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All Journal Jurnal Surya Teknika
Marta Putri, Dila
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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A CNN-based Approach for Breast Cancer Classification from Ultrasound Images Marta Putri, Dila; Ikhsan, M; Nurjanah, Siti; Fahrizal, Fahrizal; Akramunnas, Bastul Wajhi; Rahmawati, Asde
JURNAL SURYA TEKNIKA Vol. 12 No. 1 (2025): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v12i1.9378

Abstract

Breast cancer is one of the most frequently diagnosed cancers and remains a leading cause of cancer-related mortality among women worldwide. According to WHO Globocan 2020, breast cancer ranks second globally, with 2,262,419 cases out of a total of 19,292,289 cancer cases, accounting for approximately 11.7%. Early detection plays a critical role in reducing breast cancer mortality. In this study, a machine learning-based approach using Convolutional Neural Networks (CNN) was employed to classify breast cancer using ultrasound imaging. The dataset, collected by Al-Dhabyani et al. at Baheya Hospital in 2018, consists of ultrasound images of women aged between 25 and 75 years. The proposed CNN model includes stages of data input, preprocessing, training, testing, and performance evaluation. The model achieved an accuracy of 85%, demonstrating promising results for automated breast cancer detection. Further optimization is recommended to improve classification performance.
Pengembangan Sensor Elektrokimia Berbasis Material Nano untuk Deteksi Ion Timbal (Pb²⁺) Menggunakan Sistem Elektronika Terintegrasi Rahmawati, Asde; Nurjanah, Siti; Fahrizal, Fahrizal; Marta Putri, Dila; Ikhsan, M; Wajhi Akramunnas, Bastul
JURNAL SURYA TEKNIKA Vol. 12 No. 1 (2025): JURNAL SURYA TEKNIKA
Publisher : Fakultas Teknik UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jst.v12i1.9544

Abstract

Electrochemical sensors are a reliable method for detecting the presence of heavy metal ions such as lead (Pb²⁺) in aquatic environments. In this study, a sensor was developed based on a carbon paste electrode modified with ZnO nanomaterials and polyaniline, and integrated with a data acquisition system using a microcontroller. Voltammetric characterization results showed that the sensor could detect Pb²⁺ with high sensitivity at low concentrations. This system is expected to be applied for real-time and portable water quality monitoring.
Co-Authors Akramunnas, Bastul Wajhi Fahrizal Fahrizal M Ikhsan Rahmawati, Asde Siti Nurjanah Wajhi Akramunnas, Bastul