Jurnal Matematika, Statistika dan Komputasi

Clustering Provinces in Indonesia Based on Educational Facilities and Infrastructure Aspects Using K-Means and Fuzzy C Means

Sinta Septi Pangastuti

Education is one of the sectors that has an important role in the development of a country. Indonesia itself is a country that is very concerned about the implementation of education. According to the results of a survey on the world's education system in 2018 issued by PISA (Program for International Student Assessment) in 2019, Indonesia occupies a low position, namely 74th out of 79 participating countries. In order to improve the state of education in Indonesia as an effort to achieve the Sustainable Development Goals (SDGs) "Quality Education", it is necessary to review the state of education in each province. This study aims to identify groups of provinces that have similar characteristics in terms of educational facilities and infrastructure using the clustering analysis method. The methods of analysis that are used in this study are K-Means Clustering and Fuzzy C Means Clustering. Based on the results, it can be concluded that the K-means Clustering method is more suitable to be used in this analysis. K-means Clustering forms 4 groups that explain the different characteristics of basic education facilities and infrastructure from each province in Indonesia.

Factors Affecting The Number Of Domestic Flights In Indonesia During Covid-19 Pandemic Using SARIMAX Method

Abdullah Ahmad Dzikrullah

Indonesia, which consists of thousands of large and small islands, relies heavily on-air transportation to support mobility between regions. As many as 80% of Indonesia's total air transportation passengers are domestic flight passengers. This shows how vital domestic flights are in Indonesia's air transportation system. However, in 2020, the COVID-19 pandemic had an impact that resulted in a decrease in the number of domestic flights in Indonesia. Therefore, an analysis is needed to determine the factors that affect the number of domestic flights in Indonesia. This study uses the SARIMAX method with several exogenous variables, including the number of operating civil aviation airports, positive daily cases of COVID-19, calendar effects during Eid al-Fitr and New Year's Day, and social restriction policies. The results showed that the number of operating airports one week before Eid al-Fitr, one week during Eid al-Fitr, one week before New Year, and Emergency PPKM significantly influenced the number of domestic flights. The best SARIMAX model obtained is SARIMAX(1,1,1)(4,1,1)⁷ with a MAPE value of 5.35% and a coefficient of determination of 97%.

Development of Simple Statistical Software: Linear Regression Series

Regression analysis is a statistical analysis method that is often used in various fields. SPSS is a software that is often used in Indonesia for regression analysis. anxiety will arise in users who do not have a strong foundation in regression and rarely use SPSS. Anxiety will be exacerbated by how to interpret the analysis results. The aim of this study is to develop simple statistical software for regression analysis that is easy to use with few settings and is able to provide interpretation of analysis results. The development model used is System Development Life Cycle (SCLD). There are five stages in this model including, Planning, Analysis, Design, Implementation, and System. The software developed is named Simple Statistical Software series Regression Analysis or abbreviated as 3S-AR. The development results obtained were the 3S-AR application which has functionality for regression analysis. Validation of development results was carried out by comparing the results of 3S-AR analysis with SPSS software. 3S-AR has several advantages. First, it's easy to use with little setup. With just one analysis you can display the results of the regression analysis along with all the assumptions. Second, being able to provide an interpretation of the analysis results. Third, if there is an analysis that is not fulfilled, it is able to provide suggestions regarding what the user should do. Fourth, it's free. The development results can contribute to users in carrying out regression analysis easily. Especially for users who do not have a strong statistical foundation and experience in using software for regression analysis.

Indonesia's Image Analysis: International Media and Tourism Recovery

Sita Rutba

The international world's perception of a country is essential. In 2022, Indonesia attracted
international media attention, which can form the nation’s image of Indonesia in the
international public perception. Therefore, this study analyses international media, online
news, and social media Twitter perceptions towards Indonesia. For the news, aspect-based
sentiment analysis is carried out, and for Twitter opinion, sentiment classification and the
grouping of topics related to these sentiments are carried out. This research can classify
news sentiment based on aspects of forming the country's image, such as tourism, exports,
diplomacy, government policies, and people's behavior. The model can classify each
aspect into positive, negative, neutral, and none sentiment. On Twitter opinion, there are
groups of topics related to positive and negative sentiments about Indonesia. Then, it was
found that Twitter's positive sentiment about Indonesia is associated with tourism
recovery. The results are summarised in a visualization dashboard.

On Rainbow Antimagic Coloring of Corona Product of Cycle Graph (C_m) and Star Graph (S_n)

sri agista kaya, Nisky Imansyah Yahya, Djihad Wungguli

Let G be a simple and finite graph. Rainbow coloring c defined c : G ➡ {1,2,...,k} where k is the minimum color used in a graph G. Rainbow connection number (rc) are defined as determining patterns by giving different colors to all edges which are elements of graph G and vertex u - v with u,v ∈ V(G) there is a rainbow path. The rainbow antimagic coloring is a bijective function f : V ➡ (1,2,...,|V(G)| , for every two distinct vertex there is at least one rainbow path. This article discusses about the rainbow connection number (rc) and the rainbow antimagic connection number (rac) on a new graph which is the result of corona operation on a cycle graph and a star graph The results obtained in determining the rainbow connection numbers from the graph C_nʘ S_n are rc(C_nʘ S_n) = [3n/2] with n ≥ 3, and the rainbow antimagic connection numbers from the graph C_mʘ S_n are n + 3 ≤ rac(C_mʘ S_n ) ≤ n + 5 with m = 3 and n ≥ 3.

Implementation of Big Data and Crowdsourced Data on Determinants of Labor Absorption in Java Island 2022

Maria A. Hasiholan Siallagan

Java is the island with the lowest percentage of labor absorption in Indonesia in 2022. With the current technological advancements, new data sources can be utilized to obtain factors related to labor absorption besides conventional data sources. Therefore, this study aims to combine the use of big data and crowdsourced data with official statistics data to identify factors influencing labor absorption in Java Island. The data used in this research comes from remote sensing, Potensi Desa (PODES) 2021, and Survei Angkatan Kerja Nasional (SAKERNAS) 2021. This research employs Geographically Weighted Regression (GWR) because it can facilitate spatial effects in the data. The model indicates that nighttime light intensity in urban and agricultural areas, as well as environmental quality, significantly enhance labor absorption across all districts/cities in Java Island. Additionally, internet facilities, universities, and the number of micro and small industries also have a significantly positive impact in most districts/cities.

The Robust Negative Binomial Regression Model on Under-five Mortality due to Pneumonia in the Province of East Java

Anggun Qur'ani

Robust Negative Binomial regression model (RNBR) is a modelling method to overcome a problem if there are outliers and overdispersion in the data. Outliers are data points that are significantly different from other data. Outliers have a significant effect on modelling to the resulting model. Furthermore, overdispersion is indicated by the presence of too large values of Pearson statistics. In this study, the RNBR model was used to determine the factors of the toddler immune variable at post neonatal age that significantly influenced the number of under-five deaths caused by pneumonia in East Java Province. Based on the modelling obtained, it shows that the RNBR model provides more robust results in handling outlier and overdispersion problems. This can be seen from the AIC value of the RNBR model is smaller than the AIC of the Poisson regression model. In addition, s²_p and s²_RNBR, which are measures of the influence of outliers on the model, decreased from 1 for the Poisson regression model to around 0.42 for the RNBR model.

Stock Option Pricing Using Binomial Trees With Implied Volatility

Aimmatul Ummah Alfajriyah

The Black-Scholes model provides an analytical solution to option pricing and has been widely used in the world of finance. However, the assumption of constant volatility in the BlackScholes model does not represent real conditions. The Black-Scholes model can no longer explain implied volatility in the options market. In this research, option prices are modeled using an implied binomial tree, namely a binomial tree method whose volatility element uses implied volatility effects that are more consistent with real conditions. Next, a price simulation of the call option and put option was conducted using the standard binomial tree and implied binomial tree methods. The simulation results show that option prices are consistent with the BlackScholes model option prices. Based on the simulation, it was also found that the implied binomial tree method provides better option prices than the standard binomial tree based on the error values. Furthermore, increasing the time step causes the option prices obtained from the implied binomial tree converge to the option prices from the Black-Scholes model. Apart from that, factors that influence the option price are obtained, there are the stock price, strike price, interest rate, and maturity date.

Peramalan Jumlah Kedatangan Pasien Puskesmas menggunakan Metode Fuzzy Time Series Cheng

Aisyah

Puskesmas merupakan unit pelaksana sebagai inti sentral dalam pembinaan peran kesehatan dan inti pembangunan kesehatan masyarakat. Jumlah kedatangan pasien di Puskesmas mengalami peningkatan dan penurunan setiap bulannya, peningkatan dan penurunan tersebut dapat mempengaruhi fasilitas yang diberikan oleh Puskesmas, sehingga penelitian ini bermaksud untuk memprediksi jumlah kedatangan pasien di Puskesmas untuk memenuhi fasilitas pelayanan. itu harus disediakan. Penelitian ini dimulai pada bulan Januari 2022 sampai dengan Desember 2023 di Puskesmas Medan Amplas dengan menggunakan metode Cheng’s Fuzzy Time Series, sehingga hasil perhitungan prediksi ini dengan jumlah kedatangan pasien Puskesmas pada bulan Januari 2022 sampai dengan Desember 2023 sebanyak 69.369 pasien, dengan jumlah total dari Januari 2022 hingga Desember 2023 menghasilkan nilai prediksi 70.464. menghasilkan nilai MAPE sebesar 8%, bila nilai MAPE kurang dari 10% mempunyai nilai prediksi yang sangat baik.

FAKTOR-FAKTOR YANG MEMENGARUHI ANGKA GIZI BURUK DI JAWA TENGAH MENGGUNAKAN REGRESI GENERALIZED POISSON DAN BINOMIAL NEGATIF

Aini Nurmalita Ramadhani

Gizi buruk merupakan salah satu masalah kesehatan serius di Indonesia. Provinsi Jawa Tengah merupakan salah satu provinsi yang memiliki prevalensi gizi buruk cukup tinggi sebesar 3,2%. Penelitian ini bertujuan untuk memperoleh model regresi terbaik guna menjelaskan faktor-faktor yang berpengaruh signifikan terhadap jumlah kasus gizi buruk di Provinsi Jawa Tengah tahun 2022. Analisis data pada penelitian ini menggunakan model regresi Poisson, regresi binomial negatif dan regresi Generalized Poisson dengan data yang digunakan adalah data jumlah kasus gizi buruk beserta faktar-faktor yang diduga memengaruhi. Sehingga didapatkan hasil penelitian pemodelan regresi Generalized Poisson sebagai model regresi terbaik dengan nilai AIC sebesar 357,40 dan nilai BIC sebesar 362,07. Berdasarkan model tersebut, faktor yang signifikan mempengaruhi jumlah kasus gizi buruk di Provinsi Jawa Tengah tahun 2022 adalah Indeks Pembangunan Manusia.