) JOIV : International Journal on Informatics Visualization Indonesian Journal of Electrical Engineering and Computer Science Journal of Digital Market and Digital Currency
Azmi, Mohd Sanusi
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Real time hand gesture detection by using convolutional neural network for in-vehicle infortainment systems Yaakob Wan Bejuri, Wan Mohd; Asmai, Siti Azira; Ikram, Raja Rina Raja; Rahim, Nur Raidah; Khambari, Najwan; Azmi, Mohd Sanusi; Sholva, Yus
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp42-49

Abstract

Nowadays, a variety of technologies on autonomous vehicles have been extensively developed, including in-vehicle infotainment (IVI). It have been noted as one of the key services in the automobile industry. In the near future, people will be able to watch some virtual reality (VR) movies through the streaming service provided in the vehicle. However, a person sometime not tend to be joy while watching espcially when the remote controller or audio sensory controller lack of battery or too far from IVI panel. Thus, the purpose of this research is to design a scheme of real time hand gesture detection for in-vehicle infotainment system, in order to create human computer experience. In this research, the image of human palm hand will be taken by using camera for recognize the hand gesture action. This proposed scheme will recognize human gesture and convert to be computer intruction, that can be understood by IVI device. As a result, it show our proposed scheme can be the most consistent in term of accuracy and loss compared to others method. Overall, this research represents a significant step toward improving better user experience. Furthermore, the proposed scheme is anticipated to contribute significantly to the IVI field, benefiting both academia and societal outcomes.
Syllable Segmentation with Vowel Detection on Verse Quranic Recitation Setiyaningsih, Timor; Azmi, Mohd Sanusi; Draman, Azah Kamilah
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2663

Abstract

In speech recognition, segmentation involves partitioning a continuous audio signal containing speech into smaller units or segments, such as words, phonemes, or syllables. This process is paramount in speech recognition systems, as it delineates the boundaries between distinct speech elements, facilitating subsequent analysis and processing. Segmentation accuracy significantly impacts speech recognition systems' overall precision and performance, enabling more precise identification and processing of individual speech units. Moreover, proper segmentation empowers the automatic speech recognition (ASR) system to distinguish between different syllables or words effectively, leading to more efficient speech recognition outcomes.  This research paper investigates the importance of vowel detection for syllable segmentation in speech recognition, particularly in Arabic speech, such as the Quran, where changes in each syllable can alter the meaning. While existing techniques only consider pronunciation by different readers, this study employs onset detection to account for the presence of Arabic vowels. Specifically, the study focuses on detecting the onset of the recitation of Surah Al-Fatihah's fourth verse using 50 data sets in the syllable detection testing process. The results indicate that syllable detection performs excellently on syllables with /a/ and /i/ vowels. However, syllables with /u/ vowels produce results below 70%. The study suggests that the onset-based method is ideal for syllables with the presence of /a/and /i/ vowels, demonstrating the importance of considering Arabic vowel letters in speech recognition.
Word embedding and imbalanced learning impact on Indonesian Quran ontology population Utomo, Fandy Setyo; Purwati, Yuli; Azmi, Mohd Sanusi; Shafira, Lulu; Trinarsih, Nikmah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp603-613

Abstract

This research addresses limitations in Quranic instance classification, exceptionally high dimensionality, lack of semantic relationships in the term frequency-inverse document frequency (TF-IDF) technique, and imbalanced data distribution, which reduce prediction accuracy for minority classes. This study investigates the impact of word embedding and imbalance learning techniques on instance classification frameworks using Indonesian Quran translation and Tafsir datasets to handle previous research limitations. Four classification frameworks were built and evaluated using accuracy and hamming loss metrics. The results show that the synthetic minority oversampling technique (SMOTE) technique, TF-IDF model, and logistic regression classifier provide the best accuracy results of 62.74% and a hamming loss score of 0.3726 on the Quraish Shihab Tafsir dataset. This is better than the performance of previous classifiers backpropagation neural network (BPNN) and support vector machine (SVM) used in the previous framework, with accuracies of 59.91% and 62.26%, respectively. Logistic regression can also provide the best classification results with an accuracy of 67.92% and a hamming loss of 0.3208 using the previous framework. These results are better than the performance of the previous classifiers BPNN and SVM used in the previous framework, with accuracies of 62.26% and 66.98%, respectively. TF-IDF feature extraction outperforms word2vec in instance classification results due to its superior support under limited dataset conditions.
Factors Influencing User Adoption of Mobile Payment System: An Integrated Model of Perceived Usefulness, Ease of Use, Financial Literacy, and Trust Utomo, Fandy Setyo; Suryana, Nanna; Azmi, Mohd Sanusi
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.31

Abstract

In the digital age, mobile payment systems have revolutionized financial transactions by offering convenience, efficiency, and security. This study aims to explore the factors influencing the adoption of the mobile payment system in Indonesia, focusing on perceived usefulness (PU), perceived ease of use (PEU), financial literacy (FL), and perceived trust (PT). Data was collected from 400 respondents using an online survey and analyzed using SmartPLS 3 software. The results indicate that PU and PEU significantly impact users' intention to use (BI) the mobile payment system, with path coefficients of 0.928 (t-value = 28.570) and 0.955 (t-value = 154.251) respectively. PEU also positively influences PU (β = 0.955, p < 0.001). FL was found to affect PT significantly (β = 0.222, p = 0.006), which in turn influences BI (β = 0.068, p = 0.059), although the direct effect of PT on BI was marginally non-significant. The R^2 values for BI, PT, and PU were 0.977, 0.814, and 0.912 respectively, indicating a high explanatory power of the model. This study extends the Technology Acceptance Model (TAM) by integrating FL and PT, providing a comprehensive understanding of the factors driving mobile payment adoption. The findings offer valuable insights for developers, service providers, and policymakers to enhance user experience, build trust, and improve FL, ultimately promoting higher adoption rates of mobile payment systems. Future research should consider a more diverse population and explore additional factors such as social influence and facilitating conditions to validate and extend these findings further.
Co-Authors Asmai, Siti Azira Draman, Azah Kamilah Fandy Setyo Utomo Ikram, Raja Rina Raja Khambari, Najwan Rahim, Nur Raidah Shafira, Lulu Sholva, Yus Suryana, Nanna Timor Setiyaningsih, Timor Trinarsih, Nikmah Yaakob Wan Bejuri, Wan Mohd Yuli Purwati