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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Faktor Exacta Jurnal Matematika UNAND Jurnal Varian Inferensi ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Aplikasinya JAMBURA JOURNAL OF PROBABILITY AND STATISTICS PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS Indonesian Journal of Statistics and Its Applications
Siswanto Siswanto
Department Of Statistics, Faculty Of Mathematics And Natural Sciences, Hasanuddin University, Indonesia
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Social Sciences Other

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MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER ADHIYAKSA PRANANDA; Siswanto Siswanto; Sri Astuti Thamrin; A. Muh. Amil Siddik
Jurnal Matematika UNAND Vol 13, No 2 (2024)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.13.2.131-141.2024

Abstract

In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus. Indonesia is one of the countries that is aggressively implementing the COVID-19 vaccination. The vaccination process which has been carried out from February 2021 until the end of 2021 has covered approximately 160 million people or 76.83% of the target set by the government. Vaccine recipients have criteria to be able to get vaccinated to avoid side effects or complications. So it is necessary to classify groups that can receive vaccines and also delay vaccination. This research aims to determine the performance of the learning vector quantization classification method. Learning vector quantization method classification produces 95% accuracy, 97% precision, and 96% sensitivity. From these performance measurements, it can be concluded that the learning vector quantization method is very good and can be used in the classification of COVID-19 vaccination recipients at the Parumpanai Public Health Center, East Luwu Regency.
Modeling Determinants of Composite Stock Price Index Based on Multivariable Nonparametric Penalized Spline Regression Model alized Spline Dhita Hartanti Octavia; Asma Auliarani; Siswanto Siswanto; Anisa Kalondeng
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 3 (2024): May 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i3.32145

Abstract

The Composite Stock Price Index (IHSG) is a critical indicator in the Indonesian capital market, playing a central role as one of the key instruments influencing the dynamics of a country's economy. Modeling IHSG can provide a substantial contribution to stakeholders in the capital market, facilitating investment decision-making. Therefore, it is essential to obtain accurate and responsive estimates for IHSG data. The IHSG data used covers the period from January 2020 to December 2022 and tends to be fluctuating. Hence, a spline regression analysis with effective penalized spline estimation is applied to overcome the limitations of assumptions in the relationship between variables. The variables used in the modeling include inflation, exchange rates, interest rates, and IDJ. From the analysis results, optimal values based on the minimum GCV for each variable are sequentially 0.278, 0.904, 0.751, and 0.665. It is also known that these four variables collectively have a 92.1% influence, with inflation having varied impacts, exchange rates exhibiting a stronger negative effect at certain levels, interest rates showing opposite effects depending on their levels, and IDJ having a positive effect on IHSG movements. The significant variability of these impacts indicates that these variables make important contributions. In other words, IHSG fluctuations can be explained by variations in the values of inflation, exchange rates, interest rates, and IDJ.
Path Analysis of Influence of Economic and Social Factors on the Human Development Index in South Sulawesi in 2022 Anni Ivoni Parapa; Clarisa Eudia Chesynanda; Siswanto Siswanto; Anisa Kalondeng
Jurnal Matematika, Statistika dan Komputasi Vol. 20 No. 2 (2024): JANUARY 2024
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v20i2.32147

Abstract

The Human Development Index (HDI) serves as an indicator for assessing socio-economic development in a region. Each area strives to improve its HDI by considering the factors that influence it in that specific region. This research aims to identify the direct and indirect influences of economic and social factors, such as Life Expectancy (LE), Gross Regional Domestic Product per capita (GRDPpc), Labor Force Participation Rate (LFPR) through Average Years of Schooling (AYS) on the HDI in South Sulawesi in 2022. The data used in this study are secondary data obtained from the Central Statistics Agency (BPS) of South Sulawesi Province in 2022. The method applied in this research is a path analysis that examines the relationships between variables, both direct and indirect influences. The research results show that in the equation of sub-structure 1, LE and GRDP per capita ADHB have a direct influence on AYS, while LFPR does not have a direct impact on AYS. The magnitude of the influence of variables in sub-structure 1 is 53%. In the equation of sub-structure 2, LE, GRDP per capita ADHB, LFPR, and AYS have a significant direct impact on HDI. Additionally, LE and GRDP per capita ADHB have an indirect influence through AYS on HDI. The magnitude of the influence of variables in sub-structure 2 is 93.5%. Therefore, the variables that have both direct and indirect effects on HDI through AYS are LE and GRDP per capita ADHB.
Naive Bayes Algorithm with Feature Selection Using Particle Swarm Optimization Siswanto Siswanto; Iwan Kurniawan; Sri Astuti Thamrin
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.2409

Abstract

The COVID-19 vaccine in Indonesia has led to the emergence of public opinion which is conveyed on social media such as Twitter. One of the analyses that can be done to produce various information from public opinion is sentiment analysis. Sentiment analysis is used to determine whether an opinion tends to be positive or negative. This study aims to classify the public opinion of the COVID-19 vaccine in Indonesia with sentiment analysis and to visualize the location of the sentiment of the COVID-19 vaccine tweet data in Indonesia. To achieve this aim, the Naïve Bayes algorithm with Particle Swarm Optimization (PSO) feature selection was used. This study uses opinions into positive and negative class sentiments towards 2,547 tweets related to the COVID-19 vaccine in Indonesia from January to June 2021. The results show that the distribution of positive and negative class sentiments is 2,328 and 219, respectively. In addition, the positive sentiment for the COVID-19 vaccine was dominated by people on the island of Java based on a random number matrix initialized by the PSO method. The classification of public opinion on Twitter media provides accurate and optimal performance results using a combination of the Naïve Bayes algorithm with PSO feature selection. The results of the combination of these methods have accuracy and F1 score values of 91.28% and 95.38%, respectively. The visualization of geo-spatial mapping showed that positive sentiments related to the COVID-19 vaccine exist in almost all regions in Indonesia but are dominated by the Jabodetabek area.
Improved Chi Square Automatic Interaction Detection on Students Discontinuation to Secondary School Fadhil Al Anshory; Siswanto Siswanto; Sri Astuti Thamrin; Ika Inayah
Jurnal Varian Vol 7 No 1 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i1.2627

Abstract

Improved Chi Square Automatic Interaction Detection (CHAID) with bias correction is the development of the CHAID method by relying on Tschuprow's T test calculations with bias correction in the process of forming a classification tree. This study aims to obtain a classification of factors which influence students for not continuing their education from junior high school or equivalent to high school or equivalent. The results obtained in the classification tree produce nine classifications. Based on the results of the classification tree, the classification of students who do not continue their education to high school or equivalent is: students with disabilities who do not have access to Information and Communication Technology (ICTs) (0.89); students who work without disability but do not have access to ICTs (0.73); and students who do not work without disability but do not have access to in ICTs (0.60). Based on the classification obtained the factors which influence students for not continuing their education to high school or equivalent are access to ICTs, employment status, and persons with disabilities. The classification accuracy of the results uses the Improved-CHAID method with bias correction with a proportion of 80% training data and 20% testing data, namely 72.3033% on training data and an increase of 73.3300% on testing data.
Comparison of Naive Bayes Classification Methods Without and With Kernel Density Estimation Agus Hermawan; Siswanto Siswanto; Andi Kresna Jaya
Jurnal Varian Vol 7 No 2 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v7i2.3199

Abstract

Halal certification is important to give confidence to Muslim consumers around the world regarding the halalness of products. The Halal Product Assurance Organizing Body (BPJPH) is the official auditor in Indonesia that is responsible for the halal certification process. This study aims to address the need for verification and validation of data for halal certification applications in Indonesia by using the data science approach and machine learning technology. In this study, the Naïve Bayes classification method was used to optimize the data verification and validation process. However, this method needs to be improved by applying optimization methods such as Kernel Density Estimation (KDE) to improve classification results. The results showed that the Naïve Bayes classification method with KDE optimization produced better performance than the Naïve Bayes method without optimization. The performance of the Naïve Bayes classification model without optimization achieves 87.6% Accuracy, 85.4% Recall, 88.8% Precision, and 87.1% Fmeasure. Meanwhile, the Naïve Bayes classification model with KDE optimization achieves 97.5% Accuracy, 95.9% Recall, 98.9% Precision, and 97.8% Fmeasure. Thus, it can be concluded that the Naïve Bayes classification algorithm with KDE optimization results in a performance increase of 9.9% compared to the Naïve Bayes method without optimization. This research has important implications in handling complex and non-normally distributed data and providing solutions for BPJPH in the process of verifying halal certification.
Peramalan Nilai Tukar Rupiah Terhadap Dolar Singapura dengan Pendekatan Average Based Fuzzy Time Series Markov Chain Rahmah, Syifa Ur; Putri, Ayu Pratika; Siswanto, Siswanto; Kalondeng, Anisa
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.21164

Abstract

Exchange rates, representing a country's currency value in terms of another, signify currency relationships between nations. Indonesia's strong economic ties with Singapore see the Singapore Dollar boasting the highest exchange rate against the Indonesian Rupiah in Asia. The Rupiah-Singapore Dollar exchange rate is marked by fluctuations, necessitating precise forecasts. One effective forecasting method is the average-based Fuzzy Time Series (FTS) Markov Chain. This method calculates intervals based on averages and leverages the Markov Chain concept, employing a transition probability matrix to enhance accuracy. The average-based FTS Markov Chain predicts the Rupiah-Singapore Dollar exchange rate from May 16, 2023, to October 13, 2023, delivering an impressively low Mean Absolute Percentage Error (MAPE) of 0.3642%. Notably, the forecast for October 14, 2023, is 11.583.73. Consistently, this method, blending interval formation through FTS and probability transition matrix from the Markov Chain, provides reliable forecasts. These insights are invaluable for decision-makers, empowering them to proactively address potential fluctuations that might contribute to inflationary pressures on Indonesia's economy.
Pendekatan Minimum Variance Quadratic Unbiased Estimation dalam Analisis Regresi Data Panel dengan Pendugaan Komponen Galat Dua Arah Menggunakan Metode Biggers Andi Atirah Arumtiwi; Raupong Raupong; Siswanto Siswanto
Jurnal Matematika, Statistika dan Komputasi Vol. 21 No. 2 (2025): JANUARY 2025
Publisher : Department of Mathematics, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/j.v21i2.37325

Abstract

Panel data that have missing observations can be known as incomplete panel data. The model used is a two-way error component. The missing data estimation used is the Biggers method. This study aims to model the incomplete panel data regression of two-way error components on Manufacturing Company Stock Return data. The method used for estimating the error variance component is Minimum Variance Quadratic Unbiased Estimation (MIVQUE) with parameter estimation using Maximum Likelihood (ML). The method was applied to IDX data for 10 companies from 2014-2021. The results obtained using the MIVQUE method are σ ̂_v^2= 0.1142, σ ̂_μ^2=-0.0107, and σ ̂_λ^2=0.0068, for the ML method produces β ̂_0=0.0304719 〖 β ̂〗_1= -0.021107, and β ̂_2=0.0087936. Based on these methods, if there is an increase in the Debt to Equity Ratio, there is a decrease in the value of stock returns, and vice versa for Net Profit Margin.
Topic Modelling of Merdeka Belajar Kampus Merdeka Policy Using Latent Dirichlet Allocation Thamrin, Sri Astuti; Rezki, Nurul; Siswanto, Siswanto
Inferensi Vol 7, No 3 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i3.20602

Abstract

Topic modeling is the process of representing the topics discussed in text documents. In the current era of internet technology development, digital data is growing increasingly large, including tweet data from Twitter. This research aims to obtain topic modeling related to the Merdeka Belajar Kampus Merdeka policy on Twitter, which has been classified into positive and negative sentiments. The topic modeling method used is Latent Dirichlet Allocation (LDA). This method is for summarizing, clustering, connecting, or processing data from a list of topics. The data used in this research are tweets with the keyword "Kampus Merdeka" uploaded on Twitter. A total of 1579 tweets with these keywords were classified into 648 tweets and 931 tweets, respectively, with positive and negative sentiments. Each tweet with positive and negative sentiment produces 5 topics with parameter values α and β of 0.1. The coherence value in topic modeling for tweets with a positive sentiment (0.44) is more significant than for tweets with a negative sentiment (0.38) and represent for drawing conclusions about topics based on relationship between keywords in negative sentiment is more challenging compared to those in positive sentiment to the Merdeka Belajar Kampus Merdeka policy on Twitter.
PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL Siswanto Siswanto; Sri Astuti Thamrin
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v4i3.681

Abstract

In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.
Co-Authors A. Muh. Amil Siddik ADHIYAKSA PRANANDA Agus Hermawan Alfiyah Salsa Dila Sabir Alya Safira Irtiqa Miolo Andi Atirah Arumtiwi Andi Kresna Jaya Anisa Anisa Anisa Kalondeng Anisa Kalondeng Anni Ivoni Parapa Asma Auliarani Ayu Sofia Clarisa Eudia Chesynanda Dhita Hartanti Octavia Edy Saputra Rusdi Evelyn Priscilla Fadhil Al Anshory Fadilah Amirul Adhel Hedi Kuswanto Ika Inayah Iwan Kurniawan Junianto Sesa Kalondeng, Anisa Maria Yus Trinity Irsan Mirna Mirna Muhammad Yusran Musdalifah Musdalifah Nirwan Ilyas Nirwan Nirwan NURTITI SUNUSI Putri, Ayu Pratika Rahmah, Syifa Ur Raupong Raupong Raupong Raupong Rezki, Nurul Rizka Pitri Sri Astuti Thamrin Sri Astuti Thamrin Sri Astuti Thamrin Sri Astuti Thamrin Sri Mulyani Taufik Hidayat Thamrin, Sri Astuti Ummul Auliyah Syam Waode Sitti Amni Zakiyah Mar'ah Zulfanita Dien R