cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Isran K. Hasan
Contact Email
isran.hasan@ung.ac.id
Phone
+6285398740008
Journal Mail Official
redaksi.jjps@ung.ac.id
Editorial Address
Department of Statistics, 3rd Floor Faculty of Mathematics and Natural Sciences, Universitas Negeri Gorontalo Jl. Prof. Dr. Ing. B.J Habibie, Tilongkabila Kabupaten Bone Bolango, 96119
Location
Kota gorontalo,
Gorontalo
INDONESIA
JAMBURA JOURNAL OF PROBABILITY AND STATISTICS
Published by Universitas Negeri Gorontalo
ISSN : -     EISSN : 27227189     DOI : https://doi.org/10.34312/jjps
Core Subject : Science, Social,
Computer Science & IT Decision Sciences, Operations Research & Management Environmental Science Social Sciences
Probability Theory Mathematical Statistics Computational Statistics Stochastic Processes Financial Statistics Bayesian Analysis Survival Analysis Time Series Analysis Neural Network Another field which is related to statistics and the applications Another field which is related to Probability and the application
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 44 Documents
 1   2   3   4   5 
ANALISIS SPASIAL PENYEBARAN PENYAKIT SCHISTOSOMIASIS MENGGUNAKAN INDEKS MORAN UNTUK MENDUKUNG ERADIKASI SCHISTOSOMIASIS DI PROVINSI SULAWESI TENGAH BERBASIS WEB DASHBOARD Nur Sakinah; Wawan Saputra; Nurfitra Nurfitra; Satriani Satriani; Junaidi Junaidi
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.16580

Abstract

 Schistosomiasis is a parasitic disease which is caused by worm infection with worms from the Schistosoma class. This disease is zoonotic, consequently the source of transmission is not only infected on mammals but also on humans. The method used in this study is spatial autocorrelation. This is conducted to determine the presence or absence of global or local spatial autocorrelation as well as the pattern distribution of Schistosomiasis cases in Poso Regency by using Moran's I. The result in this study showed that the p-value of positive global autocorrelation is 2,2 × 10-16. This result is smaller than the 5% of significance level and also smaller than the Moran's I value (0,66).  The Moran’s I value lies in the interval  indicating that each adjacent area has the same number of Schistosomiasis cases. Meanwhile, the local spatial autocorrelation test (LISA) for Schistosomiasis cases in Poso Regency, such as villages at Lore Utara, Lore Timur and Lore Peore has the LISA value 1 determining the correlation is strong and positive. The distribution pattern of Schistosomiasis cases in Poso Regency forms a group pattern, namely disease prone areas (HH), disease spread areas (HL), disease alert areas (LH) and disease safe areas (LL) 
PERBANDINGAN METODE ANN BACKPROPAGATION DAN ARMA UNTUK PERAMALAN INFLASI DI INDONESIA M. Hadiyan Amaly; Ristu Haiban Hirzi; Basirun Basirun
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15440

Abstract

A country's development progress can be measured by good economic growth. If economic growth experiences rapid growth, it will usually trigger price increases. The occurrence of an uncontrolled increase in the price of goods or services for the needs of the community can cause inflation. inflation rate for a country is an inflation rate that has a low and stable value. One alternative is to provide an overview of the inflation in Indonesia by using forecasting analysis techniques. In this study, inflation forecasting analysis in Indonesia was carried out using the ANN Backpropagation and ARMA methods. The purpose of this research is to compare the performance results of the two methods and look at the best method for forecasting results. Based on the results of the analysis with the ANN Backpropagation method, the best network architecture model was ANN(7-4-1) using an epoch value of 400 and a learning rate of 0,1 with a value of MSE = 0,0112 and RMSE = 0,1065. While the results of the analysis using the ARMA method, the best model was obtained, namely ARMA(2,0,1) with the value MSE = 0,0648 and RMSE = 0,2545. So that the most optimal method used to predict inflation for the next period is the ANN Backpropagation method because it has a smaller error value. From this model, the results of forecasting inflation rates for the months of May to December 2022 are also obtained with a range of 0,01% to 0,5%. 
PEMODELAN VECTOR AUTOREGRESSIVE EXOGENOUS (VARX) UNTUK MERAMALKAN DATA EKSPOR TOTAL DAN IMPOR TOTAL DI INDONESIA Nur Afifah Salsabila; Sri Wahyuningsih; Ika Purnamasari
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15527

Abstract

Vector Autoregressive Exogenous (VARX) is a multivariate time series model which is a development of the Vector Autoregressive (VAR) model. VARX model is a forecasting model that involves endogenous variables and exogenous variables. The endogenous variables in this study are exports and total imports in Indonesia, then the exogenous variable in this study is the composite stock price index in Indonesia. The purpose of this study is to VARX model the export and total import data in Indonesia for the period January 2016 to December 2021 and predict it for the period January 2022 to December 2022. Based on the result of the analysis, the best model for forecasting export and total imports is the VARX(2.2) model with the MAPE value for the total export variable of 5.938% and the total import variable of 8.313%. Furthermore, the results of forecasting total exports have increased in the period January 2022 to December 2022, with forecasting results for January 2022 of US$21,383.06 million and December 2022 of US$23,569.50 million. The results of forecasting total imports have increased in the period January 2022 to December 2022, with forecasting results in January 2022 of US$17,743.17 million and December 2022 of US$20,269.07 million.
PERAMALAN NILAI TUKAR RUPIAH TERHADAP DOLLAR AMERIKA DENGAN MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Gelbi Ardesfira; Hazulil Fitriah Zedha; Iin Fazana; Julia Rahmadhiyanti; Siti Rahima; Samsul Anwar
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15469

Abstract

The Rupiah exchange rate was immensely influential in maintaining the stability of the country's economy.  The weakening of the rupiah exchange rate would have an impact on the national economy. Therefore, a forecast was needed to determine the exchange rate of the Rupiah in the future, especially against the US Dollar (USD). This study aimed to predict the rupiah exchange rate against the USD in 2022 and 2023. The data employed were the rupiah exchange rate data against the USD from January 2001 to December 2021. The forecasting method utilized in this study was the Autoregressive Integrated Moving Average (ARIMA) method. The most suitable ARIMA model in forecasting the Rupiah exchange rate against USD was ARIMA (3,1,1).  Forecasting results showed the Rupiah exchange rate weakened more significantly in 2022 and 2023, reaching IDR 14,484.5 and IDR 14,704.7 per USD, respectively, with the highest forecast limit reaching IDR 16,691.6 at the end of 2022 and IDR 17,781.8 at the end of 2023. The government needed preparing special policies in an effort to maintain the stability of the rupiah exchange rate in the future.
PERBANDINGAN METODE DOUBLE EXPONENTIAL SMOOTHING DAN TRIPLE EXPONENTIAL SMOOTHING PADA PERAMALAN NILAI EKSPOR DI INDONESIA Rindang Ndaru Puspita
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15590

Abstract

Exports are a source of foreign exchange that can affect the level of the country's economy and become a benchmark in determining the quality of the country. The value of Indonesian exports expressed in US Dollars is a monthly fluctuating time series data. In an effort to control the value of Indonesia's exports, it is necessary to have the right strategy, one of strategies is forecasting the value of exports in the future. To determine an appropriate forecasting method, the MAPE results from the Double Exponential Smoothing and Triple Exponential Smoothing are compared. From the research, the results of the prediction of the value of Indonesia's exports for the next 7 periods, from June 2022 to December 2022, the most accurate after a comparison of the MAPE value is closest to zero, the result is Triple Exponential Smoothing method more accurate for forecasting the value of Indonesian exports, this is because historical data on the value of Indonesian exports shows a trend and seasonal pattern at the same time
PEMODELAN PENYAKIT INFEKSI SALURAN PERNAFASAN AKUT DI DAERAH SEKITAR SEMBURAN LUMPUR LAPINDO SIDOARJO DENGAN PENDEKATAN MODEL MULTIVARIATE ADDITIVE REGRESSION SPLINE Mahfudhotin Mahfudhotin
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.16696

Abstract

The phenomenon of hot mudflow in Sidoarjo is interesting to be investigated further. Regarding the cause, the disaster occurred due to drilling errors resulting in the Lapindo mudflow which resulted in gas emissions causing health problems, especially those related to the respiratory tract, namely respiratory tract infections (ARI). Risk factors that can affect the incidence of ARI in general are socio-demographic, biological, housing and density factors and pollution. Therefore, this study aims to obtain a model for classifying ARI patient data in the Jabon, Tanggulangin, and Porong sub-districts, Sidoarjo district with the variables that contribute to the classification. The nonparametric approach Multivariate Adaptive Regression Spline (MARS) was chosen because several previous studies stated that this method resulted in a higher classification accuracy than other classification methods. In addition, MARS is a classification method that is able to form a model with causal interactions to produce the best MARS model obtained from a combination of Maximum Interaction (MI), Basis Function (BF), and Minimum Observation (MO) values. The results of modeling with MARS there are three variables that contribute to the grouping, namely the percentage of the distance between the house and the source of the Lapindo mudflow, the number of activities outside the house, and the number of house ventilation. The overall model classification accuracy is 97,4 percent with a GCV value of 0,096 and an R2 of 82,9 percent 
PEMODELAN STRUKTURAL EQUATION MODEL- PARTIAL LEAST SQUARE (SEM-PLS) PADA MINAT BERTRANSAKSI MENGGUNAKAN APLIKASI OVO Setia Ningsih; Hendra Dukalang; Armayani Arsal
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.16882

Abstract

The payment system in Indonesia is transforming from making payments in cash to non-cash. Non-cash payments can be made with various payment applications, one of which is the OVO application. Non-cash payments are less loved by many people, this is due to concerns about the security or ease and effectiveness of non-cash payment applications. Therefore, this study was conducted to analyze the factors that influence student interest in Gorontalo province in transacting using the OVO application using the Structural Equation Model Partial Least Square (SEM-PLS). The data analysis technique used is variance-based SEM. The results showed that the perceived convenience, effectiveness and security variables affected the interest in transacting using the OVO application with an R-square value of 70.70 percent.
STOCHASTIC MODEL ANALYSIS OF THE IMPACT OF MEDIA CAMPAIGN ON TRANSMISSION OF COVID – 19 EPIDEMIC. Kehinde Adekunle Bashiru; Taiwo Adetola Ojurongbe; Mutiu L Olaosebikan; Nureni O Adeboye; Habeeb A Afolabi; Ife Olukotun
Jambura Journal of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v3i2.15878

Abstract

The COVID - 19 pandemic is currently causing authorities and public health officials more concern. The goal of the project is to convert a deterministic model for COVID-19 transmissions to a stochastic model, and then analyze the results to see how media-driven awareness campaigns have an impact on the disease's spread. The dynamic COVID-19 model was converted to a stochastic model, which was then examined. The model includes the following categories: Susceptible (S), Exposed (E), Infected class (I),  Isolated class ( ), Aware class  and Recovered class (R), as well as the Cumulative density of awareness programs by media denoted by   . With the help of MATLAB, the converted model is then numerically solved using the Eula Maruyama approach, allowing the existence and uniqueness of the model to be examined. The implementation of awareness programs has been found to have a significant positive impact on the spread of COVID-19. As the rate of implementation of these programs rises, the population that is exposed to the virus and those who are infected with it declines, and it has been hypothesized that this will eventually cause COVID-19 to become extinct. According to the report, putting awareness campaigns into place can help stop the COVID-19 epidemic from spreading.
Comparing Logistic Regression and Support Vector Machine in Breast Cancer Problem Caecilia Bintang Girik Allo; Leonardus Sandy Ade Putra; Nicea Roona Paranoan; Vincentius Abdi Gunawan
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.19246

Abstract

There are several methods used for the classification problems. There are many different kinds of fields that can be used. Nowadays, Support Vector Machine (SVM) is a popular classification method that has been proposed by many researchers. Using the same method but different distribution methods for creating training and testing data in the same dataset can yield varying results in terms of prediction accuracy, which is crucial in classification. In this paper, we compare the prediction accuracy between SVM results and Logistic Regression results to determine the better method to  classify the current condition of the patient after undergoing some treatment.  Several treatments are used in this paper, including feature selection, feature extraction, separating the train and testing data using Holdout and K-Fold CV. Stepwise selection is done to reduce the features. Training and testing dataset is obtained using the five stratified and non-stratified holdout and five fold stratified and non-stratified cross validation. The result shows that the best method to classify the cancer dataset is five fold stratified cross validation SVM with radial kernel. The obtained accuracy is 81,816% with variance as much as 0,94%.
Analisis Faktor-Faktor Penghambat Penyelesaian Studi Mahasiswa Program Studi Matematika Universitas Sulawesi Barat Menggunakan PLS-SEM Rahmah Abubakar; Muh. Rifandi; Rahmawati Rahmawati; Fatimah Fatimah
Jambura Journal of Probability and Statistics Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjps.v4i1.19240

Abstract

This research aims to examine the factors that influence the completion of students' studies at the University of West Sulawesi Mathematics Study Program. The high frequency of alumni with a length of study above the expected time is a polemic that needs to be solved and followed up. The research method analyses the partial least squares structural equation model (PLS-SEM). This study involved 2016, 2017 and 2018, batch students. Data collection used an online questionnaire. The results showed that the self-control factor and the intelligence and interest factor had a significant effect on students' motivation to complete their study on time. On the other hand, environmental factors and campus instrument factors do not have a significant effect.

Page 4 of 5 | Total Record : 44

 1   2   3   4   5 

Filter by Year

2020 2023


Reset
Filter By Issues
All Issue Vol 4, No 1 (2023): Jambura Journal Of Probability and Statistics Vol 3, No 2 (2022): Jambura Journal Of Probability and Statistics Vol 3, No 1 (2022): Jambura Journal Of Probability and Statistics Vol 2, No 2 (2021): Jambura Journal Of Probability and Statistics Vol 2, No 1 (2021): Jambura Journal Of Probability and Statistics Vol 1, No 2 (2020): Jambura Journal of Probability and Statictics Vol 1, No 1 (2020): Jambura Journal of Probability and Statictics