Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

p-Index From 2019 - 2024

0.23

P-Index

This Author published in this journals

All Journal Media Statistika MATEMATIKA SAINS DAN MATEMATIKA Jurnal Gaussian

Yuciana Wilandari

Jurusan Statistika FSM Undip

Author-ID : 417905

Decision Sciences, Operations Research & Management Mathematics Other

Published : 20 Documents Claim Missing Document

Claim Missing Document

Articles

Title

PENERAPAN METODE KLASIFIKASI SUPPORT VECTOR MACHINE (SVM) PADA DATA AKREDITASI SEKOLAH DASAR (SD) DI KABUPATEN MAGELANG Octaviani, Pusphita Anna; Wilandari, Yuciana; Ispriyanti, Dwi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Accreditation is the recognition of an educational institution given by a competent authority, that is Badan Akreditasi Nasional Sekolah/Madrasah (BAN - S/M) after it is assessed that the institution has met the eight components of the accreditation assessment. An elementary school, as one of the compulsory basic education, should have the status of accreditation to ensure the quality of education. This study aimed to apply the classification method Support Vector Machine (SVM) on the data accreditation SD in Magelang. Support Vector Machine (SVM) is a method that can be used as a predictive classification by using the concept of searching hyperplane (separator functions) that can separate the data according to the class. SVM using the kernel trick for non-linear problems which can transform data into a high dimensional space using a kernel function, so that the data can be classified linearly. The results of this study indicate that the prediction accuracy of SVM classification using Gaussian kernel function RBF is 93.902%. It is calculated from 77 of 82 elementary schools that are classified correctly with the original classes. Keywords : Accreditation, Classification, Support Vector Machine (SVM), hyperplane, Gaussian RBF Kernel, Accuracy

PERBANDINGAN METODE KLASIFIKASI REGRESI LOGISTIK BINER DAN RADIAL BASIS FUNCTION NETWORK PADA BERAT BAYI LAHIR RENDAH (Studi Kasus: Puskesmas Pamenang Kota Jambi) Samosir, Riama Oktaviani; Wilandari, Yuciana; Yasin, Hasbi
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Low Birth Weight (LBW) is one of the main causes of infant mortality. LBW must be identified and predicted before the baby birth by observing historical data of expectant. This research aims to analyze the classification of status newborn in order to reduce the risk of LBW. The statistical method used are the Binary Logistic Regression and Radial Basis Function Network. The data used in this final project is birth weight at Pamenang Jambi City health center in 2014. In this research, the data are divided into training data and testing data. Training data will be used to generate the model and pattern formation, while testing the data is used to measure how the accuracy of the representative model or pattern formed in classifying data through confusion tables. The results of analysis showed that the Binary Logistic Regression method gives 81,7% of classification accuracy for training data and 77,4% of classification accuracy for testing data, while Radial Basis Function Network method gives 92,96% of classification accuracy for training data and 80,64% of classification accuracy for testing data. Radial Basis Function Network method has better classification accuracy than the Binary Logistic Regression method. Keywords: Low Birth Weight (LBW), Binary Logistic Regression, Radial Basis Function Network, Classification, Confusion

PERAMALAN BEBAN PUNCAK PEMAKAIAN LISTRIK DI AREA SEMARANG DENGAN METODE HYBRID ARIMA (AUTOREGRESSIVE INTEGRATED MOVING AVERAGE)-ANFIS (ADAPTIVE NEURO FUZZY INFERENCE SYSTEM) (Studi Kasus di PT PLN (Persero) Distribusi Jawa Tengah dan DIY) Kristiana, Ana; Wilandari, Yuciana; Prahutama, Alan
Jurnal Gaussian Vol 4, No 4 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Electricity become one of the basic needs in society, so that the demand level for electricity even bigger as more complex activities in society. In order to fulfill the needs of electricity in Indonesia, PT PLN have to do electrical peak load forecasting to prevent electrical crisis. In this research, we use hybrid ARIMA-ANFIS methods to forecast daily peak load of electricity in Semarang period December 2014 until January 2015. The use of hybrid ARIMA-ANFIS is to capture both linear and nonlinear patterns in the data, because sometimes time series data can contain both linear and nonlinear patterns. Since ARIMA can not deal with nonlinear patterns while ANFIS is not able to handle both linear and nonlinear patterns alone. The accuracy of the model was measured by symmetric MAPE (sMAPE) criteria, in which the best model chosen is the model with the smallest sMAPE value. The results showed that the hybrid ARIMA-ANFIS model that used to predict the daily peak load electricity in Semarang during the period of December 2014 until January 2015, comes from combination between SARIMA (0,1,1)(0,1,1)7 model and residual forecasting with ANFIS model using first lag input, Gaussian membership function in 3 clusters. Keywords: Electricity, Electrical peak load forecasting, ARIMA, ANFIS, Hybrid ARIMA-ANFIS.

PENGHITUNGAN PREMI ASURANSI LONG TERM CARE UNTUK MODEL MULTI STATUS Gumauti, Chrysmandini Pulung; Wilandari, Yuciana; Rahmawati, Rita
Jurnal Gaussian Vol 5, No 2 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Health insurance is insurance that provides health benefit in the form of a cash compensation for the cost of treatment and care. One of the health insurance’s products is Long Term Care (LTC) insurance. LTC insurance guarantees nursing and medical expense, preferred for elderly people in the future. Proper calculation the cost of premiums is needed to maintain the reserve fund appropriate for insurance company to fulfill the policy agreement. In this final project will be discussed about calculation of premiums for LTC insurance products Annuity as A Rider Benefit as a multi-state models (three states), which is based on Markov transition probability matrix. Data used is the data prevalence rate of heart disease in the United Kingdom in 2014. By calculating premiums of multi-state models, insurance products are expected to be able to guarantee health care expense according insured’s needs. Result of this premiums calculation is the older someone takes insurance, greater the annual net premium to be paid. Keywords: health insurance, Long Term Care insurance, multiple state models, Annuity as A Rider Benefit product, Markov transition.

Peramalan Inflasi Menurut Kelompok Pengeluaran Makanan Jadi, Minuman, Rokok dan Tembakau Menggunakan Model Variasi Kalender (Studi Kasus Inflasi Kota Semarang) Berlian, Amanda Lucky; Wilandari, Yuciana; Yasin, Hasbi
Jurnal Gaussian Vol 3, No 4 (2014): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Inflation is rising prices in general and continuously. Inflationary expenditure groups are divided into seven groups, and one group which spending considerable influence current inflation in Indonesia is by expenditure groups, food, beverages, cigarettes and tobacco. This is because the Indonesian people are very consumptive, especially when it coming to Eid. The movement of the month when Eid occurs once in every three years, so that changes raises a calendar variation. Calendar variation method is a method which modifies the dummy regression models with ARIMA models. In this final project, modeling and forecasting of inflation data by type of expenditure, food, beverages, cigarettes and tobacco in Semarang using variations of the calendar with holidays variation effects due to Eid. Based on the analysis and discussion shows that the best calendar variation model is ARIMA (1,0,0), with the forecasting results shows a significant increase of inflation when the month of Ramadan come.Keywords : inflation, calendar variation, the dummy regression, ARIMA

PERBANDINGAN METODE KLASIFIKASI REGRESI LOGISTIK BINER DAN NAIVE BAYES PADA STATUS PENGGUNA KB DI KOTA TEGAL TAHUN 2014 Rajagukguk, Nanci; Ispriyanti, Dwi; Wilandari, Yuciana
Jurnal Gaussian Vol 4, No 2 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Indonesia is a country that includes having the highest population density in the world.It is because the Indonesian state has a birth rate is so high. One of the efforts to control that population growth can be controlled by using the Keluraga Berencana program. In this study, the method used is the Binary Logistic Regression and Naive Bayes. To perform classification KB User Status in Tegal 2014, the variable used is the wife’s age, the age of first marriage, type of wife’s job, type of husband’s job, wife's education, husband's education, and number of children. The training data comparison testing is 70:30. Based on the research results using binary logistic regression showed that a significant predictor variables that affect the status of keluarga Berencana user are wife’s age, type of wife’s job, and number of children with a classification accuracy of testing data 83.33% .While with the Naive Bayes method obtained classification accuracy of 81.75%. From this analysis it can be concluded that the Binary Logistic Regression method is better than the Naive Bayes in classifying the status of KB users in Tegal 2014. Keywords : Binary Logistic Regression, Naive Bayes, Keluarga Berencana, Classification.

PEMBENTUKAN MODEL LOG LINIER EMPAT DIMENSI (Studi Kasus : Rata-rata Pengguna Jenis Bahan Bakar Minyak berdasarkan Jenis Kendaraan, Rasio Kompresi dan Kapasitas Mesin) Sari, Juli Sekar; Wilandari, Yuciana; Hoyyi, Abdul
Jurnal Gaussian Vol 5, No 3 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Based on the data from the Central Bureau of statistics, Indonesia's population is 237 million, an increase of 15.2% of the total population in 2000. With the increasing of the population from year to year, automatically the growth of vehicles will also experience increased. The impact of the increase in the number of motor vehicles is surely in the form of fuel consumption. Moreover, many factors will consider by the people to choose the type of fuel for their vehicle. Those factors included in the internal and external factors of the vehicle itself. At first, the internal factors in question are the type of vehicle, the compression ratio of the engine, and engine capacity. This research was conducted to find out the relationship between the internal factors with the log-linear Models. Log-linear Model was used to analyze the relationship between the variable responsesthat arewhich formed the contingency table. In this case, the researcher used log-linear Model of four dimensions with the step of analysis, as follows: outlining the possible model with diagram’s association, looking for the grade of frequency estimation of hope of any possible model, examining the Goodness of Fit of each model to find out the significant one, and determining the best model, in this case by looking at the smallest value of AIC. From the log-linear Model four dimensions is obtained the best model is the Model (WX, XY, XZ, YZ YZ) which means in case of this research there is a relationship between the type of fuel (W)*type of vehicle (X), the type of vehicle (X)*the compression ratio of the engine (Y), the type of vehicle (X)*engine capacity (Z), and the compression ratio of the engine (Y)*Engine Capacity(Z), with the value of AIC = -184. Keywords:, Log linear models four dimention, AIC

PEMBENTUKAN KURVA IMBAL HASIL (YIELD) DENGAN MODEL NELSON SIEGEL-SVENSSON (NSS) (Studi Kasus Data Obligasi Pemerintah Periode 27 Oktober 2014 Sampai 31 Oktober 2014) Hutahayan, Eugenia Septri; Widiharih, Tatik; Wilandari, Yuciana
Jurnal Gaussian Vol 4, No 3 (2015): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Medium-term debt to long-term contains a promise from the issuer to pay interest in return for a certain period and repayment of the principal debt at a specified time to the purchaser bonds are called Bonds. A method to determine the relationship between the yield (yield) were obtained with the time to maturity for a particular type of bond at a given time is described by the yield curve (yield curve). One method to describe the yield curve is the Nelson Siegel Svensson. Observed data from the Bursa Efek Indonesia (BEI) that the data of Surat Utang Negara (SUN) with code FR (Fixed Rate). In this case the entire SUN FR with a yield is not empty in the period October 27, 2014 to October 31, 2014. Construction of the yield curve on October 27, 2014, October 28, 2014 and October 30, 2014 to form the normal curve (Positive Yield Curve) while the date October 29, 2014 and October 31, 2014 to form the combined curve between the normal curve (Positive Yield Curve) and negative curves (Inverted Yield Curve).Keywords : bond, the yield curve, Government Securities, Nelson Siegel Svensson.

Co-Authors Abdul Hoyyi Agus Rusgiyono Agustina Sunarwatiningsih Alan Prahutama Amanda Lucky Berlian Ana Kristiana, Ana Arsyil Hendra Saputra Candra Silvia, Candra Chrysmandini Pulung Gumauti, Chrysmandini Pulung Di Asih I Marudani Di Asih I Maruddani Diah Safitri Dwi Ispriyanti Eugenia Septri Hutahayan, Eugenia Septri Hasbi Yasin Hesti Sari Dewi Juli Sekar Sari, Juli Sekar Moch. Abdul Mukid Nanci Rajagukguk, Nanci Noor Afifah Nurhikmah Megawati Pusphita Anna Octaviani Riama Oktaviani Samosir, Riama Oktaviani Rita Rahmawati Sudarno Sudarno Sugito - Tarno Tarno Tatik Widiharih Triastuti Wuryandari Trimono, Trimono Utami, Rianti Siswi Yulia Agnis Sutarno

Title Search

Found 8 Documents Search Journal : Jurnal Gaussian

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 8 Documents
Search
Journal : Jurnal Gaussian