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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 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

Author-ID : 697061

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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ANALISIS BAYESIAN SURVIVAL WEIBULL UNTUK MENENTUKAN FAKTOR YANG MEMPENGARUHI LAJU KESEMBUHAN PASIEN RAWAT INAP KANKER SERVIKS DI RSDU KOTA MAKASSAR Nini Harnikayani Hasa; M Nadjib Bustan; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 1 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Survival analysis is a statistical procedure for analyzing data where the response variable is the time until the occurrence of an event. In this study, Bayesian survival Weibull was used to determine the factors that influence the rate of recovery of cervical cancer inpatients. The data used in this study is cervical cancer inpatient data at the Makassar City Hospital for the 2017-2019 period. Based on the results of the analysis, it was found that a significant factor affecting the healing rate of cervical cancer inpatients was complications. Cervical cancer inpatients who experience complications tend to recover slower by 0.258 than patients who do not experience complications.

PENERAPAN METODE RANDOM FOREST UNTUK KLASIFIKASI VARIAN MINUMAN KOPI DI KEDAI KOPI KONIJIWA BANTAENG Suci Amaliah; Muhammad Nusrang; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 3 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Random Forest (RF) adalah metode yang dapat meningkatkan hasil akurasi dalam membangkitkan atribut untuk setiap node yang dilakukan secara acak. Penelitian ini bertujuan untuk mengetahui tingkat akurasi metode RF dalam memprediksi varian minuman kopi di kedai Konijiwa Bantaeng yang paling diminati pelanggan. Berdasarkan hasil analisis diperoleh bahwa model dengan error klasifikasi terkecil adalah dengan menggunakan mtry 2 dan ntree 500. Model yang dihasilkan dievaluasi dengan menggunakan confusion matrix dimana diperoleh bahwa varian minuman kategori coffee based lebih diminati daripada signature coffee dengan nilai akurasi sebesar 94,12%.

Penerapan Metode Singular Spectrum Analysis dalam Peramalan Jumlah Produksi Beras di Kabupaten Gowa Rezki Amalia Idrus; Ruliana Ruliana; Aswi Aswi
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 4 No. 2 (2022)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Total rice production in Gowa Regency from January 2018 to December 2020 has decreased which is not significant every month, so it is necessary to do a forecast to anticipate food shortages in the future. This study aims to determine the yield of rice production in Gowa Regency and to model data from October 2021 to September 2022 using the Singular Spectrum Analysis (SSA) method. Based on the results of the analysis, the MAPE value obtained is 6.32% so it can be said that forecasting using the SSA method is very accurate

PEMODELAN ARIMAX KASUS COVID-19 DIKAITKAN DENGAN CURAH HUJAN DI KOTA MAKASSAR Sukarna Sukarna; Sahlan Sidjara; Aswi Aswi; Oktaviana Oktaviana
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 4, No 2 (2022)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v4i2.25556

ABSTRACT This research applies a quantitative modelling approach and focuses on ARIMAX modelling in the Covid-19 case associated with rainfall in Makassar City. Data were obtained from the government's official website for daily confirmed case data of Covid-19 and rainfall (from June 25, 2020 to January 15, 2022). Rainfall is measured in mm which is the independent variable ( ), and confirmed Covid-19 as the dependent variable ( ). This research objective is to obtain the best ARIMAX model that informs the effect of rainfall intensity ( ) on the number of confirmed cases of Covid-19 ( ). This best model used the criteria that all parameters are significant, the residuals involved the white noise assumption and the best criteria measured from the smallest value of Akaike Information Criterion (AIC). The best model in this study is ARIMAX(3,0,5), with the smallest AIC value of 7,220,96. The results of this study indicate that rainfall ( ) has no significant effect on the number of confirmed Covid-19 ( ) in Makassar City. Keywords: ARIMAX, Covid-19, rainfall, Makassar. ABSTRAK Penelitian ini menerapkan pendekatan pemodelan kuantitatif (quantitative modelling approach) dan membahas pemodelan ARIMAX pada kasus Covid-19 dikaitkan dengan curan hujan di Kota Makassar. Data diperoleh dari website resmi pemerintah untuk data kasus terkonfirmasi harian Covid-19 dan juga curah hujan (mulai Tanggal 25 Juni 2020 s/d 15 Januari 2022). Curah Hujan diukur dalam mm yang merupakan variabel bebas ( , dan terkonfirmasi Covid-19 sebagai variabel terikat ( . Penelitian ini bertujuan untuk mendapatkan model ARIMAX terbaik yang menginformasikan pengaruh intensitas curah hujan ( terhadap jumlah kasus terkonfirmasi Covid-19 ( . Model terbaik ini memenuhi kriteria bahwa semua parameter signifikan, residual memenuhi asumsi white noise dan kriteria terbaiknya menggunakan nilai Akaike Information Criterion (AIC). Model terbaik yang diperoleh dalam penelitian ini adalah ARIMAX(3,0,5), dengan nilai AIC terkecil sebesar 7.220,96. Hasil penelitian ini menunjukkan bahwa Curah Hujan ( tidak berpengaruh signifikan terhadap jumlah terkonfirmasi Covid-19 ( di Kota Makassar. Kata kunci: ARIMAX, Covid-19, Curah hujan, Makassar

PEMODELAN TIME SERIES UNTUK NILAI TUKAR RUPIAH DI MASA PANDEMI COVID-19 Hisyam Ihsan; Abdul Rahman; Sukarna Sukarna; Aswi Aswi; Muhammad Ammar Naufal
STATMAT : JURNAL STATISTIKA DAN MATEMATIKA Vol 4, No 2 (2022)
Publisher : Math Program, Math and Science faculty, Pamulang University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/sm.v4i2.26100

Abstract: Covid-19 is an international disaster with a long occurrence interval. The research divides this disaster into four phases, namely before the Covid-19 pandemic (1 January 2019 to 31 March 2020), the implementation of PSBB (1 April 2020 to 20 January 2021), the performance of PPKM & Micro-Lockdown (21 January 2021 to 23 July 2021), and after Covid-19 is reduced (24 July 2021 to 30 June 2022). Data on IDR to USD exchange rates were obtained from the official website from 1 January 2019 to 30 June 2022. Comparing the ARIMA temporal model for the four phases was proposed in this study as an inferential and descriptive way to compare exchange rates. The results showed that the IDR exchange rate against the USD closed at IDR 14,155.63 (before the pandemic), IDR 15,581.83 (PSBB period), IDR 14,362.84 (PPKM period), and IDR 14,368.16 (after the pandemic). According to the smallest AIC or parsimony considerations, the most effective ARIMA model is ARIMA(2,1,0) for the stage before the pandemic, ARIMA(0,2,1) for the stage during PSBB, ARIMA(3,1,0) for the stage during PPKM & micro-lockdown, and ARIMA(2,1,0) for the stage after the pandemic.ABSTRAK: Pandemi Covid-19 merupakan bencana internasional yang sangat panjang interval kejadiannya. Penelitian ini membagi bencana ini menjadi 4 fase, yaitu sebelum pandemi Covid-19 (1 januari 2019 s/d 31 Maret 2020), pemberlakuan PSBB (1 April 2020 s/d 20 Januari 2021), pemberlakuan PPKM & Micro-Lockdown (21 Januari 2021 s/d 23 Juli 2021), dan setelah Covid-19 berkurang (24 Juli 2021 s/d 30 Juni 2022). Data nilai tukar IDR ke USD diambil dari situs resmi mulai 1 Januari 2019 s/d 30 Juni 2022. Tujuan penelitian ini adalah membandingkan nilai tukar secara deskriptif dan inferensial dengan membandingkan model temporal ARIMA untuk keempat fase tersebut. Hasil penelitian menunjukan bahwa nilai tukar IDR terhadap USD ditutup pada Rp 14.155,63 (sebelum pandemi), Rp 15.581,83 (masa PSBB), Rp 14.362,84 (masa PPKM), dan Rp 14.368,16 (setelah pandemi). Model ARIMA terbaik berdasarkan AIC terkecil atau pertimbangan parsimony untuk tiap fase adalah ARIMA(2,1,0) sebelum pandemi, ARIMA(0,2,1) dimasa PSBB, ARIMA(3,1,0) dimasa PPKM & micro-lockdown, dan ARIMA(2,1,0) setelah masa pandemi.Kata kunci: covid-19, nilai tukar rupiah, model temporal, ARIMA.

Application of Soft-Clustering Analysis Using Expectation Maximization Algorithms on Gaussian Mixture Model Andi Shahifah Muthahharah; Muhammad Arif Tiro; Aswi Aswi
Jurnal Varian Vol 6 No 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v6i1.2142

Research on soft-clustering has not been explored much compared to hard-clustering. Soft-clustering algorithms are important in solving complex clustering problems. One of the soft-clustering methods is the Gaussian Mixture Model (GMM). GMM is a clustering method to classify data points into different clusters based on the Gaussian distribution. This study aims to determine the number of clusters formed by using the GMM method. The data used in this study is synthetic data on water quality indicators obtained from the Kaggle website. The stages of the GMM method are: imputing the Not Available (NA) value (if there is an NA value), checking the data distribution, conducting a normality test, and standardizing the data. The next step is to estimate the parameters with the Expectation Maximization (EM) algorithm. The best number of clusters is based on the biggest value of the Bayesian Information Creation (BIC). The results showed that the best number of clusters from synthetic data on water quality indicators was 3 clusters. Cluster 1 consisted of 1110 observations with low-quality category, cluster 2 consisted of 499 observations with medium quality category, and cluster 3 consisted of 1667 observations with high-quality category or acceptable. The results of this study recommend that the GMM method can be grouped correctly when the variables used are generally normally distributed. This method can be applied to real data, both in which the variables are normally distributed or which have a mixture of Gaussian and non-Gaussian.

Pelatihan Aplikasi Mendeley dalam Upaya Optimalisasi Penulisan Referensi pada Karya Tulis Ilmiah Bagi Guru-Guru se-Kabupaten Pangkep Sukarna Sukarna; Abdul Rahman; Hisyam Ihsan; Muhammad Ammar Naufal; Aswi Aswi
Amaliah: Jurnal Pengabdian Masyarakat Vol 6 No 2 (2022): Agustus 2022
Publisher : LP3M, Universitas Muhammadiyah Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/amaliah.v6i2.739

Pelatihan ini lebih didasari oleh kendala yang dialami mitra. Banyakn sistem penulisan sitasi dan daftar pustaka pada template setiap jurnal yang berbeda-beda style. Ada yang menerapkan style Harvard, APA, IEEE, dan lain-lain. Sementara itu, mitra masih banyak yang belum mampu mengikuti bentuk style tersebut dengan cepat dan tepat. Hampir seluruh deskripsi atau penjelasan tentang tata cara (selingkung) penulisan sitasi dan daftar pustaka tersebut menggunakan bahasa Inggris. Tujuan pelatihan ini adalah untuk: 1) meningkatkan pengetahuan guru SMA/MA Kabupaten Pangkep dalam menggunakan Mendeley; 2) meningkatkan kesadaran peserta tentang pentingnya Mendeley dalam penulisan sitasi dan daftar pustaka; 3) meningkatkan wawasan, kemampuan, dan keterampilan peserta dalam mengoperasikan Mendeley sebagai aplikasi yang lebih informatif dan inovatif; dan 4) mengurangi jumlah guru SMA/MA yang menuliskan sitasi dan daftar pustaka secara manual. Metode yang digunakan adalah direct interactive paired training. Peserta yang mengikuti pelatihan ini adalah 13 orang yang sedang tertarik untuk menyusun karya tulis ilmiah seperti buku atau artikel berpontensi terbit dijurnal nasional. Hasil survey menggunakan google-form menunjukkan bahwa pelatihan ini sangat baru dan dibutuhkan bagi peserta. Sehingga, dampak yang terjadi adalah mereka tertantang dan terbantukan untuk mencoba menggunakan dan mengenal lebih jauh aplikasi Mendeley dalam menulis sitasi dan daftar pustaka.

Temporal Analysis of the Influence of the Number of Vaccinations on the Number of Covid-19 Cases in South Sulawesi Province Using ARIMAX Model Aswi Aswi; Muhammad Arif Tiro; Sudarmin Sudarmin; Ruliana Ruliana; Andi Gagah Palarungi Taufik; Zulhijrah Zulhijrah
Indonesian Journal of Fundamental Sciences Vol 8, No 2 (2022)
Publisher : Universitas Negeri Makassar

South Sulawesi is a province that has the highest number of Covid-19 cases in the Sulawesi island region. Vaccination is one way that is considered effective in controlling the infection of a disease. Covid-19 vaccination in Indonesia was carried out in January 2021. This study aims to obtain the best Autoregressive Integrated Moving Average X (ARIMAX) model in modeling the effect of the number of vaccinations on the number of Covid-19 cases in South Sulawesi Province. Data on the number of vaccinations and Covid-19 cases in South Sulawesi Province (October 1, 2021 - January 31, 2022) were used. The best ARIMAX model in modeling Covid-19 in relation to the number of vaccinations is ARIMAX (2,1,0). The results showed that the number of vaccinations had a negative effect on the number of Covid-19 cases at the significant level of 10%. This indicates that if the number of vaccinations increases then the number of Covid-19 cases will decrease.

Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) Modeling in Forecasting Covid-19 Cases in Indonesia Rahmawati Rahmawati; Suwardi Annas; Aswi Aswi
Sainsmat : Jurnal Ilmiah Ilmu Pengetahuan Alam Vol 12, No 1 (2023): Maret
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/sainsmat121442822023

Covid-19 is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The spread of Covid-19 in Indonesia has grown rapidly that the World Health Organization (WHO) has declared Covid-19 a pandemic. Covid-19 cases have spread to 34 provinces in Indonesia. Covid-19 data in Indonesia involves space and time so the appropriate modeling is the space-time model. Space-time modeling of the Covid-19 case in 34 provinces in Indonesia using the Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) model has not been carried out. The purpose of this research is to get the best GSTARIMA model and forecat the Covid-19 case for the next several times. This model incorporates time and location interdependence with different parameters for each location. Identification of the order of the AR and MA was carried out through the STACF and STPACF plots. For simplicity of interpretation, the spatial order is chosen first order. In this study, the queen contiguity and the inverse distance location weighting matrix were used. The parameter estimation used is Ordinary Least Square (OLS). The results show that the best model for predicting Covid-19 cases in 34 provinces in Indonesia is the GSTARIMA model (1,1,0)1 using an inverse distance weighting matrix with the smallest RMSE value of 1.22.Keywords: Covid-19, GSTARIMA, Queen Contiguity, Inverse Distance, OLS.

Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) dalam Peramalan Data Curah Hujan di Kota Makassar Nurul Ilmi; Aswi Aswi; Muhammad Kasim Aidid
Inferensi Vol 6, No 1 (2023)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v6i1.14347

Modeling of rainfall data using time series data involving location elements has not been widely carried out. One model that involves elements of time and location is Space Time Autoregressive (STAR). The development of the STAR model which assumes that each location has heterogeneous characteristics is the Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA) model. The purpose of this research is to get the best GSTARIMA model and forecast rainfall data in Makassar City based on the best GSTARIMA model. This model incorporates time and geographic dependencies with different parameters for each location. The data used is Makassar city's monthly rainfall data at the Bawil IV/Panaikang, Biring Romang/Panakkukang and Stammar Paotere rain stations from January 2017 to September 2021. Autoregressive (AR) and Moving Average (MA) orders were identified using the Space Time Autocorrelation plot. Function (STACF) and Space Time Partial Autocorrelation Function (STPACF). The spatial order used in this study is spatial order 1 with an inverse distance weighting matrix and normalized cross-correlation. Parameters were estimated using the Generalized Least Squares (GLS) method. The best model for predicting rainfall in the city of Makassar is the GSTARIMA (1,0,0) (1,1,0)12 model using an inverse distance weighting matrix with the smallest average Root Mean Square Error (RMSE) of 132.9661.

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 Awi Dassa Bobby Poerwanto Bobby Poerwanto Bobby Poerwanto Bustan, Muhammad Nadjib Halimah Husain Hammado, Nurussyariah Hisyam Ihsan Idul Fitri Abdullah Imam Akbar Muttaqin Ina Rahma 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 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 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

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