cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Sachnaz Desta Oktarina
Contact Email
sachnazdes@apps.ipb.ac.id
Phone
-
Journal Mail Official
ijsa@apps.ipb.ac.id
Editorial Address
sachnazdes@apps.ipb.ac.id
Location
Kota bogor,
Jawa barat
INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi), established since 2017, publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 14 Documents
 1   2 
Search results for , issue "Vol 6 No 1 (2022)" : 14 Documents clear
Comparison of The SARIMA Model and Intervention in Forecasting The Number of Domestic Passengers at Soekarno-Hatta International Airport Anistia Iswari; Yenni Angraini; Mohammad Masjkur
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p132-146

Abstract

The Covid-19 pandemic has had a massive effect on the air transportation sector. Soekarno-Hatta International Airport (Soetta) skilled a lower variety of passengers because of the Covid-19 pandemic, even though Soetta Airport persisted to perform normally. Forecasting the number of passengers needs to be done by the airport to decide the proper policy. Therefore, the airport wishes to estimate the range of passengers to determine the right coverage and prepare the facilities provided if there may be a boom withinside the range of passengers throughout the Covid-19 pandemic. Forecasting the number of domestic passengers at Soetta Airport on this examination makes use of the SARIMA model and intervention. This examination compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The effects confirmed that the best SARIMA model became ARIMA ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55,18% and 588887.4, respectively. The best intervention model  became ARIMA0,1,1) (1,0,0)12 b = 0, s = 5, r = 1  with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values acquired suggest that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport throughout the Covid-19 pandemic.
Improving Skill of SPSS Software For Biology 3rd Year Students of Samara University in 2021: Action Research Aragaw Asfaw; Abdu Hailu; Hussen Awol
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p133-142

Abstract

SPSS helped revolutionize research practices in the social sciences. Students in Department of Biology think that SPSS statistical software is very difficult for them, because SPSS statistical software is viewed as a hard science than Biology which is viewed as a soft science. The main aim of this action research is to improve the skill of SPSS software’ in the case of biology of third year Biology section A students at the Samara University in 2021. Among all 46, 35 students were included since they are present on the day of training class. The data from the student’s questionnaire were tabulated and analyzed using descriptive statistical method. For the purpose of analyzing the collected data SPSS version 20 software was used. From the summary statistics of the total 35 students the proportion of male and female students is 5(14.3%) and 30(85.7%) respectively. The residence of the student majority 22 (62.9%) comes from rural areas. Of students 23 (65.7%) are motivated for their future research works to use it for statistical data analysis and graphics. There is high demand SPSS training programs for students   as it is mandatory for data analysis. Software training programs like SPSS should be proposed on the curriculum to improve the skill of the students.
Price Prediction Model for Red and Curly Red Chilies using Long Short Term Memory Method Rizky Abdullah Falah; Meuthia Rachmaniah
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p143-160

Abstract

The price data of the Strategic Food Price Information Center from May 2018 to May 2021 in 34 provinces show a fluctuated trend. Our study aimed to build predictive modeling of red chili and curly chili prices in West Java province using the Long Short Term Memory method. The red chili and curly chili prices prediction model in our study was successfully constructed and is considered very representative of predicting prices in traditional and modern markets in West Java Province. The best parameter model for red chili in the traditional market is a neuron value of 64 and a learning rate of 0.0005, and in the modern market, there are neuron values of 48 and a learning rate of 0,005. For curly chili, the best parameter model in traditional markets is a neuron value of 48 and a learning rate of 0.00075, and in the modern market, there are neuron values of 32 and a learning rate of 0,001. All models use the number of the epoch 100. The best prediction model for the price of red chili and curly red chili in traditional markets obtained the smallest root mean square error values on the test data of 2.57% and 2.07%, respectively. Meanwhile, the best price prediction model in the modern market obtained the smallest root mean square error values on the test data of 2.11% and 2.17%, respectively. Based on the root mean square error value obtained, the model is better than the other research method and shows that the variation in the value produced by a model is close to the variation in the actual value.
Binary Logistic Regression Model of Stroke Patients: A Case Study of Stroke Centre Hospital in Makassar Suwardi Annas; Aswi Aswi; Muhammad Abdy; Bobby Poerwanto
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p161-169

Abstract

This paper aimed to determine factors that affect significantly types of stroke for stroke patients in Dadi Stroke Center Hospital. The binary logistic regression model was used to analyze the association between the types of stroke and some covariates namely age, sex, total cholesterol, blood sugar level, and history of diseases (hypertension/stroke/diabetes mellitus). Maximum Likelihood Estimation was used to estimate parameters. Combinations of covariates were compared using goodness-of-fit measures. Comparisons were made in the context of a case study, namely stroke patients (2017-2020). The results showed that a binary logistic model combining the history of diseases and blood sugar level provided the most suitable model as it has the smallest AIC and covariates included are statistically significant. The coefficient estimation of the history of diseases variable is -0.92402 with an odds ratio value exp(-0.92402)=0.4. This means that stroke patients who have a history of diseases experience a reduction of 60% in the odds of having a hemorrhagic stroke compared to stroke patients that do not have a history of diseases. In other words, stroke patients who have a history of diseases tend to have a non-hemorrhagic stroke. Furthermore, the coefficient estimation of blood sugar level is 0.74395 with an odds ratio value exp(0.74395)=2. It means that stroke patients who do not have normal blood sugar levels tend to have a hemorrhagic stroke 2 times greater than stroke patients with normal blood sugar levels. A history of diseases and blood sugar level were factors that significantly affect the types of stroke.

Page 2 of 2 | Total Record : 14

 1   2 

Filter by Year

2022 2022


Reset
Filter By Issues
All Issue Vol 9 No 2 (2025) Vol 9 No 1 (2025) Vol 8 No 2 (2024) Vol 8 No 1 (2024) Vol 7 No 2 (2023) Vol 7 No 1 (2023) Vol 6 No 2 (2022) Vol 6 No 1 (2022) Vol 5 No 2 (2021) Vol 5 No 1 (2021) Vol 4 No 4 (2020) Vol 4 No 3 (2020) Vol 4 No 2 (2020) Vol 4 No 1 (2020) Vol 3 No 3 (2019) Vol 3 No 2 (2019) Vol 3 No 1 (2019) Vol 2 No 2 (2018) Vol 2 No 1 (2018) Vol 1 No 1 (2017) More Issue