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All Journal Jurnal Gaussian
Yuciana Wilandari
Departemen Statistika, Fakultas Sains dan Matematika, Universitas Diponegoro
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Journal : Jurnal Gaussian

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PEMODELAN SISTEM ANTREAN PELAYANAN BUS JALUR BARAT TERMINAL TIRTONADI KOTA SURAKARTA DENGAN METODE BAYESIAN Nurul Khasanah; Sugito Sugito; Yuciana Wilandari
Jurnal Gaussian Vol 10, No 3 (2021): 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.v10i3.32807

Abstract

Tirtonadi is the largest bus station in Surakarta City. The departure line is devided into two lines, namely west line and east line. The west line serves buses to the west of Surakarta City. The number of buses that enter and leave the station every day causes bus queues. Modeling the queue system and analyzing the system performance measure aims to determine wether the bus service system is good or not. The queue system model is obtained by finding the distribution of arrival patterns and service patterns using the Bayesian method. This method is used because it combines the information from the current research and the prior information from the previous research. The queueing condition of the five lanes in the west line meets steady state conditions because the utility value is less than 1. The queue displant is First Come First Service (FCFS) with unlimited customers and unlimited calling sources. Based on the posterior distribution, the queue system of service bus is (GAMM/IG/1):(GD/∞/∞) for Solo-Jakarta-Bandung lane and Pedesaan lane, while for Solo-Purwokerto-Cilacap, Solo-Yogyakarta, and Solo-Semarang has the queue system (GAMM/GAMM/1):(GD/∞/∞). The queue system of service bus for each lane has good services based on the value of system performance measure. 
ESTIMASI CADANGAN KLAIM MENGGUNAKAN GENERALIZED LINEAR MODEL (GLM) DAN COPULA Yuciana Wilandari; Sri Haryatmi Kartiko; Adhitya Ronnie Effendie
Jurnal Gaussian Vol 9, No 4 (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.v9i4.29260

Abstract

In the articles of this will be discussed regarding the estimated reserves of the claim using the Generalized Linear Model (GLM) and Copula. Copula is a pair function distribution marginal becomes a function of distribution of multivariate. The use of copula regression in this article is to produce estimated reserves of claims. Generalized Linear Model (GLM) used as a marginal model for several lines of business. In research it is used three kinds of line of business that is individual, corporate and professional. The copula used is the Archimedean type of copula, namely Clayton and Gumbel copula. The best copula selection method is done using Akaike Information Criteria (AIC). Maximum Likelihood Estimation (MLE) is used to estimate copula parameters. The copula model used is the Clayton copula as the best copula. The parameter estimation results are used to obtain the estimated reserve value of the claim.
ANALISIS LAJU PERBAIKAN KONDISI KLINIS PASIEN STROKE MENGGUNAKAN REGRESI HAZARD ADITIF LIN-YING (Studi Kasus: Data Pasien Stroke di RSUD Pandan Arang Boyolali Periode Januari 2021 - Agustus 2021) Alfiya Nurwidi Hastuti; Yuciana Wilandari; Sudarno Sudarno
Jurnal Gaussian Vol 11, No 2 (2022): 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.v11i2.35465

Abstract

Additive hazard regression is a survival analysis that is an alternative to Cox proportional hazard regression. The additive hazard models that have been developed include the Aalen additive hazard model and the Lin-Ying. In this study, Lin-Ying additive hazard regression was used as an analytical method to be applied in stroke data that had been hospitalized at Pandan Arang Hospital Boyolali. This method is considered more effective because there is no assumption of proportionality. The purpose of using this method in this study are analyze the characteristics of stroke patients, form a Lin-Ying additive hazard regression model, find out the factors that affect the rate of improvement of the clinical condition of stroke patients, and interpret the model. Based on the analysis that has been done, the average length of hospitalization is 4,471 days ≈ 4 days, and the factors that significantly affect the rate of improvement of clinical conditions in stroke patients at Pandan Arang Hospital Boyolali are blood pressure and blood sugar.
PERBANDINGAN METODE HOLT WINTER’S EXPONENTIAL SMOOTHING DAN EXTREME LEARNING MACHINE UNTUK PERAMALAN JUMLAH BARANG YANG DIMUAT PADA PENERBANGAN DOMESTIK DI BANDARA UTAMA SOEKARNO HATTA Kevin Togos Parningotan Marpaung; Agus Rusgiyono; Yuciana Wilandari
Jurnal Gaussian Vol 11, No 3 (2022): 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.11.3.439-446

Abstract

The loading of goods carried out at the airport is an essential part of the transporting goods system. In this regard, it is necessary to have a prediction to make the right policy or to solve the problems that occur. Holt Winter's Exponential Smoothing, which one of the classic methods of analyzing time series data, and Extreme Learning Machine which is part of the artificial neural network method, are methods that can be used as a tool for forecasting problems. Holt Winter's Exponential Smoothing uses three times of smoothing on related data, which are level smoothing, trend smoothing, and season smoothing, while Extreme Learning Machine goes through three stages, which are normalization, training, and denormalization. In measuring the error rate in related forecasting, the symmetric Mean Absolute Percentage Error (sMAPE) value is used. The Holt Winter's Exponential Smoothing method Additive model produces a sMAPE value of 26.14%; while the Multiplicative model with the same method resulted in the sMAPE value of 25.69%. For the Extreme Learning Machine method, the sMAPE value is 49.85%. Based on the accuracy test using the sMAPE value, Holt Winter's Exponential Smoothing method Multiplicative model is the better method than Extreme Learning Machine
Co-Authors Adhitya Ronnie Effendie, Adhitya Ronnie Agus Rusgiyono Alfiya Nurwidi Hastuti Kevin Togos Parningotan Marpaung Nurul Khasanah Sri Haryatmi Kartiko Sudarno Sudarno Sugito Sugito