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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Eksakta : Berkala Ilmiah Bidang MIPA STRING (Satuan Tulisan Riset dan Inovasi Teknologi) JURNAL SURYA ENERGY Al-Tamimi Kesmas: Jurnal Ilmu Kesehatan Masyarakat (Journal of Public Health Sciences) Al-Asalmiya Nursing: Journal of Nursing Sciences Real in Nursing Journal Abdimas Universal Jurnal Pengabdian Masyarakat Teknik Jurnal Penelitian Rumpun Ilmu Teknik Electronic Journal of Education, Social Economics and Technology Riset Informasi Kesehatan Journal of Engineering Science and Technology Management Uranus: Jurnal Ilmiah Teknik Elektro, Sains dan Informatika
Romi Mulyadi
Institut Kesehatan Dan Teknologi Al Insyirah
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Journal : Journal of Engineering Science and Technology Management

 1 
Image Analysis For Breast Cancer Classification Using Learning Vector Quantization (LVQ) Method Rahmadani, Qadri; Mulyadi, Romi
Journal of Engineering Science and Technology Management (JES-TM) Vol. 5 No. 1 (2025): Maret 2025
Publisher : Journal of Engineering Science and Technology Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jestm.v5i1.267

Abstract

Breast cancer is a common disease in women, making early detection crucial to improve treatment effectiveness. This study aims to create a breast cancer classification system using MATLAB and the Learning Vector Quantization (LVQ) algorithm through mammography image analysis. The data used was taken from the public platform Kaggle. The process includes preprocessing (conversion to grayscale and normalization), texture feature extraction with Gray Level Co-occurrence Matrix (GLCM), LVQ model training, and performance evaluation using accuracy, precision, recall, and F1-score. Test results show that the LVQ model can achieve an accuracy of 80.45%, precision of 78.92%, recall of 100%, and an F1-score of 88.30%. The system is equipped with a MATLAB-based user interface (GUI) that allows for direct image classification. Although the results are positive in detecting cancer images, errors in classifying normal images are still present. Future improvements will focus on data balancing and improving model performance. This system is expected to be a tool for rapid and accurate early screening of breast cancer in clinical settings.
DICOM Image Analysis for Lung Cancer Detection Using Convolutional Neural Network (CNN) Insanul Kamil, M. Arib; Mulyadi, Romi
Journal of Engineering Science and Technology Management (JES-TM) Vol. 5 No. 2 (2025): September 2025
Publisher : Journal of Engineering Science and Technology Management

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jestm.v5i2.290

Abstract

Lung cancer remains the leading cause of cancer-related deaths worldwide, with the highest burden in Asia, including Indonesia. Early detection is critical, yet access to radiology services is often limited by infrastructure, cost, and a shortage of trained specialists. Recent advances in artificial intelligence, particularly Convolutional Neural Networks (CNNs), offer promising solutions for automated image-based diagnosis. This study aims to analyze the effectiveness of CNN in detecting lung cancer from CT scan images in DICOM format. A dataset consisting of lung CT images from Kaggle and local hospitals was preprocessed through Gaussian blur filtering, segmentation, and pixel normalization before model training. Images were classified into two categories: cancer and non-cancer. The CNN architecture was trained and validated with an 80:20 split ratio, and model performance was assessed using accuracy, precision, recall, and F1-score. The experimental results show that the proposed CNN model achieved an accuracy of 88.27%, precision of 88.96%, recall of 97.43%, and an F1-score of 92.98%. The high recall value indicates the model’s strong ability to minimize false negatives, which is essential for clinical application. Performance graphs demonstrated stable accuracy and loss across training and validation sets, suggesting minimal overfitting.In conclusion, the developed CNN model demonstrates strong potential as a supportive diagnostic tool for early lung cancer detection, particularly in resource-limited healthcare settings. Its integration into radiology workflows may accelerate screening processes and improve clinical decision-making
Co-Authors Abu Bakar Adri Yanto Albirruni Siregar Albirruni Sirregar Angelin, Nadia Apriliani Fajar Kurniawan Firmansyah, Vicky Fitra Mayenti Insanul Kamil, M. Arib Lasiyah, Nani M. Rio Oktaviano Muddasir Muddasir Muhamad Yusuf Muhammad Ikhwanul Ikhsan Mustika, Aai Nabila Noria Putri Nadia Angelin Nani Lasiyah Nofri Anto Nur Hadziqoh Nursaka Putra Okta Nola Yolanda iriani Prayitno, Rahmat Subuh Putra, Nusaka Rahmadani, Qadri Ramadhan, Farhan Rifa Yanti Rino Ferdian Surakusumah Riski Novera Yenita Rizki Eka Putra Sandra Febriani Syahputra, Ramdhani Vicky Firmansyah Yeni Pertiwi Yogi Pratama Yona Ramadhika Yona Ramadika Zaini, Zaini