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Menampilkan 11–20 dari 37 artikel
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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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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Sentiment Analysis for Political Debates on YouTube Comments using BERT Labeling, Random Oversampling, and Multinomial Naïve Bayes
Journal of Computing Theories and Applications
Vol 2
, No 3
(2025)
The 2024 Indonesian Presidential Election marked the fifth general election in the country, aimed at electing a new President and Vice President for the 2024–2029 term. Candidates competed to succeed the outgoing president, who had served two constitutional terms. A key aspect of this election was the candidate debates, where each candidate presented their vision, allowing the public to assess their policies. These debates were broadcast on platforms like YouTube, giving the public a space to co...
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ANALYSIS AND DESIGN OF WEB-BASED GROCERY SALES INFORMATION SYSTEMS AT TRI KARYA STORES
JURNAL ILMIAH KOMPUTER GRAFIS
Vol 17
, No 2
(2024)
important for various groups of people, especially in supporting appropriate decision making through the use of information technology. In the business world, an efficient system can provide real-time information, thus simplifying operational processes. However, at Toko Tri Karya, the process of selling groceries is still done manually, starting from inputting sales data to making reports. This causes various obstacles, such as delays in data processing and the possibility of errors. Therefore,...
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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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Rancang Bangun Sistem Keselamatan terhadap Gas CO2 dalam Ruang Penyimpanan Tabung Gas CO2 Menggunakan Raspberry Pi Pico W
Ocean Engineering : Jurnal Ilmu Teknik dan Teknologi Maritim
Vol 3
, No 3
(2024)
The storage space for CO2 gas cylinders is inside the ship's accommodation, so there is a risk of danger if a leak occurs because the ship's accommodation has poor air circulation. This research is devoted to removing dangerous gases from the room. This research designs and modifies a tool that can detect CO2 levels and can provide a danger signal to the surroundings. This modification uses a Raspberry Pi Pico W microcontroller. This research method uses system design, a series of tools with wir...
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Effects of Data Resampling on Predicting Customer Churn via a Comparative Tree-based Random Forest and XGBoost
Ako, Rita Erhovwo
; Aghware, Fidelis Obukohwo
; Okpor, Margaret Dumebi
; Akazue, Maureen Ifeanyi
; Yoro, Rume Elizabeth
; Ojugo, Arnold Adimabua
; Setiadi, De Rosal Ignatius Moses
; Odiakaose, Chris Chukwufunaya
; Abere, Reuben Akporube
; Emordi, Frances Uche
; Geteloma, Victor Ochuko
; Ejeh, Patrick Ogholuwarami
Journal of Computing Theories and Applications
Vol 2
, No 1
(2024)
Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ense...
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Enhanced Vision Transformer and Transfer Learning Approach to Improve Rice Disease Recognition
Rachman, Rahadian Kristiyanto
; Setiadi, De Rosal Ignatius Moses
; Susanto, Ajib
; Nugroho, Kristiawan
; Islam, Hussain Md Mehedul
Journal of Computing Theories and Applications
Vol 1
, No 4
(2024)
In the evolving landscape of agricultural technology, recognizing rice diseases through computational models is a critical challenge, predominantly addressed through Convolutional Neural Networks (CNN). However, the localized feature extraction of CNNs often falls short in complex scenarios, necessitating a shift towards models capable of global contextual understanding. Enter the Vision Transformer (ViT), a paradigm-shifting deep learning model that leverages a self-attention mechanism to trans...
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Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine
Gomiasti, Fita Sheila
; Warto, Warto
; Kartikadarma, Etika
; Gondohanindijo, Jutono
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 4
(2024)
This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precisi...
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Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellen...
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