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All Journal Media Statistika SAINSMAT Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Ekonomi Pembangunan STIE Muhammadiyah Palopo Jurnal Matematika Sains dan Teknologi Indonesian Journal of Applied Statistics Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) MATAPPA: Jurnal Pengabdian Kepada Masyarakat M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika Jurnal Review Pendidikan dan Pengajaran (JRPP) 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 Jurnal Statistika dan Aplikasinya ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Amaliah: Jurnal Pengabdian Masyarakat Journal Nursing Care Jurusan Keperawatan Politeknik Kesehatan Gorontalo 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
Department Of Statistics, Universitas Negeri Makassar
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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

 1 
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

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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.
Pemodelan Spasial Bayesian dalam Menentukan Faktor yang Mempengaruhi Kejadian Stunting di Provinsi Sulawesi Selatan Aswi, Aswi; Sukarna, Sukarna
Journal of Mathematics, Computations and Statistics Vol. 5 No. 1 (2022): Volume 05 Nomor 01 (April 2022)
Publisher : Jurusan Matematika FMIPA UNM

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Abstract

Indonesia is a country with a high prevalence of stunting. One of the provinces in Indonesia that has a fairly high number of stunting cases is South Sulawesi Province. Research on stunting cases and their causes has been done. However, these researches have not implemented the Bayesian Spatial Conditional Autoregressive (CAR) model. This study aims to determine the factors that influence the incidence of stunting in South Sulawesi Province by implementing various Bayesian spatial CAR Leroux models with and without covariates included in the model. The results showed that the best model for modeling stunting cases in South Sulawesi Province in 2020 is the Bayesian spatial CAR Leroux model with hyperprior Inverse-Gamma IG (0.5;0.0005) by including the covariates of the percentage of poverty and the percentage of children under five 0-59 months of malnutrition. The percentage of poverty and the percentage of children under five 0-59 months of malnutrition have a positive effect on the incidence of stunting. The higher the percentage of poverty and the percentage of children aged 0-59 months with malnutrition in an area, the higher the risk of stunting in that area. 50% of districts/cities in South Sulawesi Province are in the high-risk category of stunting. Parepare City is the city with the highest Relative Risk (RR) value for stunting, followed by Toraja and Enrekang Regencies. On the other hand, Wajo Regency is the district with the lowest RR, followed by Luwu Timur and Bone Regencies.
Pemetaan Kasus Tuberkulosis di Provinsi Sulawesi Selatan Tahun 2020 Menggunakan Model Bayesian Spasial BYM dan Leroux Aswi, Aswi; Sukarna, Sukarna; Nurhilaliyah, Nurhilaliyah
Journal of Mathematics, Computations and Statistics Vol. 4 No. 2 (2021): Volume 04 Nomor 02 (Oktober 2021)
Publisher : Jurusan Matematika FMIPA UNM

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Abstract

Tuberculosis (TB) is an infectious disease that is one of the ten leading causes of death in the world. Indonesia is a country with the second-highest number of TB sufferers in the world. This study aims to identify areas with a high and low relative risk (RR) of TB by using the Bayesian Spatial Conditional Autoregressive (CAR) Besag-York-Molliѐ (BYM) and Leroux models. TB case data in every 24 districts/cities in South Sulawesi province in 2020 is used. The best model was selected based on three criteria, namely Deviance Information Criteria (DIC) and Watanabe Akaike Information Criteria (WAIC). The results show that the Bayesian Spatial CAR BYM and CAR Leroux with hyperprior IG (0.5; 0.0005) are the best models that have the same RR value. Makassar City is the area with the highest RR value (1.70) which indicates that Makassar City has a TB risk 70% higher than the average. On the other hand, the Toraja district has the lowest TB risk (0.43) which indicates that Toraja has a TB risk 43% lower than the average.
Mapping the Relative Risk of Tuberculosis in Indonesia Using the Bayesian Spatial Conditional Autoregressive Leroux Model Aswi, Aswi; Nurhikmawati, Nurhikmawati; Shanty, Meyrna Vidya; Herman, Nur Taj Alya’; Sukarna, Sukarna
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6814

Abstract

Tuberculosis (TB) is an infectious disease caused by infection with the Mycobacterium Tuberculosis bacteria. Indonesia ranks second globally in terms of the number of TB cases, after India, followed by China. Modeling is needed to evaluate the relative risk (RR) of TB cases in Indonesia to identify areas that have a high RR of being infected with the bacteria. One approach used to estimate the RR of TB in Indonesia is Bayesian Conditional Autoregressive (CAR). This research aims to identify the RR rate of TB cases in Indonesia using the Bayesian spatial CAR Leroux approach based on TB case data from 2021 to 2022. The best model selection is based on Deviance Information Criteria values, the Watanabe Akaike Information, and residuals from Modified Moran's I. Analysis results shows that in 2021, the Bayesian spatial CAR Leroux Model with Inverse Gamma prior (0.5; 0.5) is the best model. DKI Jakarta Province has the highest while Bali Province has the lowest RR. In 2022, the Bayesian spatial CAR Leroux Model with Inverse Gamma prior (1;0.01) is the best model, with DKI Jakarta Province still having the highest RR, while Bali still has the lowest RR.
Implementation of the Bayesian Spatial Model for Mapping the Relative Risk of HIV Cases in Makassar City Aisyah Putri , Siti Choirotun; Aprilia Wardani Syam , Dewi; Aswi, Aswi; Hidayat , Rahmat
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9770

Abstract

Human Immunodeficiency Virus (HIV) remains a major public health challenge in Indonesia, including Makassar City. This study aims to estimate and map the relative risk (RR) of HIV cases in Makassar City using the Bayesian spatial Conditional Autoregressive (CAR) Leroux model. The dataset comprises the number of HIV cases and the population of each district, with covariates including distance to the city center and population density. Results of Moran's I test indicated significant spatial autocorrelation in HIV cases across Makassar City. Model selection based on the Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) identified the optimal model as the CAR Leroux with an Invers-Gamma (IG) hyperprior (0.5;0.0005) and distance as a covariate, yielding the lowest DIC and WAIC values. The estimation results demonstrated that distance is negatively associated with HIV incidence. The highest RR was observed in Ujung Pandang district, while the lowest was in Biringkanaya District. These findings may provide a basis for identifying priority intervention areas and support the development of more targeted and effective HIV elimination strategies.
Estimating the Relative Risk of Dengue Hemorrhagic Fever in Makassar City Using a Bayesian Spatial Localised Conditional Autoregressive Model Rahmawati; Aswi Aswi; Rahmat Hidayat; Andi Gagah Palarungi Taufik
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/z0mxxw06

Abstract

Dengue Hemorrhagic Fever (DHF) remains a significant public health challenge in Indonesia, including in Makassar City, which reported an increase of 291 cases in 2024. This study aimed to estimate the relative risk of DHF across 15 districts of Makassar by incorporating covariates such as population density, distance to the city center, and the number of hospitals, using a Bayesian Conditional Autoregressive (CAR) Localised approach. The data were obtained from the publication Makassar City in Figures 2025, issued by the Central Statistics Agency. Spatial autocorrelation analysis with Moran’s I indicated significant clustering of DHF cases. Model selection was conducted using the Deviance Information Criterion (DIC), Watanabe–Akaike Information Criterion (WAIC), and group-level area coverage. The results showed that the best-fitting model was the CAR Localised model with distance as a covariate (M9), specified at G = 3 with hyperprior IG (1; 0.01). Distance exhibited a negative association with DHF incidence, suggesting that the farther a district is from the city center, the lower its relative risk. Among the districts, Rappocini exhibited the highest relative risk followed by Panakkukang, while the lowest risks were observed in Sangkarrang Islands. These findings provide valuable insights for designing spatially targeted DHF prevention and control strategies in Makassar City.
Tourism Forecasting Using Chen and Singh Fuzzy Time Series Models Vivianti Vivianti; Aswi Aswi
Journal of Mathematics, Computations and Statistics Vol. 9 No. 1 (2026): Volume 09 Issue 01 (March 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/w2554664

Abstract

The tourism sector is one of the main drivers of the national economy, which experienced a significant decline due to the COVID-19 pandemic. In the post-pandemic era, the recovery of international tourist arrivals shows a positive trend, thus requiring accurate forecasting methods to support tourism policy planning. ARIMA method are less effective in handling nonlinear and fluctuating data. This study applies the Fuzzy Time Series (FTS) approach, specifically the Chen and Singh models, which are capable of managing data uncertainty and representing linguistic patterns adaptively. The purpose of this study is to compare the accuracy of both models using two interval determination approaches, namely the Sturges method and the mean-based method, in forecasting international tourist arrivals through Sultan Hasanuddin International Airport during the period from January 2023 to September 2025. The analytical steps include defining the universe of discourse, performing fuzzification, constructing fuzzy logical relationships (FLR) and fuzzy logical relationship groups (FLRG), and applying defuzzification to obtain forecasted values. The forecasting accuracy was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results show that the choice of interval determination method significantly affects forecasting performance, with the mean-based method producing more detailed and accurate intervals. Based on the evaluation, the FTS Singh model demonstrated the best performance, with MAPE of 2.16% and RMSE of 31.05, outperforming the Chen model under both interval approaches. Therefore, the combination of the FTS Singh model with the mean-based interval method is recommended as the optimal approach for forecasting post-pandemic international tourist arrivals, as it can capture fluctuating data patterns more precisely and consistently.
Co-Authors A. Nurul Amalia AA Sudharmawan, AA Abdul Rahman Abdul Rahmat Abidin, Muh. Zulkifli Abidin, Muhammad Rais Ahmar, Ansari Saleh Aidid, Muhammad Kasim Aisyah Putri , Siti Choirotun Ambo Upe Andi Feriansyah Andi Feriansyah Andi Gagah Palarungi Taufik Andi Gagah Palarungi Taufik Andi Muhammad Ridho Yusuf Sainon Andin P Andi Shahifah Muthahharah Ankaz As Sikib Annas, Suwardi Annas, Suwardi Annas, Suwardi Annas, Suwarni Aprilia Wardani Syam , Dewi Arbianingsih Asrirawan Assagaf, Said Fachry Awaluddin Awaluddin Awi Awi Awi Dassa, Awi Awi, Awi Bakri, Nurul Aulya Besse Sulfiani Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto Bustan, Muhammad Nadjib Cramb, Susanna Diana Eka Pratiwi Eka Hadrayani Fahmuddin, Muhammad Fahmuddin, Muhammad Fajar Arwadi Folorunso, Serifat Adedamola Haekal, Muh. Fahri Halimah Husain Hammado, Nurussyariah Herman, Nur Taj Alya’ Hidayat , Rahmat Hisyam Ihsan Huriati, Huriati Idul Fitri Abdullah Ikhwana, Nur Irwan Irwan Irwan, Irwan Ishma Azizah S Isnaini, Mardatunnisa Isnaini, Wulan Maulia Kaito, Nurlaila Lalu Ramzy Rahmanda M Nadjib Bustan M. Miftach Fakhri Mahadtir, Muhamad Mangkona, Andi Ilham Azhar Mar'ah, Zakiyah 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 Muttaqin, Imam Akbar Natalia, Derliani Nini Harnikayani Hasa Novianti, Andi Rima Nur Aziza S Nurhikmawati, Nurhikmawati Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah Nurhilaliyah, Nurhilaliyah Nurkaila Kaito Nurlia Nurlia Nurul Fadilah Syahrul Nurul Ilmi Nurwan, Nurwan Nusrang, Muhammad Oktaviana Oktaviana Oktaviana Oktaviana Palarungi, Andi Gagah Panessai Sir Poerwanto, Bobby Poerwanto, Bobby Poewanto, Bobby Putri Ananda, Elma Yulia Putri, Siti Choiratun Aisyah Putri, Siti Choirotun Aisyah Rahma, Ina Rahman, Abdul Rahmat Hidayat Rahmat Hidayat Rahmawati Rahmawati Rahmawati Rais, Zulkifli Ramadani, Reski Aulia Rezki Amalia Idrus Riska Saputri Risma Mastory Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana Ruliana, Ruliana S, Muhammad Fahmuddin Sahlan Sidjara Saleh, Andi Rahmat Salsabila, Afifah Sapriani Shanty, Meyrna Vidya Siti Choirotun Aisyah Putri Sitti Aminah Sri Ayu Astuti Sri Rahayu Stevani Stevani Suardi, Shafira Suci Amaliah Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sudarmin Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna Sukarna, Sukarna Sulistiawaty Sulistiawaty, Sulistiawaty Sumarni Sumarni Supriadi Yusuf Susanna Cramb Suwardi Annas Suwardi Annas Syafruddin Side Syamsiar, Syamsiar Taufik, Andi Gagah Palarungi Vivianti Vivianti Vivianti Wahidah Sanusi Wea, Maria Dominggo Yassar, La Ode Salman Yudi, Wanda Yunus, Sitti Rahma Zulhijrah Zulhijrah Zulhijrah Zulhijrah Zulhijrah Zulkifli Rais