Indonesian Journal of Statistics and Its Applications
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
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<![CDATA[IMPLEMENTASI TRANSFORMASI FOURIER UNTUK TRANSFORMASI DOMAIN WAKTU KE DOMAIN FREKUENSI PADA LUARAN PURWARUPA ALAT PENDETEKSIAN GULA DARAH SECARA NON-INVASIF ]]>
<![CDATA[Umam Hidayaturrohman]]>;
<![CDATA[Erfiani Erfiani]]>;
<![CDATA[Farit M Afendi]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.504
<![CDATA[Diabetes mellitus is the result of changes in the body caused by a decrease of insulin performance which is characterized by an increase of blood sugar level. Detection of blood sugar can be done with Invasive methods or non-invasive methods. However, non-invasive methods are considered better because they can check early, faster and accurate. The prototype output is values of intensity in the time domain, thus fourier transformation is very much needed to transform into the frequency domain. In this study, Fourier transformation methods used are Discrete Fourier Transform (DFT), Fast Fourier Transform Radix-2, and Fast Fourier Transform Radix-4. Evaluation for the best method is done by comparing the processing speed of each method. The FFT Radix-4 method is more effective to perform the transformation into the frequency domain. The average processing speed with the FFT Radix-4 method reaches 2.67×105 nanoseconds, and this is much faster 5.06×106 nanoseconds than the FFT Radix-2 method and 2.40×107 nanoseconds faster than the DFT method.]]>
<![CDATA[A REPEATED CROSS-SECTIONAL MODEL FOR ANALYZING UNEMPLOYMENT DATA IN BOGOR ]]>
<![CDATA[Ulfah Sulistyowati]]>;
<![CDATA[Khairil Anwar Notodiputro]]>;
<![CDATA[I Made Sumertajaya]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.513
<![CDATA[In general, the form of data encountered in statistical problems is panel data and cross-sectional data. There are times in certain conditions, the data formed in the form of a combination of panel data with cross-sectional data, which is commonly referred to as repeated cross-sectional data. Repeated cross-sectional data is often done in research with individual observations. In this study, a repeated cross-sectional analysis was carried out using a fixed influence model with observations in the form of an area (village) in Bogor, West Java to analyze unemployment factors. The results obtained are that ongoing village development affects the unemployment rate in Bogor]]>
<![CDATA[ON THE MODELLING OF LEPROSY PREVALENCE IN SOUTH SULAWESI USING SPATIAL AUTOREGRESSIVE MODEL ]]>
<![CDATA[Rezki Melany Sabil]]>;
<![CDATA[Ray Sastri]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.529
<![CDATA[The prevalence of leprosy is the number of leprosy cases per 10.000 peoples. Based on data from the Ministry of Health, the highest prevalenece of leprosy was in South Sulawesi. This is needs a special attention because leprosy is a contagious disease. The number of leprosy cases in an area may be influenced by the number of leprosy case in the neighbor area due to the movement of the air. So that, the location of area need to be included in analysis of leprosy. The aim of this study is to identify the variables that spatially affect the prevalence of leprosy in South Sulawesi and modelling it. This study uses data from the Ministry of Health for year 2016. The method of analysis is Spatial Autoregressive Model (SAR). The results is There is a positive spatial autocorrelation in the prevalence of leprosy in district level, which means that regions with high prevalence of leprosy are surrounded by areas with high prevalence of leprosy, and vice versa. The prevalence of leprosy in an area is influenced by the prevalence of leprosy in neighbor districts, the percentage of BCG vaccines recipient and the percentage of households with healthy lifestyle.]]>
<![CDATA[PEMODELAN POISSON RIDGE REGRESSION (PRR) PADA BANYAK KEMATIAN BAYI DI JAWA TENGAH ]]>
<![CDATA[Wulandari Wulandari]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.555
<![CDATA[The decline of infant mortality is one of the targets of the Indonesian government in the health sector, including the Government of Central Java. To achieve this goal, it is necessary to identify factors that affect many infant mortalities in the district/city of Central Java. Infant mortalities are count data, so Poisson regression is commonly used. The data in the study showed the existence of multicollinearity in several predictor variables, so an appropriate model was needed. Poisson Ridge Regression (PRR) is a Poisson modeling that accommodates multicollinearity. In this study, the PRR model was used to model infant mortality in Central Java district/city. The results showed that the parameter estimation of the PRR model was slightly different than the estimated Poisson regression model. Modeling infant mortality with the PRR model, out of five predictor variables, three variables harmed many infant deaths, while the other two variables had a positive effect on many infant deaths.]]>
<![CDATA[THE BIVARIATE EXTENSION OF AMOROSO DISTRIBUTION ]]>
<![CDATA[David Sam Jayakumar]]>;
<![CDATA[A Sulthan]]>;
<![CDATA[W Samuel]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.571
<![CDATA[This paper introduces the bivariate extension of the amoroso distribution and its density function is expressed in terms of hyper-geometric function. The standard amoroso distribution, cumulative distribution functions, conditional distributions, and its moments are also derived. The Product moments, Co-variance, correlations, and Shannon’s differential entropy are also shown. Moreover, the generating functions such as moment, Cumulant, Characteristic functions are expressed in Fox-wright function, and the Survival, hazard, and Cumulative hazard functions are also computed. The special cases of the bivariate amoroso distribution are also discussed and nearly 780 bivariate mixtures of distributions can be derived. Finally, the two-dimensional probability surfaces are visualized for the selected special cases and we also showed the estimation of parameters by the method of maximum likelihood approach, and the constrained maximum likelihood approach is also computed by using Non-linear Programming with a numerical application]]>
<![CDATA[METODE ANALISIS DISKRIMINAN KUADRAT TERKECIL PARSIAL UNTUK KLASIFIKASI SEGMEN LOYALITAS KONSUMEN SUSU PERTUMBUHAN ]]>
<![CDATA[Herdina Kuswari]]>;
<![CDATA[Farit Mochamad Afendi]]>;
<![CDATA[Khairil Anwar Notodiputro]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.586
<![CDATA[Consumer segmentation is the process of dividing consumers into different segments based on consumer characteristics, making it easier for companies to develop marketing strategies. The segmentation is carried out based on consumer loyalty using the RFM (Recency, Frequency, Monetary) approach a number of 7753 members of a nutritional product loyalty program is considered in the analysis. Partial least square discriminant analysis classification modeling is built using the results of consumer segmentation being the a response variable. The model is not good enough based on the AUC (Area Under Curve) value of the ROC (Relative Operating Characteristic) curve that quite low for each segment. The explanatory variables that have high contribution to the model is X5, X9, and X2 with VIP (Variable Importance in the Projection) values more than 1.]]>
<![CDATA[VARIABEL-VARIABEL YANG MEMENGARUHI WAKTU HINGGA SESEORANG MENGGUNAKAN NARKOBA PERTAMA KALI MENGGUNAKAN ANALISIS SURVIVAL ]]>
<![CDATA[Widya Larasati]]>;
<![CDATA[Mohammad Dokhi]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.587
<![CDATA[Indonesia is currently in a state of emergency of drugs because of its increasing abuse rate and its spread is widespread not only in big cities. The average of first drug use is in adolescence. This study aims to determine the variables that influence the time for someone to use drugs for the first time and the acceleration factor. The data analyzed by using Survival Analysis with the frailty variable is secondary data from BNN and Puslitkes UI survey. The result is, the average of first drug use in eight towns/districts of research locus was 18-year. Smoking, alcohol consumption, the environment, and gender are significant variables that influence the first drug use. A person who has smoked, use alcohol, lived in a drug-exposed environment, and male sex will have a resilient time not to use drugs faster with the acceleration factor of each variable are 0.3184, 0.3985, 0.3501, and 0.6773. The results conclude that regulations on cigarettes and alcohol need to be revisited since both influence drug initiation. In addition, prevention programs need to focus more on adolescents and young children so that they have strong self-defense from the influence of a drug-exposed environment.]]>
<![CDATA[STUDY ON EMD METHOD FOR PREDICTING THE PRICE OF CURLY RED CHILI IN INDONESIA ]]>
<![CDATA[Zilrahmi Zilrahmi]]>;
<![CDATA[Hari Wijayanto]]>;
<![CDATA[Farit M Afendi]]>;
<![CDATA[Rizal Bakri]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.600
<![CDATA[The fluctuations of curly red chili price affect the inflation rate in Indonesia. So that, the basic characteristics of price movement and correctly prediction for curly red chili price become concern in various studies. Empirical Mode Decomposition (EMD) method helps to examine behavioral characteristics of curly red chili prices in Indonesia easily. Ensemble EMD (EEMD) and modified EEMD are the decomposition method of time series which is development of EMD method. The decomposed data with EMD methods can also used for price forecast. The forecasting with ARIMA and trend polynomial performed to assess the effect of decomposition with EMD methods for forecast stability of curly red chili price in Indonesia under various conditions. The results show the most influence factor for price fluctuation of curly red chili in Indonesia is season and growing season. In this case, the ability of a decomposition method to produce the actual components that describe the pattern of data signals affect the accuracy of the predicted value obtained using the model. The predicted value using the decomposed data by modified EEMD always better than EEMD on the overall condition.]]>
<![CDATA[IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI ]]>
<![CDATA[Muhammad Luthfi Setiarno Putera]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.603
<![CDATA[Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.]]>
<![CDATA[PENGGEROMBOLAN DERET WAKTU DENGAN PENDEKATAN UKURAN KEMIRIPAN PICCOLO UNTUK PERAMALAN CURAH HUJAN PROVINSI BANTEN ]]>
<![CDATA[Sarah Fadhlia]]>;
<![CDATA[I Made Sumertajaya]]>;
<![CDATA[Anik Djuraidah]]>
<![CDATA[ Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020) ]]>
Publisher : <![CDATA[Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT) ]]>
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DOI: 10.29244/ijsa.v4i2.607
<![CDATA[Time series data modeling can be done by modeling each object one by one. Monthly rainfall data is an example of time series data. The purpose of time series analysis is to find patterns of past data and then forecast the future characteristics of data. The data used in this study is the Banten Province rainfall data which contained 19 rainfall stations. So it will require 19 models to forecast the rainfall data. The pattern of time series data in Banten Province monthly rainfall data in several locations has similarities. So that the similarity of this pattern can be considered in the clusters. In time series clustering, the idea is to investigate the similarity of time series in a cluster. The accuracy of distance similarity size measurements is performed on the generation data generated from 3 models, namely AR (1), AR (2), and AR (3). The piccolo method has an average accuracy of 0.62. While the maharaj method has an average accuracy of 0.41. This means that the Ward hierarchical clustering method using the Piccolo distance approach has a greater accuracy value than the Maharaj distance approach. Furthermore, the Piccolo method can be used as an alternative to the excellent distance method for grouping time series data in case data. The Banten Province rainfall station has 3 optimal clusters. Modeling individual level and cluster level has accuracy values that are not much different.]]>
