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
14 Documents
Search results for
, issue
"Vol 6 No 1 (2022)"
:
14 Documents
clear
<![CDATA[Comparison of C4.5 and C5.0 Algorithm Classification Tree Models for Analysis of Factors Affecting Auction: Perbandingan Model Pohon Klasifikasi Algoritma C4.5 dan C5.0 untuk Analisis Faktor yang Mempengaruhi Keberhasilan Lelang ]]>
<![CDATA[Mohammad Fajri]]>;
<![CDATA[Iut Tri Utami]]>;
<![CDATA[Muh. Maruf]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p13-22
<![CDATA[Auction in Indonesia is carried out by the Office of State Assets and Auction Services (KPKNL). Goods auctioned at KPKNL are quite diverse including land, wood, inventory, vehicles, and other goods. However, not all of the items auctioned were sold. Because not a few items have been auctioned but no one has made an offer. The Purpose of this study is to compare two classification methods, C4.5 and C5.0 algorithm and to determine which items were successfully auctioned with those that did not and its factors. The methods that used were comparing the classification tree C4.5 algorithm and C5.0 algorithm with cross validation. From the results of the comparison of the two methods, it was found that the C5.0 Algorithm method was rated better than the C4.5 algorithm in classifying the auction results with an accuracy of 96.43% and 92.86% respectively. In this case, C5.0 has a higher precision than C4.5.]]>
<![CDATA[Implementation of Ensemble Self-Organizing Maps for Missing Values Imputation ]]>
<![CDATA[Titin Siswantining]]>;
<![CDATA[Kathan Gerry Vivaldi]]>;
<![CDATA[Devvi Sarwinda]]>;
<![CDATA[Saskya Mary Soemartojo]]>;
<![CDATA[Ika Mattasari]]>;
<![CDATA[Herley Al-Ash]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p1-12
<![CDATA[The purpose of this study is to implement the ensemble self-organizing maps (E-SOM) method to impute missing values at the preprocessing data stage, which is an important stage when making predictions or classifications. The Ensemble Self-Organizing Maps (E-SOM) is the development of the SOM imputation method, in which the E-SOM method is implemented by applying an ensemble framework using several SOMs to improve generalization capabilities. In this study, the E-SOM imputation method is implemented in South African heart disease data using random forest as a classification model. The results of the model evaluation showed that for accuracy in testing data, the Random Forest model formed from E-SOM imputed data yields better accuracy values than the Random Forest model formed from SOM-imputed data for variations of 36, 49, 64, and 81 neurons, while for variation of 25 neurons both models produce the same accuracy value. From the variation of the number of ensembles applied, the E-SOM imputation method with a combination of 81 neurons and 15 ensemble numbers produced a Random Forest model with the most optimal value of accuracy.]]>
<![CDATA[A New Perspective to Measuring Interdependence among Stock, Oil and Currency Markets: A Canonical Correlation Analysis ]]>
<![CDATA[Idowu Oluwasayo Ayodeji]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p23-40
<![CDATA[With a view to explaining various seemingly-contrasting results often reported in financial linkages literature, the study investigates the possibility of the existence of more than one unique relationship among stock, oil and currency markets. It also quantified the impact of selected macroeconomic variables on these relationships. Three prominent markets of stock, oil and exchange rates were examined from the United States, United Kingdom and Nigeria. The model adopted was the canonical correlation specification. Canonical solution identified two significant unique association patterns each among US, UK and Nigerian markets, indicating that their linkages vary with time. We also observed that the effect of macroeconomic variables on the link among financial markets vary by country and data frequency. Overall, inflation rates played the most significant role in the linkages among financial markets. The study concluded that the previous results on interdependence among financial markets are not conflicting but rather complimentary as they evidenced the multiple patterns of association among markets.]]>
<![CDATA[A Study on Accuracy of Paddy Harvest Area Estimation on Area Sampling Frame Method: Kajian Ketepatan Pendugaan Luas Panen Padi pada Metode Pengambilan Kerangka Sampel Area ]]>
<![CDATA[Mulianto Raharjo]]>;
<![CDATA[Anang Kurnia]]>;
<![CDATA[Hari Wijayanto]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p41-49
<![CDATA[There was unsynchronized national rice data until 2017, which indicating that influenced by the differences in calculation methods between government agencies. The Indonesian Central Bureau of Statistics (BPS Statistics), the most responsible agency for national rice data, collected rice plant areas data using the paddy statistical assessment method (SP-Padi). Subjective elements from various parties potentially influenced the result of this assessment method. The development of a new method to overcome this matter has been started by the government since 1993. In 2018 the method, which is named the Area Sample Frame (ASF) method, was officially used by the government under the coordination of BPS. The ASF method divides the area into grids: blocks, segments, and sub-segments. This new method has several issues related to the methodology used in determining the sampling method. This study was conducted to evaluate the accuracy of paddy harvest area estimation on the ASF method through a sampling simulation process of the ASF method with various scenarios. With 20 simulated scenario combinations, it was found that the difference percentage average between the harvested area of the population and the harvested area of the sample to the sub-district area was 0.00062%, and the mean square error (MSE) was 0.0041%. So it can be concluded that the ASF methodology is an unbiased method and is good enough to accommodate various strata diversity in any region.]]>
<![CDATA[Study of Clustering Time Series Forecasting Model for Provincial Grouping in Indonesia Based on Rice Price: Kajian Model Peramalan Clustering Time Series untuk Penggerombolan Provinsi Indonesia berdasarkan Harga Beras ]]>
<![CDATA[Muhammad Ulinnuha]]>;
<![CDATA[Farit M Afendi]]>;
<![CDATA[I Made Sumertajaya]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p50-62
<![CDATA[Most indonesians consume rice as the main staple. The high low price of rice has an impact on farmers and communities, especially those who cannot afford it. Rice price forecasting is one of the important information to be considered for future rice prices. The data used is secondary data sourced from bps publication, Rural Consumer Price Statistics: Food Group, from January 2008 to December 2019 for 32 provinces in Indonesia. Time series modeling and forecasting is usually done on a single variable using ARIMA. however, modeling becomes inefficient if there are many variables, so clustering time series analysis is performed using correlation distance with the clustering method of average linkage hierarchy. Cluster level ARIMA modeling with 4 clusters provides high efficiency because only by doing 4 times modeling results in accuracy values not much different from individual level modeling. the results obtained by individual-level ARIMA Modeling resulted in an average MAPE of 3.36%, while cluster-level ARIMA modeling with 4 clusters resulted in an average MAPE value of 4.27%, with a second MAPE difference of -0.91%. Formally conducted z test, the results obtained there is no difference between individual-level MAPE and cluster-level MAPE. This means that cluster-level modeling is relatively good and representative.]]>
<![CDATA[Bayesian-Structural Equation Modeling on Learning Motivation of Undergraduate Students During Covid-19 Outbreak ]]>
<![CDATA[Reny Rian Marliana]]>;
<![CDATA[Maya Suhayati]]>;
<![CDATA[Sri Bekti Handayani N.]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p63-76
<![CDATA[The aim of this study is to explore the relationship model between e-learning readiness, self-directed learning readiness, and learning motivation of the students at STMIK Sumedang during the COVID-19 outbreak. Bayesian-Structural Equation Modeling and Markov Chain Monte Carlo Algorithm are used in the estimation of the parameters. The posterior distribution is formed using informative prior i.e., inverse-Gamma distribution on variance parameters, inverse-Wishart distribution on residual covariance, and normal distribution on other parameters of the model. The calculation is performed using the blavaan package on R-Software version 4.1.0 with 19000 iteration and 9000 samples of burn-in period. Data were taken from 214 samples of the students at STMIK Sumedang. The outcome from the calculation showed there is a significant effect from self-directed learning readiness to motivation learning of students and there is no significant effect from e-learning readiness to learning motivation. The direct effect on learning motivation is 7.25 from self-directed learning readiness and 0.045 from e-learning readiness.]]>
<![CDATA[Application of Fuzzy C-Means and Weighted Scoring Methods for Mapping Blankspot Villages in Pemalang Regency ]]>
<![CDATA[Imam Adiyana]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Farit M Afendi]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p77-89
<![CDATA[Covid-19 pandemic affects habits people around the world. The education sector in Indonesia is also undergoing policy changes, namely policy of transitioning face-to-face teaching and learning process to distance learning process (PJJ/online learning). Several studies have been conducted to examine the constraints PJJ process, resulting in finding that quality of internet network is majority obstacle in PJJ process. Conditions where there is no internet network in an area is commonly called a blankspot. In order to minimize the problem of blankspots, President and Ministry of Communication and Informatics of Indonesia realized the program "Indonesia is free signals to the corners of the country". This program involves all districts in Indonesia to conduct network quality surveys in the smallest areas of the village. Basically, network quality survey activities require relatively no small resources and costs. So as to conduct the efficiency of field survey activities, early detection of village blankspot status is required based on the characteristics blankspot village in general. While the commonly used method of grouping village based on village characteristics is the fuzzy c-means and weighted scoring method. These two methods were chosen because they have good cluster convergence rate and easily interpreted display results of the group by user in the form diagrams and scores. This study aims to prove that fuzzy c-means and weighted scoring method are good for grouping cases of blankspot villages according to previous studies with different cases. The result comparison goodness value of clustering, it is known that fuzzy c-means method more suitable for clustering characteristics blankspot village than the k-means method. Meanwhile, weighted scoring method cannot be said better method for village classification than the decision tree method.]]>
<![CDATA[GSTARIMA Model with Missing Value for Forecasting Gold Price ]]>
<![CDATA[Fadhlul Mubarak]]>;
<![CDATA[Atilla Aslanargun]]>;
<![CDATA[İlyas Sıklar]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p90-100
<![CDATA[Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.]]>
<![CDATA[Modeling Dengue Fever by using Conditional Autoregressive Bessag-York-Mollie: Pemodelan Demam Berdarah dengan Menggunakan Conditional Autoregressive Bessag-York-Mollie ]]>
<![CDATA[Jajang Jajang]]>;
<![CDATA[Budi Pratikno]]>;
<![CDATA[Mashuri Mashuri]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p101-113
<![CDATA[Dengue fever is a tropical disease caused by the dengue virus. The small proportional of this dengue fever disease can develops into a more severe dengue hemorrhagic fever (DHF). This research discussed about the model for disease mapping in Ciamis District. The purpose of this research is to characterize relative risk and factors correlated with case DHF. Independent variables used in this research are population density, attitude of region, and the number of health worker. To analysis this data, we used conditional autoregressive Bessag-York-Mollie (CAR-BYM) model. Based on descriptive statistic, the maximum and minimum DHF cases are Ciamis and Sukamantri, respectively. Furthermore, basedon model results, we found that the maximum and minimum relative risk are Cijeungjing and Sukamantri, respectively. Furthermore, there were 7 sub districts which relative risk are greater than one and 20 sub districts which relative risk are less than one. The sub districts which relative risk are greater than one show that DHF cases in these sub districts are greater than expectation. Based on the CAR-BYM model result showed that Each increase the population density by one unit contributes to the addition of DHF cases by 0.0012 units. Each additional health worker one unit, it will reduce the number of DHF cases by 0.0675 units. Each additional altitude of one unit will reduce the number of dengue cases by 0.0011 units. Based on relative risk (RR) value of the CAR-BYM model we found that the Cijengjing and Ciamis Districts have darkest color. The RR values in the two sub-districts are 3,449 and 3,240, respectively. The RR values of the two sub-districts are more than expected values.]]>
<![CDATA[Identification of Factors Affecting Smoking Prevalence in West Java using Spatial Modeling: Identifikasi Faktor-Faktor Yang Memengaruhi Prevalensi Merokok di Jawa Barat Menggunakan Pemodelan Spasial ]]>
<![CDATA[Aditya Firman Baktiar]]>;
<![CDATA[Toza Sathia Utiayarsih]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29244/ijsa.v6i1p114-131
<![CDATA[Smoking behavior is certainly a serious problem that needs to be resolved immediately in Indonesia. It because smoking has been shown to trigger various diseases. More than that, smoking can also causes death. Based on the results of Riskesdas in 2013 and 2018, the province with the highest smoking prevalence in Indonesia is Jawa Barat. Moreover, the prevalence of smoking in Jawa Barat also shows a stable trend and has always been above the national prevalence since 2001. If we look at the spatial distribution, the prevalence of smoking in Jawa Barat shows a grouping where close districts/cities have a similar values to each other. It indicates spatial dependencies that need to be accommodated in the modeling. Therefore, this study was conducted to determine the factors that influence the prevalence of smoking in Jawa Barat by using spatial analysis. Based on the spatial lag model, it was found that the percentage of the population graduating from high school and the percentage of the highland area had a significant effect on smoking prevalence in Jawa Barat. While the percentage of the married population, the percentage of the working population, and tobacco production had no significant effect.]]>
