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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Indonesian Journal of Statistics and Its Applications
Afendi, Farit M
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation Other

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FUNCTION GROUP SELECTION OF SEMBUNG LEAVES (BLUMEA BALSAMIFERA) SIGNIFICANT TO ANTIOXIDANTS USING OVERLAPPING GROUP LASSO kusnaeni, kusnaeni; Soleh, Agus M; Afendi, Farit M; Sartono, Bagus
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 2 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (476.663 KB) | DOI: 10.30598/barekengvol16iss2pp721-728

Abstract

Functional groups of sembung leaf metabolites can be detected using FTIR spectrometry by looking at the spectrum's shape from specific peaks that indicate the type of functional group of a compound. There were 35 observations and 1866 explanatory variables (wavelength) in this study. The number of explanatory variables more than the number of observations is high-dimensional data. One method that can be used to analyze high-dimensional data is penalized regression. The overlapping group lasso method is a development of the group-based penalized regression method that can solve the problem of selecting variable groups and members of overlapping groups of variables. The results of selecting the variable groups using the overlapping group lasso method found that the functional groups that were significant for the antioxidants of sembung leaves were C=C Unstructured, CN amide, Polyphenol, Sio2.
TIME SERIES IMPUTATION USING VAR-IM (CASE STUDY: WEATHER DATA IN METEOROLOGICAL STATION OF CITEKO) Rizal, Muhammad Edy; Wigena, Aji H; Afendi, Farit M
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 4 (2022): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (851.852 KB) | DOI: 10.30598/barekengvol16iss4pp1373-1384

Abstract

Univariate imputation methods are defined as imputation methods that only use the information of the target variable to estimate missing values. While univariate imputation methods are convenient and flexible since no other variable is required, multivariate imputation methods can potentially improve imputation accuracy given that the other variables are relevant to the target variable. Many multivariate imputation methods have been proposed, one of which is Vector Autoregression Imputation Method (VAR-IM). This study aims to compare imputation results of VAR-IM-based methods and univariate imputation methods on time series data, specifically on long lag seasonal data such as daily weather data. Three modified VAR-IM methods were studied using simulations with three steps: deletion, imputation, and evaluation. The deletion step was conducted using six different schemes with six missing proportions. The simulations were conducted on secondary daily weather data collected from meteorological station of Citeko from January 1, 1991, to June 22, 2013. Nine weather variables were examined, that is the minimum, maximum, and average temperatures, average humidity, rainfall rate, duration of solar radiation, maximum and average wind speed, as well as wind direction at maximum speed. The simulation results show that the three modified VAR-IM methods can improve the accuracies in around 75% of cases. The simulation results also show that imputation results of VAR-IM-based methods tend to be more stable in accuracy as the missing proportion increase compared to the imputation results of univariate imputation methods.
Grouping Provinces in Indonesia Based on the Causes of Stunting Variables using Hierarchical Clustering Analysis: Pengelompokan Provinsi di Indonesia Berdasarkan Peubah Penyebab Stunting Menggunakan Analisis Cluster Hierarki Meilani, Detia; Masjkur, Mohammad; Afendi, Farit M
Indonesian Journal of Statistics and Applications Vol 7 No 1 (2023)
Publisher : Statistics and Data Science Program Study, SSMI, 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.v7i1p32-43

Abstract

Stunting is a condition due to chronic malnutrition that causes children to be shorter in height compared to their age. The prevalence of stunting in Indonesia still exceeds the standards set by WHO. This study aims to classify provinces in Indonesia based on the characteristics of the causes of stunting. Cluster analysis is a statistical method used to group objects with similar characteristics. Province grouping is done using hierarchical cluster analysis consisting of Single Linkage, Complete Linkage, Average Linkage, Ward's method, and Centroid method. The Cophenetic correlation coefficient was used to determine the best cluster method and the optimal number of clusters using the Silhouette coefficient. The results show that the centroid method has the highest Cophenetic correlation coefficient with four clusters. The first cluster consists of 1 province with low stunting characteristics, the second cluster consists of 3 provinces with high stunting characteristics, the third cluster consists of 22 provinces with very high stunting characteristics, and the fourth cluster consists of 8 provinces with moderate stunting characteristics.
IMPLEMENTASI TRANSFORMASI FOURIER UNTUK TRANSFORMASI DOMAIN WAKTU KE DOMAIN FREKUENSI PADA LUARAN PURWARUPA ALAT PENDETEKSIAN GULA DARAH SECARA NON-INVASIF Hidayaturrohman, Umam; Erfiani, Erfiani; Afendi, Farit M
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, 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.v4i2.504

Abstract

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.
STUDY ON EMD METHOD FOR PREDICTING THE PRICE OF CURLY RED CHILI IN INDONESIA Zilrahmi, Zilrahmi; Wijayanto, Hari; Afendi, Farit M; Bakri, Rizal
Indonesian Journal of Statistics and Applications Vol 4 No 2 (2020)
Publisher : Statistics and Data Science Program Study, SSMI, 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.v4i2.600

Abstract

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.
Application of Fuzzy C-Means and Weighted Scoring Methods for Mapping Blankspot Villages in Pemalang Regency Adiyana, Imam; Sumertajaya, I Made; Afendi, Farit M
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Statistics and Data Science Program Study, SSMI, 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.v6i1p77-89

Abstract

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.
Co-Authors Adiyana, Imam Agus Mohamad Soleh Bagus Sartono Erfiani Erfiani Hari Wijayanto I Made Sumertajaya Kusnaeni Kusnaeni, Kusnaeni Meilani, Detia Mohammad Masjkur Muhammad Edy Rizal Rizal Bakri Umam Hidayaturrohman Wigena, Aji H Zilrahmi, Zilrahmi