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All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF)
Ismail, Mohd Norasri
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Computer Science & IT

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Modified Alexnet Architecture for Classification of Cassava Based on Leaf Images Sholihin, Miftahus; Md Fudzee, Mohd Farhan; Ismail, Mohd Norasri; Wati, Efi Neo; Arshad, Mohamad Syafwan; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2966

Abstract

The objective of this study is to address the drawbacks of conventional classification approaches through the implementation of deep learning, specifically a modified AlexNet. The primary aim of this study is to precisely categorize the four distinct varieties of cassava, namely Manggu, Gajah, Beracun, and Kapok. The cassava dataset was obtained from farmers in Lamongan, Indonesia, and was used as a source of information. Data collection on cassava leaves was carried out with agricultural research specialists. A total of 1,400 images are included in the dataset, with 350 images corresponding to each variety of cassava produced. The central focus of this research lies in a comprehensive evaluation of the modified AlexNet architecture's performance compared to the original AlexNet architecture for cassava classification. Multiple scenarios were examined, involving diverse combinations of learning rates and epochs, to thoroughly assess the robustness and adaptability of the proposed approach. Among the evaluation criteria that were rigorously examined were accuracy, recall, F1 score, and precision. These metrics were used to determine the predictive capabilities of the model as well as its potential utilization in the actual world. The results show that the modified AlexNet design has better performance than the original AlexNet for recall, accuracy, precision, and F-1 score, all achieving a rate of 87%. In situations where a learning rate of 0.0001 and an epoch count of 150 are utilized, the performance of the approach stands out significantly, displaying an excellent level of competency. Nevertheless, it is crucial to recognize that distinct fluctuations in performance were noted within particular contexts and with diverse learning rates.
Fine-Tuned Transfer Learning with InceptionV3 for Automated Detection of Grapevine Leaf Diseases Sholihin, Miftahus; Zamroni, Moh. Rosidi; Anifah, Lilik; Fudzee, Mohd Farhan Md; Ismail, Mohd Norasri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.4717

Abstract

Grape leaf diseases pose a major threat to vineyard productivity, making early and accurate detection essential for modern grape plantation management. Despite advancements in computer vision, challenges remain in differentiating diseases with visually similar symptoms. This study addresses that gap by developing a grape leaf disease classification system using a fine-tuned deep learning model based on the InceptionV3 architecture. Three training scenarios were conducted with fixed parameters batch size of 32 and learning rate of 0.001while varying the number of epochs (25, 50, and 75). Results showed a consistent improvement in classification accuracy with increased training epochs, reaching 98.64%, 98.78%, and 99.09% respectively. Confusion matrix analysis revealed that most misclassifications occurred between visually similar diseases such as Black Rot and ESCA, but error rates declined as the number of epochs increased. Rather than merely applying transfer learning, this research highlights the impact of systematic tuning specifically epoch count optimization in enhancing model accuracy for difficult to distinguish disease classes. These findings underscore the urgency of developing high performance, automated disease detection tools to support precision agriculture and sustainable crop health monitoring.
Bridging Usability and Accessibility of User Authentication using Usable Accessed (UAce) for Online Payment Applications Mohamed, Juliana; Md Fudzee, Mohd Farhan; Ramli, Sofia Najwa; Ismail, Mohd Norasri; Defni, -
JOIV : International Journal on Informatics Visualization Vol 5, No 4 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.4.740

Abstract

Usability and accessibility are significant authentication aspects for online applications. Despite the fact that there are ongoing efforts to improve the interface design, some existing research only focuses on a single aspect of it. Thus, it is vital to investigate how to merge these two features into a practical and workable solution. This study presents a preliminary process for designing accessible and usable applications for online banking payment using Usable Accessed (UAce by adopting Design Science Research (DSR) as its methodology. The UAce standard considers attributes and characteristics from the user authentication. The standard establishes a development method and tool for assessing subjectively and quantitatively usable, as well as the user authentication while taking into account specific elements, qualities, and features. The DSR technique for developing highly usable and accessible interactive apps was utilized in designing this approach.
A Survey on Smart Campus Implementation in Malaysia Musa, Masitah; Ismail, Mohd Norasri; Md Fudzee, Mohd Farhan
JOIV : International Journal on Informatics Visualization Vol 5, No 1 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.1.434

Abstract

Universities around the world are actively implementing smart campuses. A smart campus is a campus environment capable of providing efficient technology and infrastructure in providing services to support and improve the teaching process, research, and student experience. It comprises initiatives to better support and enhances the better experience in the teaching and learning process and other services in the campus environment. To successfully implement the initiatives, a framework is required to define the scope of the implementation. Several universities in Malaysia are currently developing initiatives to implement their smart campus. This paper surveyed the literature and resources from universities in Malaysia to identify smart campus initiatives implemented following the smart campus domain. Due to the lack of resources available in the prominent database of indexed journal articles, the main source of review is based on official university sources such as official websites and so on. The result shows that all universities implemented all smart campus domains. Smart Management domain has the highest number of 58% of the overall initiatives. The second highest domain is Smart Learning at 13%, followed by Green Campus at 10%. We also identify that there is new domain of smart campus that was introduced. The new domain is Smart Research. Based on the survey, most universities in Malaysia are actively improving their work processes and the environment by implementing smart campus.
Co-Authors Arshad, Mohamad Syafwan Defni, - Fudzee, Mohd Farhan Md Gusman, Taufik Lilik Anifah M. Rosidi Zamroni Miftahus Sholihin Mohamed, Juliana Mohd Farhan MD Fudzee, Mohd Farhan Musa, Masitah Ramli, Sofia Najwa Wati, Efi Neo