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All Journal Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Madani: Multidisciplinary Scientific Journal
Simamora, Tabita Paulina
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Pengaruh Kenyamanan, Kesulitan, dan Persepsi Terhadap Penggunaan Bahasa Baku pada Mahasiswa Statistika Universitas Negeri Medan Simamora, Tabita Paulina; Ingrid, Antonia; Simanullang, Junitro Andreas; Septianingtias, Indri Avisa; Damanik, Ayu Lestari; 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.15075343

Abstract

Standard language is a variety of language that is accepted for use in official or formal situations. Standard language is the standard of communication used in the academic and professional world. Its use is important to ensure that the message conveyed is clear, formal, and in accordance with linguistic rules. However, in everyday life, students tend to use non-standard language more often in informal conversations, academic assignments, and discussions on campus. This study uses a quantitative approach with descriptive and inferential analysis methods to determine the effect of comfort, difficulty, and perception of the importance of standard language on the frequency of its use among Statistics students at Medan State University. Data were obtained by distributing questionnaires to 50 Statistics students selected using simple random sampling techniques. This study aims to analyze the frequency of use of standard language among Statistics students at UNIMED and to see the effect of comfort, difficulty, and perception on its use. The results of the analysis show that the majority of students use standard language in the categories "Sometimes" to "Often", with male students tending to use it more often than female students.
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 Perbedaan Skor Pre-Test dan Post-Test pada Pembelajaran Bahasa Inggris Berbasis IoT dengan Uji Wilcoxon Simamora, Tabita Paulina; Sianturi, Tiurmaida
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 5 (2025): Volume 3, Nomor 5, June 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

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

Abstract

The utilization of Internet of Things (IoT) technologies in the world of education opens up new opportunities in creating more adaptive, interactive, and data-driven learning. This study aims to analyze the differences in students pre-test and post-test scores after following English Language learning using IoT-based platforms. The data used was secondary data from the Kaggle platform, which loaded students learning outcome information, including pre-test scores, post-test scores, engagement levels, and device types used. The sample size in this study was 30 students. The approach used was a quantitative approach with One Group Pretest-Posttest Design. The normality test against the difference in scores was performed using the ShapiroWilk test and showed that the data were not normally distributed (0.0053). Therefore, the analysis proceeded with the Wilcoxon Signed-Rank test as a nonparametric test for two paired data. The test results showed there was a significant difference between the pre-test and post-test scores , with the median of the post-test score being higher (77) compared to the pre-test (67). Data visualizations in the form of histograms, QQ plots, boxplots, and scatters of individual plots were used to support the analysis results and interpretation. The results of this study show that IoT-based learning contributes to the improvement of students learning outcomes in English Language, so it is recommended as an alternative to technology-based learning strategies.
Analisis Hubungan PDRB per Kapita dan Rata Rata Lama Sekolah terhadap Indeks Pembangunan Manusia di Provinsi Bali Beatrice, Chelsea; Zahra, Hafizha; Simamora, Tabita Paulina
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 5 (2025): Volume 3, Nomor 5, June 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15697390

Abstract

Pembangunan manusia merupakan tujuan utama dari proses pembangunan suatu daerah, yang tidak hanya berfokus pada pertumbuhan ekonomi semata, tetapi juga mencakup peningkatan kualitas hidup masyarakat secara menyeluruh. Indeks Pembangunan Manusia (IPM) adalah salah satu indikator yang sering digunakan sebagai tolok ukur utama untuk menilai kualitas pembangunan suatu wilayah dan menjadi dasar perumusan kebijakan pembangunan yang berorientasi pada manusia. Penelitian ini menganalisis pengaruh Produk Domestik Regional Bruto (PDRB) per kapita dan Rata-rata Lama Sekolah (RLS) terhadap Indeks Pembangunan Manusia (IPM) di Provinsi Bali dengan menggunakan pendekatan kuantitatif deskriptif-asosiatif. Data panel dari sembilan kabupaten/kota selama periode 20162024 dianalisis menggunakan regresi linear berganda dan Generalized Least Squares (GLS) untuk mengatasi permasalahan heteroskedastisitas. Hasil penelitian menunjukkan bahwa baik PDRB per kapita maupun RLS berpengaruh positif dan signifikan terhadap IPM, dengan model akhir menjelaskan 96,77% variasi IPM di wilayah studi. Koefisien regresi yang signifikan secara statistik menegaskan bahwa peningkatan kesejahteraan ekonomi dan akses pendidikan formal secara langsung meningkatkan kualitas pembangunan manusia di Bali. Implikasi temuan ini menyoroti pentingnya integrasi antara kebijakan ekonomi dan pendidikan dalam upaya meningkatkan IPM secara berkelanjutan, serta memberikan rekomendasi berbasis data bagi pembuat kebijakan daerah untuk merancang strategi pembangunan yang efektif dan inklusif, khususnya di daerah dengan karakteristik sosial-ekonomi unik seperti Bali.
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.
Co-Authors Beatrice, Chelsea Christoffel Mario Damanik, Ayu Lestari Ingrid, Antonia Mario, Christoffel Septianingtias, Indri Avisa Siahaan, Linda Natasya Sianturi, Tiurmaida Simanullang, Junitro Andreas Syairal Fahmy Dalimunthe Zahra, Hafizha