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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Simantec Jurnal Ilmiah Kursor Network Engineering Research Operation [NERO] BAREKENG: Jurnal Ilmu Matematika dan Terapan Systemic: Information System and Informatics Journal Indonesian Journal of Electrical Engineering and Computer Science Insand Comtech : Information Science and Computer Technology Journal International Conference on Digital Advanced Tourism, Management, and Technology
Khozaimi, Ach.
University Of Trunojoyo Madura
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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New insight in cervical cancer diagnosis using convolution neural network architecture Khozaimi, Ach; Firdaus Mahmudy, Wayan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3092-3100

Abstract

The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.
Advanced cervical cancer classification: enhancing pap smear images with hybrid PMD filter-CLAHE Khozaimi, Ach; Darti, Isnani; Anam, Syaiful; Kusumawinahyu, Wuryansari Muharini
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp644-655

Abstract

Cervical cancer remains a significant health problem, especially in developing countries. Early detection is critical for effective treatment. Convolutional neural networks (CNN) have shown promise in automated cervical cancer screening, but their performance depends on pap smear image quality. This study investigates the impact of various image preprocessing techniques on CNN performance for cervical cancer classification using the SIPaKMeD dataset. Three preprocessing techniques were evaluated: PeronaMalik diffusion (PMD) filter for noise reduction, contrast-limited adaptive histogram equalization (CLAHE) for image contrast enhancement, and the proposed hybrid PMD filter-CLAHE approach. The enhanced image datasets were evaluated on pretrained models, such as ResNet-34, ResNet-50, SqueezeNet-1.0, MobileNet-V2, EfficientNet-B0, EfficientNet-B1, DenseNet121, and DenseNet-201. The results show that hybrid preprocessing PMD filter-CLAHE can improve the pap smear image quality and CNN architecture performance compared to the original images. The maximum metric improvements are 13.62% for accuracy, 10.04% for precision, 13.08% for recall, and 14.34% for F1-score. The proposed hybrid PMD filter-CLAHE technique offers a new perspective in improving cervical cancer classification performance using CNN architectures.
Optimized pap-smear image enhancement: hybrid Perona-Malik diffusion filter-CLAHE using spider monkey optimization Khozaimi, Ach; Darti, Isnani; Muharini Kusumawinahyu, Wuryansari; Anam, Syaiful
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp2765-2775

Abstract

Pap-smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance pap-smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). Blind/reference-less image spatial quality evaluator (BRISQUE) and contrast enhancement-based image quality (CEIQ) are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson’s contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving pap-smear image quality.
Implementation Of The Rivest Cipher 4 Method Web-Based Employee Data Dwi Kuswanto; Ach. Khozaimi; Deden Nur Eka Abdi 
International Conference On Digital Advanced Tourism Management And Technology Vol. 1 No. 1 (2023): International Conference on Digital Advanced Tourism, Management, and Technolog
Publisher : Sekolah Tinggi Ilmu Ekonomi Pariwisata Indonesia Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56910/ictmt.v1i1.67

Abstract

Data security has always been an exciting topic to discuss in the era of globalization and industrial revolution 5.0. Along with the development of Cryptographic techniques, which continue to develop. Security of data storage techniques is essential in a data security system. The method used in this research is the Rivest Chiper 4 Algorithm. This research takes a case study of employee data storage security in a company that uses local storage as a medium for storing employee data. A web-based employee data collection system with the Rivest Chiper 4 algorithm implemented on the server side. The research results show that in testing the Avalanche Effect Rivest Chiper 4 algorithm with three key character length variations, an average value of 50.87% was obtained. The test results show that the average Avalanche Effect value is more than 50%, indicating that small changes to the plaintext can impact the ciphertext. With the help of Cryptool 1, using Brute Force time testing results with variations in key length, the password cracking time was 33 years with a 6-character key length. The longer the key is used, the longer the completion process will take to crack the ciphertext. Meanwhile, plaintext length is linearly correlated with the length of Brute Force testing time but is insignificant. Hardware performance also affects the estimated time of Brute Force.
New perspective in enhancing Papanicolaou-smear image using CLAHE and spider monkey optimization Khozaimi, Ach; Muharini Kusumawinahyu, Wuryansari; Darti, Isnani; Anam, Syaiful; Nahdhiyah, Ulfatun
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10250

Abstract

High-quality Papanicolaou (Pap) smear images are essential for reliable early detection of cervical cancer, yet low contrast and noise often hinder accurate interpretation. This study introduces spider monkey optimization (SMO)-contrast-limited adaptive histogram equalization (CLAHE), an optimized CLAHE framework guided by the SMO algorithm. A novel signal contrast (SC) objective function is proposed, combining perceptual enhancement contrast enhancement-based image quality (CEIQ) with fidelity preservation peak signal-to-noise ratio (PSNR) to adaptively tune CLAHE parameters. Experiments on the publicly available SIPaKMeD and Mendeley LBC datasets demonstrate that SMO-CLAHE consistently outperforms manual settings and flower pollination algorithm (FPA)-based optimization, and achieves performance comparable to pelican optimization algorithm (POA) across key quality metrics including entropy, structural similarity index (SSIM), PSNR, enhancement measure estimation (EME), root mean square contrast (RMSC), standard deviation (STD-DEV), and CEIQ. Furthermore, downstream evaluation using a MobileNetV3-S classifier shows that the enhanced images lead to improved cervical cancer classification performance. These results highlight SMO-CLAHE as a robust and clinically relevant preprocessing framework, offering a new perspective for Pap smear image enhancement and diagnostic support.
ENHANCING CERVICAL CANCER IMAGES QUALITY: HYBRID SMO-PMD FILTER FOR NOISE REDUCTION Khozaimi, Ach; Darti, Isnani; Anam, Syaiful; Kusumawinahyu, Wuryansari Muharini
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 2 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss2pp1437-1452

Abstract

This study presents an image denoising method for cervical cancer images using the Perona–Malik Diffusion (PMD) filter optimized with the Spider Monkey Optimization (SMO) algorithm. The BRISQUE is proposed as the new objective function. The method was simulated on three datasets: SIPaKMeD, Herlev, and Mendeley Liquid-Based Cytology (LBC). Enhanced image quality was evaluated using MSE, SSIM, PSNR, and Entropy. On the SIPaKMeD dataset, the SMO-PMD filter achieved an average MSE of 0.0454, SSIM of 0.9984, PSNR of 62.27 dB, and Entropy of 5.425. The Mendeley dataset recorded an MSE of 0.3991, SSIM of 0.9994, PSNR of 53.08 dB, and Entropy of 5.489. The Herlev dataset achieved an MSE of 8.1191, SSIM of 0.9688, PSNR of 55.77 dB, and Entropy of 5.203. The SMO algorithm was compared with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). SMO showed better results across all metrics. The proposed method produces images with lower noise, higher structural similarity, and improved visual quality. The stable entropy values across the datasets indicate that essential diagnostic information was preserved. These findings provide a new perspective for enhancing cervical cancer images using a hybrid SMO-PMD filter. A limitation of this study is that experiments were limited to three datasets, and SMO’s reliance on extreme κ values might reduce stability in other contexts
Co-Authors Achmad Jauhari Aeri Rachmad Annisa Ulfa, Annisa Ari Kusumaningsih Arik Kurniawati Budi Soesilo Deden Nur Eka Abdi  Dwi Kuswanto Fifin Ayu Murfarroha Firdaus Solihin Firmansyah Adiputra Ika Oktavia Suzanti Isnani Darti Muharini Kusumawinahyu, Wuryansari Mujibur Rohman Nahdhiyah, Ulfatun Pramudita, Yoga Dwitya Rima Tri Wahyuningrum Rochman, Eka Mala Sari Sigit Susanto Putro Syaiful Anam Vina Angelina Savitri Wayan Firdaus Mahmudy Wuryasari Muharini Kusumawinahyu Yudha Dwi Putra Negara, Yudha