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Menampilkan 11–20 dari 29 artikel
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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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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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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27 Sitasi
Hybrid Quantum Key Distribution Protocol with Chaotic System for Securing Data Transmission
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
This research proposes a combination of Quantum Key Distribution (QKD) based on the BB84 protocol with Improved Logistic Map (ILM) to improve data transmission security. This method integrates quantum key formation from BB84 with ILM encryption. This combination creates an additional layer of security, where by default, the operation on BB84 is only XOR-substitution, with the addition of ILM creating a permutation operation on quantum keys. Experiments are measured with several quantum measureme...
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9 Sitasi
Butterflies Recognition using Enhanced Transfer Learning and Data Augmentation
Adityawan, Harish Trio
; Farroq, Omar
; Santosa, Stefanus
; Islam, Hussain Md Mehedul
; Sarker, Md Kamruzzaman
; Setiadi, De Rosal Ignatius Moses
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
Butterflies’ recognition serves a crucial role as an environmental indicator and a key factor in plant pollination. The automation of this recognition process, facilitated by Convolutional Neural Networks (CNNs), can expedite this task. Several pre-trained CNN models, such as VGG, ResNet, and Inception, have been widely used for this purpose. However, the scope of previous research has been somewhat constrained, focusing only on a maximum of 15 classes. This study proposes to modify the CNN Ince...
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14 Sitasi
PKM: Penyuluhan tentang Pelaporan Keuangan pada UMKM Dapur Aura dan UMKM Sayur Kriyur di Kecamatan Selo Kabupaten Boyolali
Putri, Fiani Lindia
; Mardiyanto, Doni
; Ismawati, Kun
; Atas Aji, Ambyah
; Giyono, Giyono
; Savitri, Savitri
; Nugroho, Ignatius Novie Endi
; Candrakusuma, Desy Amalia
; Widayani, Astrid
; Budiwinarto, Kim
Adi Widya: Jurnal Pengabdian Masyarakat
Vol 7
, No 2
(2023)
The service was carried out based on findings from the Dapur Aura UMKM and Sayur Kriyur UMKM whose bookkeeping was still in the form of cash flow report only. The aim of this activity is to develop the bookkeeping of the two MSMEs, so that they can start making general journals, compiling ledgers, including balance sheets, income statements and notes to financial reports in accordance with the provisions in SAK EMKM. The service method is carried out through simple counseling to business owners...
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Image Encryption using Half-Inverted Cascading Chaos Cipheration
Setiadi, De Rosal Ignatius Moses
; Robet, Robet
; Pribadi, Octara
; Widiono, Suyud
; Sarker, Md Kamruzzaman
Journal of Computing Theories and Applications
Vol 1
, No 2
(2023)
This research introduces an image encryption scheme combining several permutations and substitution-based chaotic techniques, such as Arnold Chaotic Map, 2D-SLMM, 2D-LICM, and 1D-MLM. The proposed method is called Half-Inverted Cascading Chaos Cipheration (HIC3), designed to increase digital image security and confidentiality. The main problem solved is the image's degree of confusion and diffusion. Extensive testing included chi-square analysis, information entropy, NCPCR, UACI, adjacent pixel...
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14 Sitasi