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Respatiwulan Respatiwulan
Universitas Sebelas Maret
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Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier Lingga Aji Andika; Pratiwi Amalia Nur Azizah; Respatiwulan Respatiwulan
Indonesian Journal of Applied Statistics Vol 2, No 1 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i1.29998

Abstract

Indonesia is one of the countries that adheres to a democratic system. In the course of a democratic system it is marked by periodic general elections. In 2019 Indonesia held a general election simultaneously to elect the President, DPR, DPRD and DPD. After the election, a lot of opinion arise within the community, including on social media twitter. One of the topics discussed was the results of the quick count of the presidential election. Therefore, a method that can be used to analyze sentiment from the quick count opinion is needed, that is naive Bayes method. The aims of this study are to find the best naive Bayes model and to classify sentiments. The result shows the best accuracy of 82.90% with α = 0.05. The classification obtained is 34.5% (471) positive tweets and 65.5% (895) negative tweets on the results of the quick count.Keywords : sentiment analysis, naive Bayes classifier, elections, quick count
Analisis Faktor Indeks Harga Konsumen Kota Semarang Novia Nafisah; Respatiwulan Respatiwulan
Indonesian Journal of Applied Statistics Vol 2, No 2 (2019)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v2i2.34903

Abstract

The Consumer Price Index (CPI) can describe consumption patterns in the community. The CPI is also used to calculate inflation rates that reflect a country's economic conditions. The CPI for sub-expenditure consists of 7 groups divided into 35 sub-groups. Factor analysis on CPI was conducted to reduce variables, to identify underlying factors, and to classify variables in the Semarang City CPI expenditure group from January 2014 to August 2017. As the result, there is only one underlying factor, namely the primary needs of urban communities with cumulative variance value of 88.509%, eigenvalues of 23.012 consisting of 27 subgroup variables.Keywords : Consumer Price Index (CPI), factor analysis, eigen value
Model Penyebaran Penyakit SIR Tipe Rantai Binomial dengan Kontak Random dan Waktu Penyembuhan Bernilai Tak Hingga Ilham Asyifa Maulana Rosyid; Respatiwulan Respatiwulan; Sri Sulistijowati Handajani
Indonesian Journal of Applied Statistics Vol 3, No 2 (2020)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v3i2.44307

Abstract

Susceptible-Infected-Recovered (SIR) epidemic model is an epidemic model that illustrates the pattern of disease spread with the characteristics of individuals who have recovered cannot be re-infected and have a permanent immune system. The binomial chain type epidemic model assumes that infection spreads in discrete time units and the number of the infected individuals follows a binomial distribution. This research aims to discuss  binomial chain type SIR epidemic model by simulating the model. The transition probability depends on  the number of infected individuals in the period   the number of individuals encountered, and  the transmission probability. This model also assumes an infinite recovery time ( = ∞). This situation illustrates that infected individuals remain contagious during the period of spread of the disease. This situation can arise when the causative agent of the disease has a long life. Then simulations are performed by giving different transmission probability  The results show that the greater transmission probability will cause the probability of a new individual being infected in the next period to be greater.Keywords : SIR epidemic model, binomial chain, infinite recovery time
Model Simulation of Continuous Time Markov Chain Susceptible Infected Recovered-Bacterial Population for Cholera Disease Aulia Maulani Syifa Nur Hidayati; Respatiwulan Respatiwulan; Sri Subanti
Indonesian Journal of Applied Statistics Vol 6, No 1 (2023)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijas.v6i1.71801

Abstract

Epidemic is an outbreak of an infectious disease rapidly in a population at a certain place and time. Epidemic models are used to explains the spread pattern of disease. The continuous time Markov chain susceptible infected recovered-bacterial population in the aquatic reservoir (CTMC SIR-B) model is a stochastic model, which considers the effect of bacterial population. The human population are classified into 3 groups. There are susceptible, infected, and recovered groups. Then, there are bacterial population which can infectious the cholera disease to human. CTMC SIR-B model considers treatment and water sanitation parameters. The spread of cholera disease can be modeled as CTMC SIR-B. Cholera is an acute intestinal infectious disease caused by the bacterium Vibrio cholerae. Cholera can be transmitted through the human digestive system. The symptoms of cholera disease are diarrhea, vomiting, and dehydration. The dehydration if not handled properly, may cause death. The aims of this research are to build and simulate the CTMC SIR-B model for cholera disease. The result of the model simulation shows that there is no significant difference between various values of treatment and water sanitation parameters. The pattern of the cholera disease spread describes that the transmission of cholera can occur from human to human even though there is no population of bacteria in the aquatic reservoir.Keywords: cholera; ctmc sir-b; epidemic model; stochastic. 
Co-Authors Aulia Maulani Syifa Nur Hidayati Handajani, Sri Sulistijowati Ilham Asyifa Maulana Rosyid Lingga Aji Andika Novia Nafisah Pratiwi Amalia Nur Azizah Sri Subanti