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Menampilkan 1–10 dari 28 artikel
Integrating Quantum, Deep, and Classic Features with Attention-Guided AdaBoost for Medical Risk Prediction
Kusuma, Muh Galuh Surya Putra
; Setiadi, De Rosal Ignatius Moses
; Herowati, Wise
; Sutojo, T.
; Adi, Prajanto Wahyu
; Dutta, Pushan Kumar
; Nguyen, Minh T.
Journal of Computing Theories and Applications
Vol 3
, No 2
(2025)
Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, dee...
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2 Sitasi
A Machine Learning Based Approach to Course and Career Recommendation System: A Systematic Literature Review
Iorzua, Joseph Tersoo
; Moses, Timothy
; Eke, Christopher Ifeanyi
; Agushaka, Ovre Jeffery
; Kwaghtyo, Dekera Kenneth
; Godswill, Theophilus
Journal of Computing Theories and Applications
Vol 3
, No 1
(2025)
Learners are continually faced with choosing appropriate courses or making career choices due to increased educational opportunities. The emergence of machine learning-based course and career recommender systems has the potential to address this issue, offering personalized course recommendations tailored to individual learning pathways, preferences, and learning history. The optimization and feature engineering techniques and practical deployment environments have not been collectively examined...
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3 Sitasi
Integrating Hybrid Statistical and Unsupervised LSTM-Guided Feature Extraction for Breast Cancer Detection
Setiadi, De Rosal Ignatius Moses
; Ojugo, Arnold Adimabua
; Pribadi, Octara
; Kartikadarma , Etika
; Setyoko, Bimo Haryo
; Widiono, Suyud
; Robet, Robet
; Aghaunor, Tabitha Chukwudi
; Ugbotu, Eferhire Valentine
Journal of Computing Theories and Applications
Vol 2
, No 4
(2025)
Breast cancer is the most prevalent cancer among women worldwide, requiring early and accurate diagnosis to reduce mortality. This study proposes a hybrid classification pipeline that integrates Hybrid Statistical Feature Selection (HSFS) with unsupervised LSTM-guided feature extraction for breast cancer detection using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Initially, 20 features were selected using HSFS based on Mutual Information, Chi-square, and Pearson Correlation. To addres...
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3 Sitasi
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow
Setiadi, De Rosal Ignatius Moses
; Warto, Warto
; Muslikh, Ahmad Rofiqul
; Nugroho, Kristiawan
; Safriandono, Achmad Nuruddin
Journal of Computing Theories and Applications
Vol 2
, No 4
(2025)
Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset wit...
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7 Sitasi
Awareness of Pre-Service Teachers Technological Pedagogical Content Knowledge for Instruction in Universities in Ogun State
Journal of Emerging Technology in Teaching and Learning
Vol 1
, No 1
(2025)
This research investigates the awareness of Technological Pedagogical Content Knowledge (TPACK) among pre-service teachers in Ogun State, Nigeria universities. Understanding how future educators perceive and utilise technology is crucial for effective teaching and learning in the context of increasing technological integration in education. The primary purpose of this study was to assess the availability of technology resources, evaluate pre-service teachers' awareness of TPACK, and explore pote...
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Feature Fusion with Albumentation for Enhancing Monkeypox Detection Using Deep Learning Models
Pratama, Nizar Rafi
; Setiadi, De Rosal Ignatius Moses
; Harkespan, Imanuel
; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications
Vol 2
, No 3
(2025)
Monkeypox is a zoonotic disease caused by Orthopoxvirus, presenting clinical challenges due to its visual similarity to other dermatological conditions. Early and accurate detection is crucial to prevent further transmission, yet conventional diagnostic methods are often resource-intensive and time-consuming. This study proposes a deep learning-based classification model by integrating Xception and InceptionV3 using feature fusion to enhance performance in classifying Monkeypox skin lesions. Giv...
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15 Sitasi
A Quantum Circuit Learning-based Investigation: A Case Study in Iris Benchmark Dataset Binary Classification
Journal of Computing Theories and Applications
Vol 2
, No 3
(2025)
This study presents a Quantum Machine Learning (QML) architecture for perfectly classifying the Iris flower dataset. The research addresses improving classification accuracy using quantum models in machine-learning tasks. The objective is to demonstrate the effectiveness of QML approaches, specifically the Variational Quantum Circuit (VQC), Quantum Neural Network (QNN), and Quantum Support Vector Machine (QSVM), in achieving high performance on the Iris dataset. The proposed methods result in pe...
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4 Sitasi
The Meritorious Learning Rewards Promoted by Chat GPT in Academic Writing Classroom Contexts
Educalingua Journal
Vol 2
, No 2
(2024)
One of the most conspicuous and serious hindrances oftentimes confronted in these presently-situated academic writing learning dynamics is the constant presence of an emotionally-exhausting and anxiety-inducing learning environment in which EFL learners experience the absence of writing enjoyment. To better lessen this debilitating hurdle, second language educators are highly recommended to start activating the proper usage of artificial intelligence platforms in their regular academic writing l...
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Discourse Analysis: Language Issues in Indigenous Language Learning in Indonesia
Educalingua Journal
Vol 2
, No 2
(2024)
This article examines the complexities of language issues in the context of Indigenous language learning in Indonesia, a nation with over 700 living languages. Indigenous languages are at risk of extinction due to socio-political and economic factors. The research examines the role of language policies, educational practices, and community engagement in preserving and revitalizing Indigenous languages. It highlights the challenges Indigenous communities face in accessing quality language learnin...
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2 Sitasi
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction
Setiadi, De Rosal Ignatius Moses
; Muslikh, Ahmad Rofiqul
; Iriananda, Syahroni Wahyu
; Warto, Warto
; Gondohanindijo, Jutono
; Ojugo, Arnold Adimabua
Journal of Computing Theories and Applications
Vol 2
, No 2
(2024)
Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or densit...
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23 Sitasi