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  • 1
    Online Resource
    Online Resource
    Prediksi Harga Saham Harian Menggunakan Cascade Forward Neural Network (CFNN) Dengan Particle Swarm Optimization (PSO)
    Universitas Islam Bandung (Unisba) ; 2019
    In:  STATISTIKA Journal of Theoretical Statistics and Its Applications Vol. 19, No. 2 ( 2019-12-01), p. 71-82
    Details
    In: STATISTIKA Journal of Theoretical Statistics and Its Applications, Universitas Islam Bandung (Unisba), Vol. 19, No. 2 ( 2019-12-01), p. 71-82
    Type of Medium: Online Resource
    ISSN: 2599-2538 , 1411-5891
    URL: Article
    DOI: 10.29313/jstat.v19i2.4878
    Language: Unknown
    Publisher: Universitas Islam Bandung (Unisba)
    Publication Date: 2019
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  • 2
    Online Resource
    Online Resource
    PREDIKSI CURAH HUJAN EKSTREM DI KOTA SEMARANG MENGGUNAKAN SPATIAL EXTREME VALUE DENGAN PENDEKATAN MAX STABLE PROCESS (MSP)
    Institute of Research and Community Services Diponegoro University (LPPM UNDIP) ; 2019
    In:  MEDIA STATISTIKA Vol. 12, No. 1 ( 2019-07-24), p. 39-
    Details
    In: MEDIA STATISTIKA, Institute of Research and Community Services Diponegoro University (LPPM UNDIP), Vol. 12, No. 1 ( 2019-07-24), p. 39-
    Type of Medium: Online Resource
    ISSN: 2477-0647 , 1979-3693
    URL: Article
    DOI: 10.14710/medstat.12.1.39-49
    Language: Unknown
    Publisher: Institute of Research and Community Services Diponegoro University (LPPM UNDIP)
    Publication Date: 2019
    detail.hit.zdb_id: 3069374-3
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  • 3
    Online Resource
    Online Resource
    Live Expectancy Modelling using Spatial Durbin Robust Model
    IOP Publishing ; 2020
    In:  Journal of Physics: Conference Series Vol. 1655, No. 1 ( 2020-10-01), p. 012098-
    Details
    In: Journal of Physics: Conference Series, IOP Publishing, Vol. 1655, No. 1 ( 2020-10-01), p. 012098-
    Abstract: Spatial regression model is a model used to determine the relationship between response variables and predictor variables that have spatial influence in them. If the two variables have spatial influence, then the model that will be formed is the Spatial Durbin Model. One of the causes of the inaccuracy of the spatial regression model in predicting is observations of outliers. Removing outliers in spatial analysis can change the composition of spatial effects on the data. One method of settlement due to outliers in the spatial regression model is to use robust spatial regression. The application of the M-estimator parameter estimator principle is done in estimating the coefficient of spatial regression parameters that are robust to outliers. The results of modelling by applying the principle of M-estimator estimator on estimating the robust Spatial Durbin Model regression parameters are expected to be able to accommodate the existence of outliers in the spatial regression model. One example of the application of the Spatial Durbin Model Robust is the case of life expectancy modelling.
    Type of Medium: Online Resource
    ISSN: 1742-6588 , 1742-6596
    URL: Article
    DOI: 10.1088/1742-6596/1655/1/012098
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2020
    detail.hit.zdb_id: 2166409-2
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  • 4
    Online Resource
    Online Resource
    APLIKASI NAÏVE BAYES CLASSIFIER (NBC) PADA KLASIFIKASI STATUS GIZI BALITA STUNTING DENGAN PENGUJIAN K-FOLD CROSS VALIDATION
    Institute of Research and Community Services Diponegoro University (LPPM UNDIP) ; 2022
    In:  Jurnal Gaussian Vol. 11, No. 1 ( 2022-05-13), p. 130-139
    Details
    In: Jurnal Gaussian, Institute of Research and Community Services Diponegoro University (LPPM UNDIP), Vol. 11, No. 1 ( 2022-05-13), p. 130-139
    Abstract: The case of stunting in Indonesia is a problem that has been discussed for a long time. One of many efforts to overcome this problem is through an accelerated stunting reduction program to improve the nutritional status of the community and also to reduce the prevalence of stunting or stunted toddlers. Generally, the index used to determine the nutritional status of stunting toddlers height compared to age. This study aims to identify the classification results, evaluate the model, and predict the nutritional status of stunting toddlers using the Naïve Bayes Classifier algorithm with K-Fold Cross Validation testing. The data processing system used is the GUI-R (Graphical User Interface) in order to facilitate the analysis process by implementing the Shiny Package in the Rstudio program. The results of accuracy using Naïve Bayes Classifier with 10-Fold Cross Validation test obtained the highest accuracy on the 6th iteration with an accuracy 94.39%, while the lowest accuracy on the 8th iteration with an accuracy 82.08%. Overall, the average accuracy in each iteration is 88.46%, so it can be concluded that Naïve Bayes Classifier model considered good enough to classified data on the nutritional status of stunting toddlers.Keywords: Stunting, Data Mining, Naïve Bayes Classifier, K-Fold Cross Validation, Shiny Package
    Type of Medium: Online Resource
    ISSN: 2339-2541
    URL: Article
    DOI: 10.14710/j.gauss.v11i1.33991
    Language: id
    Publisher: Institute of Research and Community Services Diponegoro University (LPPM UNDIP)
    Publication Date: 2022
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  • 5
    Online Resource
    Online Resource
    The Step Construction of Geographically Weighted Panel Regression in Air Polluter Standard Index (APSI) Data
    EDP Sciences ; 2018
    In:  E3S Web of Conferences Vol. 73 ( 2018), p. 12006-
    Details
    In: E3S Web of Conferences, EDP Sciences, Vol. 73 ( 2018), p. 12006-
    Abstract: Geographically Weighted Panel Regression or GWPR is a local linear regression model that combines GWR model and panel data regression model with considering spatial effect, especially spatial heterogeneity problem. This article is focused on the soft computation of GWPR model using Fixed Effect Model (FEM). Parameter estimation in GWPR is obtain by Weighted Least Squares (WLS) methods and the resulting model for each location will be different from one to another. This study will compare the fixed-effect GWPR model with several weighting functions. The best model is determined based on the biggest coefficient of determination (R2) value. In this study, the model is applied in the Air Polluter Standard Index (APSI) in Surabaya City, East Java. The results of this study indicate that Fixed Effect GWPR model with a fixed exponential kernel weighting function is the best model to describe the APSI because it has the smallest AIC.
    Type of Medium: Online Resource
    ISSN: 2267-1242
    URL: Article
    DOI: 10.1051/e3sconf/20187312006
    Language: English
    Publisher: EDP Sciences
    Publication Date: 2018
    detail.hit.zdb_id: 2755680-3
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  • 6
    Online Resource
    Online Resource
    Forecasting of Rainfall in Central Java using Hybrid GSTAR-NN-PSO Model
    EDP Sciences ; 2019
    In:  E3S Web of Conferences Vol. 125 ( 2019), p. 23015-
    Details
    In: E3S Web of Conferences, EDP Sciences, Vol. 125 ( 2019), p. 23015-
    Abstract: Forecasting of rainfall trends is essential for several fields, such as airline and ship management, flood control and agriculture. The rainfall data were recorded several time simultaneously at a number of locations and called the space-time data. Generalized Space Time Autoregressive (GSTAR) model is one of space-time models used to modeling and forecasting the rainfall. The aim of this research is to propose the nonlinear space-time model based on hybrid of GSTAR, Feed Forward Neural Network (FFNN) and Particle Swarm Optimization (PSO) and it called GSTAR-NN-PSO. In this model, input variable of the FFNN was obtained from the GSTAR model. Then use PSO to initialize the weight parameter in the FFNN model. This model is applied for forecasting monthly rainfall data in Jepara, Kudus, Pati and Grobogan, Central Java, Indonesia. The results show that the proposed model gives more accurate forecast than the linear space-time model, i.e. GSTAR and GSTAR-PSO. Moreover, further research about space-time models based on GSTAR and Neural Network is needed to improving the forecast accuracy especially the weight matrix in the GSTAR model.
    Type of Medium: Online Resource
    ISSN: 2267-1242
    URL: Article
    DOI: 10.1051/e3sconf/201912523015
    Language: English
    Publisher: EDP Sciences
    Publication Date: 2019
    detail.hit.zdb_id: 2755680-3
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  • 7
    Online Resource
    Online Resource
    LIFE EXPECTANCY MODELING USING MODIFIED SPATIAL AUTOREGRESSIVE MODEL
    Institute of Research and Community Services Diponegoro University (LPPM UNDIP) ; 2022
    In:  MEDIA STATISTIKA Vol. 15, No. 1 ( 2022-07-27), p. 72-82
    Details
    In: MEDIA STATISTIKA, Institute of Research and Community Services Diponegoro University (LPPM UNDIP), Vol. 15, No. 1 ( 2022-07-27), p. 72-82
    Abstract: The presence of outliers will affect the parameter estimation results and model accuracy. It also occurs in the spatial regression model, especially the Spatial Autoregressive (SAR) model. Spatial Autoregressive (SAR) is a regression model where spatial effects are attached to the dependent variable. Removing outliers in the analysis will eliminate the necessary information. Therefore, the solution offered is to modify the SAR model, especially by giving special treatment to observations that have potentially become outliers. This study develops to modeling the life expectancy data in Central Java Province using a modified spatial autoregressive model with the Mean-Shift Outlier Model (MSOM) approach. Outliers are detected using the MSOM method. Then the result is used as the basis for modifying the SAR model. This modification, in principle, will reduce or increase the average of the observed data indicated as outliers. The results show that the modified model can improve the model accuracy compared to the original SAR model. It can be proved by the increased coefficient of determination and decreasing the Akaike Information Criterion (AIC) value of the modified model. In addition, the modified model can improve the skewness and kurtosis values of the residuals getting closer to the Normal distribution.
    Type of Medium: Online Resource
    ISSN: 2477-0647 , 1979-3693
    URL: Article
    DOI: 10.14710/medstat.15.1.72-82
    Language: English
    Publisher: Institute of Research and Community Services Diponegoro University (LPPM UNDIP)
    Publication Date: 2022
    detail.hit.zdb_id: 3069374-3
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  • 8
    Online Resource
    Online Resource
    PEMODELAN PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION
    Institute of Research and Community Services Diponegoro University (LPPM UNDIP) ; 2018
    In:  MEDIA STATISTIKA Vol. 11, No. 1 ( 2018-09-29), p. 53-64
    Details
    In: MEDIA STATISTIKA, Institute of Research and Community Services Diponegoro University (LPPM UNDIP), Vol. 11, No. 1 ( 2018-09-29), p. 53-64
    Abstract: Economic growth can be measured by amount of Gross Regional Domestic Product (GRDP). Based on official news of statistics BPS, Economic growth in Banten region has increase up to 5.59%. It supported by several sector, there are agriculture, business, industry and from various fields. Mixed Geographically Weighted Regression (MGWR) methods have been developed based on linear regression by giving spatial effect or location (longitude and latitude), the resulting model from Economic growth in Banten will be local or different based on each location. MGWR mixed method between linear regression and GWR, parameters in linear regression are global and GWR parameters are local. The results more specific because economic growth in Banten region assessed by location.Keywords: Banten, Economic growth, MGWR.
    Type of Medium: Online Resource
    ISSN: 2477-0647 , 1979-3693
    Uniform Title: PEMODELAN PERTUMBUHAN EKONOMI DI PROVINSI BANTEN MENGGUNAKAN MIXED GEOGRAPHICALLY WEIGHTED REGRESSION
    URL: Article
    DOI: 10.14710/medstat.11.1.53-64
    Language: English , English
    Publisher: Institute of Research and Community Services Diponegoro University (LPPM UNDIP)
    Publication Date: 2018
    detail.hit.zdb_id: 3069374-3
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