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All Journal Media Statistika MATEMATIKA Jurnal Gaussian

Triastuti Wuryandari

Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro

Author-ID : 420696

Decision Sciences, Operations Research & Management Mathematics Other

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PEMODELAN REGRESI 3-LEVEL DENGAN METODE ITERATIVE GENERALIZED LEAST SQUARE (IGLS) (Studi Kasus: Lamanya pendidikan Anak di Kabupaten Semarang) Amanda Devi Paramitha; Suparti Suparti; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

In a research, data was used often hierarchical structure. Hierarchical data is data obtained through multistage sampling from a population with independent variables can be defined within each level and dependent variable can be defined at the lowest level. One analysis that can be used for data with a hierarchical structure is a multilevel regression analysis. The purpose of this final three-level regression analyzes to establish regression models about the length of a child's education in the District of Semarang where the individual level-1 with a factor of gender, lodged at the family level-2 by a factor of the length of father's education and duration of maternal education and nesting on the environment level-3 with factor of residence, number of elementary school the large number of junior high school and the large number of high school. Parameter estimation in 3-level regression models can use several methods, one of which is a method of Iterative Generalized Least Square (IGLS). Of cases the length of education in the district of Semarang indicate that factors influencing factor is the length of father's education and the duration of the mother's education. Keywords : Hierarchical structure, multistage sampling, multilevel regression, Iterative Generalized Least Square.

ANALISIS KLASIFIKASI NASABAH KREDIT MENGGUNAKAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) Desy Ratnaningrum; Moch. Abdul Mukid; Triastuti Wuryandari
Jurnal Gaussian Vol 5, No 1 (2016): Jurnal Gaussian
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Credit is one of the facilities provided by banks to lend money to someone or a business entity within the prescribed period. The smooth repayment of credit is essential for the bank because it influences the performance as well as its presence in daily life. Acceptance of prospective credit customers should be considered to minimize the occurrence of bad credit. Classification and Regression Trees (CART) is a statistical method that can be used to identify potency of credit customer status such as current credit and bad credit. The predictor variables used in this study are gender, age, marital status, number of children, occupation, income, tenor / period, and home ownership. To improve the stability and accuracy of the prediction were used the Bootstrap Aggregating Classification and Regression Trees (Bagging CART) method. The classification of credit customers using Bagging CART gives the classification accuracy 81,44%. Key words : Credit, Bootstrap Aggregating Classification and Regression Trees (Bagging CART), Classification Accuracy

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

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 SURVIVAL PADA DATA KEJADIAN BERULANG MENGGUNAKAN PENDEKATAN COUNTING PROCESS Ulya Tsaniya; Triastuti Wuryandari; Dwi Ispriyanti
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.11.3.377-385

Asthma is a disorder that attacks the respiratory tract and causes bronchial hyperactivity to various stimuli characterized by recurrent episodic symptoms such as wheezing, coughing, shortness of breath, and heaviness in the chest. Asthma sufferers will experience exacerbations, namely episodes of asthma recurrence which gradually worsens progressively accompanied by the same symptoms. The length of time a person experiences an exacerbation can be influenced by various factors. To analyze this, the Cox regression model can be used which is within the scope of survival analysis where time is the dependent variable. In the survival analysis, asthma exacerbations were identical/recurrent events where the individual experienced the event more than once during the study. If the survival data contains identical/recurrent events, the analysis uses a counting process approach. Counting Process is an approach used to deal with survival data with identical recurrent events, meaning that recurrences are caused by the same thing, which in this case is the narrowing of the bronchioles in asthmatics. The purpose of this study was to determine the factors that cause asthma exacerbations by using a counting process approach as a data treatment for recurrent events at Diponegoro National Hospital. Based on the results of the analysis, the factors that influence the length of time a patient experiences an exacerbation are the age, gender, and type of cases

IMPLEMENTASI ALGORITMA K-MEDOIDS DAN K-ERROR UNTUK PENGELOMPOKAN KABUPATEN/KOTA DI PROVINSI JAWA TENGAH BERDASARKAN JUMLAH PRODUKSI PETERNAKAN TAHUN 2020 Fahrur Rozzi Iskak; Iut Tri Utami; Triastuti Wuryandari
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.11.3.366-376

The livestock sub-sector is one of the sub-sectors that contribute to the national economy and can significantly absorb labour so that it can be relied upon in efforts to improve the national economy. One of the steps used to increase livestock production in each region in Central Java Province is regional mapping. Cluster analysis is one of the regional mapping methods that can increase livestock production by grouping regencies/cities with characteristics of the same level of livestock production based on the type of livestock production. The k-error and k-medoids method is a non-hierarchical cluster analysis method, where the k-error is a method developed to overcome the problem of data measurement errors in classical cluster analysis, while the k-medoids is a method used to overcome the problem of outliers contained in the data. The validity test of the standard deviation ratio and the WB Index was used to determine the quality of the clustering results. The small validity value of the standard deviation ratio and the WB Index shows the best results of clustering and selecting method. Based on the results of the clustering, the optimal cluster was obtained at k=7 using the k-medoids algorithm, where the validation value of the standard deviation ratio=0.773 and WB Index=0.531.

PERAMALAN PENDAPATAN BULANAN MENGGUNAKAN FUZZY TIME SERIES CHEN ORDE TINGGI Muhammad Rizky Yuliyanto; Triastuti Wuryandari; Iut Tri Utami
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.12.1.61-70

Cooperatives need consideration in the making of business strategy decisions. Forecasting can assist cooperatives in deciding on their business strategy. This study used n-orde Fuzzy Time Series Chen. n-orde Fuzzy Time Series Chen captures data patterns formed by two or more historical data in each period called fuzzy logic relation (FLR). The pattern of FLR is used to be projected in forecasting future conditions. This study used 2-orde, 3-orde, and 4-orde with 1-orde as the comparison. This study used data on the monthly revenue of the Employee Cooperative of PT. Telekomunikasi Indonesia Semarang Region for the period of January 2019 to May 2022 to predict revenue for the period of June and July 2022. This study used symmetric Mean Absolute Percentage Error (sMAPE) in calculating the forecasting error rate. 1-orde, 2-orde, 3-orde, and 4-orde of Fuzzy Time Series Chen produced different forecasting results for the period of June and July 2022. 1-orde has sMAPE value of 23.15% (good enough forecasting), 2-orde and 3-orde have sMAPE value of 10.06% (good forecasting), and 4-orde has sMAPE value of 4.52% (very good forecasting). This study showed that the larger orde used in Fuzzy Time Series Chen, the lower forecasting error rate.

PERBANDINGAN ANALISIS SURVIVAL MENGGUNAKAN REGRESI COX PROPORTIONAL HAZARD DAN REGRESI WEIBULL PADA PASIEN COVID-19 DI RSUD TAMAN HUSADA BONTANG Damayanti, Sindi; Wuryandari, Triastuti; Sudarno, Sudarno
Jurnal Gaussian Vol 12, No 3 (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.12.3.453-464

COVID-19 is brought on by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and transmitted to humans through animal. SARS-CoV-2 infection affects patient's metabolism and causes hyperinflammatory. This condition affects individuals with risk factors such as age, gender, diabetes, heart disease, hypertension, Chronic Obstructive Pulmonary Disease (COPD), obesity, and Acute Respiratory Distress Syndrome (ARDS). One approach to figuring out the association between the time of an occurrence and the independent factors is the Cox Proportional Hazard Regression. The Cox PH regression is a semiparametric model because it doesn’t require a specific distribution test. There is a parametric model used in modeling and analyzing failure time data, namely Weibull regression. The case study is patients with COVID-19 at Taman Husada Bontang Regional Public Hospital who underwent hospitalization from August 2021 to September 2021 data. Based on the Cox PH Regression and Weibull Regression models, variables that affect the survival time of COVID-19 patients are heart disease and ARDS. The AIC value obtained using the Cox Proportional Hazard regression is 635.6149, this value is smaller than the Weibull regression which is 745.5509 so the use of survival analysis with the Cox Proportional Hazard regression is better than the Weibull regression in this case.

PENERAPAN REGRESI COX PROPORTIONAL HAZARD PADA KEJADIAN BERSAMA (TIES) DENGAN METODE BRESLOW, EFRON, DAN EXACT Zega, Nesty Novita Sari; Mustafid, Mustafid; Wuryandari, Triastuti
Jurnal Gaussian Vol 12, No 4 (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.12.4.520-530

Dengue Hemorrhagic Fever (DHF) is a contagious disease that continues to be public health concern. This disease can cause death in a short time and often causes an epidemic. Semarang city has a high number of deaths due to DHF. Reducing the mortality rate due to DHF can be done by knowing the factors that affect the patient's recovery rate. Cox proportional hazard regression is a method of survival analysis that represents the relationship between the independent variable and the dependent variable in the form of survival time. This study examined hospitalized DHF patients at RSI Sultan Agung Semarang. The data contains ties, so parameter estimation is carried out using the Breslow, Efron, and Exact methods. These three methods have different levels of computational intensity and size of data ties, so these three methods will be used in this study to determine the most appropriate method for handling DHF data ties at RSI Sultan Agung Semarang. the analysis reveals that the Cox proportional hazards regression model with the Exact method is the most suitable method for handling ties and the recovery rate of DHF patients is affected by age, platelets, and hemoglobin category.

PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON Ridho, Wahyu Anwar; Wuryandari, Triastuti; Hakim, Arief Rachman
Jurnal Gaussian Vol 12, No 3 (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.12.3.372-381

The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.

PERAMALAN INDEKS HARGA SAHAM GABUNGAN (IHSG) MENGGUNAKAN MODEL INTERVENSI FUNGSI PULSE Rosilawati, Elsa Dwi; Tarno, Tarno; Wuryandari, Triastuti
Jurnal Gaussian Vol 12, No 3 (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.12.3.382-391

The intervention model is one model that is frequently used to explain how interventions from both internal and external sources can lead to dramatic fluctuations in a time series of data. The Composite Stock Price Index, known as the IDX Composite, is an index that tracks all stock price performance. For the Composite Stock Price Index from 2 October 2020 to 6 June 2022, daily close price data are used in this study. The data showed a sharp reduction starting on 9 May 2020 (T=386) and lasting for the following 4 days, which made the pulse function the likely intervention model. Rising interest rates and high inflation figures from the United States are to blame for the drop in IDX Composite close price. In addition, a lot of profit-taking was done because of the Eid holidays and the expectation of a substantial increase in COVID-19. The best intervention model created is ARIMA ([3],1,0) with an intervention order of b=0, r=0, and s=11, which can then be used to forecast Composite Stock Price Index for the following period. This is based on the outcomes and analyses. The sMAPE value in the research utilizing this model was 0.98%, suggesting very strong forecasting capabilities.

Co-Authors Abdul Hoyyi Abdur Rofiq Agung Waluyo Agus Rusgiyono Alin Citra Suardi Amanda Devi Paramitha Anes Desduana Selasakmida Annisa Rahmawati Aprilia Sekar Khinanti Arief Rachman Hakim Damayanti, Sindi Danardono Danardono Desy Ratnaningrum Desy Ratnaningrum, Desy Dewi Setya Kusumawardani Dwi Ispriyanti DWI RAHMAWATI Etik Setyowati, Etik Fahra Pracendi Astrelita, Fahra Pracendi Fahrur Rozzi Iskak Faiqotul Himmah Fitri Juniaty Simatupang, Fitri Juniaty Hanik Rosyidah, Hanik Iut Tri Utami Moch. Abdul Mukid Muhammad Rizky Yuliyanto Mustafid Mustafid Noor Afifah Oktaviana Prayudhani Pritha Sekar Wijayanti Restu Sri Rahayu, Restu Ridho, Wahyu Anwar Rita Rahmawati Rosilawati, Elsa Dwi Sayekti Dewi Anggraini Sri Haryatmi Kartiko Sudarno Sudarno Sudarno Sudarno Sugito - Sugito Sugito Suparti Suparti Tarno Tarno Tarno Tarno Tatik Widiharih Tuan Hanni Ulya Tsaniya Viona Alliza Diandra Putri Yogi Isna Hartanto Yuciana Wilandari Zega, Nesty Novita Sari

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