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All Journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Letters in Information Technology Education (LITE) International Journal of Robotics and Control Systems
Chuttur, Mohammad Yasser
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Requirements for an Online Automated Project Allocation System in Higher Education Institutions – A Case Study Khurwolah, Mooneerah; Chuttur, Mohammad Yasser
Letters in Information Technology Education (LITE) Vol 3, No 2 (2020)
Publisher : Universitas Negeri Malang

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Abstract

This paper presents the requirements gathered for an online automated project allocation system that can be used to assign final year projects to students registered in Higher Education Institutions (HEIs). The requirements are gathered for a well-known University in Mauritius. This research is motivated by several issues encountered with the current manual system in place at the studied institution and the need for adopting online systems following the COVID-19 outbreak. Following document analysis and a survey, important functional and non-functional requirements for an online automated project allocation system were uncovered. Gathered requirements also helped in determining a recommended workflow that can be adopted as best practice for final year project allocation. We posit that requirements presented in this paper can help develop a system that can be very useful and ultimately streamline the process for allocating projects typically important for Higher Education Institutions and other similar training institutions.
Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Handayani, Anik Nur; Chuttur, Mohammad Yasser
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1328

Abstract

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.
Understanding Student Acceptance of AI in Mojokerto Regency High Schools and a Framework for Effective Integration Iswanti, Usmanur Dian; Ridwandono, Doddy; Faroqi, Asif; Chuttur, Mohammad Yasser; Suryanto, Tri Lathif Mardi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24993

Abstract

Background: The use of AI in education is growing rapidly, especially in adaptive learning and automated feedback. Recent studies show widespread adoption of AI in higher education, but research at the secondary school level is limited. Factors such as ease of use, motivation, and institutional support play an important role accepting these technologies. Objective: The objective of this study is to investigate the acceptance and usage of the Question.AI application among high school students in Mojokerto Regency, to identify the factors that influence its adoption and effectiveness in enhancing learning outcomes. Methods: The methodology adopted for this research comprises a quantitative study design using a probability sampling method, specifically the Stratified Random Sampling technique. A total of 400 high school students from Mojokerto Regency participated. Data collection was conducted through structured questionnaires designed to evaluate factors influencing the adoption of the Question.AI application. Result: The result revealed that Facilitating Conditions (FC), Habit (H), and Hedonic Motivation (HM) significantly influence students' behavioral intention to use the Question.AI application. Among these, Habit and Hedonic Motivation showed the strongest effect, indicating that students are more likely to adopt AI tools when their use becomes routine and satisfied. Conclusion: These results support the UTAUT2 framework and highlight the need for enjoyable user experiences and adequate support systems to drive sustained adoption. The findings contribute to understanding AI acceptance at the secondary education level and offer practical insights for integrating AI applications more effectively into school environments.
Flood Prediction using Artificial Neural Networks: Empirical Evidence from Mauritius as a Case Study Dhunny, A. Z.; Seebocus, Reena H.; Allam, Z.; Chuttur, Mohammad Yasser
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Artificial Neural Networks (ANN) has been well studied for flood prediction. However, there is not enough empirical evidence to generalize ANN applicability to small countries with microclimates prevailing in a small geographical space. In this paper, we focus on the climatic conditions of Mauritius for which we seek to investigate the accuracy of using ANN to predict flooding using locally collected data from 11 meteorological stations spread across the country. The ANN model for flood prediction presented in this work is trained using 20,000 climate data records, collected over a period of two years for Mauritius. Our input climate features are minimum temperature, maximum temperature, rainfall and humidity and our output decision is „flood‟ or „no flood‟. Using ANN, we achieved an accuracy of 98% for flood prediction and hence, we conclude that ANN is indeed a good predictor for flood occurrence even for regions with predominantly microclimatic conditions.
A Comparison of Machine Learning Models to Prioritise Emailsusing Emotion Analysis for Customer Service Excellence Chuttur, Mohammad Yasser; Parianen, Yashinee
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further.
Optimizing Random Forest Algorithm to Classify Player's Memorisation via In-game Data Alzuhdi, Akmal Vrisna; Ar Rosyid, Harits; Chuttur, Mohammad Yasser; Nazir, Shah
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. Then, we trained and optimized three Random Forest (RF) classifiers to predict the player's Memorization. We chose RF because it can generalize well given the high-dimensional dataset. We used RF as the classifier, subject to optimization using its hyperparameter: n_estimators. We implemented a Grid Search Cross Validation (GSCV) method to identify the best value of n_estimators. We utilized the statistics of GSCV results to reduce the weight of n_estimators by observing the region of interest shown by the graphs of performances of the classifiers. Overall, the classifiers fitted using the BEST n_estimators (i.e., 89, 31, 89, and 196 trees) from GSCV performed well with around 80% accuracy. Moreover, we successfully identified the smaller number of n_estimators (OPTIMAL), at least halved the BEST n_estimators. All classifiers were retrained using the OPTIMAL n_estimators (37, 12, 37, and 41 trees). We found out that the performances of the classifiers were relatively steady at ~80%. This means that we successfully optimized the Random Forest in predicting a player's Memorization when playing the Chem Fight game. An automated technique presented in this paper can monitor student interactions and evaluate their abilities based on in-game data. As such, it can offer objective data about the skills used.
Co-Authors Aji Prasetya Wibawa Allam, Z. Alzuhdi, Akmal Vrisna Anik Nur Handayani Asif Faroqi Dhunny, A. Z. Harits Ar Rosyid Iswanti, Usmanur Dian Khurwolah, Mooneerah Nazir, Shah Parianen, Yashinee Ridwandono, Doddy Seebocus, Reena H. Utama, Agung Bella Putra