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All Journal Jurnal Informatika dan Teknik Elektro Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Sharef: Journal of Sharia Economics and Finance
Setiyani, Safira Hasna
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

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Pengaruh Human Capital, UMR, dan Pertumbuhan Ekonomi terhadap Pengangguran pada Muria Raya Wijanarko, Fauzuna Naufal; Yusiana Rahma; Safira Hasna Setiyani
Sharef: Journal of Sharia Economics and Finance Vol 3 No 2 (2025): Journal of Sharia Economics and Finance Vol. 3 No. 2 (2025)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jsef.v3i2.1525

Abstract

Tujuan penelitian ini adalah penggunaan regresi data panel dalam analisis pengaruh Human Capital, UMR dan Laju Pertumbuhan Ekonomi terhadap Pengangguran pada Kawasan Muria Raya (Periode 2011-2023). Metode yang digunakan adalah metode penelitian deskriptif asosiatif dengan pendekatan kuantitatif. Populasi yang digunakan merupakan 6 Kota dan Kabupaten dalam Kawasan Muria Raya. Teknik pengumpulan data menggunakan data sekunder melalui dokumentasi dan studi kepustakawan dengan metode Purposive Sampling. Hasil penelitian ini menunjukkan bahwa Human Capital, UMR dan Laju Pertumbuhan Ekonomi secara simultan memiliki pengaruh positif dan signifikan terhadap pengangguran. Secara partial, variabel Human Capital memiliki pengaruh negatif dan tidak signifikan terhadap pengangguran. Variabel UMR memiliki pengaruh negatif dan signifikan terhadap pengangguran secara partial. Variabel Laju Pertumbuhan Ekonomi memiliki pengaruh negatif dan tidak signifikan terhadap pengangguran secara partial.
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
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 deadliest types of cancer among women worldwide. Early detection plays a crucial role in increasing the chances of successful treatment and reducing the risk of death. Various efforts have been made by both the general public and medical professionals to raise awareness, promote early screening, and ensure timely medical intervention. With advances in technology, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is histopathological image analysis to support the diagnostic process in breast cancer cases. Histopathological image classification has gained significant attention from researchers in recent years, and various machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of the deep learning framework, have shown promising results in identifying tissue patterns in histopathological images. However, despite their high accuracy, CNNs are often less interpretable, making it difficult to understand the reasoning behind their predictions—especially when dealing with subtle features such as small spots, dots, or fine lines that may be overlooked. This study addresses these limitations by proposing a method that not only classifies histopathological images with high accuracy but also enhances readability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets like BreakHIS, the proposed method achieves a classification accuracy of up to 97.50%, demonstrating its potential as a reliable tool in medical diagnostics and breast cancer research.
Penerapan Dekomposisi Matriks untuk Reduksi Kompleksitas Komputasi pada Algoritma Machine Learning Setiyani, Safira Hasna; Rahma, Yusiana
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11166

Abstract

The increasing complexity of machine learning algorithms is often accompanied by higher computational costs particularly when dealing with high-dimensional data. This condition poses significant challenges in terms of computational efficiency and resource utilization. One mathematical approach that can address this issue is the application of linear algebra concepts, specifically matrix decomposition techniques. This study aims to apply matrix decomposition methods to reduce computational complexity in machine learning algorithms without significantly degrading model performance. The proposed approach employs matrix decomposition, such as Singular Value Decomposition (SVD), during the data preprocessing and model training stages. The performance of the algorithms is evaluated by comparing their behavior before and after the application of matrix decomposition in terms of computational time, accuracy, and memory efficiency. The experimental results demonstrate that matrix decomposition can significantly reduce computational complexity and improve learning efficiency, while maintaining stable or only slightly reduced accuracy. These findings indicate that matrix decomposition is an effective and practical approach for optimizing machine learning algorithms, particularly for large-scale and high-dimensional datasets.
Co-Authors Affandy Edi Noersasongko Rahma, Yusiana Wijanarko, Fauzuna Naufal Yusiana Rahma