Indonesian Journal of Statistics and Its Applications
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802): diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. Artikel yang dimuat berupa hasil penelitian bidang statistika dan aplikasinya dengan topik (tapi tidak terbatas): rancangan dan analisis percobaan, metodologi survey dan analisis, riset operasi, data mining, pemodelan statistika, komputasi statistika, time series dan ekonometrika, serta pendidikan statistika.
Articles
163 Documents
<![CDATA[Improving Skill of SPSS Software For Biology 3rd Year Students of Samara University in 2021: Action Research ]]>
<![CDATA[Aragaw Asfaw]]>;
<![CDATA[Abdu Hailu]]>;
<![CDATA[Hussen Awol]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i1p133-142
<![CDATA[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.]]>
<![CDATA[Price Prediction Model for Red and Curly Red Chilies using Long Short Term Memory Method ]]>
<![CDATA[Rizky Abdullah Falah]]>;
<![CDATA[Meuthia Rachmaniah]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i1p143-160
<![CDATA[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.]]>
<![CDATA[Binary Logistic Regression Model of Stroke Patients: A Case Study of Stroke Centre Hospital in Makassar ]]>
<![CDATA[Suwardi Annas]]>;
<![CDATA[Aswi Aswi]]>;
<![CDATA[Muhammad Abdy]]>;
<![CDATA[Bobby Poerwanto]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i1p161-169
<![CDATA[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.]]>
<![CDATA[Proposing Additional Indicators for Indonesia Youth Development Index with Smaller Level Analysis: A Case Study in South Kalimantan Province ]]>
<![CDATA[Suryo Adhi Rakhmawan]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v5i2p220-227
<![CDATA[South Kalimantan is a province in Indonesia with many youths and has the lowest score in Indonesia Youth Development Index (YDI) 2017. However, the lowest score is the gender and discrimination dimension which incomplete to be analyzed because there are some indicators that are not included in the dimension. To solve the problems, it is necessary to build a measurement that can monitor a smaller level. Through this research, the author provides a measurement for describing the level of youth development in classifications for South Kalimantan in 2018. This index is built with the analysis factor method. It consists of five dimensions used in Indonesian YDI 2017 with some additional indicators. The result of this research shows that the index is a valid measure due to its significant correlation with Indonesia YDI 2017. The other result is the youth living in urban areas tend to have a higher index than youth who live in rural areas. While the youth who are male, also tend to have a higher development index than the female population. The suggestion for the South Kalimantan government is to improve the youth, the development priority for every classification can be started from the classification and dimension of youth index with the lowest achievement.]]>
<![CDATA[Nested Mixed Models with Repeated Measurements for Analyzing Gross Profit of Public Companies in West Java: Model Campuran Tersarang dengan Pengamatan Berulang untuk Analisis Data Laba Bruto Perusahaan Terbuka di Jawa Barat ]]>
<![CDATA[Alina Witri]]>;
<![CDATA[Khairil Anwar Notodiputro]]>;
<![CDATA[Rahma Anisa]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i2p296-308
<![CDATA[The company's gross profit plays an important role in boosting the Gross Regional Domestic Product (PDRB) which will affect the revenue of local governments, known as Pendapatan Asli Daerah. Local governments often need information how gross profits of companies are different within each sector. It is not easy to investigate this matter especially if these companies are observed repeatedly and subsectors are nested within the sector. In this study, three factors were involved, i.e., sectors, subsectors which are nested in a particular sector, and time. It is assumed that the sectors and time of observation are fixed, whereas the subsectors are random. The response variable is the average gross profit per subsector of public companies in West Java. The objective of this study is to identify the variation of the subsectors, the effects of sectors as well as time on the average of the gross profit. Since the study involves fixed and random factors and the gross profit rate was observed more than one time, then a nested mixed model with repeated measurement is used. The results showed that there was no sector effect on the average gross profit, there is a variation in the average gross profit per subsector that is nested within the sector, and the time of observation did not influence the average gross profit.]]>
<![CDATA[Comparison of Negative Binomial Regression Model and Geographically Weighted Poisson Regression on Infant Mortality Rate in South Sulawesi Province ]]>
<![CDATA[Siswanto Siswanto]]>;
<![CDATA[Edy Saputra R]]>;
<![CDATA[Nurtiti Sunusi]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i2p170-179
<![CDATA[The number of infant mortality cases is an important indicator to assess the quality of a country's public health. A number of studies argue that the case of infant mortality has a close relation to the living area condition and the social status of the parents. Indirectly, the quality of life of babies in a country will impact the nation's quality of life in general. Therefore, many efforts are required to reduce the infant mortality in Indonesia. One of the steps that could be done to overcome this issue is to analyze the causative factors. The statistical method that has been developed for data analysis taking into account current spatial factors is the Geographically Weighted Poisson Regression (GWPR) with a weighted Bisquare kernel function. Based on the partial estimation with the GWPR model, there are seven groups based on significant variables that affect the number of infant deaths in South Sulawesi Province. Of the seven groups formed, the first group is the Selayar Islands where all variables have a significant effect. This needs to be a concern for the South Sulawesi provincial government to improve facilities and infrastructure in the Selayar Islands, of course the location which is very far from the city center can affect access to drug reception, medical personnel and so on. Based on the results of the analysis of the factors that affect the number of infant deaths in South Sulawesi Province using a negative binomial regression approach and GWPR with a bisquare kernel weighting, it can be concluded that the GWPR model used is the best for analyzing the number of infant deaths in South Sulawesi Province because it has an AIC value. The smallest is 167.668.]]>
<![CDATA[A Dynamic Factor Model for Nowcasting Household Consumption ]]>
<![CDATA[Az Zahra Amon Ra]]>;
<![CDATA[Khairil Anwar Notodiputro]]>;
<![CDATA[Pika Silvianti]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i2p202-212
<![CDATA[A Dynamic Factor Model (DFM) is one of the time series models that can be used to forecast within a very short period in the future known as nowcasting. This model can be used to accommodate the frequency difference that exists between monthly explanatory variables and a response variable which is measured quarterly. This model has been commonly used in economics especially to forecast household consumption for the purpose of constructing economic policies. The economic condition of a country can be reflected in the country's Gross Domestic Product (GDP). Consumption is an important component of GDP because of its large proportion of GDP. One of the household economic activities to meet the various needs of goods and services is referred to as household consumption. This paper discusses the DFM to forecast household consumption based on the varimax and quartimax rotations. The results show that both rotational methods can be used for transmitting household consumption with the same precision.]]>
<![CDATA[On the Statistical Learning Analysis of Rain Gauge Data over the Natuna Islands ]]>
<![CDATA[Sandy Herho]]>;
<![CDATA[Faiz Fajary]]>;
<![CDATA[Dasapta Irawan]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v6i2p347-357
<![CDATA[Located in the middle of South China Sea with distance more than 700 m to nearby main lands, Natuna Islands settings remain the focus of scientific conversation. This article presents state-of-the-art statistical learning methods for analyzing rain gauge data over the Natuna Islands. By using shape preserving piecewise cubic interpolation, we managed to interpolate 671 null values from the daily precipitation data. Dominant periodicity analysis of daily precipitation signals using Lomb-Scargle Power Spectral Density shows annual, intraseasonal, and interannual precipitation patterns over the Natuna Islands. Unsupervised anomaly analysis using the Isolation Forest algorithm shows there are 146 anomaly daily precipitation data points. We also conducted an experiment to predict the accumulation of monthly precipitation over the Natuna Islands using the Bayesian structural time series algorithm. The results show that the local linear trend with seasonality model is able to model the value of accumulated monthly precipitation for a twelve-month prediction horizon. The work presented here has profound implications for rainfall observations in this area.]]>
<![CDATA[K-prototypes Algorithm for Clustering Schools Based on The Student Admission Data in IPB University ]]>
<![CDATA[Sri Sulastri]]>;
<![CDATA[Lismayani Usman]]>;
<![CDATA[Utami Dyah Syafitri]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v5i2p228-242
<![CDATA[The new student admissions was regularly held every year by all grades of education, including in IPB University. Since 2013, IPB University has a track record of every school that has succeeded in sending their graduates, even until they successfully completed their education at IPB University. It was recorded that there were 5,345 schools that included in the data. It was necessary to making every school in the data into the clusters, so IPB could see which schools were classified as good or not good in terms of sending their graduates to continue their education at IPB based on the characteristics of the clusters. This study using the k-prototypes algorithm because it can be used on the data that consisting of categorical and numerical data (mixed type data). The k-prototypes algorithm could maintain the efficiency of the k-means algorithm in handling large data sizes, but eliminated the limitations of k-means. The results showed that the optimal number of clusters in this study were four clusters. The fourth cluster (421 school members) was the best cluster related to the student admission at IPB University. On the other hand, the third cluster (391 school members) was the worst cluster in this study.]]>
<![CDATA[Comparison of Short-Term Load Forecasting Based on Kalimantan Data ]]>
<![CDATA[Syalam Ali Wira Dinata]]>;
<![CDATA[Muhammad Azka]]>;
<![CDATA[Primadina Hasanah]]>;
<![CDATA[Suhartono Suhartono]]>;
<![CDATA[Moh Danil Hendry Gamal]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 5 No 2 (2021) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v5i2p243-259
<![CDATA[This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.]]>
