Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat (JHP2M) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Zakiyah Mar'ah
Universitas Negeri Makassar
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Mathematics Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Clustering Biplot on Tourist Visits in Indonesia Isma Muthahharah; Zakiyah Mar'ah
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3890

Abstract

This research aims to find out whether or not many tourists visited Indonesia after Covid-19 by clustering. This will generate foreign exchange earnings and contribute directly to the country's economic growth. The analytical method used in this research is K-Medoids. K-Medoids is a partition clustering technique that groups a collection of n objects into k clusters by utilizing the objects in the collection of objects to represent a cluster called a medoid. The data in this research used secondary data related to foreign tourist visits to Indonesia from several publication sources in 2017-2021. The results of this research show that there were 3 clusters obtained: Cluster 1 shows the number of tourist visits visiting Indonesia in 2017, 2018 and early 2019 because the Covid-19 pandemic has not yet occurred, Cluster 2 shows that there were no tourist visits in 2020 due to the start of the Covid-19 pandemic, and Cluster 3 indicates low tourist arrivals in 2021 due to the Covid 19 pandemic which temporarily prohibited foreign tourists from visiting Indonesia.
Pendampingan Belajar Matematika dan Bahasa Inggris Gratis untuk Meningkatkan Literasi dan Numerasi Anak-anak Marginal di Kelurahan Pannampu Kota Makassar : Pendampingan Belajar Matematika dan Bahasa Inggris Gratis untuk Meningkatkan Literasi dan Numerasi Anak-anak Marginal di Kelurahan Pannampu Kota Makassar SITTI MASYITAH MELIYANA R; Hardianti Hafid; Sitti Nailah Rustam; Zakiyah Mar'ah; Isma Muthahharah
Jurnal Hasil-Hasil Pengabdian dan Pemberdayaan Masyarakat Vol. 3 No. 1 (2024): Volume 03 Nomor 01 (April 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jhp2m.v3i1.2248

Abstract

Bagian yang perlu ditingkatkan dari anak-anak marginal dari sisi akademik adalah kemampuan berhitung dan berbicara dalam Bahasa Inggris. Tim pengabdi mengajar secara rutin di Rumah Baca Kampung Bersih Nusantara setiap seminggu sekali, tepatnya di hari sabtu, untuk meningkatkan kemampuan berhitung dan Bahasa Inggris anak-anak tersebut. Pelatihan yang berkelanjutan sangat diperlukan untuk memberikan efek yang mendalam bagi peningkatan kemampuan tersebut. Pengajaran dilakukan oleh tim pengabdi yang memiliki background Pendidikan baik dari latar belakang keilmuan matematika maupun Bahasa Inggris. Pelatihan sudah memberikan hasil peningkatan yang signifikan karena program ini sejatinya sudah berlangsung selama beberapa bulan. Motivasi belajar matematika dan Bahasa Inggris para siswa juga menjadi lebih besar yang terbukti dari besarnya antusiasme mereka dalam mempraktekkan apa yang diinstruksikan oleh pengajar. Berdasarkan temuan tersebut dapat disimpulkan bahwa pelaksanaan pengabdian ini sudah efektif. Selain itu, pengetahuan para anak-anak terhadap materi yang telah disampaikan lebih meningkat.
A Systematic Simulation Study of Semiparametric Spline Estimators for Nonlinear Data Structures in R Rahmat Hidayat; Aswi Aswi; Zakiyah Mar'ah
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/7k3vqe29

Abstract

In many real-world applications, the assumption of linearity in classical regression models is often violated, leading to model misspecification and inaccurate estimation when data exhibit complex nonlinear patterns. Although nonparametric approaches provide flexibility, they frequently suffer from poor interpretability and instability in high-dimensional settings. To address these limitations, this study examines the implementation of semiparametric spline regression as a flexible yet interpretable alternative. The model integrates a linear component for certain predictors and a spline-based nonparametric component to capture local data fluctuations. Through a simulation study using the R programming language, the performance of the spline estimator was evaluated based on the Generalized Cross Validation (GCV) criterion for optimal knot selection. The results demonstrate that the semiparametric spline model achieves superior accuracy, with a coefficient of determination (R²) reaching 97.35%, compared to 81.18% for the classical linear model. In addition, the Mean Square Error (MSE) is significantly reduced from 2.158 to 0.303. Residual diagnostic analysis confirms that the model satisfies normality and homoscedasticity assumptions. These findings highlight the effectiveness of spline-based semiparametric regression in modeling complex nonlinear data structures.
Integrating Spatial Lag and Error Components in a SARMA Model for Tuberculosis Analysis across Indonesian Provinces Zakiyah Mar'ah; Rahmat H.S.
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/107vez06

Abstract

Tuberculosis (TB) remains a critical public health challenge, necessitating an in-depth understanding of its regional determinants to formulate effective, targeted interventions. This study investigates the underlying factors driving TB cases and identifies the optimal spatial regression model for analyzing its regional distribution. Utilizing cross-sectional data from 34 observation areas during the year 2023, the prevalence of TB was evaluated against five independent variables: life expectancy (X1), access to basic sanitation (X2), availability of primary healthcare facilities (X3), smoking prevalence (X4), and treatment success rates (X5). Initial exploratory analysis revealed a significant spatial autocorrelation of TB cases across the regions (Moran’s I = 0.566, p-value = 0.0003). Consequently, spatial regression modeling was applied using Spatial Autoregressive (SAR), Spatial Error Model (SEM), and Spatial Autoregressive Moving Average (SARMA) approaches. By comparing the Akaike Information Criterion (AIC), Log-Likelihood, and R² metrics, the SARMA model emerged as the most robust fit for the dataset (R² = 0.674, AIC =283.82). The empirical results demonstrate that, at a 10% significance level, access to basic sanitation negatively impacts TB cases. Furthermore, the significance of the spatial parameters confirms that neighboring regional dynamics and geographical proximity play a crucial role in the spread of Tuberculosis.
Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models (Case Study: Crime in South Sulawesi) Indi Nur Ridwan; Sudarmin; Zakiyah Mar'ah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm503

Abstract

The Geographically Weighted Regression (GWR) model operates by taking into account how the relationships between different factors change across geographic space. Meanwhile, the Mixed Geographically Weighted Regression (MGWR) model permits certain variables to exhibit spatially varying (local) effects, while other variables are assumed to have constant effects across all locations. Both models are relevant to be applied in crime studies influenced by variations in regional conditions. The objective of this study is to evaluate the GWR and MGWR approaches in selecting the best model to explain factors associated with crime cases in South Sulawesi. The data used include the number of crime cases in South Sulawesi in 2024 along with factors presumed to influence them. The investigation's outcomes suggest the GWR model demonstrates higher appropriateness compared to the MGWR model, evidenced by its reduced Akaike Information Criterion (AIC) score and a 98.44% coefficient of determination . Based on the best-fitting model, population density and the number of poor residents were identified as the main factors influencing criminality in South Sulawesi in 2024.
Co-Authors Aswi, Aswi Hardianti Hafid Indi Nur Ridwan Isma Muthahharah Rahmat H.S. Rahmat Hidayat Sitti Masyitah Meliyana R. Sitti Nailah Rustam Sudarmin