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Department of Statistic, Faculty of Science and Mathematics , Universitas Diponegoro Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro Gedung F lt.3 Tembalang Semarang 50275
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Jurnal Gaussian
Published by Universitas Diponegoro
ISSN : -     EISSN : 23392541     DOI : -
Core Subject : Education,
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Jurnal Gaussian terbit 4 (empat) kali dalam setahun setiap kali periode wisuda. Jurnal ini memuat tulisan ilmiah tentang hasil-hasil penelitian, kajian ilmiah, analisis dan pemecahan permasalahan yang berkaitan dengan Statistika yang berasal dari skripsi mahasiswa S1 Departemen Statistika FSM UNDIP.
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Articles 15 Documents
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Search results for , issue "Vol 9, No 3 (2020): Jurnal Gaussian" : 15 Documents clear
VALUE at RISK (VaR) DAN CONDITIONAL VALUE at RISK (CVaR) DALAM PEMBENTUKAN PORTOFOLIO BIVARIAT MENGGUNAKAN COPULA GUMBEL Dina Rahma Prihatiningsih; Di Asih I Maruddani; Rita Rahmawati
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28913

Abstract

One way to minimize risk in investing is to form of portfolio by combining several stocks.Value at Risk (VaR) is a method for estimating risk but has a weakness that is VaR is incoherent because it does not have the subadditivity. To overcome the weakness of VaR, Conditional Value at Risk (CVaR) can use. Stock data is generally volatile, so ARIMA-GARCH is used to model it. The selection of ARIMA models on R software can be automatically using the auto.arima() function. Then Copula Gumbel is a method for modeling joint distribution and flexible because it does not require the assumption of normality and has the best sensitivity to high risk so that it is suitable for use in stock data.The first step in this research is to modeling Copula Gumbel-GARCH with the aim to calculate VaR and CVaR on the portfolio of PT Bank Mandiri Tbk (BMRI) and PT Indo Tambangraya Megah Tbk (ITMG). At the confidence level 99%, 95%, and 90% obtained the VaR results sequentially amounted to 3.977073%; 2.546167%; and 1.837288% and the CVaR results sequentially amounted to 4.761437%; 3.457014%; and 2.779182%. The worst condition is a loss with VaR and it is still possible if a worse condition occurs is a loss with CVaR so that investors can be more aware of the biggest loss that will be suffered.Keywords: Value at Risk, Conditional Value at Risk, Auto ARIMA, Copula Gumbel.
ANALISIS ARIMA DAN WAVELET UNTUK PERAMALAN HARGA CABAI MERAH BESAR DI JAWA TENGAH Chrisentia Widya Ardianti; Rukun Santoso; Sudarno Sudarno
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28906

Abstract

Time series is a type of data collected according to the sequence of times in a certain time span. Time series data can be used as a predictor of future conditions. Analysis of time series data, one of the ARIMA units, is a parametric method that requires an assumption to get valid results. Data stationarity is one of the factors that must be fulfilled. Wavelet is a non-parametric method that is able to represent time and frequency information simultaneously, so that it can analyze non-stationary data. This research presents forecasting the price of red chili in Central Java using ARIMA and wavelet with the approach of the Multiscale Autoregressive (MAR) model. The best model is the one with the smallest MSE value. The results showed that the ARIMA(0,1,1) model was said to be the best model with MSE = 2252142. However, because the assumption of normality is not fulfilled, an alternative process is done with wavelet. Wavelet approach results show that the MAR model Haar filter level (j) = 4 with MSE = 2175906 is better than Daubechies 4 filter 4 level (j) = 1 with MSE = 3999669. Therefore, the Haar wavelet is considered better in the time series analysis. Keyword : ARIMA, wavelet, MAR, forecasting, MSE
PREDIKSI JUMLAH KEBERANGKATAN PENUMPANG PESAWAT TERBANG MENGGUNAKAN MODEL VARIASI KALENDER DAN DETEKSI OUTLIER (Studi Kasus di Bandara Soekarno-Hatta) Alvi Waldira; Abdul Hoyyi; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28914

Abstract

 Transportation has a strategic role, even becoming one of the main needs of the community, especially air transportation services. A large number of passengers in air transportation always experiences a difference every month. One of the differences occurred when approaching Eid al-Fitr, which changes every year based on an Islamic calendar that is different from Masehi calendar. The lunar shift in the occurrence of Eid al-Fitr forms a pattern called calendar variation. The effects of calendar variations can be overcome by using an additional variable, such as a dummy variable, this variable which will be used in the ARIMAX model. Observation of time series is often influenced by several unexpected events such as outliers. This outlier causes the results of data analysis to be less valid. So the researchers added the detection of outliers in this study. Based on the analysis results, the ARIMA calendar variation model is obtained (1.0, [12]), with time variable t, dummy variable , and the addition of one outlier. This model has a MAPE value of 0.07079609 which means this model is very good for forecasting. Forecasting results showed an increase in the number of passengers during the two months before Eid. Keywords: Passenger, calendar variation, outlier detection
PENERAPAN ANALISIS KLASTER K-MODES DENGAN VALIDASI DAVIES BOULDIN INDEX DALAM MENENTUKAN KARAKTERISTIK KANAL YOUTUBE DI INDONESIA (Studi Kasus: 250 Kanal YouTube Indonesia Teratas Menurut Socialblade) Ahmad Badruttamam; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28907

Abstract

YouTube is one of the most popular online platforms today. The popularity of YouTube has makes it an effective advertising medium. In April 2019, Socialblade released the top 250 YouTube channels in Indonesia based on their gradations with various characteristics. YouTube channel data will be grouped into several clusters to make it easier for advertisers to choose channels with characteristics as needed. The purpose of this study is to determine the best number of clusters and determine their characteristics. The method used is the k-Modes cluster analysis with values k = 3, 4, 5, ..., 8. The k-Modes method can group objects that have categorical type variables into relatively homogeneous groups. The best number of clusters (k) can be checked using the Davies Bouldin Index (DBI). Based on the analysis carried out, obtained the best number of six clusters with a Davies-Bouldin Index value of 1.080509. The most recommended cluster for advertising is cluster 6, which has grade A characteristics, gold title, and has an estimated annual income of 5 million USD < income ≤ 10 million USD. Keywords: Youtube, Cluster Analysis, k-Modes, Categorical Data, Davies-Bouldin Index
PERBANDINGAN METODE SMOTE RANDOM FOREST DAN SMOTE XGBOOST UNTUK KLASIFIKASI TINGKAT PENYAKIT HEPATITIS C PADA IMBALANCE CLASS DATA Muhamad Syukron; Rukun Santoso; Tatik Widiharih
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28915

Abstract

Hepatitis causes around 1.4 million people die every year. This number makes hepatitis to be the largest contagious disease in the number of deaths after tuberculosis. Liver biopsy is still the best method for diagnosing the stage of hepatitis C, but this method is an invasive, painful, expensive, and can cause complications. Non-invasively method needs to be developed, one of non-invasif method is machine learning. Random Forest and XGboost are classification methods that are often used, since they have many advantages over classical classification methods. The SMOTE algorithm can be used to improve the accuracy of predictions from imbalanced data. the data in this study have 24 independent variables in the form of patients self-data, hepatitis C symptoms, and laboratory test results. The dependent variable in this study is a binary category, namely the level of hepatitis C disease (fibrosis and cirrhosis). The results showed that the random forest and XGboost had an accuracy of around 74% but the recall value was less than 2%. SMOTE random forest dan SMOTE XGboost have an accuracy & recall value more than 75%. SMOTE random forest has a higher accuracy for predicting fibrosis class while SMOTE XGboost is better in cirrhosis class. Variables that are more influental in determining hepatitis C stage are variables from laboratory test. Keyword : Fibrosis, Cirrhosis, Random Forest, SMOTE, XGboost
PENERAPAN RESPONSE BASED UNIT SEGMENTATION IN PARTIAL LEAST SQUARE (REBUS-PLS) UNTUK ANALISIS DAN PENGELOMPOKAN WILAYAH (Studi Kasus: Kesehatan Lingkungan Perumahan di Provinsi Jawa Tengah) Febriana Sulistya Pratiwi; Sudarno Sudarno; Agus Rusgiyono
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28927

Abstract

Residental environmental health is a complex problem that depends on several dimensions. One of the statistical method that can be used to analyze the relation between complex dimensions is Structural Equation Modeling (SEM) with a variant/component based approach or Partial Least Square. The purpose of this study is to develop a structural model of the relation between household economy, education, housing facilities, and residental environmental health in Central Java Province in 2018 based on 12 valid and reliable indicators. In the structural equation model there is a significant positive effect path that is the influence of household economy towards education and towards housing facilities, and influence housing facility on the residential environment health. In SEM analysis it is generally assumed that the data taken comes from a homogeneous population but often the data consists of several segments. Therefore, we need a method to detect heterogeneity problems, namely Response Based Unit Segmentation in Partial Least Square (REBUS-PLS). Based on the dendogram produced, by forming 2 classes/segments,  values as the accuracy of the prediction model on the local model had a higher value (except  values for Education in local model 2) than  values on the global model. In addition, the Goodnes of Fit value as a measure of model suitability for each local model is also had a higher value, so that it indicates the goodness of the model in the local model is better than the global model.Keywords: environmental health, SEM, PLS, REBUS-PLS
KOMPUTASI METODE MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING AVERAGE (MEWMA) UNTUK PENGENDALIAN KUALITAS PROSES PRODUKSI MENGGUNAKAN GUI MATLAB (STUDI KASUS: PT. Pismatex Textile Industry Pekalongan) Riza Fahlevi; Hasbi Yasin; Dwi Ispriyanti
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28908

Abstract

Control chart is one of the effective statistical tools to overcome the problem of process quality in a production. Multivariate Exponentially Weighted Moving Average (MEWMA) control chart is an effective quality control tool in processes with more than one variable and correlated (multivariate). The MEWMA control chart has a weight value (λ) which makes this chart more sensitive in detecting small shifts process mean. The weight (λ) has values ranging from 0 to 1 ( ), where this weight will be given to each data. The MEWMA control chart in this study was used to form a control chart by the product defects percentage of grade B and grade B at PT. Pismatex Textile Industry Pekalongan. In this study, GUI Matlab was formed to assist the computational process in forming MEWMA control charts to control the quality of production at  PT. Pismatex Textile Industry Pekalongan. Based on the result, the optimal weight is obtained at the weight value λ = 0.9. Keywords: Multivariate Exponentially Weighted Moving Average (MEWMA), Weight (λ), GUI Matlab, Percentage of product defects.
KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) Isna Wulandari; Hasbi Yasin; Tatik Widiharih
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.27416

Abstract

The recognition of herbs and spices among young generation is still low. Based on research in SMK 9 Bandung, showed that there are 47% of students that did not recognize herbs and spices. The method that can be used to overcome this problem is automatic digital sorting of herbs and spices using Convolutional Neural Network (CNN) algorithm. In this study, there are 300 images of herbs and spices that will be classified into 3 categories. It’s ginseng, ginger and galangal. Data in each category is divided into two, training data and testing data with a ratio of 80%: 20%. CNN model used in classification of digital images of herbs and spices is a model with 2 convolutional layers, where the first convolutional layer has 10 filters and the second convolutional layer has 20 filters. Each filter has a kernel matrix with a size of 3x3. The filter size at the pooling layer is 3x3 and the number of neurons in the hidden layer is 10. The activation function at the convolutional layer and hidden layer is tanh, and the activation function at the output layer is softmax. In this model, the accuracy of training data is 0.9875 and the loss value is 0.0769. The accuracy of testing data is 0.85 and the loss value is 0.4773. Meanwhile, testing new data with 3 images for each category produces an accuracy of 88.89%. Keywords: image classification, herbs and spices, CNN. 
PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN ADAPTIVE BOOSTING PADA KASUS KLASIFIKASI MULTI KELAS Ade Irma Prianti; Rukun Santoso; Arief Rachman Hakim
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28924

Abstract

The company's financial health provides an indication of company’s performance that is useful for knowing the company's position in industrial area. The company's performance needs to be predicted to knowing the company's progress. K-Nearest Neighbor (KNN) and Adaptive Boosting (AdaBoost) are classification methods that can be used to predict company's performance. KNN classifies data based on the proximity of the data distance while AdaBoost works with the concept of giving more weight to observations that include weak learners. The purpose of this study is to compare the KNN and AdaBoost methods to find out better methods for predicting company’s performance in Indonesia. The dependent variable used in this study is the company's performance which is classified into four classes, namely unhealthy, less healthy, healthy, and very healthy. The independent variables used consist of seven financial ratios namely ROA, ROE, WCTA, TATO, DER, LDAR, and ROI. The data used are financial ratio data from 575 companies listed on the Indonesia Stock Exchange in 2019. The results of this study indicate that the prediction of company’s performance in Indonesia should use the AdaBoost method because it has a classification accuracy of 0,84522 which is greater than the KNN method’s accuracy of 0,82087. Keywords: company’s performance, classification, KNN and AdaBoost, classification accuracy. 
KOMPUTASI GUI-R UNTUK PEMODELAN REGRESI NONPARAMETRIK BIRESPON POLINOMIAL LOKAL PADA PENGARUH SUKU BUNGA BI TERHADAP INDEKS HARGA SAHAM GABUNGAN DAN KURS USD Rudi Saputro Setyo Purnomo; Suparti Suparti; Sudarno Sudarno
Jurnal Gaussian Vol 9, No 3 (2020): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/j.gauss.v9i3.28911

Abstract

Economy is one of important indicator of development country. Capital market is one of important tool in economy. The development of the capital market in Indonesian can be seen based on the composite stock price index (CSPI). Other than capital market, international trade is an important tool in the economy. Existence of the international trade generates exchange rate, one of which is USD exchange rate. Exchange rate can be increased and weakened, so it’s stability needs to be maintained. One of the factor that can influence CSPI and USD exchange rate is the BI interest rate. To be able to predict the value of CSPI and USD exchange rate then do the birespon regression modelling because between CSPI and USD exchange rate there are relationship. The regression model approach  which used in this research is local polynomial. This approach has high adaptability with data. To make the modelling easier so this research arrange Graphycal User Interface (GUI) by using R software. The local polynomial birespon regression is applied to CSPI and USD exchange rate data based on BI interest rate by using GUI. The optimal modal is obtained by General Cross Validation (GCV) optimation. The optimal model is model by combination of sequences two and three, bandwidths 6 and 2,7, and local points 5,75 and 6. The value of R Square is 66,68% and the mean absolute percentage error (MAPE) is 4,0798%. This MAPE shows that the optimal model has very high accuration in prediction the data because this value of MAPE less than 10%.Keywords: CSPI, USD exchange rate, BI interest rate, birespon, local polynomial, GUI.

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