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Epidemiological Mapping Of Tuberculosis In South Sulawesi Using Local Indicators Of Spatial Association (LISA) And K-Means Clustering Mar'ah, Zakiyah; Hafid, Hardianti; Meliyana R, Sitti Masyitah
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141665022025

Spatial statistics is a statistical approach that links data to the location of events. The most basic way to test whether data can be analyzed using spatial statistics is to find the spatial dependence. Local spatial dependence is tested using Local Indicators of Spatial Association (LISA). This research aims to use a form of LISA, Local Moran, to cluster and map epidemiological data, the number of tuberculosis (TB) cases in South Sulawesi. The novelty of this research is that the mapping of TB infectious disease in South Sulawesi was carried out using Local Moran, as well as clustering area using K-Means. The distribution pattern of TB cases in South Sulawesi tended to be clustered and the areas that had significant spatial dependency were Makassar, Maros and Takalar. The positive Moran value in Makassar shows that the characteristics of TB cases in Makassar tended to be similar to its neighbor. Meanwhile, the negative Moran values in Maros and Takalar indicates that the characteristics of TB cases in both areas were not similar to their neighbors. The result of K-Means shows that the areas with the highest number of TB cases in South Sulawesi were Bone, Gowa and Makassar.

Workshop Perancangan Modul Ajar dan Asesmen Pembelajaran Menggunakan Artificial Intelligence Firdiani, Dian; Pratiwi, Andi Citra; Khalidatunnisa, Besse; Daud, Firdaus; Mar'ah, Zakiyah
Ininnawa : Jurnal Pengabdian Masyarakat Vol. 3 No. 1 (2025): Vol. 3 No. 1 (2025): Volume 03 Nomor 01 (April 2025)
Publisher : Program Studi Manajemen FEB UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26858/ininnawa.v3i1.8395

This community service activity aims to enhance teachers' capacity in designing instructional modules and learning assessments based on the Merdeka Curriculum by utilizing artificial intelligence (AI) technology. The background of this activity stems from the need for teachers to develop adaptive and meaningful learning experiences that align with students’ characteristics, especially in response to the challenges of digital transformation in education. The activity was conducted in the form of a participatory workshop involving elementary school teachers from the Manuju District, Gowa Regency, as participants. The implementation methods included a preliminary study, needs identification, hands-on practice in developing AI-based teaching modules and assessments, as well as reflective discussions. The results of the activity indicate that participants experienced improved understanding of the principles of the Merdeka Curriculum and enhanced technical skills in using various AI platforms to design more effective and efficient lesson plans. The workshop also contributed positively to raising teachers’ awareness of the importance of utilizing technology to support the development of the Pancasila Student Profile. Overall, this community service activity demonstrates that the integration of technology into instructional planning holds significant potential for improving the quality of both the educational process and outcomes.

Epidemiological Mapping Of Tuberculosis In South Sulawesi Using Local Indicators Of Spatial Association (LISA) And K-Means Clustering Mar'ah, Zakiyah; Hafid, Hardianti; Meliyana R, Sitti Masyitah
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 14, No 1 (2025): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat141665022025

Spatial statistics is a statistical approach that links data to the location of events. The most basic way to test whether data can be analyzed using spatial statistics is to find the spatial dependence. Local spatial dependence is tested using Local Indicators of Spatial Association (LISA). This research aims to use a form of LISA, Local Moran, to cluster and map epidemiological data, the number of tuberculosis (TB) cases in South Sulawesi. The novelty of this research is that the mapping of TB infectious disease in South Sulawesi was carried out using Local Moran, as well as clustering area using K-Means. The distribution pattern of TB cases in South Sulawesi tended to be clustered and the areas that had significant spatial dependency were Makassar, Maros and Takalar. The positive Moran value in Makassar shows that the characteristics of TB cases in Makassar tended to be similar to its neighbor. Meanwhile, the negative Moran values in Maros and Takalar indicates that the characteristics of TB cases in both areas were not similar to their neighbors. The result of K-Means shows that the areas with the highest number of TB cases in South Sulawesi were Bone, Gowa and Makassar.

A Seasonal ARIMA (SARIMA) Model for Forecasting Domestic Passenger Traffic at Sultan Hasanuddin Airport Meliyana, Sitti Masyitah; Hafid, Hardianti; Mar'ah, Zakiyah; Muthahharah, Isma
Quantitative Economics and Management Studies Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.qems3935

The growth of the domestic aviation industry in Indonesia has led to a significant increase in passenger numbers, particularly at major airports such as Sultan Hasanuddin Airport. Accurate forecasting of passenger traffic is essential for effective planning and resource allocation. This study aims to develop a suitable time series model to forecast the number of domestic air passengers departing from Sultan Hasanuddin Airport. Using monthly passenger data from January 2019 to April 2024 obtained from the Indonesian Badan Pusat Statistik (BPS), the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied. The modelling process followed the Box-Jenkins methodology, involving data exploration, stationarity testing, model identification, parameter estimation, diagnostic checking, and model validation. Among several candidate models, the ARIMA (0,1,1)(0,0,1)12 model was identified as the most appropriate, producing normally distributed, independent residuals and yielding a Mean Absolute Percentage Error (MAPE) of 4.5%. The results demonstrate that the SARIMA model provides a reliable tool for forecasting short-term domestic passenger flows at the airport.

GEOGRAPHICALLY WEIGHTED LASSO (GWL) MODELING TO IDENTIFY FACTORS INFLUENCE STUNTING INCIDENTS IN SOUTH SULAWESI Novianti, Andi Rima; Aswi, Aswi; Mar'ah, Zakiyah
Jurnal Statistika dan Aplikasinya Vol. 9 No. 1 (2025): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.09109

The Geographically Weighted Lasso (GWL) method is a technique that employs the Lasso approach within the Geographically Weighted Regression (GWR) model, which can also simultaneously select non-significant variables by shrinking the regression coefficient values to zero. Consequently, any variable assigned to a zero coefficient is considered statistically insignificant. In 2022, stunting remained a significant public health issue in South Sulawesi, ranking 10th nationwide with a prevalence of 27.2%. This underscores the urgent need for spatially sensitive analytical methods that can address regional heterogeneity and reveal key determinants at the district level. Notably, the application of GWL to analyze stunting in South Sulawesi using data from the Indonesian Nutrition Status Survey (SSGI 2022) represents a significant contribution that addresses an important research gap. This study aims to model stunting prevalence and identify its influential factors using GWL. The analysis yielded a tuning parameter λ = 0.04, achieving a model goodness of fit of R² = 0.957, demonstrating GWL’s effectiveness in mitigating multicollinearity. Four primary predictors of stunting emerged: low birth weight (LBW), access to safe drinking water, the human development index (HDI), and the average length of parental schooling.

GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) FOR COVID-19 CASE IN INDONESIA Mar'ah, Zakiyah; Sifriyani, Sifriyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0879-0886

Coronavirus disease 2019 (COVID-19) is a newly emerging infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) which was declared a pandemic by the World Health Organization (WHO) on March 11th, 2020. The response to this ongoing pandemic requires extensive collaboration across the scientific community to contain its impact and limit further transmission. Modeling to see cause-and-effect relationships in an event usually uses the Multiple Linear Regression (Ordinary Least Square) method. But in the case of Covid-19, the spread of the virus occurred from one location to another, so there was an indication that there was a spatial effect on the incident. In this study, we did not only look at spatial perspective but also considered time series data, so the method used was Geographically Weighted Panel Regression (GWPR). This study modeled the number of positive cases of Covid-19 in 34 provinces in Indonesia that occurred from March 2020 to August 2021 and looked at what factors influenced the number of positive cases of Covid-19 in each province. GWPR was performed with the assumption of a Fixed Effect Model (FEM). The FEM assumption was used by considering that the conditions of each observation unit were different. Based on the results, the best GWPR model obtained was the GWPR model with a Fixed Gaussian Kernel. The predictor variables that influenced the number of positive cases of Covid-19 were different at each location and tent to cluster at certain locations.

Peningkatan Literasi Data Guru melalui Pelatihan Penyajian Data di SMAN 7 Takalar Mar'ah, Zakiyah; Aidid, Muhammad Kasim; Muthahharah, Isma; Syalsabila, Annisa
Jurnal Pengabdian Masyarakat Bhinneka Vol. 4 No. 1 (2025): Bulan September
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v4i1.370

Tujuan kegiatan ini adalah untuk memberikan pemahaman dan keterampilan kepada guru SMAN 7 Takalar agar mereka dapat lebih efisien dalam mengelola data hasil belajar siswa, merencanakan pembelajaran, serta membuat laporan dengan menggunakan Microsoft Excel yang sudah tersedia. Microsoft Excel merupakan salah satu perangkat lunak yang sangat penting dalam pengolahan dan penyajian data. Namun, masih banyak siswa SMA dan tenaga kependidikan, yang mengalami kesulitan dalam menggunakan Excel. Sosialisasi dalam kegiatan ini mencakup identifikasi kebutuhan guru, penyuluhan dan diskusi serta penyebaran informasi. Selama pelatihan, para guru diberikan materi secara langsung tentang materi dasar Microsoft Excel, membuat grafik dan diagram serta melakukan simulasi pengolahan data analisis ulangan harian siswa. Kesimpulan kegiatan ini adalah 1) telah memberikan kontribusi yang signifikan dalam peningkatan literasi data melalui pengenalan penyajian data, 2) peserta mampu mengolah dan menyajikan data ujian siswa secara mandiri, 3) penyajian data yang baik sangat penting untuk membantu dalam memahami informasi kompleks serta pada pengambilan keputusan berbasis data.

GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) IN MODELING THE RISK FACTORS OF PNEUMONIA DISEASE AMONG TODDLERS IN THE CENTRAL SULAWESI PROVINCE Mar'ah, Zakiyah; Rais, Zulkifli; Haris, A. Sulfiana
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm151

This research was conducted to map and model the number of Pneumonia cases in Central Sulawesi Province using the Geographically Weighted Negative Binomial Regression (GWNBR) approach. The data used were Pneumonia case data in Central Sulawesi Province obtained from the Health Publication of Central Sulawesi Province in 2021. The analysis results with the GWNBR method indicated that predictor variables significantly influencing the number of Pneumonia cases in each district/city of Central Sulawesi Province were Exclusive Breastfeeding Percentage (X1), Complete Basic Immunization Percentage (X2), Percentage of Toddlers Receiving Vitamin A (X3), and Percentage of Coverage of Toddler Services (X5). Meanwhile, the variable Low Birth Weight (X4) does not significantly affect the cases.

Statistical Downscaling Modeling with Time Lag Components for Forecasting Rainfall in Wet and Dry Seasons Meyliana, Sitti Masyitah; Mar'ah, Zakiyah; Hafid, Hardianti
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm180

Climate change in Indonesia often poses a serious threat to the agricultural sector. The impacts can include reduced agricultural productivity. In this context, rainfall variables are frequently used in research related to the impacts of climate change. In this study, precipitation data from the global circulation model (GCM) outputs are used as predictor variables and rainfall data from the Indramayu station are used as response variables in statistical downscaling modeling. The cross-correlation function between these variables plays an important role in statistical downscaling modeling. The cross-correlation function can enhance the correlation between predictor variables and response variables. Therefore, this research aims to compare the rainfall prediction results using initial GCM data (GCM) and GCM data with lag components (lagged GCM) determined based on the cross-correlation function. The methods used in statistical downscaling modeling are partial least squares regression (PLSR) and principal component regression (PCR). The modeling results using data from the period 1993-2020 show that the PLSR model on lagged GCM data is the best compared to other models (PLSR on GCM data, PCR on GCM data, and PCR on lagged GCM data). This model produces the highest coefficient of determination and the smallest RMSE value. Furthermore, the PLSR model on lagged GCM data can predict the 2008 rainfall data, following the actual rainfall pattern with the smallest RMSEP value. In general, modeling using lagged GCM data provides better rainfall prediction results compared to GCM data

Metode Radial Basis Function Neural Network Untuk Klasifikasi Kab/Kota Tertinggal Di Provinsi Sulawesi Selatan Ruliana, Ruliana; Rais, Zulkifli; Mar'ah, Zakiyah; Hasnita, Hasnita
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 03 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm197

A disadvantaged area is an area that has the characteristics of tending to be left behind compared to other areas. Radial basis function neural networks are a part of Artificial Neural Networks, which use radial basis activation functions and are commonly used in classification cases. All districts/cities in South Sulawesi province have different characteristics from other districts/cities. Therefore, districts/cities are grouped into 2 groups to identify districts/cities that have characteristics that tend to be the same based on indicators of regional underdevelopment. The grouping results are then used as actual values for classification using the RBFNN method, to determine the classification results and performance of the RBFNN method. In classifying districts/cities in South Sulawesi province based on indicators of regional underdevelopment using the radial basis function neural network method, an accuracy value of 91% was obtained using a comparison of 55% training data and 45% testing data and an f-measure value of 92% was obtained

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