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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 192 Documents
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Analyzing Low Birthweight in Java Based on Logistic Regression Model for Matched Pair Data: Analisis Berat Badan Lahir Rendah di Pulau Jawa Berdasarkan Model Regresi Logistik untuk Data Berpadanan Putri, Christiana Anggraeni; Irfani, Rini; Notodiputro, Khairil Anwar
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p75-85

Abstract

Low birthweight is one of the leading causes of neonatal death. Generally, the study of low birth weight is done by modeling logistic regression without considering the influence of confounding variables that can deviate the actual relationship between the explanatory variables and the response. This paper aims to identify low birth weight determinants in Java based on the logistic regression model for conditional study design, in which the analysis is based on matching the education level of the mother with one control. The results of the analysis showed that matched logistic regression can be used to correct bias due to the influence of a confounding variable. It reveals that based on the results of modeling, the frequency of pregnancy examinations and the parity of children are significantly affect the risk of low birth weight in Java Island.
Loopy Orthogonal Signal Correction Scatter Correction in Non-Invasive Blood Glucose: Koreksi Pencaran Loopy Orthogonal Signal Correction pada Glukosa Darah Non-Invasif Misrika, Dahlia; Erfiani, Erfiani; Wigena, Aji
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p105-113

Abstract

Spectroscopy is the study of matter based on light, sound, or particles emitted, absorbed, or reflected as well as the study of methods for generating and analyzing spectra. The spectrum has systematic diversity, namely the presence of light scattering and differences in the size of objects. The spectroscopic output allows for scattering shifts, because the same object measured several times does not exactly produce the same spectrum. Problems found in the spectrum can be overcome by pre-processing the data, namely the scatter correction method. Scatter correction is used to reduce the physical properties in the spectrum so that the information obtained is relatively the same for each spectrum, produces good estimates, and can be interpreted well. One of the spectroscopic tools that utilize infrared light is a non-invasive blood glucose level measuring device. The output of the tool is the time domain and intensity spectrum. Each object from the resulting spectrum still has noise, so scatter correction can be applied to this data. The purpose of this study was to perform a loopy Orthogonal Signal Correction (OSC) scatter correction method on time domain spectrum data on intensity on a non-invasive blood glucose level measuring device. The OSC method uses the concept of orthogonality to the mean by drawing the intensity value, weighting it, calculating the vector loading and then making corrections to the initial intensity. Based on the analysis, the loopy OSC method is better than OSC because the convergence is more accurate, the mean difference is smaller, the variance is smaller and the value converges on all the values tested. Based on exploration and the average difference, the loopy OSC method is better able to form the same pattern for each replication. This also shows that an object that is measured repeatedly has been able to be identified as the same object.
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.
Statistical Downscaling Model with Jackknife Ridge Regression and Modified Jackknife Ridge Regression to Forecast Rainfall Sahriman, Sitti; Upa, Dewi
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p155-165

Abstract

Statistical downscaling (SD) is a transfer function that connects local scale rainfall data with global scale rainfall. Global-scale rainfall can be obtained from the Global Circulation Model (GCM) output. GCM simulates climate variables in the form of large-scale grids, causing a high correlation between the grids (multicollinearity). The methods used in SD modeling to overcome multicollinearity are Jackknife Ridge Regression (JRR) and Modified Jackknife Ridge Regression (MJR). The method is the development of the Ridge Regression (RR) method. This study aims to predict local rainfall data in Pangkep Regency (response variables) based on local scale GCM output rainfall data (predictor variables) with the JRR and MJR approaches. In addition, K-means cluster technique is used in determining dummy variables to overcome the heterogeneity of the various remaining models. Results using training data (1990-2017 period) show that the MJR method is better at explaining the diversity of data based on a higher R2 value (68%) and a lower Root Mean Square Error / RMSE value (165.57) than the JRR method (R2 amount is 67 and RMSE amount is 167.72). Model validation using data testing (2018 period) also shows the same results, namely MJR is better than JRR. Other than that, the addition of dummy variables can improve the accuracy of the model in estimating rainfall data. Adding a dummy variable to the model results in a high R2 (range between 94% -95%) with a lower RMSE value (range between 66.60-67.69).
An Empirical Comparison of Some Product Estimators Sahoo, R.K.; Sabat , Ajit Kumar; Nayak, R.K.; Sahoo, L.N.
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p318-335

Abstract

In this paper, we undertake an extensive comparative study of some biased, almost unbiased and unbiased product estimators on the ground of different performance measures through Monte Carlo simulation that has not yet been initiated in the survey sampling literature. The simulation experiment is conducted using data on 20 natural populations available in the literature, and the performance indicators taken into consideration are the absolute relative bias, percentage relative efficiency, coverage rate of confidence intervals, standard deviation of the student t-statistic, and approach to symmetry (normality). This empirical study will not only facilitate to assess the overall relative performance of different competing product or product-type estimators but will also be beneficial to provide some guidelines towards further research in this direction.
Economic Order Quantity (EOQ) for Perishable Goods with Weibull Distribution and Exponential Demand Rate Proportional to Price Bankole, Motunrayo; Ajiboye, Adegoke S; Egbon, Osafu Augustine; Popoola, Jumoke
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p261-269

Abstract

Business organizations that deal with consumable and perishable items have consistently incurred enormous loss as a result of the nature of their goods. The losses have direct negative impact on revenues. Unplanned and lack of precise production prediction models are responsible for this. An appropriate prediction model, developed to guide production plan and processes will help manufacturers in deciding which product to make and in what quantity. In this study, the Economic Order Quantity (EOQ) for perishable goods with Weibull lifetime distribution and exponential demand rate proportional to price was developed for perishable goods. The differential equations governing the instantaneous state of inventory in the interval [0, t2] were obtained and solved for the equation of the quantity of inventory at time t. Using fixed parameters for the weibull and exponential distributions, simulation study was conducted on the derived EOQ model using R programming language. The simulation shows that the EOQ increases with increase in Weibull parameter. Real data on six loafs of bread obtained from Afe Babalola University bakery was used to illustrate how the model works. Result shows a good fit to the data and the average EOQ ranges from 60 to 400 loafs with ordering times of either 1 or two days interval. The pattern of EOQ varies between type of loafs of bread. The EOQ model developed is shown by this result to be appropriate for perishable goods with weibull lifetime distribution and exponential demand rate proportional to price.
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.
Determinants of Antenatal Care Visits in Indonesia with Synthetic Minority Over-Sampling Techniques for Imbalance Data: Determinan Kunjungan Antenatal Care di Indonesia dengan Teknik Synthetic Minority Over-Sampling untuk Imbalanced Data Thamrin, Nurafiza; Baktiar, Aditya Firman; Addawiyah, Firda Aini; Husna, Miftahul; Irwati, Tati
Indonesian Journal of Statistics and Applications Vol 7 No 2 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i2p86-104

Abstract

Maternal mortality and infant mortality are two indicators that describe the degree of public health as well as indicators of sustainable development in Indonesia. The acceleration of reduction these two indicators must be supported by antenatal care services since pregnancy for the safety of mothers and babies. Based on the results of the IDHS 2017, antenatal care coverage in Indonesia (77.4%) is still far from the target in 2024 (95%.) This study used logistic regression analysis with Synthetic Minority Oversampling Technique (SMOTE) resampling method because of imbalance data to explore the determinants of complete antenatal care visits in Indonesia and descriptive analysis to find out an overview of complete antenatal care associated with factors that are considered influencing it. Data that was used in this study is the Indonesia Demographic and Health Survey (IDHS) 2017 with unit of analysis for women of childbearing age who are married or live together and gave birth to their last child in the period 2012-2017. The logistic regression results of the SMOTE method show that the variables of mother's education, husband's work status, knowledge of pregnancy danger signs, distance to health facilities, timing first antenatal check, mother's age, economic status, birth order, and number of problems during pregnancy significantly affect the completeness of antenatal care visits. The policy recommendations in this study are expected to be adopted by government to increase antenatal care visits in Indonesia as an effort to reduce maternal and infant mortality.
Sentiment Analysis of Twitter Users’ Opinion Towards Face-to-Face Learning: Analisis Sentimen Tanggapan Masyarakat Pengguna Twitter terhadap Pembelajaran Tatap Muka Manaf, Silmi Annisa Rizki; Alamudi, Aam; Fitrianto, Anwar
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v7i1p15-31

Abstract

In early 2022, the government allowed face-to-face learning again after approximately one year of online learning. When face-to-face learning will be held again in several areas, the number of Covid-19 has increased and the government has imposed the enforcement of restrictions on community activities. The pros and cons of face-to-face learning also occur on social media, one of them is on Twitter. This study used twitter data for January 30th – February 7th 2022. Opinions on twitter regarding face-to-face learning were studied by sentiment analysis using the binary logistic regression method with sentiment classes being positive and negative. Labeling uses based on the final score of the difference between the number of positive and negative words. The purpose of this study is to determine the public’s perception of the policy of implementing face-to-face learning in the era of the Covid-19 on social media especially Twitter. From this study, public’s perception tends to be in a negative direction which indicates that they have not agreed enough with the existence of face-to-face learning in the period of February 2022 with the accuracy was 85%, sensitivity was 77%, specificity was 88%, and AUC was 91%.

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