Jurnal Matematika, Statistika dan Komputasi https://journal.unhas.ac.id/index.php/jmsk <table style="border-collapse: collapse; width: 693px;"> <tbody> <tr> <td style="width: 40%;"><img src="https://journal.unhas.ac.id/public/site/images/budin258/jurnal.jpg" alt="" width="770" height="956" /></td> <td style="width: 2%;"> </td> <td style="width: 58%;"> <p style="text-align: justify;"><strong>e-ISSN: <a href="https://issn.brin.go.id/terbit/detail/1517041991" target="_blank" rel="noopener">2614-8811</a>, p-ISSN:<a href="https://issn.brin.go.id/terbit/detail/1180427019" target="_blank" rel="noopener">1858-1382</a></strong></p> <p style="text-align: justify;"><strong><span style="font-weight: normal;">Welcome to Jurnal Matematika, Statistika dan Komputasi (Supported by The Indonesian Mathematician Society -IndoMS). Jurnal Matematika, Statistika dan Komputasi is published on</span></strong> <strong><span style="font-weight: normal;">January, May and September by Department of Mathematics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia.<br /></span></strong><strong><span style="font-weight: normal;">JMSK welcomes original papers in Indonesia Language (Bahasa) or in English for scope:</span></strong> <strong><span style="font-weight: normal;">Mathematics for the development of mathematical sciences, statistics, computation, or mathematics Education. </span></strong></p> </td> </tr> </tbody> </table> <p style="text-align: justify;"><strong>ACCREDITED BY SINTA 3</strong></p> <table style="border-collapse: collapse; width: 550px;"> <tbody> <tr> <td style="width: 95.9219px;"><strong>INDEXED BY:</strong></td> <td style="width: 146.688px;"><a href="http://id.portalgaruda.org/index.php?ref=browse&amp;mod=viewjournal&amp;journal=2164" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_IPI.png" alt="" width="94" height="76" /></a></td> <td style="width: 9.98438px;"> </td> <td style="width: 159.656px;"><a title="DOI Crossreff" href="http://dx.doi.org/10.20956" target="_blank" rel="noopener"><strong><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_copernicus2.jpg" alt="" width="185" height="45" /></strong></a></td> <td style="width: 10.9688px;"><strong> </strong></td> <td style="width: 115.75px;"><strong><a title="DOI Crossreff" href="http://dx.doi.org/10.20956" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/Logo_Crossref1.PNG" alt="" width="65" height="48" /></a></strong></td> <td style="width: 10.0312px;"><strong> </strong></td> </tr> <tr> <td style="width: 95.9219px;"><strong> </strong></td> <td style="width: 146.688px;"><strong><a title="INDEX IOS" href="http://onesearch.id/Search/Results?lookfor=jmsk&amp;type=AllFields&amp;filter%5B%5D=institution%3A%22Universitas+Hasanuddin%22&amp;filter%5B%5D=collection%3A%22JURNAL+MATEMATIKA+STATISTIKA+DAN+KOMPUTASI%22" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_IOS4.jpg" alt="" width="192" height="63" /></a></strong></td> <td style="width: 9.98438px;"> </td> <td style="width: 159.656px;"><strong><a title="INDEX ROAD" href="http://road.issn.org/issn/2614-8811#.WrRkeH--mpp" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/logo_Road3.jpg" alt="" width="206" height="68" /></a></strong></td> <td style="width: 10.9688px;"><strong> </strong></td> <td style="width: 115.75px;"><strong><a title="INDEX GOOGLE SCHOLAR" href="https://scholar.google.co.id/citations?user=s2e2GIgAAAAJ&amp;hl=en" target="_blank" rel="noopener"><img src="http://journal.unhas.ac.id/public/site/images/budi258/lOGO_GOOGLE_SCHOLAR.jpg" alt="" width="147" height="72" /></a></strong></td> <td style="width: 10.0312px;"><strong> </strong></td> </tr> </tbody> </table> en-US <p><a href="http://creativecommons.org/licenses/by/4.0/" rel="license"><img src="https://i.creativecommons.org/l/by/4.0/88x31.png" alt="Creative Commons License" /></a><br /><span>This work is licensed under a </span><a href="http://creativecommons.org/licenses/by/4.0/" rel="license">Creative Commons Attribution 4.0 International License</a><span>.</span></p><p><strong>Jurnal Matematika, Statistika dan Komputasi</strong> is an Open Access journal, all articles are distributed under the terms of the <strong><a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</a></strong>, allowing third parties to copy and redistribute the material in any medium or format, transform, and build upon the material, provided the original work is properly cited and states its license.<strong> </strong> This license allows authors and readers to use all articles, data sets, graphics and appendices in data mining applications, search engines, web sites, blogs and other platforms by providing appropriate reference.</p> budinurwahyu@unhas.ac.id (Budi Nurwahyu) budinurwahyu@unhas.ac.id (Budi Nurwahyu) Sun, 24 Dec 2023 14:34:14 +0000 OJS http://blogs.law.harvard.edu/tech/rss 60 On Rainbow Antimagic Coloring of Corona Product of Cycle Graph (C_m) and Star Graph (S_n) https://journal.unhas.ac.id/index.php/jmsk/article/view/34523 <p>Let G&nbsp; be a simple and finite graph. Rainbow coloring c defined&nbsp; c : G ➡ {1,2,...,k} where k&nbsp; is the minimum color used in a graph&nbsp; G. Rainbow connection number (rc)&nbsp; 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)&nbsp; 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)&nbsp; 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&nbsp; The results obtained in determining the rainbow connection numbers from the graph&nbsp; C<sub>n</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub> are rc(C<sub>n</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub>) = [3n/2]&nbsp; with n ≥ 3, and the rainbow antimagic connection numbers from the graph C<sub>m</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub> are n + 3 ≤ rac(C<sub>m</sub><sub>&nbsp;</sub>ʘ S<sub>n</sub> ) ≤ n + 5&nbsp; with m = 3 and n ≥ 3.</p> sri agista kaya, Nisky Imansyah Yahya, Djihad Wungguli Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34523 Implementation of Big Data and Crowdsourced Data on Determinants of Labor Absorption in Java Island 2022 https://journal.unhas.ac.id/index.php/jmsk/article/view/34518 <p>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.</p> Maria A. Hasiholan Siallagan Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34518 The Robust Negative Binomial Regression Model on Under-five Mortality due to Pneumonia in the Province of East Java https://journal.unhas.ac.id/index.php/jmsk/article/view/34512 <p>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, s<sup>2</sup><sub>p</sub> and s<sup>2</sup><sub>RNBR</sub>, 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.</p> Anggun Qur'ani Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34512 Stock Option Pricing Using Binomial Trees With Implied Volatility https://journal.unhas.ac.id/index.php/jmsk/article/view/34476 <p>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.</p> Aimmatul Ummah Alfajriyah Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34476 Peramalan Jumlah Kedatangan Pasien Puskesmas menggunakan Metode Fuzzy Time Series Cheng https://journal.unhas.ac.id/index.php/jmsk/article/view/34460 <p><span style="vertical-align: inherit;"><span style="vertical-align: inherit;">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.</span></span></p> Aisyah Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34460 FAKTOR-FAKTOR YANG MEMENGARUHI ANGKA GIZI BURUK DI JAWA TENGAH MENGGUNAKAN REGRESI GENERALIZED POISSON DAN BINOMIAL NEGATIF https://journal.unhas.ac.id/index.php/jmsk/article/view/34421 <p>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 <em>Generalized</em> Poisson dengan data yang digunakan adalah data jumlah kasus gizi buruk beserta faktar-faktor yang diduga memengaruhi. Sehingga didapatkan hasil penelitian pemodelan regresi <em>Generalized</em> 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.</p> Aini Nurmalita Ramadhani Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34421 the Log Kumaraswamy Weibull Regression Model with Application https://journal.unhas.ac.id/index.php/jmsk/article/view/34412 <p><strong>:</strong> In this research paper, we propose a new regression for the Kumar-Samy-Weibull distribution, The coefficients of the proposed regression model were estimated using the Maxim method &nbsp;&nbsp;,in the finally the proposed model was applied to a set of real data. The proposed model was compared to some models through some statistical criteria</p> <p>&nbsp;</p> ahamed12763@gmail.com ahamed Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34412 Prediksi Hubungan Beberapa Unsur pada Cangkang Bekicot Menggunakan Metode Interpolasi Lagrange https://journal.unhas.ac.id/index.php/jmsk/article/view/34406 <p>Interpolation is a numerical technique used to determine the value between known data points. Based on the results of laboratory tests on the relationship of several elements contained in snail shells, a series of data was obtained. These data are data pairs between calcium-silicon, calcium-iron, calcium-phosphorus, and calcium-titanium. However, to analyze the relationship between these data is not easy to do with the regression method. Lagrange interpolation is one method that can be used to determine the relationship between these data sets. Through the four pairs of data, a Lagrange polynomial equation of degree 7 is obtained that describes the relationship of each element contained in the snail shell. From the equation, a polynomial value with a negative coefficient is obtained in the initial term of the polynomial for the calcium-silicon, calcium-iron, and calcium-titanium element pairs, with values of -102.895, -365.694, and -11.6132, respectively, which means that initially the percentage of calcium increases to a maximum point and then decreases. However, on the contrary, in the calcium-phosphorus element pair, a polynomial with a positive coefficient is obtained in the initial term of the polynomial, which is 77.5394, indicating the opposite relationship that initially the percentage of calcium decreases to a minimum point and then increases. These different results may be due to the different shell formation processes in each snail sample. The process of snail shell formation can be influenced by various factors, including the type of snail, environmental conditions, as well as the composition of the soil in which the snail lives.</p> Irene Devi Damayanti Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34406 Determinan Indeks Inovasi Daerah Tahun 2022 dalam Upaya Menghadapi Era Volatility, Uncertainty, Complexity, and Ambiguity dengan Pendekatan Regresi Robust S-Estimation https://journal.unhas.ac.id/index.php/jmsk/article/view/34307 <p>Currently, life is changing rapidly, information is increasingly difficult to filter, competition is increasingly open, and demands to adapt quickly. These conditions are known as Volatility, Uncertainty, Complexity, and Ambiguity (VUCA). In facing the VUCA era, government innovation needed to increase anticipatory and adaptive power in overcoming various challenges and disruptions. However, World Intellectual Property Organization data shows that Indonesia's innovation index in 2022 is still ranked 75th out of 132 countries. Thus, Indonesia still has problems and weaknesses in managing the innovation cycle, especially government innovation. Due to the differences in the characteristics of each region in Indonesia, it is essential to carry out research to identify the determinants of the regional innovation index (IID) in 2022. This research uses descriptive analysis and also inferential analysis using robust regression S-estimation to handle data with outlier problems. The research results show that the variables of the degree of fiscal decentralization, bureaucratic reform index, and innovation capability score have a significant positive effect on IID in 2022, while the percentage of students who access the internet has a significant negative effect on IID in 2022. On the other hand, the variables of digital skills score and ratio of students and lecturers do not have a significant effect on IID in 2022.</p> Erina Herwindalita, Siskarossa Ika Oktora Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34307 Perbandingan Model GSTAR dan SGSTAR dalam Memprediksi Temperatur https://journal.unhas.ac.id/index.php/jmsk/article/view/34305 <p>Recent research in time series analysis indicates that events at a particular location are not only influenced by events at previous times but also by proximity between locations. Events influenced by both space and time can be modeled using a space-time model. GSTAR model is oe such space-time model. In its development, time series data exhibiting seasonal patterns are modeled using Seasonal GSTAR (S-GSTAR). The GSTAR and S-GSTAR models are used to model temperature in the Banjar, Cilacap, and Sleman Districts. The purpose of employing both methods is to compare the best model for modeling temperatsure at these three locations. Spatial weights used include inverse distance weighting using the Euclidean distance formula, uniform weighting, and cross-correlation normalization weighting. Ordinary Least Squares (OLS) is the estimation method used in this study. The best model obtained is SGSTAR with inverse distance weighting, as this model has the smallest RMSE value<strong>.</strong></p> Riani Utami, Utriweni Mukhaiyar, Nabila Mardiyah, Yalela Sa’adah, Erni Widyawati Copyright (c) https://journal.unhas.ac.id/index.php/jmsk/article/view/34305