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All Journal Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Madani: Multidisciplinary Scientific Journal Matematika Socius: Social Sciences Research Journal
Siahaan, Linda Natasya
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Analisis Faktor-faktor yang Mempengaruhi Keputusan Pembelian Mobil Menggunakan Regresi Logistik Biner Haliza, Putri Yusra; Tamara, Angga; Mario, Christoffel; Hondro, Yizhar Saputra; Siahaan, Linda Natasya; Dalimunthe, Syairal Fahmy
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 2 (2025): March
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15077946

Abstract

This study aims to analyze the factors influencing car purchase decisions using binary logistic regression. Data were obtained from 1,000 respondents with independent variables including age, marital status, gender, car ownership, and income. The analysis results show that marital status, gender, and income significantly influence purchase decisions. Married respondents tend to have a lower likelihood of purchasing a car compared to single respondents, while females have a smaller tendency compared to males. On the other hand, higher income significantly increases the probability of car purchase. The constructed binary logistic regression model has a prediction accuracy of 93.8%, demonstrating its reliability in classifying purchase decisions. This study provides valuable insights for the automotive industry in designing effective and targeted marketing strategies. Additionally, further exploration of other factors such as brand preferences, geographic location, and psychological factors is recommended to enrich the understanding of automotive market behavior.
Analisis Spasial Persebaran COVID-19 di Indonesia Menggunakan Metode K-Means Clustering dan ESD Mario, Christoffel; Siahaan, Linda Natasya; Simanullang, Junitro Andreas; Simamora, Tabita Paulina
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 3 (2025): April 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15276145

Abstract

The COVID-19 pandemic, which began in March 2020, has had a significant impact on public health in Indonesia. Although case numbers have started to decline, understanding the spatial spread of the virus remains crucial for effective response efforts. Conventional analyses that rely solely on descriptive statistics often overlook spatial relationships between regions. This study combines Exploratory Spatial Data Analysis (ESDA) and K-Means Clustering to examine the spatial distribution of COVID-19 cases and group Indonesian provinces based on the number of cases, recovery rates, and mortality rates. The data used include Indonesias provincial shapefiles from GADM and COVID-19 case data from Data Wrapper. The analysis reveals three main clusters. Cluster one includes DKI Jakarta, West Java, and Central Java, characterized by high case numbers and mortality rates, with below-average recovery rates. Cluster two consists of East Java, North Sumatra, and South Sulawesi, with relatively low case numbers, very low recovery rates, and high mortality rates. Cluster three comprises 26 other provinces with lower case numbers, high recovery rates, and low mortality rates. These findings indicate that COVID-19 transmission in Indonesia is not spatially uniform, highlighting the need for targeted intervention in high-risk areas.
Analisis Faktor-Faktor yang Mempengaruhi Indeks Pembangunan Manusia di Jawa Tengah Menggunakan Regresi Log-Linier Sarah, Auta Shintha; Siahaan, Linda Natasya; Nazwa, Syahira
Socius: Jurnal Penelitian Ilmu-Ilmu Sosial Vol 2, No 12 (2025): July 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi Indeks Pembangunan Manusia (IPM) di Provinsi Jawa Tengah pada tahun 2022–2023. Metode yang digunakan adalah regresi linier log-linier dengan pendekatan kuantitatif menggunakan data sekunder dari 35 kabupaten/kota. Variabel independen yang dianalisis meliputi PDRB per kapita, persentase penduduk miskin, jumlah penduduk, dan angka harapan hidup. Hasil analisis menunjukkan bahwa seluruh variabel independen berpengaruh signifikan terhadap IPM. Model regresi log-linier yang dibangun telah memenuhi asumsi klasik seperti normalitas, homoskedastisitas, dan tidak adanya autokorelasi. Evaluasi model menggunakan Mean Absolute Error (MAE) sebesar 0,0167 dan koefisien determinasi (R²) sebesar 0,8062, yang menunjukkan bahwa model memiliki kemampuan prediksi yang baik dan dapat menjelaskan sekitar 80,62% variasi dalam IPM. Temuan ini mengindikasikan bahwa peningkatan kesejahteraan ekonomi, pengurangan kemiskinan, pertumbuhan penduduk yang terkendali, serta peningkatan angka harapan hidup berperan penting dalam meningkatkan pembangunan manusia di Jawa Tengah. Penelitian ini diharapkan dapat menjadi landasan dalam perumusan kebijakan pembangunan daerah yang lebih tepat sasaran dan berbasis data.
Penerapan Model Geometric Brownian Motion Untuk Prediksi Saham dan Analisis Risiko Kerugian Sianturi, Tiurmaida; Christoffel Mario; Simamora, Tabita Paulina; Siahaan, Linda Natasya
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 8 No. 3 (2025): Volume 8 Nomor 3 Tahun 2025 (July - September)
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v8i3.5813

Abstract

This study aims to predict the stock price of Apple Inc. (AAPL) using the Geometric Brownian Motion (GBM) model and to analyze risk through a Monte Carlo Simulation-based Value at Risk (VaR) approach. Daily stock price data of Apple Inc. from January 1, 2022, to December 31, 2024, is used and split into training and testing datasets. The data analysis techniques involve calculating stock returns using the geometric return approach, testing normality with the Kolmogorov-Smirnov test, estimating GBM model parameters, simulating stock prices using Monte Carlo simulation in R software, evaluating prediction accuracy with Mean Absolute Percentage Error (MAPE), and assessing risk using Value at Risk (VaR) along with backtesting. The results show that the GBM model has good accuracy, with a Mean Absolute Percentage Error (MAPE) of 12.32%. The VaR risk analysis at 95% and 99% confidence levels shows no violations, indicating a conservative model. This study contributes to stock price prediction and investment risk management.
Analisis Aspek-Aspek yang Memengaruhi Penyakit Jantung Menggunakan Model Regresi Logistik Biner Menggunakan Software RStudio Sianturi, Tiurmaida; Siahaan, Linda Natasya; Aritonang, Siska Dwi
Matematika: Jurnal Teori dan Terapan Matematika Vol. 24 No. 1 (2025): Jurnal Matematika
Publisher : UPT Publikasi Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/matematika.v24i1.5105

Abstract

Penyakit jantung adalah salah satu penyakit paling umum yang menjadi penyebab kematian seseorang di seluruh dunia. Seiring dengan kemajuan dalam teknik analisis data, terutama di bidang kesehatan, terdapat kehadiran teknik analisis berbasis regresi logistik yang berguna untuk menganalisis resiko-resiko yang berhubungan dengan penyakit jantung. Penelitian ini bertujuan untuk mengidentifikasi variabel yang signifikan dalam menentukan risiko penyakit jantung pada pasien. Dataset terdiri dari 14 variabel , namun setelah seleksi variabel menggunakan p-value dan Variance Inflation Factor (VIF), hanya 11 variabel yang digunakan dalam model akhir. Variabel yang signifikan termasuk jenis kelamin, kadar kolesterol, hasil EKG, detak jantung maksimal, dan angina induksi olahraga. Model ini dievaluasi menggunakan Confusion Matrix, dengan akurasi sekitar 81%, sensitivitas 83%, dan spesifisitas 79%. Selain itu, ROC curve menghasilkan AUC sebesar 0.89, menunjukkan bahwa model memiliki kinerja yang baik dalam membedakan pasien dengan dan tanpa penyakit jantung. Hasil penelitian menunjukkan bahwa jenis kelamin laki-laki, kadar kolesterol tinggi, dan detak jantung maksimal yang tinggi meningkatkan risiko penyakit jantung, sementara faktor seperti angina induksi olahraga berhubungan dengan penurunan risiko.
Co-Authors Aritonang, Siska Dwi Christoffel Mario Haliza, Putri Yusra Hondro, Yizhar Saputra Mario, Christoffel Nazwa, Syahira Sarah, Auta Shintha Sianturi, Tiurmaida Simamora, Tabita Paulina Simanullang, Junitro Andreas Syairal Fahmy Dalimunthe Tamara, Angga