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All Journal International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah International Journal of Robotics and Control Systems Advance Sustainable Science, Engineering and Technology (ASSET)
Andrew Nafalski
University of South Australia
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Classification of Engineering Journals Quartile using Various Supervised Learning Models Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rasyid; Anik Nur Handayani; Andrew Nafalski; Edinar Valiant Hawali; Jehad A.H. Hammad
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1483.101-106

Abstract

In scientific research, journals are among the primary sources of information. There are quartiles or categories of quality in journals which are Q1, Q2, Q3, and Q4. These quartiles represent the assessment of journal. A classification machine learning algorithm is developed as a means in the categorization of journals. The process of classifying data to estimate an item class with an unknown label is called classification. Various classification algorithms, such as K-Nearest Neighbor (KNN), Naïve Bayes, and Support Vector Machine (SVM) are employed in this study, with several situations for exchanging training and testing data. Cross-validation with Confusion Matrix values of accuracy, precision, recall, and error classification is used to analyzed classification performance. The classifier with the finest accuracy rate is KNN with average accuracy of 70%, Naïve Bayes at 60% and SVM at 40%. This research suggests assumption that algorithms used in this article can approach SJR classification system.
Boosting and bagging classification for computer science journal Nastiti Susetyo Fanany Putri; Aji Prasetya Wibawa; Harits Ar Rasyid; Andrew Nafalski; Ummi Rabaah Hasyim
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.985

Abstract

In recent years, data processing has become an issue across all disciplines. Good data processing can provide decision-making recommendations. Data processing is covered in academic data processing publications, including those in computer science. This topic has grown over the past three years, demonstrating that data processing is expanding and diversifying, and there is a great deal of interest in this area of study. Within the journal, groupings (quartiles) indicate the journal's influence on other similar studies. SCImago provides this category. There are four quartiles, with the highest quartile being 1 and the lowest being 4. There are, however, numerous differences in class quartiles, with different quartile values for the same journal in different disciplines. Therefore, a method of categorization is provided to solve this issue. Classification is a machine-learning technique that groups data based on the supplied label class. Ensemble Boosting and Bagging with Decision Tree (DT) and Gaussian Nave Bayes (GNB) were utilized in this study. Several modifications were made to the ensemble algorithm's depth and estimator settings to examine the influence of adding values on the resultant precision. In the DT algorithm, both variables are altered, whereas, in the GNB algorithm, just the estimator's value is modified. Based on the average value of the accuracy results, it is known that the best algorithm for computer science datasets is GNB Bagging, with values of 68.96%, 70.99%, and 69.05%. Second-place XGBDT has 67.75% accuracy, 67.69% precision, and 67.83 recall. The DT Bagging method placed third with 67.31 percent recall, 68.13 percent precision, and 67.30 percent accuracy. The fourth sequence is the XGBoost GNB approach, which has an accuracy of 67.07%, a precision of 68.85%, and a recall of 67.18%. The Adaboost DT technique ranks in the fifth position with an accuracy of 63.65%, a precision of 64.21 %, and a recall of 63.63 %. Adaboost GNB is the least efficient algorithm for this dataset since it only achieves 43.19 % accuracy, 48.14 % precision, and 43.2% recall. The results are still quite far from the ideal. Hence the proposed method for journal quartile inequality issues is not advised.
Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems Tri Lathif Mardi Suryanto; Aji Prasetya Wibawa; Hariyono Hariyono; Andrew Nafalski
International Journal of Robotics and Control Systems Vol 3, No 4 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i4.1195

Abstract

Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.
Comparative Performance of Transformer Models for Cultural Heritage in NLP Tasks Tri Lathif Mardi Suryanto; Aji Prasetya Wibawa; Hariyono Hariyono; Andrew Nafalski
Advance Sustainable Science Engineering and Technology Vol. 7 No. 1 (2025): November-January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i1.1211

Abstract

AI and Machine Learning are crucial in advancing technology, especially for processing large, complex datasets. The transformer model, a primary approach in natural language processing (NLP), enables applications like translation, text summarization, and question-answer (QA) systems. This study compares two popular transformer models, FlanT5 and mT5, which are widely used yet often struggle to capture the specific context of the reference text. Using a unique Goddess Durga QA dataset with specialized cultural knowledge about Indonesia, this research tests how effectively each model can handle culturally specific QA tasks. The study involved data preparation, initial model training, ROUGE metric evaluation (ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-Lsum), and result analysis. Findings show that FlanT5 outperforms mT5 on multiple metrics, making it better at preserving cultural context. These results are impactful for NLP applications that rely on cultural insight, such as cultural preservation QA systems and context-based educational platforms.
Fixed sherwood duel optimization for time series imputation Agung Bella Putra Utama; Aji Prasetya Wibawa; Anik Nur Handayani; Andrew Nafalski
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2396

Abstract

Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased analyses and inaccurate predictions. Traditional imputation methods, such as mean, median, and mode substitution, provide computational efficiency but oversimplify temporal structures. At the same time, more advanced approaches, including Multiple Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN), offer improvements yet remain sensitive to data distribution and model configuration. To address this gap, this study introduces Sherwood Duel Optimization (SDO). This socio-inspired framework reconceptualizes imputation as a deterministic duel-based optimization problem. In its fixed form, SDO generates multiple candidate imputations and selects the most robust replacement value using a composite multi-metric scoring mechanism that integrates forecasting accuracy and explanatory power. The framework was evaluated using multivariate educational time-series data and further validated across heterogeneous SDG-related domains, and compared against classical and advanced baselines across three forecasting models. Experimental results demonstrate that SDO consistently outperforms existing methods, reducing forecasting error (MAPE) by more than 40%, achieving the lowest RMSE, and producing R² values exceeding 0.95. Statistical testing confirms that these improvements are significant across experimental configurations. These findings highlight the potential of SDO as a reliable, interpretable, and computationally efficient optimization-based imputation framework. By strengthening data reliability at the reconstruction stage, SDO enhances the credibility of downstream forecasting and decision-making in institutional and sustainability-oriented monitoring systems.
Minangkabau Language Stemming: A New Approach with Modified Enhanced Confix Stripping Fadhli Almu'iini Ahda; Aji Prasetya Wibawa; Didik Dwi Prasetya; Danang Arbian Sulistyo; Andrew Nafalski
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6511

Abstract

Stemming is an essential procedure in natural language processing (NLP), which involves reducing words to their root forms by eliminating affixes, including prefixes, infixes, and suffixes. The employed method assesses the efficacy of stemming, which differs according to language. Complex affixation patterns in Indonesian and regional languages such as Minangkabau pose considerable difficulties for traditional algorithms. This research adopts the enhanced fixed-stripping method to tackle these issues by integrating linguistic characteristics unique to Minangkabau. This study has three phases: data acquisition, pseudocode development, and algorithm execution. Testing revealed an average accuracy of 77.8%, indicating the algorithm's proficiency in managing Minangkabau’s intricate morphology. Nevertheless, constraints persist, particularly with irregular affixation patterns. Possible improvements could include adding more datasets, improving the rules for handling affixes, and using machine learning to make the system more flexible and accurate. This study emphasizes the significance of customized solutions for regional languages and provides insights into the advancement of NLP in various linguistic environments. The findings underscore the progress made in processing Minangkabau text while also emphasizing the need for further research to address current issues.
Co-Authors Agung Bella Putra Utama Aji Prasetya Wibawa Anik Nur Handayani Danang Arbian Sulistyo Didik Dwi Prasetya Edinar Valiant Hawali Fadhli Almu’iini Ahda Harits Ar Rasyid Hariyono Hariyono Hariyono Hariyono Jehad A.H. Hammad Nastiti Susetyo Fanany Putri Tri Lathif Mardi Suryanto Tri Lathif Mardi Suryanto Ummi Rabaah Hasyim