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Hybrid ARIMA-Spatial Autocorrelation (Moran Index and LISA) for Covid-19 Vaccination in All Indonesian Provinces Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
Jambura Journal of Biomathematics (JJBM) Volume 4, Issue 2: December 2023
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v4i2.20915

Numerous issues arise from stochastic processes with temporal and spatial index parameters. From 2020, Covid-19 has occurred worldwide. Combining time series with geographical analysis is crucial. ARIMA and spatial autocorrelation analysis using Moran's Index and LISA are prominent models for the two analyses. ARIMA predicts future values. The ARIMA model is applied to all recorded locations since it involves a stochastic process with a time and location parameter index. Then the prediction results at each location were examined using spatial autocorrelation, starting with the Moran index to see global relationships, then LISA (to look at the relationship between locations locally, to see which locations have a significant effect). The Queen Contiguity weight matrix calculates spatial autocorrelation (assuming that locations that are directly adjacent to each other have a spatial effect). Spatial autocorrelation will divide each place into four quadrants: High-High (HH), High-Low (HL), Low-High (LH), and Low-Low (LL). This approach was applied to 2021 Indonesian vaccination rates in all 34 provinces (354 days). Hence, the ARIMA model was applied to the 34 provinces, and each location received three forecasting. Moran's Index revealed spatial autocorrelation in the 354th and 355th time forecasts. LISA shows that Aceh (LL), West Sumatra (LH), South Sumatra (HH), Lampung (LH), and North Maluku (LL) influence other provinces (LH).

APPLICATION OF C4.5 ALGORITHM WITH FEATURE SELECTION IN CLASSIFICATION OF DISCHARGE STATUS OF HEAD INJURY PATIENTS ., Putri; Sulistianingsih, Evy; Imro'ah, Nurfitri; Debataraja, Naomi Nessyana
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol6iss2page165-174

Head trauma is a medical emergency that can cause brain damage and disability, leading to death. The discharge status of injured patients is classified into two: alive and dead. The purpose of this study is to apply the C4.5 algorithm without feature selection and by using Chi-Square and Mutual Information feature selection to show independent variables that significantly influence the discharge status of head injury patients. This research data is secondary data of patients who suffered head injuries at Dr. Abdul Aziz Hospital, Singkawang City, in 2019-2021. The independent variables used were age, gender, length of hospitalization, etiology of head injury, Suprasellar Cistern, and Glasscow Coma Scale, with the dependent variable being discharge status. Based on the study results, the Chi-Square feature selection results identified two variables that had a significant effect. In contrast, for the Mutual Information feature selection results, five variables had a significant impact on the dependent variable. The C4.5 Algorithm classification model without feature selection produces an accuracy of 88.57%, the C4.5 Algorithm classification model with Chi-Square feature selection produces an accuracy of 88.57%, and the C4.5 Algorithm classification model with Mutual Information feature selection produces an accuracy value of 91.42% with the highest accuracy obtained from the results of the C4.5 Algorithm model formation with Mutual Information feature selection.

Utilization of the ARIMA Model for Predicting the Value of Coconut Export in Kalimantan Barat Marda, Marda; Imro'ah, Nurfitri; Novita, Irene
Jurnal Forum Analisis Statistik Vol. 4 No. 2 (2024): Jurnal Forum Analisis Statistik (FORMASI)
Publisher : Badan Pusat Statistik Provinsi Kalimantan Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57059/formasi.v4i2.87

The manufactured coconut classified under HS code 08011100 is available in either shredded or dried form. In 2022, this variety of coconut is projected to account for 8.68% of the export value of manufactured coconuts in Indonesia. While West Kalimantan has not yet achieved the status of the largest coconut exporter in Indonesia, coconut remains the primary commodity in the plantation sub-sector and significantly contributes to the Regional Original Income (PAD) of West Kalimantan Province. West Kalimantan, with a potential coconut plantation area of 94,204 ha and a growing array of processed coconut products, stands poised to enhance the value of its coconut exports. This study seeks to examine prospective market conditions by predicting the export value of coconuts in shredded or dried form, serving as a foundational strategy for enhancing the value of coconut exports. The ARIMA (Autoregressive Integrated Moving Average) model is employed to forecast the value of coconut exports (HS 08011100) for the upcoming 4 periods. During the process of identifying the best model, the ARIMA model (2,1,1) was selected, yielding a MAPE value of 26.63%. This indicates that the forecasts for coconut export values (HS 08011100) remain acceptable. The estimated coconut export value serves as a valuable planning reference for stakeholders aiming to enhance future coconut exports.

WEST KALIMANTAN FOREST FIRE PROBABILITY MAPPING USING BINARY LOGISTIC REGRESSION Imro'ah, Nurfitri; Dadan Kusnandar; Debatraja, Naomi Nessyana
Jurnal Statistika dan Aplikasinya Vol. 8 No. 2 (2024): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.08207

Forest fires, which occur almost annually, are common in West Kalimantan during the dry season. There is little question that the rate of regional development will slow down as a result of the huge effects this condition has on the social, economic, and environmental domains. Naturally occurring factors are one of the many potential causes of forest fires. The goal of this research was to identify the factors that significantly influence forest fires and to produce a map showing the likelihood of forest fires occurring in various West Kalimantan cities and districts. The analytical technique that enabled us to achieve our goal was logistic regression. The existence or absence of forest fires is one of the dependent variables being used. The temperature, geography, vegetation, and human influences are the independent variables during this time. The bulk of forest fires that occurred in West Kalimantan were caused by human activity as opposed to natural causes, per the study's findings. There are several reasons why humans set off forest fires, whether on purpose or accidentally, but one of them is the distance that people can go to conduct activities inside the forest. Beyond the variables listed above, there are two other criteria that can start a forest fire: the distance from the point to the road and the distance from the point to the air. Using logistic regression, it was discovered that the variable distance between the site and the river contributed thirty percent to the likelihood of forest fires.

The GSTAR (1;1) Modelling with Three Combination of the Grid Sizes and Spatial Weight Matrix in Forest Fires Cases Ayyash, Muhammad Yahya; Huda, Nur'ainul Miftahul; Imro'ah, Nurfitri
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27543

One of the models that is utilized in spatio-temporal analysis is known as the Generalized Space-Time Autoregressive (GSTAR). This model incorporates two dimensions, namely the geographical and temporal aspects of the situation. This approach assists in the identification of patterns and correlations between data by taking into account both spatial and temporal elements. From modeling the confidence level of forest fire hotspot cases in Kubu Raya and its surrounds using the GSTAR (1;1) model with three different combinations of grids and special weight matrices, the purpose of this study is to discover which combination of grids and spatial weight matrices is the most effective. The results of diagnostic tests and the degrees of MAPE accuracy are used to determine which model is the most suitable. The data was obtained from the FIRMS-NASA platform, ranging from January 2014 to August 2024. A grid with a dimension of 1.25 x 1.25 degrees and a rook contiguity weight matrix is a combination of grids and spatial weight matrices that meet the white noise assumption, according to the findings of the study. This conclusion is based on the diagnostic test. As a result, the combination of a grid with a size of 1.25 x 1.25 and a rook contiguity weight matrix is the best in this modeling. This combination has a MAPE of 11.797%, which indicates that this model has a good level of accuracy.

COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Ayyash, Muhammad Yahya
Jurnal Matematika UNAND Vol 14, No 1 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.1.31-45.2025

This research aims to investigate the usefulness of the Generalized Space- Time Autoregressive (GSTAR) approach in predicting the number of fire hotspots in West Kalimantan Province. Specifically, the study compares the performance of the Queen contiguity method and the uniform weight matrix. Fires in the forests and on the land in West Kalimantan are severe problems that cause harm to the environment and other adverse effects. Data on fire hotspots were collected from four different regencies in West Kalimantan between January 2018 and March 2023 to provide the information used in this study. Compared to the uniform weight matrix, the study results reveal that the Queen contiguity weight matrix produces more accurate results. This is evidenced by the fact that the Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) values are lower in the Queen contiguity weight matrix. Based on these findings, more effective techniques for preventing forest and land fires are anticipated to be considered for planning purposes.

Double Intervention Analysis on The Arima Model of Covid-19 Cases in Bali Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
Journal of the Indonesian Mathematical Society Vol. 31 No. 1 (2025): MARCH
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.v31i1.1347

The time series process is not only influenced by previous observations, but some phenomena result in drastic changes to observations in the time series process so that there is a change in the average or only a temporary change in observations. For example, there is a policy from the government towards handling a case. This is referred to as an intervention. Therefore, it is necessary to do time series modeling with intervention factors. One form of intervention in the current pandemic era is a policy issued by the government. In this study, the time series model used is ARIMA. This study aimed to analyze the effect of an intervention on the ARIMA model on Covid-19 cases in Bali. This study uses data on the number of new Covid-19 cases in Bali from 24 April 2020 to 31 May 2021. There are two interventions used in this study, namely restrictions on activities for the Panca Yadnya ceremony and crowds in Bali and restrictions on traveling outside the area and/or going home and/or leaving for employees of the State Civil Apparatus during the Covid-19 pandemic. The results of this study show that two policies issued by the Bali provincial government can handle the addition of new cases of Covid-19. It can be seen from the decline in the number of new Covid-19 cases in Bali until the end of May 2021.

COMPARISON OF WEIGHT MATRIX IN HOTSPOT MODELING IN WEST KALIMANTAN USING THE GSTAR METHOD Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul; Ayyash, Muhammad Yahya
Jurnal Matematika UNAND Vol. 14 No. 1 (2025)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.14.1.31-45.2025

This research aims to investigate the usefulness of the Generalized Space- Time Autoregressive (GSTAR) approach in predicting the number of fire hotspots in West Kalimantan Province. Specifically, the study compares the performance of the Queen contiguity method and the uniform weight matrix. Fires in the forests and on the land in West Kalimantan are severe problems that cause harm to the environment and other adverse effects. Data on fire hotspots were collected from four different regencies in West Kalimantan between January 2018 and March 2023 to provide the information used in this study. Compared to the uniform weight matrix, the study results reveal that the Queen contiguity weight matrix produces more accurate results. This is evidenced by the fact that the Root Mean Squared Error (RMSE) and Mean Absolute Deviation (MAD) values are lower in the Queen contiguity weight matrix. Based on these findings, more effective techniques for preventing forest and land fires are anticipated to be considered for planning purposes.

Analisis Pembentukan Portofolio Optimal pada Indeks Saham LQ-45 dengan Metode Safety First Criterion Amalia, Disya Recita; Sulistianingsih, Evy; Imro'ah, Nurfitri
Jambura Journal of Mathematics Vol 6, No 2: August 2024
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjom.v6i2.24438

An optimal portfolio of stocks is a combination of various stock investment assets chosen to provide the maximum level of return for a specified level of risk or provide a minimal level of risk for a specified level of return. Investors form an optimal stock portfolio with the aim of minimizing the risk of investment activities. This research discusses the formation of the optimal portfolio on LQ-45 index stocks with the Safety First Criterion method. There are three criteria in the Safety First method, namely Roy Safety First, Kataoka Safety First, and Telser Safety First. The three criteria of Safety First have the main similarity in focusing on investment risk and have different objectives. The optimal portfolio with Roy Safety First criteria aims to reduce the possibility of a high level of risk. Then, the optimal portfolio with Kataoka Safety First criteria, has the goal of maximizing returns, with a level of risk determined by investors. While the optimal portfolio of Telser Safety First criteria aims to achieve the highest expected return within a predetermined risk level. The data in this study are secondary data on the weekly closing price of the LQ-45 index for the period February 2021 to January 2023, which is 105 weeks. Based on the results of the analysis, the optimal portfolio formation for risk-loving investors is the Telser criteria portfolio. This portfolio consists of ADRO, BBNI, BMRI, ITMG, and MEDC stocks. Then, the optimal portfolio for risk-averse investors is the Roy criteria portfolio consisting of ADRO, BBNI, BMRI, MDKA, and MEDC stocks.

FOREST FIRE ANALYSIS FROM PERSPECTIVE OF SPATIAL-TEMPORAL USING GSTAR (p;λ_1,λ_2,…,λ_p) MODEL Pratiwi, Hesty; Imro'ah, Nurfitri; Huda, Nur'ainul Miftahul
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1379-1392

West Kalimantan is particularly susceptible to the devastating effects of forest fires, among the natural disasters that have a significant impact. One of the indicators that can be used to identify forest fires is the presence of hotspots. The term "hotspot" refers to data that has both spatial and temporal characteristics. Using the Generalized Space-Time Autoregressive (GSTAR) model combined with the Queen Contiguity weight matrix, this research aims to model and forecast the confidence level of hotspots in Kubu Raya Regency and its surrounding areas. We chose the GSTAR model because of its ability to model spatial interactions between locations and temporal change patterns over time. According to NASA FIRMS, the data used in this study were confidence level hotspot data, covering the period from January 2014 to August 2024. To define locations for modeling, the study area was divided into grids measuring degrees. The maximum confidence level value in each grid was used to represent the highest potential fire risk. The research process consists of the following stages: data preparation, stationarity testing, calculation of the Queen Contiguity spatial weight matrix, identification of model orders based on STACF and STPACF plots, and estimation of model parameters to predict hotspot confidence levels. The GSTAR (3;1) model was selected as the best model because it satisfies the white-noise assumption and has a MAPE value of 14.78%. Based on the MAPE, the GSTAR (3;1) model can provide reasonably accurate predictions for the confidence level of fire points over the following three periods. The prediction results indicate a decline in the fire point confidence level across all locations during the following three periods. The findings of this study can support the optimization of resource allocation in the prevention of forest fires.

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