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All Journal JOIV : International Journal on Informatics Visualization International Journal of Advanced Science Computing and Engineering
Ramli, Azizul Azhar
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Print It To Me Development System Dickenn, Haezel Ann; Ramli, Azizul Azhar; Ali Shah, Zuraini; Kasim, Shahreen
International Journal of Advanced Science Computing and Engineering Vol. 1 No. 1 (2019)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.326 KB) | DOI: 10.62527/ijasce.1.1.16

Abstract

This paper addresses the project where, by the end of the project, the web-based system is able to provide an online printing service system to any party or individual who are staying in-campus to make money with their personal printer. In-campus student and staff are allowed to register as service provider (SP) and they can view and accept order via this webbased system. The system are able to display and show requests by customers while the service providers can select the requests and provide proper service to them. Furthermore, the system should have limitless availability of access, compared to the current printing request process.
An Improved Flower Pollination Algorithm for Global and Local Optimization Kamboh, M. Iqbal; Mohd Nawi, Nazri Bin; Ramli, Azizul Azhar; Sukma, Fanni
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.5.4.738

Abstract

Meta-heuristic algorithms have emerged as a powerful optimization tool for handling non-smooth complex optimization problems and also to address engineering and medical issues. However, the traditional methods face difficulty in tackling the multimodal non-linear optimization problems within the vast search space. In this paper, the Flower Pollination Algorithm has been improved using Dynamic switch probability to enhance the balance between exploitation and exploration for increasing its search ability, and the swap operator is used to diversify the population, which will increase the exploitation in getting the optimum solution. The performance of the improved algorithm has investigated on benchmark mathematical functions, and the results have been compared with the Standard Flower pollination Algorithm (SFPA), Genetic Algorithm, Bat Algorithm, Simulated annealing, Firefly Algorithm and Modified flower pollination algorithm. The ranking of the algorithms proves that our proposed algorithm IFPDSO has outperformed the above-discussed nature-inspired heuristic algorithms.
Karonese Sentiment Analysis: A New Dataset and Preliminary Result Karo Karo, Ichwanul Muslim; Md Fudzee, Mohd Farhan; Kasim, Shahreen; Ramli, Azizul Azhar
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1119

Abstract

Amount social media active users are always increasing and come from various backgrounds. An active user habit in social media is to use their local or national language to express their thoughts, social conditions, socialize, ideas, perspectives, and publish their opinions. Karonese is a non-English language prevalent mostly in North Sumatra, Indonesia, with unique morphology and phonology. Sentiment analysis has been frequently used in the study of local or national languages to obtain an overview of the broader public opinion behind a particular topic. Good quality Karonese resources are needed to provide good Karonese sentiment analysis (KSA). Limitation resources become an obstacle in KSA research. This work provides Karonese Dataset from multi-domain social media. To complete the dataset for sentiment analysis, sentiment label annotated by Karonese transcribers, three kinds of experiments were applied: KSA using machine learning, KSA using machine learning with two variants of feature extraction methods. Machine learning algorithms include Logistic Regression, Naïve Bayes, Support Vector Machine and K-Nearest Neighbor. Feature extraction improves model performance in the range of 0.1 – 7.4 percent. Overall, TF-IDF as feature extraction on machine learning has a better contribution than BoW. The combination of the SVM algorithm with TF-IDF is the combination with the highest performance. The value of accuracy is 58.1 percent, precision is 58.5 percent, recall is 57.2, and F1 score is 57.84 percent
Co-Authors Ali Shah, Zuraini Dickenn, Haezel Ann Ichwanul Muslim Karo Karo Kamboh, M. Iqbal Mohd Farhan MD Fudzee, Mohd Farhan Mohd Nawi, Nazri Bin Shahreen Kasim, Shahreen Sukma, Fanni