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All Journal Jurnal Bumigora Information Technology (BITe) Journal Of Information System And Artificial Intelligence
Resmiranta, Dading Oktaviadi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN): Pengenalan Citra Wajah Menggunakan Model Hybrid Filtering dan Convolutional Neural Networks (CNN) Resmiranta, Dading Oktaviadi; Krismono, Bambang; Hidjah, Khasnur
Journal Of Information System And Artificial Intelligence Vol. 6 No. 1 (2025): Vol. 6 No.1(2025): Journal of Information System and Artificial Intelligence Vo
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v6i1.271

Abstract

The face is an important object in the biometric identification system. However, low image quality due to uneven lighting, noise, and variations in facial expressions can interfere with the accuracy of the recognition system. The study investigated the use of Convolutional Artificial Neural Network (CNN) combined with hybrid screening techniques to improve image quality, thereby improving the accuracy of facial recognition systems. Filters used include weight mean filtering, median filtering, Contrast-Limited Adaptive Histogram Equalization and gaussian filtering, wavelet filtering. The pre-processed image was then trained using image denoising measurements of the Structural Similarity Index, Mean Squared Error, and Peak Signal to Noise Ratio. The main objective of this study is to evaluate the best filtration combination to produce high accuracy in face classification. The datasets used were 55 classes and 100 images per class. The inceptionV3 architecture model is used for classifications with a number of epochs of 10. Evaluation was carried out on a facial data set with an 80%:20% scheme. The results of the experiment showed that the hybrid method produced the best performance with 94.5% validation accuracy, 94.2% precision, and 94.6% recall, an increase of +1.4% compared to baseline. The (original) baseline itself recorded 93.1% validation accuracy, 92.8% precision, and 93.2% recall. In addition, the loss graph shows that the pre-process model has faster and more stable convergence than the non-pre-processing model. These results confirm that the application of preprocessing, especially the hybrid approach, is able to improve the accuracy and stability of the model in image classification tasks.
Peningkatan Kinerja Klasifikasi Scabies Sapi MenggunakanEdited Nearest Neighbours (ENN) pada Model Random Forestdan XGBoost Ihsan, M. Khaerul; Maulana, Muhammad; Tanwir, Tanwir; Mas’ud, Abi; Hanif, Naufal; Resmiranta, Dading Oktaviadi
Jurnal Bumigora Information Technology (BITe) Vol. 7 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v7i2.6055

Abstract

Background: Scabies disease in cattle causes significant economic losses for farmers due to declines in the animals’physical condition and productivity.Objective: This study aims to evaluate the effectiveness of the Edited Nearest Neighbours (ENN) method in improvingclassification performance for scabies in cattle.Methods: This research employs machine learning methods, including Random Forest and XGBoost. A dataset of 600clinical symptom samples was converted to numerical data and cleaned of noise using the ENN technique.Result: Applying ENN significantly improved the accuracy of both the Random Forest and XGBoost models, increasing itfrom around 0.60 to 0.91. In addition, both models achieved a perfect recall of 1.00, indicating maximum capability todetect positive cases.Conclusion: This study concludes that noise reduction using ENN can produce a more accurate and reliable diagnosticsystem. This method is highly recommended to optimize the performance of classification algorithms on animal clinicaldata with high levels of inconsistency. 
Co-Authors Ihsan, M. Khaerul Khasnur Hidjah Mas’ud, Abi Muhammad Maulana Naufal Hanif Tanwir, Tanwir Triwijoyo, Bambang Krismono