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All Journal Jurnal Informatika dan Teknik Elektro Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Setiyani, Safira Hasna
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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PREDIKSI PERGERAKAN HARGA SAHAM MENGGUNAKAN QUANTUM MACHINE LEARNING BERBASIS VARIATIONAL QUANTUM CIRCUITS Setiyani, Safira Hasna
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3S1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3S1.8038

Abstract

Pergerakan harga saham bersifat kompleks, non-linear dan dipengaruhi oleh berbagai faktor ekonomi sehingga prediksinya menjadi tantangan bagi metode tradisional. Penelitian ini bertujuan untuk memanfaatkan Quantum Machine Learning (QML) berbasis Variational Quantum Circuits (VQC) dalam memprediksi arah pergerakan harga saham di Bursa Efek Indonesia. Dataset yang digunakan merupakan data harga harian saham (open, high, low, close, volume) selama periode 2020 - 2025 yang diperoleh dari Yahoo Finance dan IDX. Metode penelitian meliputi preprocessing data, transformasi time series menggunakan sliding window, serta pelatihan model QML untuk memprediksi tren naik atau turun saham. Hasil eksperimen menunjukkan bahwa model QML mampu mencapai akurasi prediksi sebesar 99,70%. Evaluasi dilakukan menggunakan metrik akurasi, mean squared error (MSE) dan confusion matrix, menunjukkan kemampuan VQC menangkap pola non-linear yang kompleks. Penelitian ini menegaskan potensi QML sebagai teknologi inovatif untuk analisis pasar saham dan membuka peluang pengembangan sistem prediksi saham berbasis komputasi kuantum di masa depan.
Classification of Breast Cancer Histopathology Images with Attention-Based Multiple Instance Learning Method Setiyani, Safira Hasna; Noersasongko, Edi; Affandy
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2310

Abstract

Breast cancer is one of the cancers with the highest mortality rate among women worldwide. Early detection plays a crucial role in improving the chances of successful treatment and reducing the risk of death. Numerous efforts have been made both by the general public and healthcare professionals to promote awareness, early screening, and timely medical intervention. In line with technological advancements, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is the analysis of histopathology images to support the diagnosis process in breast cancer cases. Histopathological image classification has attracted considerable attention from researchers in recent years, and a variety of machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of deep learning frameworks, have shown promising results in identifying tissue patterns in histopathology images. However, despite their high accuracy, CNNs often lack interpretability, making it difficult to understand the reasoning behind their decisions—especially when dealing with subtle features such as small spots, dots, or fine lines, which may go undetected. This study addresses those limitations by proposing a method that not only classifies histopathology images with high accuracy but also improves interpretability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets such as BreakHIS, the proposed method achieved a classification accuracy of up to 97.50%, demonstrating its potential as a reliable tool in medical diagnostics and breast cancer research.
Co-Authors Affandy Edi Noersasongko