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All Journal Media Statistika Jurnal Gaussian
Sudarno Sudarno
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Decision Sciences, Operations Research & Management Other

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HAZARD PROPORTIONAL REGRESSION STUDY TO DETERMINE STROKE RISK FACTORS USING BRESLOW METHOD Sudarno Sudarno; Eri Setiani
MEDIA STATISTIKA Vol 12, No 2 (2019): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (492.514 KB) | DOI: 10.14710/medstat.12.2.200-213

Abstract

Cox proportional hazard regression is a regression model that is often used in survival analysis. Survival analysis is phrase used to describe analysis of data in the form of times from a well-defined time origin until occurrence of some particular be death. In analysis survival sometimes ties are found, namely there are two or more individual that have together event. The objectives of this research are applied Cox proportional hazard regression on ties event using Breslow methodand determine factors that affect survival of stroke patients in Tugurejo Hospital Semarang. The response variable is length of stay at hospital, and the predictors are gender, age, type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, blood sugar levels, and body mass index. The factors cause stroke disease by significant are type of stroke, history of hypertension, systolic blood pressure, diastolic blood pressure, and blood sugar level. By the survivorship function that the patients have been looked after at hospital greater than 20 days, they have probability of healthy be little even go to death. A person in order to be healthy must notice and prevent some factors cause disease.
ANALISIS LAJU PERBAIKAN KONDISI KLINIS PASIEN COVID-19 DENGAN MENGGUNAKAN PENDEKATAN MULTIPLE PERIOD LOGIT (Studi Kasus: Penderita COVID-19 yang Menjalani Rawat Inap di RSUD Depok Pada September 2021) Viona Alliza Diandra Putri; Sudarno Sudarno; Triastuti Wuryandari
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.v11i2.35461

Abstract

Coronavirus Disease-2019, known as Covid-19, is one of infectious diseases that occurred in Wuhan and named as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV 2). This infectious disease is caused by a type of virus groups which can cause disease in animals or humans called Coronavirus. The quality of patient treatment can be seen from time that the patient needs to have clinical improvement and able to get out of the hospital. Survival analysis is a statistical procedure to analyse data with time until a certain event occurs as a response variable One of the methods that can be used is Logit Regression with multiple period logit approach. This research discusses the rate of clinical condition improvement of Covid-19 patients using survival analysis with multiple period logit approach. This logit approach called multiple period logit is used because the predictor variable in this research can change at any time until an event occurs. This research data obtained from medical records at RSUD Depok which are Covid-19 patient data who have been hospitalized in September 2021. The dependent variables consist of the hospitalization length and patient status (cured or censored), while the independent variables consist of age, gender, symptoms, systolic blood pressure, diastolic blood pressure, number of pulse rates, respiration, temperature, saturation, comorbid conditions, and smoking. The data consist of 68 patients which 53 patients go home in better condition. The results of analysis using multiple period logit approach obtained factors that affect the rate of clinical condition improvement of Covid-19 patients, there are age, symptoms, respiration, and congenital disease
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.
PEMODELAN KURS DOLLAR AMERIKA SERIKAT TERHADAP RUPIAH MENGGUNAKAN REGRESI PENALIZED SPLINE DILENGKAPI GUI R Gina Wangsih; Suparti Suparti; 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.v11i2.35469

Abstract

United States Dollar (USD) exchange rate movement against Rupiah is the main guideline for economic actors in making decisions. Exchange rate movement of USD against Rupiah is a time series data. One of the statistical methods that can be used for modelling time series data is ARIMA. ARIMA method data must be stationery and residuals must be normally distributed, independent, and constant variance, which means an alternative model is needed so that it is not bound by any assumptions, namely a nonparametric penalized spline regression model. Selling rate data of USD against Rupiah is modeled using nonparametric penalized spline regression because the assumptions in the ARIMA model are not fulfilled. Penalized spline regression modeling is using full search algorithm in determining knot points. Lambda values are tested from 0 to 100000 on order 2, 3, and 4. Optimal penalized spline model is a model with minimum GCV value. R GUI facilitate the process of selecting the best model. Data is divided into 2 parts, namely in sample data for model formation and out sample data for evaluating the best model performance based on MAPE value. Penalized spline regression modeling produces the best model, namely optimal penalized spline model with minimum GCV value achieved on 3rd order with 35 knot points and lambda value = 2007. 96,20% value of R Squared model indicates the model is a strong model. In the evaluation of the best model, the MAPE data out sample value is 0.65%. MAPE value indicates the model has very good forecasting ability.
PEMODELAN FAKTOR EKONOMI MAKRO TERHADAP HARGA SAHAM TELKOM MENGGUNAKAN REGRESI SPLINE TRUNCATED MULTIVARIABEL DILENGKAPI GUI R Lulu Maulatus Saidah; Suparti Suparti; Sudarno Sudarno
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.11.3.344-354

Abstract

Stock prices are an important thing that investors should know before investing. Volatile stock prices require investors to know the factors that influence their changes. Stock price instability makes it very difficult for investors to make investments and affects the integrity they get. One of the factors that affect stock prices is macroeconomic factors consisting of rupiah exchange rate (X1), inflation (X2), and SBI interest rate (X3). A statistical method that can be used to model fluctuating data is spline nonparametric regression. This study aims to model macroeconomic factors against Telkom's stock price using multivariable truncated spline nonparametric regression with optimal knot point selection methods that minimize Generalized Cross Validation (GCV). Many knots used are a combination of 1 and 2 and the order used is a combination of 2, 3, and 4. The best multivariable truncated spline model is achieved on a knot combination (2,2,2) with the order X1, X2, X3 being 3, 2, 2 which results in an R2 value of 92.71% included in the strong model criteria. In the evaluation of model performance obtained a MAPE value of 1.857% which shows the model has excellent forecasting ability. In this study, a Graphical User Interface (GUI) program was formed R that can facilitate data analysis and produce more attractive display output.
ANALISIS SENTIMEN PENERAPAN PPKM PADA TWITTER MENGGUNAKAN NAÏVE BAYES CLASSIFIER DENGAN SELEKSI FITUR CHI-SQUARE Pualam Wahyu Ratiasasadara; Sudarno Sudarno; Tarno Tarno
Jurnal Gaussian Vol 11, No 4 (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.11.4.580-590

Abstract

Dissemination of information related to the implementation of PPKM takes place very quickly, especially on social media networks. Positive and negative news certainly has an impact on public opinion or sentiment on the implementation of PPKM. Sentiment analysis is needed to determine behavior or opinions in the form of reviews, ratings, or tendencies of the author towards a particular topic. In this study, the data used is public opinion on Twitter social media with the keyword "PPKM" from November 2, 2021 to November 8, 2021 and obtained data as many as 12,616 tweets which then deleted duplicate data to become 6,465 data. Data classification was performed using Naïve Bayes with Chi-Square feature selection and the data were classified into positive and negative classes. The results of the classification performance using Nave Bayes with Chi-Square feature selection obtained an accuracy of 83% which means that the Nave Bayes classification model with Chi-Square feature selection is quite effective in classifying public opinion on the implementation of PPKM.
PENGUKURAN NILAI RISIKO PORTOFOLIO SAHAM PADA INDEKS LQ45 DI BIDANG TELEKOMUNIKASI MENGUNAKAN METODE KOPULA CLAYTON Salsabila Syifa Binsanno; Sudarno Sudarno; Di Asih I Maruddani
Jurnal Gaussian Vol 12, No 1 (2023): 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.12.1.81-91

Abstract

The characteristic of copula is non strict on certain distribution assumptions, can explain nonlinier relationship, and easily construct distribution through the marginals that do not need to come from the same distribution family. Copula will be useful for stock data that has price charts fluctuate rapidly and risk will always follow in investing. The relation between risk and copula in this study is to calculate the risk value in the stock portfolio using VaR with the generation of Monte Carlo simulation through Clayton copula on four companies engaged in telecommunications sector, namely EXCL.JK (PT XL Axiata Tbk), TLKM.JK (PT Telekomunikasi Indonesia Tbk), TOWR.JK (PT Sarana Menara Nusantara Tbk), and TBIG.JK (PT Tower Bersama Infrastructure Tbk) for period 2 January 2020 to 31 December 2021. This study resulted that the selected stock portfolios are EXCL and TBIG which had the highest risk value of -0,062741 at 99% confidence level, so when an investor will invest Rp100.000.000,00 the maximum estimated risk is Rp.6.274.100 within one day.
PEMODELAN HYBRID ARIMA-ANFIS UNTUK DATA PRODUKSI TANAMAN HORTIKULTURA DI JAWA TENGAH Tarno Tarno; Agus Rusgiyono; Budi Warsito; Sudarno Sudarno; Dwi Ispriyanti
MEDIA STATISTIKA Vol 11, No 1 (2018): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (506.342 KB) | DOI: 10.14710/medstat.11.1.65-78

Abstract

The research purpose is modeling adaptive neuro fuzzy inference system (ANFIS) combined with autoregressive integrated moving average (ARIMA) for time series data. The main topic is application of Lagrange Multiplier (LM) test for input selection, determining the number of membership function and generating rules in ANFIS. Based on partial autocorrelation (PACF) plot, the lag inputs which are thought have an effect to data are evaluated by using LM-test. Procedure of LM test is applied to determine the optimal number of membership functions. Based on the result, a number of rule-bases are generated. The best model is applied for forecasting potato production data in Central Java. The case study of this research is modeling monthly data of potato production from January 2004 up to December 2016. From empirical study, ANFIS optimal was obtained with lag-1 and lag-11 as inputs with two membership functions and two fuzzy rules. The hybrid method based on ARIMA and ANFIS is also implemented. The result of the prediction with a hybrid method is compared to the ANFIS and ARIMA. Based on the value of Mean Absolute Percentage Error (MAPE), hybrid model ARIMA-ANFIS has a good performance as a model of ANFIS and ARIMA individually.Keywords: Time Series, Potato production, hybrid, ANFIS, ARIMA, LM-test
Co-Authors Abdul Hoyyi Adestya Ayu Maharani Affan Hanafaie Agus Rusgiyono Ahmad Badruttamam Alan Prahutama Alfiya Nurwidi Hastuti Alifia Hanifah Mumtaz Aprilia Sekar Khinanti Asri Lutfia Silmi Bimbi Ardhana Rizky Bramadita Kunni Fauziyyah Budi Warsito Chrisentia Widya Ardianti Dea Manuella Widodo Deanira Qinanty Alamsyah Di Asih I Maruddani Diah Safitri Dwi Ispriyanti Dwi Nooriqfina Eko Siswanto Enggartya Andini Eri Setiani Farhah Izzatul Jannah Febriana Sulistya Pratiwi Gina Wangsih Hasbi Yasin Helmi Panjaitan Inarotul Amani Rizki Ananda Lulu Maulatus Saidah Mochammad Iffan Zulfiandri Mustafid Mustafid Nathasa Erdya Kristy Pualam Wahyu Ratiasasadara Puspita Kartikasari Reno Penggalih Surya Wardhani Rita Rahmawati Rizka Nur Faizah Rudi Saputro Setyo Purnomo Rukun Santoso Salsabila Syifa Binsanno Sang Nur Cahya Widiutama Silvy ‘Aina Salsabila Sintia Rizki Aprilianti Sola Fide Sugito Sugito Suparti Suparti Syavhana Yusricha Zuhri Putri Tarno Tarno Tatik Widiharih Triastuti Wuryandari Viona Alliza Diandra Putri Widi Rahayu Yuciana Wilandari