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All Journal Media Statistika SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Matematika Sains dan Teknologi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) MATAPPA: Jurnal Pengabdian Kepada Masyarakat Jurnal Varian Inferensi ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) Variansi : Journal of Statistics and Its Application on Teaching and Research Amaliah: Jurnal Pengabdian Masyarakat SAINSMAT: Jurnal Ilmiah Ilmu Pengetahuan Alam VARIANSI: Journal of Statistics and Its Application on Teaching and Research Indonesian Journal of Fundamental Sciences Seminar Nasional Hasil Penelitian LP2M UNM SMART: Jurnal Pengabdian Kepada Masyarakat Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Aswi, Aswi
Prodi Statistika, FMIPA, Universitas Negeri Makassar
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Pemodelan Faktor-Faktor yang Mempengaruhi Kasus Stunting di Sulawesi Selatan Menggunakan Geographically Weighted Regression Putri, Siti Choirotun Aisyah; Salsabila, Afifah; Suardi, Shafira; Mutmainnah, Mutmainnah; Aswi, Aswi
ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v5i2.30142

Abstract

One of the prevalent nutritional issues affecting toddlers worldwide is stunting. Several studies on stunting cases have been conducted in Indonesia. However, modeling using the Geographically Weighted Regression (GWR) method in South Sulawesi has not been carried out. This study aims to identify the variables that affect the incidence of stunting in each district in South Sulawesi based on spatial modeling using the GWR method. Data on the number of stunting cases, the pproportion of low-birth-weight infants, the percentage of under-five who are malnourished, the percentage of proper drinking water, and the percentage of poor people in South Sulawesi in 2020 were used. The results show that the GWR model has an  value of 86.64%, which is higher than that of the global regression model. The factors that influence the percentage of stunting based on the GWR modeling results are the percentage of under-five who are malnourished and the percentage of proper drinking water. The findings of this study are anticipated to help the government address the issue of stunting in South Sulawesi. Early prevention may then be implemented.
PEMODELAN BAYESIAN SPASIAL CONDITIONAL AUTOREGRESSIVE (CAR) PADA KASUS DEMAM BERDARAH DENGUE DI INDONESIA Aswi; Sukarna
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 10 No 1 (2022): VOLUME 10 NOMOR 1 TAHUN 2022
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v10i1.29113

Abstract

Demam Berdarah Dengue (DBD) merupakan salah satu penyakit menular yang masih merupakan masalah utama dalam kesehatan masyarakat di Indonesia. Total kasus DBD di Indonesia pada tahun 2020 masih cukup tinggi yaitu 108.303 kasus. Beberapa penelitian terkait pemodelan DBD telah menggunakan metode Bayesian spasial. Akan tetapi, penelitian tersebut masih fokus pada salah satu provinsi yang ada di Indonesia. Penelitian ini bertujuan untuk memodelkan risiko relatif (RR) kasus DBD tahun 2020 di Indonesia dengan 34 provinsi dan menghasilkan peta tematik RR. Data yang digunakan data kasus DBD serta data jumlah penduduk di Indonesia tahun 2020 yang diperoleh dari Publikasi Kementerian Kesehatan Republik Indonesia 2021. Model Bayesian spasial Conditional Autoregressive (CAR) Leroux digunakan dengan pemilihan model terbaik didasarkan pada Deviance Information Criteria (DIC), Watanabe Akaike Information Criteria (WAIC), dan Modifikasi Moran’s I (MMI) untuk residual. Hasil yang diperoleh menunjukkan bahwa model Bayesian spasial CAR Leroux dengan hyperprior IG(1; 0,1) merupakan model terbaik dalam pemodelan kasus DBD tahun 2020 di Indonesia. Sekitar 53% provinsi yang ada di Indonesia merupakan wilayah dengan RR tinggi, dimana Provinsi Bali memiliki nilai RR tertinggi (6,84), diikuti oleh Provinsi Nusa Tenggara Timur (RR=2,70), dan Daerah Istimewa Yogyakarta (RR=2,33). Sebaliknya, provinsi dengan RR terendah adalah Provinsi Maluku (RR=0,11), diikuti oleh Provinsi Papua (RR = 0,13), dan Provinsi Kalimantan Barat (RR =0,38).
PEMODELAN KASUS COVID-19 DI INDONESIA MENGGUNAKAN ANALISIS SPASIAL DENGAN PENDEKATAN BAYESIAN Aswi; Sukarna; Nurhilaliyah
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 10 No 2 (2022): VOLUME 10 NOMOR 2 TAHUN 2022
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v10i2.33221

Abstract

Kasus terkonfirmasi positif Covid-19 dilaporkan pertama kali di Indonesia pada tanggal 2 Maret 2020. Hingga 30 September 2022, Indonesia memiliki 6.465.207 kasus. Berbagai penelitian mengenai pemodelan kasus Covid-19 telah dilakukan. Akan tetapi, penelitian menggunakan model Bayesian spasial Conditional Autoregressive (CAR) Leroux (BSCL) untuk kasus Covid-19 di 34 provinsi di Indonesia belum sepenuhnya dilakukan. Penelitian ini bertujuan untuk mendapatkan model BSCL terbaik dalam mengestimasi risiko relatif (RR) kasus Covid-19 di 34 provinsi di Indonesia dan menghasilkan peta tematik RR. Data agregat kasus positif Covid-19 (2 Maret 2020-30 September 2022) digunakan dalam penelitian ini. Selain itu, data jumlah penduduk di 34 provinsi di Indonesia tahun 2021 juga digunakan. Kriteria dalam memilih model terbaik adalah dengan melihat nilai residual dari Modifikasi Moran’s I (MMI) yang lebih dekat ke nol, nilai Watanabe Akaike Information Criteria yang terkecil serta nilai Deviance Information Criteria (DIC) terkecil. Hasil pengolahan data Covid-19 menunjukkan bahwa model BSCL dengan hyperprior IG (0,1;0,1). merupakan model terbaik dalam mengestimasi RR kasus Covid-19 di Indonesia. Sekitar 29,41% (10 provinsi) di Indonesia yang memiliki nilai RR kategori tinggi terjangkit Covid-19. Provinsi dengan RR tertinggi dan terendah masing-masing adalah Provinsi DKI Jakarta (RR=5,68) dan Provinsi Nusa Tenggara Barat (RR=0,28).
Analisis Dampak Covid-19 Terhadap Tingkat Inflasi di Indonesia Aswi; Zulhijrah; Isnaini, Mardatunnisa; Sri Sulastri
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 11 No 2 (2023): VOLUME 11 NO 2 TAHUN 2023
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v11i2.40304

Abstract

Penyebaran corona virus diseases 19 (Covid-19) telah meluas ke seluruh penjuru dunia dan membawa dampak terhadap pendidikan, pariwisata maupun ekonomi. Inflasi adalah salah satu dampak pandemi COVID-19 terhadap kondisi makro Indonesia. Penelitian tentang dampak Covid-19 terhadap tingkat inflasi di Indonesia telah dilakukan, tetapi hasilnya tidak konsisten. Analisis yang mengaitkan antara Covid-19 dan tingkat inflasi di Indonesia menggunakan model Autoregressive Integrated Moving Average with exogenous variables (ARIMAX) belum dilakukan. Studi ini bertujuan untuk mendapatkan model ARIMAX terbaik dan menentukan apakah terdapat hubungan antara kasus Covid-19 dengan tingkat inflasi di Indonesia. Data inflasi bulanan dan data rata-rata Covid-19 bulanan (Maret 2020-September 2022) di Indonesia digunakan pada studi ini. Data Inflasi dituliskan sebagai variabel Zt yakni variabel independen yang diperoleh dari Badan Pusat Statistik dan data Covid-19 sebagai peubah Xt yang merupakan variabel independen yang diperoleh dari situs resmi Kementerian Kesehatan Republik Indonesia. Berdasarkan hasil analisis data, disimpulkan bahwa model ARIMA terbaik adalah ARIMA (0, 1, [6]). Dari hasil estimasi model ARIMAX diperoleh bahwa dampak Covid-19 terhadap inflasi di Indonesia tidak berpengaruh secara signifikan.
Implementasi Backpropagation Neural Network Pada Status Preeklampsia Ibu Hamil Sapriani; Bobby Poerwanto; Aswi
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 11 No 2 (2023): VOLUME 11 NO 2 TAHUN 2023
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v11i2.40657

Abstract

Preeklampsia adalah penyakit yang diderita ibu hamil yang ditandai adanya kenaikan tekanan darah. Preeklampsia dapat membahayakan ibu dan juga janin karena dapat menyebabkan komplikasi, hingga kondisi terburuk yaitu kematian. Tujuan dari penelitian ini adalah untuk mengetahui hasil klasifikasi sekaligus prediksi berdasarkan lima variabel yang diduga mempengaruhi status preeklampsia ibu hamil. dengan menggunakan metode Backpropagation Neural Network (BNN). Data rekam medis ibu hamil di RSIA Sitti Khadijah 1 Makassar dan RS TK II Pelamonia Makassar yang berjumlah 167 data digunakan pada penelitian ini. Terdapat lima variabel yang diduga mempengaruhi status preeklampsia ibu hamil yaitu usia ibu saat kehamilan, paritas, riwayat penyakit, indeks massa tubuh, dan status pekerjaan ibu. Hasil studi menghasilkan bahwa skenario pembagian data terbaik yaitu data training 80% dan data testing 20% dengan hasil akurasi sebesar 67,65%, sensitifitas 69,23%, spesifikasi 66,67%, presisi 56,25%, Score 62,07%, nilai hidden layer 7 dan learning rate 0,001.
Modeling Factors Influencing Covid-19 Cases in South Sulawesi Using Bayesian Conditional Autoregressive Localised Yassar, La Ode Salman; Shanty, Meyrna Vidya; Mahadtir, Muhamad; Aswi, Aswi; Annas, Suwardi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 1 (2024): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat131606362024

Abstract

South Sulawesi Province is listed as the province with the highest number of Covid-19 cases in the Sulawes island. Research on Covid-19 modeling has been carried out by many researchers, but until now, there has been no research using the Bayesian spatial Conditional Autoregressive Localized model which involves a combination of factors such as distance to the provincial capital, population density, and the number of elderly people in each district in South Sulawesi Province. The aim of this research is to get the best Bayesian Conditional Autoregressive Localized model. The best model is based on four criteria, namely: Deviance Information Criteria, Watanabe Akaike Information Criteria, residuals from Modified Moran's I, and the number of areas included in a group. It was found that model with G=3 by including population density covariates was the best model. A significant factor influencing the increase in Covid-19 cases is the population density factor which has a positive effect. This shows that the more densely populated an area is, the greater the chance of being infected with Covid-19. Makassar has the highest relative risk value for Covid-19 followed by Toraja district and Pare-Pare City. Meanwhile, Bone district has the lowest relative risk value for Covid-19, followed by Wajo district and Enrekang district.
Statistical Modeling and Factors Influencing School Dropout in Indonesia: A Review Shanty, Meyrna Vidya; Mahadtir, Muhamad; Awaluddin, Awaluddin; Natalia, Derliani; Ramadani, Reski Aulia; Aswi, Aswi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 13, No 1 (2024): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat131608032024

Abstract

The education enrollment rate is crucial for Indonesia to improve its human resources and sustain its economic development. In reality, the dropout student rate is still relatively high. Previous research has highlighted several factors and models related to the dropout student rate in Indonesia. The purpose of the study is to identify the most popular statistical modeling and factors influencing school dropout in Indonesia. We searched in February 2023 using ScienceDirect, ProQuest, and Google Scholar. The search was restricted to refereed journal articles published in English from January 2013 to December 2022. This study underwent four stages: identification, screening, eligibility, and inclusion. The study finds that the most popular statistical modeling is the Logistic Regression Model, and the most significant factor increasing the school dropout rate in Indonesia is family and economic factors. The findings suggest that children who were not attending school came from families with lower levels of education. The well-being of these families was directly linked to their children's educational status. The primary reasons for young students dropping out of elementary and junior schools include an inability to pay school fees and a desire to work on farms to support their parents.
Penerapan Metode Generalized Space Time Autoregressive (GSTAR) Untuk Memprediksi Sebaran Covid-19 di Pulau Jawa Wea, Maria Dominggo; aswi; Aidid, Muhammad Kasim
Journal of Mathematics, Computations and Statistics Vol. 7 No. 1 (2024): Volume 07 Nomor 01 (April 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i1.1944

Abstract

Virus Corona merupakan keluarga besar virus (antara hewan dan manusia) yang ditularkan secara zoonik dan menimbulkan gejala ringan hingga berat. Kasus COVID-19 terus bertambah secara global dan berfluktuasi, termasuk di Indonesia dan sebaran kasus infeksi virus terbesar di Pulau Jawa. Penelitian ini bertujuan untuk memprediksi sebaran kasus COVID-19 di Pulau Jawa dengan data spasial yaitu 6 provinsi sehingga menggunakan model ruang waktu yaitu Model Generalized Space Time Autoregressive (GSTAR). Matriks pembobot yang digunakan adalah invers jarak dan queen contiguity. Tahapan pembentukan model terdiri dari uji stasioneritas data, penentuan urutan ruang-waktu, penggunaan kedua matriks pembobot, estimasi parameter dengan Ordinary Least Square, uji kesesuaian model dengan uji asumsi white noise dan uji asumsi normalitas, perhitungan Root Mean Square Error (RMSE), pemilihan model terbaik dengan matriks pembobotan queen contiguity yang menghasilkan nilai RMSE terkecil, peramalan berdasarkan model GSTAR terbaik, dan pemetaan total hasil peramalan. Model GSTAR terbaik yang dihasilkan adalah GSTAR (1;1) I(1) dengan menggunakan matriks pembobotan queen contiguity dengan nilai RMSE terkecil. Hasil peramalan dan pemetaan berdasarkan hasil ramalan total kasus COVID-19 di Pulau Jawa pada tanggal 1 April 2022 – 7 April 2022 menunjukkan bahwa hampir semua provinsi mendekati nilai pada data aktual dilihat dari plot dan peta sebaran.
The Impact of Covid-19 on Stunting Cases in Indonesia: A Bayesian Spatial Modeling Approach Ankaz As Sikib; Aswi, Aswi; Ruliana
Journal of Mathematics, Computations and Statistics Vol. 7 No. 2 (2024): Volume 07 Nomor 02 (Oktober 2024)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v7i2.3065

Abstract

The high number of COVID-19 cases has impacted various sectors. One of the notable consequences of the COVID-19 pandemic is its effect on food security and nutrition. Social restrictions implemented to curb the spread of the virus have resulted in worsening economic conditions, limited access to healthcare facilities, difficulties in obtaining nutritious food, and school closures. Changes in the routines and activities of COVID-19 patients may contribute to an increase in the prevalence of stunting in Indonesia. While research has been conducted on the impact of COVID-19 on the rise of stunting cases in Indonesia, previous studies have typically focused on individual provinces and have not utilized the Bayesian Conditional Autoregressive (CAR) model. This study aims to investigate the relationship between COVID-19 and the increase in stunting cases across Indonesia. We analyze data on stunting cases in each Indonesian province and the number of COVID-19 patients between March 23, 2020, and December 31, 2021. To assess the relationship, we employ the Bayesian spatial CAR Leroux model with several Inverse-Gamma hyperpriors. We compare these models using various fit criteria. The results indicate that the Bayesian spatial CAR Leroux model with Inverse-Gamma hyperpriors (0.1;0.1) performs best, as it yields the smallest Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) values. In conclusion, our analysis reveals a positive correlation between the number of COVID-19 cases and the increase in stunting cases in Indonesia. Approximately 50% of the regions in Indonesia face a high relative risk of stunting, with Nusa Tenggara Timur having the highest relative risk, followed by Kalimantan Barat and Sulawesi Barat
Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Sulawesi Selatan Menggunakan Model ARFIMA Sukarna; Abdy, Muhammad; Aswi; Kaito, Nurlaila
Journal of Mathematics, Computations and Statistics Vol. 5 No. 2 (2022): Volume 05 Nomor 02 (Oktober 2022)
Publisher : Jurusan Matematika FMIPA UNM

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

Abstract

Tourism is a potential and strategic asset to encourage the development of a region, especially for areas that have potential tourist objects. Exchange rates, inflation, and geography influence foreign tourist visits to an area. What may be unexpected is the increase in the number of tourists, which makes tourist workers have difficulties in providing the best services, and vice versa if there is a sudden drop, it will increase the number of unemployed. Therefore, we need a scientific study of forecasting that can provide information on the number of tourists. The ARFIMA model is an ARIMA whose differencing value is a fraction. The main goal of this research is to discover the best ARFIMA model to predict the number of foreign tourist arrivals in South Sulawesi. From the data of foreign tourists in South Sulawesi from 2015 to 2020, the result of this research is the AIC value of 710.44 for ARFIMA([1,8],d,0) with. The average difference between the actual and forecasted data in the out sample data for the two models is 38.6667 points. Therefore, the two models can still be classified as the best for forecasting foreign tourists from South Sulawesi. It depends on who applied this models into this cases.
Co-Authors A. Nurul Amalia AA Sudharmawan, AA Abdul Rahman Aidid, Muhammad Kasim Andi Feriansyah Andi Feriansyah Andi Gagah Palarungi Taufik Andi Muhammad Ridho Yusuf Sainon Andin P Andi Shahifah Muthahharah Ankaz As Sikib Annas, Suwardi Asrirawan Awaluddin Awaluddin Awi Awi Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto Bustan, Muhammad Nadjib Fahmuddin, Muhammad Halimah Husain Hammado, Nurussyariah Hisyam Ihsan Idul Fitri Abdullah Irwan Irwan Isnaini, Mardatunnisa Kaito, Nurlaila M Nadjib Bustan Mahadtir, Muhamad Mardatunnisa Isnaini Mauliyana, Andi Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Abdy Muhammad Ammar Naufal Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro Muhammad Arif Tiro, Muhammad Arif Muhammad Fahmuddin Muhammad Fahmuddin Muhammad Fahmuddin Sudding Muhammad Kasim Aidid Mutmainnah Mutmainnah Natalia, Derliani Nini Harnikayani Hasa Nur Aziza S Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah, Nurhilaliyah Nurkaila Kaito Nurul Fadilah Syahrul Nurul Ilmi Nusrang, Muhammad Oktaviana Oktaviana Poerwanto, Bobby Putri, Siti Choirotun Aisyah Rahma, Ina Rahman, Abdul Rahmawati Rahmawati Ramadani, Reski Aulia Rezki Amalia Idrus Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana, Ruliana Sahlan Sidjara Salsabila, Afifah Sapriani Shanty, Meyrna Vidya Siti Choirotun Aisyah Putri Sri Ayu Astuti Sri Rahayu Suardi, Shafira Suci Amaliah Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Supriadi Yusuf Susanna Cramb Suwardi Annas Suwardi Annas Syafruddin Side Wahidah Sanusi Wea, Maria Dominggo Yassar, La Ode Salman Zulhijrah Zulhijrah Zulhijrah Zulkifli Rais