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All Journal JOIV : International Journal on Informatics Visualization Jurnal Teknik Informatika (JUTIF) International Journal of Advanced Science Computing and Engineering
Ramli, Azizul Azhar
Unknown Affiliation
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
Development of Smart Study Web Application for Classifying Student Material Understanding Levels Using Naive Bayes Classifier Susilo, Purnomo Hadi; Mujtahidah, Vita Ihwatin; Nawafilah, Nur Qomariyah; Ramli, Azizul Azhar
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5507

Abstract

The rapid development of information and communication technology requires adaptive digital learning systems that are able to evaluate students’ learning outcomes objectively. However, the Smart Study application previously functioned only as a quiz delivery platform and lacked analytical capabilities to assess students’ levels of material understanding, particularly in practical courses such as Computer Networks. This study aims to design and develop a web-based Smart Study application integrated with the Naive Bayes classification algorithm to determine students’ understanding levels based on quiz performance data. The research methodology includes data collection from Informatics Engineering students at Universitas Islam Lamongan, followed by data preprocessing through cleaning and categorical conversion of features, including final score, average response time, response time variability, and correct incorrect response time ratio. The dataset was divided into 80% training data and 20% testing data. The Naive Bayes model was trained and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The results show that the proposed model achieved an accuracy of 75%, correctly classifying 15 out of 20 testing samples. The model demonstrated strong performance in identifying the Comprehended class with an F1-score of 0.83, while performance for the Not Comprehended class was lower with an F1-score of 0.55 due to class imbalance. This study contributes to the fields of learning analytics and educational data mining by demonstrating the integration of a simple machine learning method into an e-learning application to support early detection of learning difficulties and data-driven evaluation of digital learning processes in higher education.
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 Mujtahidah, Vita Ihwatin Nawafilah, Nur Qomariyah Purnomo Hadi Susilo, Purnomo Hadi Shahreen Kasim, Shahreen Sukma, Fanni