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Sianturi, Tiurmaida
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Analisis Kesalahan Umum dalam Penyusunan Makalah Akademik: Studi Kasus Mahasiswa Perguruan Tinggi Zahra, Hafizha; Padang, Chelsea Beatrice; Sianturi, Tiurmaida; Nazwa, Syahira; Audina, Rizka; 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.15072723

Abstract

This study aims to analyze errors in scientific writing by students in several universities in Indonesia, identify the factors that cause them, and provide recommendations for improvement. This study uses a quantitative descriptive method with a questionnaire survey distributed to 100 students from various universities in North Sumatra selected by convenience sampling method. The questionnaire covered understanding of paper format, frequency of writing, level of difficulty, training experience, factors causing errors, and proposed solutions. The results showed that students generally have a good understanding of the basic structure of term papers, but there are still weaknesses in technical aspects such as spacing, margins, and title writing. Factors causing errors include lack of understanding of the material, technical formatting errors, and lack of attention to detail. This study recommends more specific and practical writing training to improve the quality of students' academic writing.
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

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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 Spasial Jumlah Kejadian Bencana di Indonesia: Pendekatan ESDA dan Local Indicators of Spatial Association Sianturi, Tiurmaida; Aritonang, Siska Dwi Febyola; Panjaitan, Sinsi Setiawati; Harahap, Adi Gunawan
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.15259972

Abstract

This study aims to analyze the spatial patterns of the number of disaster events in Indonesia with the spatial data exploration approach (ESDA) and the Local Indicators of Spatial Association (LISA) to identify the existence of spatial autocorrelation and disaster prone area clusters. This research is an exploratory quantitative research using Exploratory Spatial Data Analysis (ESDA). The data used in this study are secondary data obtained from Indonesian Disaster Information Data (Dibi) on Flood Disaster Statistics in Indonesia in 2024. The variable used in this study was the number of flood events in Indonesia in 2024. Spatial analysis in this study reached the explore stage conducted by the Esda method. The ESDA method is an exploration data analysis (EDA) to detect the spatial properties of the data where for each variable value there is location data. This location data refers to the point or area referred to by variables. This esda method is also a visual and numerical method used for hypothesis testing and identifying spatial relationships and patterns through the use of spatial weight matrices. The results of the study show that the results of spatial data exploration analysis of flood disasters in Indonesia in 2024, found that the distribution of events is not random, but rather forms certain spatial patterns. This is supported by a significant global autocorrelation test results, both through the Morans I and Gearys C index. Morans I value is 0.182 (p-value = 0.020) and Gearys C of 0.798 (P-value = 0.033) shows the presence of positive spatial spatial autocor tall.
Analisis Regresi Linier Berganda Dalam Estimasi Faktor-Faktor yang Mempengaruhi IPM di Sulawesi Selatan Sianturi, Tiurmaida; Anggraini, Sepi; Deswan, Meisyy Laisa Usrini
Madani: Jurnal Ilmiah Multidisiplin Vol 3, No 5 (2025): Volume 3, Nomor 5, June 2025
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

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Abstract

The Human Development Index (HDI) is one of the indicators in assessing the development progress of a region that reflects the quality of life of the community based on the dimensions of health, education, and decent living standards. This study aims to analyze the effect of PDRB, IPG , and Average Years of Schooling (RLS) on HDI in South Sulawesi Province. This study uses multiple linear regression analysis methods on secondary data from BPS South Sulawesi for the period 2020-2023 which includes 24 districts / cities in South Sulawesi. The results showed that PDRB, IPG, and RLS had a positive and significant effect on HDI both partially and simultaneously. The model is able to explain 89% of the variation in HDI and fulfills all classical regression assumptions. The conclusion of this study emphasizes the importance of regional development policies that focus on improving the quality of education, sustainable economic growth, and gender equality to accelerate the achievement of human development in South Sulawesi.
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 Anggraini, Sepi Aritonang, Siska Dwi Aritonang, Siska Dwi Febyola Audina, Rizka Christoffel Mario Deswan, Meisyy Laisa Usrini Harahap, Adi Gunawan Nazwa, Syahira Padang, Chelsea Beatrice Panjaitan, Sinsi Setiawati Siahaan, Linda Natasya Simamora, Tabita Paulina Syairal Fahmy Dalimunthe Zahra, Hafizha