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Menampilkan 11–20 dari 33 artikel
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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10 Sitasi
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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23 Sitasi
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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22 Sitasi
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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27 Sitasi
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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27 Sitasi
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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33 Sitasi
Exploring DQN-Based Reinforcement Learning in Autonomous Highway Navigation Performance Under High-Traffic Conditions
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
Driving in a straight line is one of the fundamental tasks for autonomous vehicles, but it can become complex and challenging, especially when dealing with high-speed highways and dense traffic conditions. This research aims to explore the Deep-Q Networking (DQN) model, which is one of the reinforcement learning (RL) methods, in a highway environment. DQN was chosen due to its proficiency in handling complex data through integrated neural network approximations, making it capable of addressing h...
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5 Sitasi
Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients
Journal of Computing Theories and Applications
Vol 1
, No 3
(2024)
Music genre classification is one part of the music recommendation process, which is a challenging job. This research proposes the classification of music genres using Bidirectional Long Short-Term Memory (BiLSTM) and Mel-Frequency Cepstral Coefficients (MFCC) extraction features. This method was tested on the GTZAN and ISMIR2004 datasets, specifically on the IS-MIR2004 dataset, a duration cutting operation was carried out, which was only taken from seconds 31 to 60 so that it had the same durat...
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29 Sitasi