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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Control Systems and Optimization Letters
Irianna Futri
Khon Kaen University
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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An Innovative Artificial Intelligence-Based Extreme Learning Machine Based on Random Forest Classifier for Diagnosed Diabetes Mellitus Dimas Chaerul Ekty Saputra; Elvaro Islami Muryadi; Raksmey Phann; Irianna Futri; Lismawati Lismawati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28690

Abstract

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.
An Extreme Gradient Boosting for Blood Disease Classification Using Hematological Parameters: A Comparative Evaluation with Ensemble and Non-Ensemble Models Dimas Chaerul Ekty Saputra; Vessa Rizky Oktavia; Irianna Futri; Affifah Mutiara Pertiwi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31659

Abstract

The early detection of hematological disorders remains challenging because many conditions share similar clinical characteristics and show substantial variation in laboratory measurements. Existing machine learning systems often struggle to maintain consistent accuracy in multi-class settings with imbalanced data. The research contribution is a multi-class diagnostic framework that identifies nine hematological disease categories using only routine laboratory parameters, supported by a leakage-free evaluation protocol and a comprehensive comparison across baseline classifiers. The proposed solution uses an extreme gradient boosting model as the primary classifier and evaluates it against support vector machine, random forest, and extra trees. The method includes data cleaning and numerical standardization, and class balancing with the Synthetic Minority Oversampling Technique applied only to the training subset within each fold of ten-fold cross-validation to prevent optimistic bias. Model performance is assessed using accuracy, precision, recall, and F1-score, together with computational efficiency measured through processing time and memory usage. The results show that the extreme gradient boosting model achieves the best overall performance, with an average accuracy of 98.67%, precision of 98.80%, recall of 98.67%, and an F1-score of 98.66%. It also demonstrates efficient memory usage and shorter processing time compared with the other tested methods. The competing models perform adequately but exhibit higher variability and weaker recognition for minority classes. In conclusion, these findings indicate that extreme gradient boosting provides an accurate and efficient approach for hematology-based multi-class disease classification when evaluated under a strict, leakage-free resampling protocol.
Broad Learning System: A Derivation-Based Mathematical Formulation Dimas Chaerul Ekty Saputra; Dyah Putri Rahmawati; Affifah Mutiara Pertiwi; Muhammad Ijaz Shafarin; Kharisma Monika Dian Pertiwi; Thinzar Aung Win; Irianna Futri; Pima Hani Safitri
Control Systems and Optimization Letters Vol 4, No 1 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i1.294

Abstract

Broad Learning System is a wide learning framework that constructs nonlinear feature representations while enabling efficient model training through analytical solutions. This paper presents a derivation-based formulation of Broad Learning System that explains the mathematical structure underlying the learning process. The model constructs an expanded feature representation through feature mapping nodes followed by enhancement nodes that further enrich the learned representation. The learning problem is then expressed as a linear model in the constructed feature space, and the output weights are obtained using ridge regularized least squares optimization. This formulation allows the training process to be solved directly using matrix operations without iterative gradient based procedures. In addition, an incremental learning mechanism is introduced to enable efficient parameter updates when new samples or additional nodes are incorporated into the model. The presented formulation highlights how Broad Learning System combines nonlinear feature construction with computationally efficient closed form learning, providing a clear theoretical interpretation of the learning process.
Co-Authors Affifah Mutiara Pertiwi Dimas Chaerul Ekty Saputra Dimas Chaerul Ekty Saputra Dyah Putri Rahmawati Elvaro Islami Muryadi Kharisma Monika Dian Pertiwi Lismawati Lismawati Muhammad Ijaz Shafarin Pima Hani Safitri Raksmey Phann Thinzar Aung Win Vessa Rizky Oktavia