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All Journal Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informatika Universitas Pamulang Jurnal Telematika Jurnal Riset Informatika Building of Informatics, Technology and Science Jurnal Dinamika UMT JURNAL TEKNIK JIKA (Jurnal Informatika) Jurnal Inovasi Informatika Jurnal Teknik Informatika (JUTIF) Journal Social Science And Technology For Community Service Journal Industrial Manufacturing Insearch: Information System Research Journal International Journal of Management Science and Information Technology (IJMSIT) Jurnal Keuangan dan Akuntansi Terapan (KUAT) JSITP Proceeding of The International Conference on Religious Education and Cross - Cultural Understanding
Destriana, Rachmat
Universitas Muhammadiyah Tangerang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Journal : journal of applied informatics and computing

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Magnetic Resonance Imaging for Breast Cancer Classification Using Convolutional Neural Networks Mahiruna, Adiyah; Destriana, Rachmat; Riansyah, Rahmat
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9101

Abstract

Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the urgent need for accurate diagnostic methods. This research addresses the challenges of early detection by leveraging Convolutional Neural Networks (CNNs) for the classification of Magnetic Resonance Imaging (MRI) data. Using a publicly available Kaggle dataset consisting of 54,676 MRI images categorized into "Normal" and "Cancer" classes, the dataset was split into 80% for training and 20% for validation. A modified CNN architecture was developed, incorporating optimized layers and hyperparameters, such as the ADAM optimizer, a learning rate of 0.0001, and a mini-batch size of 128. The proposed model achieved exceptional performance, with an accuracy of 99.72%, precision and recall of 99.98% and 99.97%, respectively, and an F1-score of 99.98%, as evaluated through a confusion matrix. These results demonstrate the model’s robustness in distinguishing between healthy and cancerous tissues, providing a reliable and efficient diagnostic tool. This study highlights the potential of CNNs to improve diagnostic precision in medical imaging, aiding clinicians and advancing AI applications in healthcare.
Enhancing Clustering Accuracy Using K-Means with Seeds Optimization Mahiruna, Adiyah; Ngatimin, Ngatimin; Destriana, Rachmat; Rachmawanto, Eko Hari; Yuliansyah, Herman; Hidayat, Muhammad Taufiq
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10458

Abstract

In this study, the development of the Mean-based method proposed by Goyal and Kumar will be carried out by changing the initial cluster center determination step, which was originally based on the origin point O (0,0), to be replaced with the arithmetic mean. To assess the performance of the proposed method, it will be compared with the Global K-means method and the Mean-based K-means method. In this study, the performance of these methods will be measured using the Davies-Bouldin Index, and the significance of the proposed method will be measured using the Friedman Test. This study proposes a method of Improving K-Means Performance through Initial Center Optimization based on Second Global Average for Clustering Osteoporosis Diagnosis of lifestyle factors. Evaluation of K-Means performance through Initial Center Optimization based on Second Global Average with DBI measurements. The targeted experimental results of this study include improving the performance of K-means optimized through the initial center based on Second Global Average. From the results of nine experiments with the number of clusters [2,3,4,5,6], it can be seen that the method proposed in this study has the same superior performance compared to the Mean Based method and compared to the Global K-means method.
Co-Authors Aditya Prayoga Suyitno Aditya Tegar Siswanto Adiyah Mahiruna Ahmad Fauzi Aksani, Muhamad Luthfi Alamsyah, Dedy Alexander, Allan D Alfry Aristo Jansen Sinlae Algadri, Wulandari Angga Aditya Permana Atmojo, Wahyu Tisno Bintang Eka Suryana Daniarti, Yeni Darmawan, Umar Denis Chandra Prabowo Desy Nurnaningsih Dirgahayu Erri Djamaludin Syahlan Mubaroq3 Dyas Yudi Priyanggodo Dyas Yudi Priyanggodo Eko Hari Rachmawanto Ernawati, Tia Faridi Faridi Farzani, Revalina Firmansyah, Rahmat Dwi Fitriawati, Nora Handayani, Nurdiana Hengki Rusdianto Herdiansah, Arief Herman Yuliansyah, Herman Hidayat, Muhammad Taufiq Hidayatullah, M. Fajri Huasin, Syepry Maulana Husain, Syepry Maulana Ida Ayu Putu Sri Widnyani Iqbal Hidayatsyah Noor Jordy Lasmana Putra Layina, Andiny Liesnaningsih Liesnaningsih Liesnaningsih, Liesnaningsih Lisa Fitriani Ishak Lutfi, Yunanda Mahiruna, Adiyah Mamdukh Budiman Mayatopani, Hendra Muhammad Jonni Nanda, Indra Ngatimin Ngatimin NGATIMIN, NGATIMIN Nur Fadilah Fatma Nur'aini, Aliya Nurdiana Handayani Nurhasan Nugroho nurnaningsih, Desi Paweloi, Fatahillah Prasetyoadi, Erwin Prastyawan, Bayu Enggar Rachel, Figo Reynaldy, Deva Alfian Riansyah, Rahmat RIFQI RIADHI Rijal Wakhid Rizkillah Rina Pratiwi Pudja I. A Rini Nuraini Rochmat Taufiq Rohmat Taufiq Rusdianto, Hengki Safuan Safuan Sidiq, Sofian Suhendar, Asep Syakur, Muhamad Abdan Tamam, Gusti Syihabuddin Taufiq, Rochmat Tia Ernawati Triansyah, Jully Wijaya, Mirza Mahbub Yani Sugiyani, Yani Yanuardi Yanuardi Yanuardi Yanuardi Yudi Priyanggodo, Dyas