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Menampilkan 1–5 dari 5 artikel
Evaluating Open-Source Machine Learning Project Quality Using SMOTE-Enhanced and Explainable ML/DL Models
Journal of Computing Theories and Applications
Vol 3
, No 2
(2025)
The rapid growth of open-source software (OSS) in machine learning (ML) has intensified the need for reliable, automated methods to assess project quality, particularly as OSS increasingly underpins critical applications in science, industry, and public infrastructure. This study evaluates the effectiveness of a diverse set of machine learning and deep learning (ML/DL) algorithms for classifying GitHub OSS ML projects as engineered or non-engineered using a SMOTE-enhanced and explainable modelin...
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Pentingnya Packaging Yang Menarik Dan Digital Marketing Pada Kerupuk Lala 73
Fajrie, Mahfudlah
; Permatasari, Citra
; Zahro, Nafissatus
; Artalopa, Ryan
; Fibrian Noor, Isnan
; Putri Zuniawati, Eka
; Rizqi Afrizal, Edo
; Nur Asri, Miftakhul Huda
; Bahrul Ilmi, Muhammad
; Fatkhurridlo Mahendra, Muhammad
; Tiyas, Puspitaning
; Khalif Akbar, Tyas Wahyudi
; Rizieq Hussain, Muhammad
; Rohmah, Ainur
; Fathur Rohman, Muhammad Zidan
; Amanda, Lisna
Adi Widya: Jurnal Pengabdian Masyarakat
Vol 9
, No 2
(2025)
Usaha Mikro Kecil dan Menengah (UMKM) memiliki peran krusial dalam perekonomian Indonesia, namun masih menghadapi kendala dalam aspek pemasaran dan branding produk. Salah satu tantangan yang dihadapi oleh UMKM di Desa Kalipucang Wetan, Kecamatan Welahan, adalah minimnya inovasi dalam desain kemasan serta kurangnya pemanfaatan strategi pemasaran digital. Penelitian ini bertujuan untuk meningkatkan daya tarik produk UMKM, khususnya Kerupuk Lala 73, dengan mengembangkan desain kemasan yang lebih me...
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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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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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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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