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All Journal Telematika Sistemasi: Jurnal Sistem Informasi Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Jurnal Komputasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom Akrab Juara : Jurnal Ilmu-ilmu Sosial Infotech: Jurnal Informatika & Teknologi Journal of Informatics Management and Information Technology Jurnal Abdimas Ekonomi dan Bisnis Reputasi: Jurnal Rekayasa Perangkat Lunak IMTechno: Journal of Industrial Management and Technology Sinergi: Jurnal Pengabdian Kepada Masyarakat Bulletin of Informatics and Data Science Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Agustiani, Sarifah
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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Journal : Telematika

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Convolutional Neural Networks for Classification of Lung Cancer Based on Histopathological Images Agustiani, Sarifah; Pribadi, Denny; Junaidi, Agus; Wildah, Siti Khotimatul; Mustopa, Ali; Arifin, Yoseph Tajul
Telematika Vol 16, No 2: August (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i2.2356

Abstract

Lung cancer is one of the deadliest types of cancer characterized by the uncontrolled growth of cancer cells in the lung tissue due to the accumulation of carcinogens. Lung cancer ranks second in the most cases with 2.206 million new cases and ranks first in deaths. This lung cancer often does not cause symptoms in the early stages, because it only appears after the tumor is large enough or the cancer has spread to surrounding tissues or organs, so it is necessary to have early detests to prevent severity and determine follow-up treatment. This study aims to classify lung cancers using digital pathology images with data of 15000 images obtained from the LC25000 dataset containing 5,000 images for each class. The method used in this classification process uses convolutional neural networks (CNN) which is one of the implementations of Deep Learning used for digital image processing. Using this method, the doctor can diagnose and find out the type of lung cancer quickly without spending much time. Thus, the faster the prediction results received by the doctor / health expert, the faster the next action or handler will be, this study produces a fairly accurate accuracy value even though it uses a shallow CNN architecture because it only consists of 5 layers with 3 convolution layers and 2 fully connected layers, with the resulting accuracy value of 98.53%.
Deep Learning for Histopathological Image Analysis: A Convolutional Neural Network Approach to Colon Cancer Classification Agustiani, Sarifah; Rianto, Yan
Telematika Vol 17, No 1: February (2024)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v17i1.2831

Abstract

Colon cancer is a type of cancer that attacks the last part of the human digestive tract. Factors such as an unhealthy diet, low fiber consumption, and high animal protein and fat intake can increase the risk of developing this disease. Diagnosis of colon cancer requires sophisticated diagnostic procedures such as CT scan, MRI, PET scan, ultrasound, or biopsy, which are often time-consuming and require particular expertise. This study aims to classify colon cancer based on histopathological images using a dataset of 10,000 images. This data is divided into 7,950 images for training, 2,000 for testing, and 50 for validation, aiming to achieve effective generalization. The Convolutional Neural Network (CNN) method was applied in this research with a relatively shallow architecture consisting of 4 convolution layers, 2 fully connected layers, and 1 output layer. Research results were evaluated by looking at the accuracy value of 99.55%, precision value of 99.49%, recall of 99.59%, prediction experiments on several images, and loss and accuracy graphs to detect signs of overfitting. However, this research has limitations in determining hyperparameters and layer depth, which was only tested from 1 to 5 convolution layers. Therefore, there are still opportunities for further development, such as applying unique feature extraction before the classification process.
Co-Authors Abdul Latif Agung Sasongko Agus Junaidi Agus Junaidi Ali Mustopa, Ali Andi Saryoko Andreas Roy Prasetya Arifin, Yosep Tajul Atang Saepudin Atang Saepudin Cucu Ika Agustyaningrum Emiliana, Meutia Raissa Fachry Abda El Rahman Ganda Wijaya Gata, Windu Haliza Ramadhanti, Pristya Haryani Haryani Haryani Hendri Mahmud Nawawi Kamil, Anton Abdul Basah Laksono Trisnantoro M. Rangga Ramadhan Saelan Maysaroh, Maysaroh Mustopa, Ali Nanik Wuryani Nawawi, Hendri Mahmud Prawikas, Amanda Pribadi, Denny Pristya Haliza Ramadhanti Putri, Rizky Rachma Rahmat Hidayat Rahmat Hidayat Rianto, Yan Riska Aryanti Rizky Ade Safitri Roy Prasetya, Andreas Rusda Wajhillah, Rusda Siti Khotimatul Wildah Siti Marlina, Siti Sopiyan Dalis Suharyanto Syamsul Bahri Titik Misriati tri wahyuni Tri Wahyuni Tya Septiani Nurfauzia Koeswara Wahyudin Wahyudin Windu Gata Windu Gata Yoseph Tajul Arifin Yudhistira Yudhistira yunita yunita