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All Journal FORUM STATISTIKA DAN KOMPUTASI Media Statistika International Journal of Science, Engineering and Information Technology Xplore: Journal of Statistics Indonesian Journal of Statistics and Its Applications

Itasia Dina Sulvianti

Department Of Statistics, IPB University, Indonesia

Author-ID : 391649

Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Other

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METODE PENDUGAAN MATRIKS RAGAM-PERAGAM DALAM ANALISIS REGRESI KOMPONEN UTAMA (RKU) Itasia Dina Sulvianti; Dian Kusumaningrum; Yani Suryani
FORUM STATISTIKA DAN KOMPUTASI Vol. 14 No. 2 (2009)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.386 KB)

Regresi komponen utama (RKU) merupakan salah satu analisis regresi yang menggunakan komponen utama untuk mengatasi adanya multikolinearitas pada regresi berganda. Metode kemungkinan maksimum (MLE) biasanya digunakan untuk menduga matrik ragam-peragam pada analisis regresi komponen utama. Namun, metode pendugaan ini sangat sensitif terhadap adanya data pencilan multivariat. Oleh karena itu, salah satu cara untuk mengatasi masalah ini adalah dengan menggunakan metode minimum covariance determinant (MCD) dalam menduga matriks ragam-peragamnya. Penelitian ini menggunakan metode MLE dan MCD untuk menduga matriks ragam-peragam pada analisis regresi komponen utama. Sedangkan parameter regresinya diduga oleh metode kuadrat terkecil (MKT). Sementara itu, untuk pemilihan jumlah komponen utama digunakan kriteria 80% proporsi keragaman dari data contoh. Hasil penelitian ini menunjukkan bahwa dampak adanya pencilan multivariat pada analisis regresi komponen utama yang matriks ragam-peragamnya diduga oleh metode MCD akan menghasilkan nilai rata-rata akar ciri pertama yang tetap stabil pada komponen utama pertama (KU1), walaupun rasio pencilan multivariat dengan banyaknya data terus bertambah. Saat rasio pencilan multivariat dengan banyaknya data sebesar 5%, metode pendugaan parameter regresi komponen utama dengan MKT-MLE dan MKT-MCD menunjukkan hasil yang sama baik karena kedua metode ini cenderung menghasilkan nilai bias dan mean squared error (MSE) yang relatif sama kecil. Namun, pada saat rasio pencilan multivariat dengan banyaknya data lebih besar dari 5% (10%,15%,20%), metode MKT-MCD menunjukkan hasil yang lebih baik dibandingkan metode MKT-MLE dalam menduga parameter regresi komponen utama. Hal ini terjadi karena metode MKT-MCD cenderung menghasilkan nilai bias dan MSE yang lebih kecil dibandingkan MKT-MLE.

Pemodelan Harga Beras di Pulau Sumatera dengan Menggunakan Model Generalized Space Time ARIMA Dwi Yulianti; I Made Sumertajaya; Itasia Dina Sulvianti
Xplore: Journal of Statistics Vol. 2 No. 2 (2018): 31 Agustus 2018
Publisher : Department of Statistics, IPB

Generalized space time autoregressive integrated moving average (GSTARIMA) model is a time series model of multiple variables with spatial and time linkages (space time). GSTARIMA model is an extension of the space time autoregressive integrated moving average (STARIMA) model with the assumption that each location has unique model parameters, thus GSTARIMA model is more flexible than STARIMA model. The purposes of this research are to determine the best model and predict the time series data of rice price on all provincial capitals of Sumatra island using GSTARIMA model. This research used weekly data of rice price on all provincial capitals of Sumatra island from January 2010 to December 2017. The spatial weights used in this research are the inverse distance and queen contiguity. The modeling result shows that the best model is GSTARIMA (1,1,0) with queen contiguity weighted matrix and has the smallest MAPE value of 1.17817 %.

Pemodelan Faktor Risiko Penyakit Campak pada Balita di Provinsi DKI Jakarta: Pemodelan Faktor Risiko Penyakit Campak pada Balita di Provinsi DKI Jakarta Ayu Annisa Rahmah; Itasia Dina Sulvianti; Cici Suhaeni; Bimandra Adiputra Djaafara
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

Measles is one of the infectious caused by virus. The disease is easily transmitted and has become one of the main causes of child mortality especially toddlers. In 2016, Jakarta experienced the highest measles case in the last ten years and found the difference in the number of measles cases in each sub-district of Jakarta. This can be caused by the existence of effect of spatial location i.e. spatial heterogeneity. Geographically weighted regression (GWR) is a method that can be applied to address the presence of spatial heterogeneity in the process of developing the model. In this study, the weighting function used was the Gaussian kernel. The modelling process generated 42 local models at sub-district level. Explanatory variables that influence the incidence rate of measles in toddlers (Y) signiï¬cantly are the percentage of immunization coverage measles (X1), the total annual rainfall (X4), and the percentage of the number of toddlers (X5). In this study, the GWR model is better than multiple linear regression model which were indicated by higher value of and smaller value of AIC.

Pemodelan Produksi Ayam Ras di Indonesia Menggunakan Regresi dengan Sisaan Deret Waktu Akhbamah Primadaniyah Febrin; Itasia Dina Sulvianti; Aji Hamim Wigena
Xplore: Journal of Statistics Vol. 8 No. 1 (2019): 30 April 2019
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/xplore.v8i1.192

The production of broiler chicken has fluctuated in recent years and many factors alleged to influence the production. The purpose of this study is modeling a structural equation of forecasting the production of broiler chicken. The study use a dependent variable (Y) that is production of broiler chickens (kilo ton) and five independent variables (X) consist of broiler chicken population (million), national chicken consumption (ton/year), retail price (Rp/kg), real price of corn (Rp), and real price of Kampung chicken (Rp). The variables are time series data with errors does not spread out randomly. Modeling method used and suitable to the conditions is regression with time series errors combined with ARIMA (Autoregressive Integrated Moving Average). The results of the regression analysis showed that only population variable and retail price variable are influencing the production of broiler chicken in Indonesia. Those two independent variables then modeled by a dependent variable using regression with time series errors. The best modeling is regression with time series errors ARIMA(1,1,0) with MAPE (Mean Average Percentage Error) value of 2.4%, RMSE (Root Mean Square Error) value of 39.800, and correlation value 0.980. The results has proved that the production of broiler chicken in Indonesia is influenced by those two variables.

Perbandingan Quadratic Discriminant Analysis dan Support Vector Machine untuk Klasifikasi Tutupan Lahan di DKI Jakarta Kamaluddin Junianto Dimas; Rahma Anisa; Itasia Dina Sulvianti
Xplore: Journal of Statistics Vol. 9 No. 1 (2020)
Publisher : Department of Statistics, IPB

DKI Jakarta is a center of government as well as economy and business of Indonesia, thus development projects in Jakarta continue every year. Therefore, monitoring for land use has to be improved in accordance to DKI Jakarta Spatial Planning. The attempt needs to be supported by continuous data availability regarding land cover condition in Jakarta. The aforementioned data collecting process become easier due to remote sensing technology development. Remote sensing technology can be utilized for analyzing the size of land use area by using classification analysis. It has been found that the level of accuracy depends on the type of classification method and number of training data. This research evaluated the level of overall accuracy, sensitivity, and specificity of Quadratic Discriminant Analysis (QDA) and Support Vector Machine (SVM) along with number of data training used in classifying Jakarta land cover in 2017. The results showed that in both methods, the variance of all the aforementioned criteria were getting smaller along with the increasing number of training data. QDA and SVM had similar performance based on overall accuracy and specificity. However, SVM was better than QDA on sensitivity.

Analisis Korelasi Kanonik pada Parameter Kualitas Fisik dan Parameter Kualitas Kimia Air Sungai Ciliwung Nadya Amelia Dewi Suryana; Itasia Dina Sulvianti; Muhammad Nur Aidi
Xplore: Journal of Statistics Vol. 10 No. 2 (2021)
Publisher : Department of Statistics, IPB

Water is an important factor in fulfilling the needs of living things, therefore the water that is used must be free from bacterias and do not contain any toxic substances. The most common water source comes from the river. Ciliwung River as one of the main rivers used for drinking, household needs, industrial needs, and transportation must have good water quality. Therefore, the Ciliwung River water quality needed to be known. The water quality is measured based on the parameters such as the physical water quality and the chemical water quality. The measurement of those parameters are classified to be complicated as it measured by laboratorium research, so that the identification of the chemical water quality parameter could be done through the physical water quality that is easier and simpler to be measured. This study aims to determine the variable of the physical water parameters that can be used to identify the chemical water quality parameters, so that the water quality of the Ciliwung River can be known in a simpler way. Statistical method that can be used to see the relationship between the two variable groups is the canonical correlation analysis. Canonical correlation analysis is a method in multiple variable analysis used to investigate the relationship of two groups of variables using the linear combination principle of the two variables. Based on the results of the canonical correlation analysis, it can be concluded that there is a relationship between the physical quality of water and the chemical quality of water. The correlation exists between the variables of physical quality of water, which are the water temperature and the content of suspended substances in water, with the variables of chemical quality of water, namely groups of metals (manganese levels in water and iron content in water) and groups of acid (the level of deep phosphate in water, the level of sulfate in water, the level of nitrite in water, and the level of nitrate in water). The relationship between the physical quality of water is positive between the temperature of water and the chemical quality of water whereas negative between the levels of suspended substances in water and the chemical quality of water.

Penerapan Two Step Cluster untuk Mengklasifikasikan Desa di Kabupaten Madiun Berdasarkan Data Potensi Desa Alif Supandi; Asep Saefuddin; Itasia Dina Sulvianti
Xplore: Journal of Statistics Vol. 10 No. 1 (2021)
Publisher : Department of Statistics, IPB

Village development is a fundamental part of national development. Developing villages requires information on society necessities. This research aims at clustering villages in Kabupaten Madiun which has similar characteristics among each other and identify characteristics of the built clusters. Therefore, specific problems in the clusters of villages may become the foundation to implement development. The method that used for grouping objects with combined variables is two-step cluster. This analysis was used 14 variables consist of six categorical variables and eight numerical variables. The clustering analysis produces four clusters. The clusters that need more attention to be developed was Cluster 2 which had minimum facilities and resources. The average Silhouette coefficient for the clusters built was 0.3 which can be considered as fair category.

Pemodelan dengan Geographically Weighted Negative Binomial Regression (Studi kasus: Banyaknya Penderita Kusta di Jawa Barat) Khusnul Khotimah; Itasia Dina Sulvianti; Pika Silvianti
Xplore: Journal of Statistics Vol. 10 No. 3 (2021)
Publisher : Department of Statistics, IPB

The number of leper in West Java is an example of the count data case. The analyzes commonly used in count data is Poisson regression. This research will determine the variables that influence the number of leper in West Java. The data used is the number of leper in West Java in 2019. This data has an overdispersion condition and spatial heterogenity. To handle overdispersion, the negative binomial regression model can be employed. While spatial heterogenity is overcome by adding adaptive bisquare kernel weight. This research resulted Geographically Weighted Negative Binomial Regression (GWNBR) with a weighting adaptive bisquare kernel classifies regency/city in West Java into ten groups based on the variables that sigfinicantly influence the number of leper. In general, the variable in the percentage of households with Clean and Healthy Behavior (PHBS) has a significant effect in all regency/city in West Java. Especially for Bogor Regency, Depok City, Bogor City, and Pangandaran Regency, the variable of the percentage of people poverty does not have a significant effect on the number leper.

Regresi Elastic Net dengan Peringkasan Luas untuk Mengukur Keakuratan Alat Non-Invasive Produk Tahun 2017 dan 2019 Fariz Mufti Rusdana; Itasia Dina Sulvianti; . Erfiani
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Diabetes melitus is one of dangerous disease because itâ€™s hard to be cured. This is shows itâ€™s important for everyone to always control and checking their blood glucose levels to prevent make the diabetes melitus is getting worse. Non-invasive biomarking team from IPB currently developing blood glucose device measurement with non-invasive method. Now, the non-invasive biomarking team from IPB already created 2 products, design product for 2017â€™s and 2019â€™s with the output in the form of a residual intensity spectrum with respect to the time-domain. Therefore, calibration modeling is needed to predict blood glucose level. The best calibration modeling method for 2017â€™s device discovered by Herianti (2020) with elastic net regression and DDC algorithm for resolve the outlier. In 2019, measuring the blood glucose level were using different tools. This research aims to determine a more stable tool for measuring the blood glucose level with non-invasive method from 2 available tools, and to determine a more accurate summarization method of the intensity residual spectrum. More stable tool for measuring the blood glucose level is a 2017â€™s device. The summarization method in this research uses a trapezoidal area and 3 digit summarization approach. The result showed that the 2 summarization method didnâ€™t have a significant different in accuracy.

Penerapan Support Vector Machine dengan SMOTE Untuk Klasifikasi Sentimen Pemberitaan Omnibus Law Pada Situs CNNIndonesia.com Widiananda Putri Hutami; Hari Wijayanto; Itasia Dina Sulvianti
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

The declaration of the omnibus law reaped the pros and cons in the community. In a situation like this, the media should be neutral. One of the media that still maintains neutrality is Detik (Rumata 2017). Detik owns several channels such as detikNews, detikFinance, and CNN Indonesia. In this study, the neutrality of the CNN Indonesia media as part of Detik will be studied based on the tendency of sentiment on the omnibus law-related news. Sentiment analysis is used to examine the trend of opinion on news headlines. In conducting sentiment analysis, a method that supports classification is needed. The classification method that will be used in this research is the Support Vector Machine (SVM). There is an imbalance of data in the three categories of sentiment so that the Synthetic Minority Oversampling Technique (SMOTE) method is used to overcome this imbalance. The omnibus law tends to be reported neutrally by CNNIndonesia.com site. The one vs all method has a better classification result than the one vs one method. The application of SMOTE only gives slightly better results than data classification without the application of SMOTE because the imbalance in the data is not too extreme. Modeling using the one vs all method with SMOTE and distribution of data 90% train data 10% test data gives the best classification results with a macro average f1-score of 60,33%.

Co-Authors . Erfiani Aam Alamudi Abdul Aziz Nurussadad Aji Hamim Wigena Akbar Rizki Akhbamah Primadaniyah Febrin Alif Supandi Anik Djuraidah Anindia Nufitasari ASEP SAEFUDDIN Auzi Asfarian Ayu Annisa Rahmah Bagus Sartono Barokaturrizkia Ameliani Bimandra Adiputra Djaafara Cici Suhaeni Dewi Fortuna Dian Kusumaningrum Dwi Yulianti Eria, Raisa Nida Farit Mochamad Afendi Fariz Mufti Rusdana Gerry Alfa Dito Hari Wijayanto Hari Wijayanto I Made Sumertajaya Kamaluddin Junianto Dimas Khusnul Khotimah Mohamad Arif Pramarta Muhammad Nur Aidi Nadya Amelia Dewi Suryana Pika Silvianti Pika Silvianti Rahardiantoro, Septian Rahma Anisa Rossi Azmatul Barro Suci Pujiani Prahesti Widiananda Putri Hutami Yani Suryani Yenni Angraini

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