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.
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<![CDATA[LQ45 Stock Portfolio Selection using Black-Litterman Model in Pandemic Time Covid-19 ]]>
<![CDATA[Siska Yosmar]]>;
<![CDATA[S Damayanti]]>;
<![CDATA[S Febrika]]>
<![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.v5i2p343-354
<![CDATA[The world was shocked by the emergence of a virus that spread very quickly to several countries including Indonesia at the end of 2019. This virus infection is called Corona Virus Disease 2019 (Covid-19). The outbreak of Covid-19 not only threatens human lives but also disrupts various economic, financial, and business activities, especially in Indonesia. A stock portfolio is a collection of financial assets in a unit that is held or created by an investor, investment company, or financial institution. The Black-Litterman model of the stock portfolio is a portfolio model that involves the CAPM equilibrium return and investor views. The purpose of this study is to determine the stock portfolio with the Black-Litterman model using company data listed in the LQ45 stock index from January 2020 to June 2020. Four of the twenty-nine LQ45 stocks were selected as assets in the stock portfolio. The stock portfolio containing the four stocks, namely ICBP, KLBF, MNCN, and TLKM with the Black-Litterman model resulted in an expected return of 2.07% and a risk of 2.82%.]]>
<![CDATA[Nowcasting Indonesia’s GDP Growth Using Machine Learning Algorithms ]]>
<![CDATA[Nadya Dwi Muchisha]]>;
<![CDATA[Novian Tamara]]>;
<![CDATA[Andriansyah Andriansyah]]>;
<![CDATA[Agus M Soleh]]>
<![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.v5i2p355-368
<![CDATA[GDP is very important to be monitored in real time because of its usefulness for policy making. We built and compared the ML models to forecast real-time Indonesia's GDP growth. We used 18 variables that consist a number of quarterly macroeconomic and financial market statistics. We have evaluated the performance of six popular ML algorithms, such as Random Forest, LASSO, Ridge, Elastic Net, Neural Networks, and Support Vector Machines, in doing real-time forecast on GDP growth from 2013:Q3 to 2019:Q4 period. We used the RMSE, MAD, and Pearson correlation coefficient as measurements of forecast accuracy. The results showed that the performance of all these models outperformed AR (1) benchmark. The individual model that showed the best performance is random forest. To gain more accurate forecast result, we run forecast combination using equal weighting and lasso regression. The best model was obtained from forecast combination using lasso regression with selected ML models, which are Random Forest, Ridge, Support Vector Machine, and Neural Network.]]>
<![CDATA[Clustering with Euclidean Distance, Manhattan - Distance, Mahalanobis - Euclidean Distance, and Chebyshev Distance with Their Accuracy ]]>
<![CDATA[Said Al Afghani]]>;
<![CDATA[Widhera Yoza Mahana Putra]]>
<![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.v5i2p369-376
<![CDATA[There are several algorithms to solve many problems in grouping data. Grouping data is also known as clusterization, clustering takes advantage to solve some problems especially in business. In this note, we will modify the clustering algorithm based on distance principle which background of K-means algorithm (Euclidean distance). Manhattan, Mahalanobis-Euclidean, and Chebyshev distance will be used to modify the K-means algorithm. We compare the clustered result related to their accuracy, we got Mahalanobis - Euclidean distance gives the best accuracy on our experiment data, and some results are also given in this note.]]>
<![CDATA[Ensemble Learning For Television Program Rating Prediction ]]>
<![CDATA[Iqbal Hanif]]>;
<![CDATA[Regita Fachri Septiani]]>
<![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.v5i2p377-395
<![CDATA[Rating is one of the most frequently used metrics in the television industry to evaluate television programs or channels. This research is an attempt to develop a prediction model of television program ratings using rating data gathered from UseeTV (interned-based television service from Telkom Indonesia). The machine learning methods (Random Forest and Extreme Gradient Boosting) were tried out utilizing a set of rating data from 20 television programs collected from January 2018 to August 2019 (train dataset) and evaluated using September 2019 rating data (test dataset). Research results show that Random Forest gives a better result than Extreme Gradient Boosting based on evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). On the training dataset, prediction using Random Forest produced lower RMSE and MAE scores than Extreme Gradient Boosting in all programs, while on the testing dataset, Random Forest produced lower RMSE and MAE scores in 16 programs compared with Extreme Gradient Boosting. According to MAPE score, Random Forest produced more good quality prediction (4 programs in the training dataset, 16 programs in the testing dataset) than Extreme Gradient Boosting method (1 program in the training dataset, 12 programs in the testing dataset) both in training and testing dataset.]]>
<![CDATA[Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm ]]>
<![CDATA[N Cahyani]]>;
<![CDATA[Sinta Septi Pangastuti]]>;
<![CDATA[K Fithriasari]]>;
<![CDATA[Irhamah Irhamah]]>;
<![CDATA[N Iriawan]]>
<![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.v5i2p396-404
<![CDATA[A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.]]>
<![CDATA[Estimation of Value at Risk by Using GJR-GARCH Copula Based on Block Maxima ]]>
<![CDATA[Hasna Afifah Rusyda]]>;
<![CDATA[Fajar Indrayatna]]>;
<![CDATA[Lienda Noviyanti]]>
<![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.v5i2p405-414
<![CDATA[This paper will discuss the risk estimation of a portfolio based on value at risk (VaR) using a copula-based asymmetric Glosten – Jagannathan – Runkle - Generalized Autoregressive Conditional Heteroskedasticity (GJR-GARCH). There is non-linear correlation for dependent model structure among the variables that lead to the inaccurate VaR estimation so that we use copula functions to model the joint probability of large market movements. Data is GEV distributed. Therefore, we use Block Maxima consisting of fitting an extreme value distribution as a tail distribution to count VaR. The results show VaR can estimate the risk of portfolio return reasonably because the model has captured the data properties. Data volatility can be accommodated by GJR-GARCH, Copula can capture dependence between stocks, and Block maxima can accommodate extreme tail behavior of the data.]]>
<![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[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.]]>
<![CDATA[Analysis of Multiple Correspondence Against Crimes in Sleman Regency ]]>
<![CDATA[E Widodo]]>;
<![CDATA[R Maggandari]]>
<![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.v5i2p260-272
<![CDATA[Crime is bad behavior, from social and religious norms and it makes psychology and economics harm. Stealing, ill-treatment, embezzlement, deception, deception/embezzlement, and adultery are the most crime in the last 9 months. Therefore, for identify the type of crime in the community we need a method to see the tendency of a category using multiple correspondence analysis methods. Analysis of multiple correspondences is one of the descriptive statistics that use to describe a pattern of relationships from contingency’s table with the aim of finding liability between categories. The results of the correspondence analysis are that the tendency of criminal suspect to be related to this types of crime of stealing and ill-treatment to be done by students or students less than 25 years old and were male, suspect of deception and adultery tends to be done by women over 40 years old and does not work, and suspect of embezzlement tends by workers and their ages around 25 to 40 years. The liability of the relation between criminal incidents and the types of crime is the types of crime of ill-treatment and adultery that are most prone to occur in shops with vulnerable hours 00:00-05:59 and 18:00-23:59.]]>
