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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Matematika dan Statistika serta Aplikasinya (Jurnal MSA) Jurnal Matematika Sains dan Teknologi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Variansi : Journal of Statistics and Its Application on Teaching and Research VARIANSI: Journal of Statistics and Its Application on Teaching and Research Seminar Nasional Hasil Penelitian LP2M UNM
Muhammad Fahmuddin
Program Studi Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Negeri Makassar, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Energy Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Forecasting Bank Indonesia Currency Inflow and Outflow Using ARIMA, Time Series Regression (TSR), ARIMAX, and NN Approaches in Lampung Laila Qadrini; Asrirawan Asrirawan; Nur Mahmudah; Muhammad Fahmuddin; Ihsan Fathoni Amri
Jurnal Matematika, Statistika dan Komputasi Vol. 17 No. 2 (2021): JANUARY 2021
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/jmsk.v17i2.11803

Abstract

There are various types of data, one of which is the time-series data. This data type is capable of predicting future data with a similar speed as the forecasting method of analysis.  This method is applied by Bank Indonesia (BI) in determining currency inflows and outflows in society. Moreover, Inflows and outflows of currency are monthly time-series data which are assumed to be influenced by time. In this study, several forecasting methods were used to predict this flow of currency including ARIMA, Time Series Regression (TSR), ARIMAX, and NN. Furthermore, RMSE accuracy was used in selecting the best method for predicting the currency flow. The results showed that the ARIMAX method was the best for forecasting because this method had the smallest RMSE.
MODEL HIBRIDA DEKOMPOSISI-ARIMA UNTUK PERAMALAN INFLASI DI KOTA MAKASSAR Muhammad Fahmuddin; Zulkifli Rais
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 2 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm23889

Abstract

Forecasting is an art and predicting science about future events. Forecasting could be basic for short-term, mid-term, and long-term planning. The aim of this study is to create a hybrid decomposition model - ARIMA to forecast inflation data in Makassar City. The decomposition method is used for decomposition the inflation data into trend components, seasonal, and random. Furthermore, the decomposition method could be used to forecasting the tren component dan seasonal. Whereas, the ARIMA method was used to forecasting the random component. The result of this study shows ARIMA model used for forecasting the random component is ARIMA (0,0,[3]) with an AIC score of 171,6973Keywords: Decomposition, ARIMA, inflation
Pendekatan persamaan struktural pada model regresi error spasial (Kasus: PDRB Sulawesi Selatan) Muhammad Kasim Aidid; Zulkifli Rais; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol 3, No 3 (2021)
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm26380

Abstract

The spatial autocorrelation model studied in the framework of structural equations is the spatial error regression model. The results of this study are applied to South Sulawesi's Gross Regional Domestic Product (GRDP) data. For parameter estimation using open source software Mx. To implement the spatial error model in SEM, two new sets of weighted spatial variables need to be formed, namely W based on the dependent variable (PW) and ηW based on the independent variable (PW) and ξW based on the independent variable (QW). Since in the case of the latent model, the variables P and Q cannot be observed directly, then ηW and ξW are directly defined by the observation variables (indicators) Y yW and Y xW which are related to each other as Yy and Yx to η and ξ. obtained a model that represents the spatial error in SEM. By using South Sulawesi GRDP data where y represents the per capita GRDP in the Regency/City, x1 and x2 respectively represent the value of the Mining sector and the building sector in the Regency/City. XW1 represents first-order contiguity spatially lagged for trade and XW2 represents first-order contiguity spatially lagged for agriculture. yW denotes spatially lagged first-order contiguity for GRDP. (1−λ)γ0 represents the unit variable coefficient. From the model it can be stated that GRDP (y) is influenced by several sectors in the economy such as mining (x1) and building (x2). In addition, there is a location effect (Spatial Effect) that affects the GRDP in South Sulawesi. Based on the final results obtained, it is known that λ = 0,16 which indicates that there is a dependency on the GRDP data in South Sulawesi in 2008 between one district/city and another district/city based on the spatial correction. Areas that are centers of mining and construction in South Sulawesi are mutually dependent, causing dependence on GRDP data, this can be seen in the positive covariance value between mining lagged, and building lagged, and lagged GRDPKeywords: Effect Spatial, Error Spatial, SEM, GRDP
Implementation of K-Means Clustering on Poverty Indicators in Indonesia Suwardi Annas; Bobby Poerwanto; Sapriani Sapriani; Muhammad Fahmuddin S
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 2 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (411.441 KB) | DOI: 10.30812/matrik.v21i2.1289

Abstract

This study aims to cluster all districts/cities in Indonesia related to poverty indicators. The attributes used are poverty gap index and poverty severity index. The data used comes from BPS. The method used is K-Means clustering, and the results show that by using the elbow and silhouette index methods, the optimal number of clusters is 2, where for cluster 1, it can be defined as a cluster with an area with a high poverty gap index and poverty severity index compared to cluster 2. As a result, cluster 1 has 42 districts/cities, and 472 for cluster 2.
Pengaruh Keikutsertaan Dosen Pada Pelatihan Pekerti Terhadap Kemampuan Pedagogisnya Sudding Sudding; Halimah Husain; Muhammad Fahmuddin S
Seminar Nasional LP2M UNM SEMINAR NASIONAL 2021 : PROSIDING EDISI 11
Publisher : Seminar Nasional LP2M UNM

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

Abstract

Abstrak. Program PEKERTI dan AA merupakan program pelatihan yang dirancang Direktorat Jenderal Pendidikan Tinggi untuk peningkatan kompetensi pedagogic bagi para dosen, baik di perguruan tinggi negeri, maupun di perguruan tinggi swasta. Penelitian ini bertujuan untuk mempelajari sejauhmana manfaat keikut sertaan dosen dalam kegiatan pelatihan PEKERT terhadap perannya sebagai tenaga pengajar di prodi masing-masing, mempelajari kelemahan-kelemahan atau kekurangan-kekuranganh dalam pelaksanaan pelatihan PEKERTI di Universitas Negeri Makassar, yang akan dijadikan modal atau dasar dalam mengevaluasi atau perbaikan pelaksanaan berikutnya, dan mengetahui tingkat kepuasan dosen-dosen peserta pelatihan PEKERTI akan materi peltihan yang diperoleh. Survei tentang PEKERTI dilaksanakan melalui angket di goggle form. Pertanyaan diberikan menyangkut pelatihan (tujuan, waktu, pemateri, sarana dan prasarana, media, serta materi), motivasi (suka mengatasi rintangan dan ingin maju), kinerja mengajar (perencanaan pembelajaran, pelaksanaan pembelajaran, dan pelaksanaan evaluasi pembelajaran), dan tugas mandiri. Hasil penelitian menunjukkan bahwa sebagian besar dosen-dosen yang telah mengikuti PEKERTI sangat setuju bahwa pelatihan PEKERTI bermanfaat dalam meningkatkan kemampuan pedagogisnya.Kata Kunci: Pelatihan, PEKERTI, Dosen
Penerapan Metode Analisis Regresi Linier Pada Faktor-Faktor Penguasaan Kosa Kata Bahasa Inggris Mahasiswa Fauzan Hari Sudding Sally; Muhammad Fahmuddin S
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 5 No. 01 (2023)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm85

Abstract

This research aims to determine whether social media and students’ motivation to learn significantly affect students’ English vocabulary mastery using regression analysis. The findings indicate that students’ motivation to know has a significant effect on students' English vocabulary mastery. The coefficient of determination obtained is 0.301, which means that the motivation to learn variable can explain the vocabulary mastery variable by 30.1%. In comparison, the remaining 69.9% is explained by other variables not included in this research. However, there is no significant relationship was found between the use of social media by the students and their English vocabulary mastery
Forecasting Consumer Price Index Expenditure Inflation for Food Ingredients Using Singular Spectrum Analysis Nur Aziza S; Aswi Aswi; Muhammad Fahmuddin S; Asrirawan
Jurnal Matematika Sains dan Teknologi Vol. 24 No. 2 (2023)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/jmst.v24i2.4868.2023

Abstract

Inflation is an economic problem that significantly impacts the macro economy and people's real income if it occurs continuously. South Sulawesi Province often experienced significant inflation fluctuations during 2005-2019. In 2015, inflation in South Sulawesi reached 3.32%, ranking the highest in Eastern Indonesia. Ten food ingredients played an essential role in influencing inflation that year. However, until now, research on forecasting Consumer Price Index expenditure inflation for food ingredients in South Sulawesi using the Singular Spectrum Analysis method has never been carried out. The novelty in this research lies in using the Singular Spectrum Analysis method, which provides a new contribution to forecasting inflation trends in South Sulawesi and deepens understanding of regional inflation problems. This research aims to forecast consumer price index expenditure inflation for food ingredients in South Sulawesi using the Singular Spectrum Analysis method. This research used CPI expenditure inflation data for food ingredients from the official website of the Central Statistics Agency of South Sulawesi for the monthly period from January 2014 - June 2022. The forecasting results show that the lowest inflation rate is predicted to occur in December 2022 at -0,12%, while the highest level is expected to be reached in May 2023 at 0.43%. Furthermore, the mean absolute percentage error value of 3.54% indicates that the forecasting model has a very good level of accuracy. The results of this forecasting have the potential to be used by economic policymakers in South Sulawesi in designing more effective policies to overcome the problem of inflation, especially in the food ingredients and its impact on society. The practical implications of this research can help improve regional economic stability and community welfare.
Bayesian Spatio-Temporal Conditional Autoregressive Modelling of Factors Affecting Pneumonia Cases in Indonesia Risma Mastory; Aswi, Aswi; Muhammad Fahmuddin; Lalu Ramzy Rahmanda
Jurnal MSA (Matematika dan Statistika serta Aplikasinya) Vol 13 No 2 (2025): VOLUME 13 NO 2, 2025
Publisher : Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/msa.v13i2.56315

Abstract

The Bayesian Spatio-Temporal Conditional Autoregressive (BST CAR) method is a statistical approach used to analyze data with both spatial and temporal components. While the BST CAR model has been widely applied in various studies, no research has yet explored using the Localized BST CAR model for pneumonia cases in Indonesia. This study aims to identify and model the factors influencing pneumonia incidence in Indonesia using the Localized BST CAR framework. The data analyzed in this study consist of the number of pneumonia cases in Indonesia from 2018 to 2022, along with variables believed to affect the incidence. The findings indicate that the Localized BST CAR model with G=3 provides the best fit for modeling the relative risk of pneumonia cases in Indonesia. Key factors found to significantly influence pneumonia cases include the percentage of exclusively breastfed infants, the percentage of infants with complete basic immunization, and the percentage of the population living in poverty. Notably, the percentage of exclusively breastfed infants and the percentage of fully immunized infants were positively associated with pneumonia cases, while the percentage of the poor population had a negative effect
Co-Authors Annas, Suwardi Asrirawan Aswi, Aswi Aswi, Aswi Bobby Poerwanto Fauzan Hari Sudding Sally Ihsan Fathoni Amri Laila Qadrini Lalu Ramzy Rahmanda Muhammad Kasim Aidid Nur Aziza S Nur Mahmudah Risma Mastory Sapriani Sapriani Sudding Sudding Zulkifli Rais