Journal of Mathematics Computations and Statistics最新文献

{"title":"Peramalan Jumlah Kedatangan Wisatawan Mancanegara di Sulawesi Selatan Menggunakan Model ARFIMA","authors":"S. Sukarna, Muhammad Abdy, A. Aswi, Nurkaila Kaito","doi":"10.35580/jmathcos.v5i2.38793","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.38793","url":null,"abstract":"Pariwisata dianggap sebagai suatu aset yang strategis untuk mendorong pembangunan pada wilayah-wilayah tertentu yang mempunyai potensi objek wisata. Faktor-faktor yang mempengaruhi wisatawan mancanegara berkunjung ke suatu wilayah negara, diantaranya nilai tukar mata uang, inflasi disuatu wilayah kunjungan wisatawan, dan letak geografis suatu wilayah negara. Peningkatan yang tidak terduga pada jumlah kunjungan wisatawan ini dapat berdampak kesulitan bagi para pelaku wisatawan dalam hal memberikan pelayanan terbaik dan sebaliknya jika terjadi penurunan jumlah kunjungan wisatawan hal yang dikhawatirkan akan terjadi pengangguran.Oleh karena itu, diperlukan suatu peramalan yang dapat memberikan informasi atau gambaran pada proses jumlah kunjungan wisatawan mancanegara. Dalam analisis runtun waktu terdapat data yang memiliki ciri proses jangka pendek dan data yang memiliki ciri proses jangka panjang. Model yang dapat menangani kedua jenis data ini adalah model autoregressive fractionally integrated moving average (ARFIMA). Model ARFIMA merupakan pengembangan dari model ARIMA, dengan differencing bernilai pecahan. Penelitian ini bertujuan untuk menentukan model ARFIMA pada peramalan jumlah kunjungan wisatawan mancanegara Sulawesi Selatan di masa yang akan datang. Pada penelitian ini, nilai AIC antara ARFIMA([1,8],d,0) dengan d ̂_gph=0,02 dan ARFIMA(0,d,1) dengan d ̂_(R/S)=0,12  relatif sama, hasil komparasi dengan model ARIMA memberikan hasil bahwa tidak diperoleh nilai ARIMA yang sesuai sehingga penulis menggunakan model ARFIMA untuk peramalan dan hasil peramalan jumlah kedatangan wisatawan mancanegara Sulawesi Selatan dapat dilihat dengan perbandingan data out sample.Keywords: Parawisata, peramalan, ARFIMA.","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122834836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solusi Persamaan Difusi Adveksi Dengan Metode Pemisahan Variabel","authors":"Hisyam Ihsan, Ilmi Nurfaizah Rustam","doi":"10.35580/jmathcos.v5i2.39086","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.39086","url":null,"abstract":"Penelitian ini merupakan penelitian murni berupa kajian teori tentang solusi persamaan difusi adveksi dengan metode pemisahan variabel. Tujuan dari penelitian ini adalah untuk mengetahui penurunan persamaan difusi adveksi, menemukan solusi persamaan difusi adveksi dengan menggunakan metode pemisahan variabel dan melakukan simulasi Solusi Persamaan dengan menggunakan software Matlab. Persamaan Difusi Adveksi diperoleh dari penurunan dengan Hukum Fick. Solusi persamaan difusi adveksi dengan menerapkan metode pemisahan variabel, menentukan syarat batas, memisahkan variabel, mendapatkan solusi umum, dan mendapatkan solusi khusus. Dimana solusi khusus tersebut akan disimulasikan.Kata Kunci : Persamaan Difusi Adveksi, Metode Pemisahan VariabelThis research is pure research in the form of a theoretical study of the solution of advection-diffusion using separation of variable method. The purpose of this study was to determine the derivation of the advection-diffusion equation, find a solution to the advection-diffusion equation using the separation of variable method and perform simulations. Solutions of the equation using Matlab Software. The Advection Diffusion Equation is obtained from the derivation by Fick's Law. The solution of the advection-diffusion equation is by applying the separation of variable method, determining boundary conditions, separating variables, obtaining general solutions, and obtaining special solutions. Where the specific solution will be simulated. Keywords : Advection-Diffusion Equation, Separation of Variable Method.","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116871522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penerapan K-Means Clustering dalam Pengelompokan Data (Studi Kasus Profil Mahasiswa Matematika FMIPA UNM)","authors":"A. Zaki, I. Irwan, Imanuel Agung Sembe","doi":"10.35580/jmathcos.v5i2.38820","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.38820","url":null,"abstract":"Penelitian ini adalah penelitian terapan yang bertujuan untuk mengetahui cluster yang ada pada mahasiswa jurusan Matematika FMIPA UNM menggunakan K-Means Clustering. Metode penelitian ini adalah studi literatur. Hasil penelitian diperoleh 4 cluster dimana durasi belajar mandiri dan IPK dari tertinggi ke terendah berturut-turut adalah Cluster 1, Cluster 2, Cluster 4, dan Cluster 3. Cluster 1 didominasi mahasiswa SBMPTN, Semester 3, dengan rata-rata berumur 19,20 tahun, durasi belajar mandiri 2,49 jam, 23,97 SKS, IPS 3,69, dan IPK 3,67.  Cluster 2 didominasi mahasiswa SBMPTN, Semester 1, dengan rata-rata berumur 18,08 tahun, durasi belajar mandiri 2,07 jam, 22 SKS, dan IPK 3,63. Cluster 4 didominasi mahasiswa MANDIRI, Semester 5, dengan rata-rata berumur 19,78 tahun, durasi belajar mandiri 1,89 jam, 21,62 SKS, IPS 3,48, dan IPK 3,36. Cluster 3 didominasi mahasiswa SBMPTN dan IPS 3,64, SNMPTN bersama-sama, Semester 3, dengan rata-rata berumur 18,52 tahun, durasi belajar mandiri 1,29 jam, 21,87 SKS, IPS 3,13, dan IPK 3,19. Variabel yang paling berpengaruh dalam pembentukan cluster secara berturut-turut adalah Semester, Jumlah SKS, IPK, Umur, IPS, Rata-rata Durasi Belajar Mandiri, dan Jalur Masuk.   Kata Kunci: Cluster, K-Means Clustering, IPK, Durasi Belajar Mandiri.This research is an applied research that aims to determine the existing clusters in students majoring in Mathematics FMIPA UNM using K-Means Clustering. This research method is literature study. The results obtained 4 clusters where the duration of self-study and the GPA from the highest to the lowest were Cluster 1, Cluster 2, Cluster 4, and Cluster 3. Cluster 1 was dominated by SBMPTN students, Semester 3, with an average age of 19.20 years, duration of independent study 2.49 hours, 23.97 course credits, IPS 3.69, and IPK 3.67. Cluster 2 is dominated by SBMPTN students, Semester 1, with an average age of 18.08 years, independent study duration 2.07 hours, 22 course credits, IPS 3.64, and IPK 3.63. Cluster 4 is dominated by MANDIRI students, Semester 5, with an average age of 19.78 years, duration of independent study 1.89 hours, 21.62 course credits, IPS 3.48, and IPK 3.36. Cluster 3 is dominated by SBMPTN and SNMPTN students together, Semester 3, with an average age of 18.52 years, duration of independent study 1.29 hours, 21.87 course credits, IPS 3.13, and IPK 3.19. The most influential variables in the formation of clusters are Semester, Number of Course Credits, IPK, Age, IPS, Average Duration of Independent Study, and Pathway.Keywords: Cluster, K-Means Clustering, IPK, Duration of Independent Learning","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130935566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifikasi Sebaran Karakteristik Kriminal di Indonesia Tahun 2021 Menggunakan Model-Based Clustering","authors":"Debora Chrisinta, Leonard Peter Gelu, Budiman Baso","doi":"10.35580/jmathcos.v5i2.36956","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.36956","url":null,"abstract":"Kriminalitas merupakan suatu aspek yang mempengaruhi kelancaran perekonomian dalam masyarakat. Pentingnya peranan pemerintah dalam meminimalisir terjadinya kriminalitas dapat dilakukan dengan mengetahui sebaran karakteristik kriminalitas yang ada di setiap provinsi. Metode Model-Based Clustering  dapat membantu mengidentifikasi karakteristik tersebut. Proses Clustering dilakukan dengan memastikan terlebih dahulu sebaran peubah mendekati sebaran normal dilihat dari QQplot dan kebebasan antar peubah. Hasil Clustering menunjukkan bahwa terdapat dua karakteristik kriminalitas yang tersebar di provinsi Indonesia. Identifikasi karakteristik sebaran kriminalitas menggunakan nilai rata-rata pada masing-masing cluster optimal yang sudah didapatkan. Cluster pertama menunjukkan sebaran 16 provinsi dengan kategori kriminalitas cenderung lebih rendah, sedangkan cluster kedua menunjukkan sebaran 18 provinsi dengan kategori kriminalitas tinggi.","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114781047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis K-Medoid Untuk Pemetaan Tingkat Pencemaran Udara di Provinsi Sulawesi Selatan","authors":"I. Irwan, Maya Sari Wahyuni, Sulaiman Sulaiman, Andi Muhammad Mu'adz","doi":"10.35580/jmathcos.v5i2.38215","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.38215","url":null,"abstract":"Analisis gerombol berfungsi untuk mengelompokkan objek-objek dengan kesamaan karakteristik yang tinggi dalam 1 gerombol sementara objek-objek dengan ketidaksamaan karakteristik berada dalam gerombol yang berbeda. Analisis gerombol terbagi menjadi 2 yaitu hierarki dan non-hierarki. Penelitian ini menerapkan analisis gerombol non-hierarki yaitu metode k-medoid untuk menggerombolkan kabupaten/kota beserta empat sektornya yaitu transportasi, industri/agro industri, pemukiman, perkantoran/komersial di Provinsi Sulawesi Selatan berdasarkan indikator penyusun nilai Indeks Kualitas Udara (IKU) tahun 2019 dan 2020. IKU dikategorikan berdasarkan enam status Indeks Kualitas Lingkungan Hidup (IKLH). Untuk mendapatkan gerombol terbaik dari proses k-medoid maka setiap gerombol perlu dievaluasi menggunakan nilai koefisien silhouette. Hasil penelitian ini menunjukkan k = 2 gerombol dari metode k-medoid merupakan inisiasi gerombol terbaik dengan nilai koefisien silhouette terbaik sebesar 0,56. Hasil analisis terhadap hasil gerombol menunjukkan bahwa dengan penggunaan 2 gerombol, untuk data passive sampler 2019 menghasilkan gerombol 1 termasuk kategori IKLH sangat baik dengan nilai IKU sebesar 84,14 dan gerombol 2 masuk kategori IKLH kurang dengan nilai IKU sebesar 60,04. Untuk data passive sampler 2020 menghasilkan gerombol 1 termasuk kategori IKLH baik dengan nilai IKU sebesar 80,68 dan gerombol 2 masuk kategori IKLH kurang dengan nilai IKU sebesar 61,53.Kata Kunci: Analisis gerombol, k-medoid, IKU, koefisien silhouetteCluster analysis serves to group objects with high similarity of characteristics in one cluster while objects with dissimilarity of characteristics are in different clusters. Cluster analysis is divided into two, namely hierarchical and non-hierarchical. This study applies a non-hierarchical cluster analysis, namely the k-medoid method to group districts/cities and their four sectors, namely transportation, industrial/agroindustrial, residential, office/commercial in South Sulawesi Province based on indicators that make up the 2019 Air Quality Index (AQI) value and 2020. AQI are categorized based on six Environmental Quality Index (EQI) statuses. To get the best clusters from the k-medoid process, each cluster needs to be evaluated using the silhouette coefficient value. The results of this study indicate that k = 2 clusters from the k-medoid method are the best cluster initiations with the best silhouette coefficient value of 0.56. The results of the analysis of the cluster results show that with the use of 2 clusters, for 2019 passive sampler data, cluster 1 is included in the very good EQI category with a AQI value of 84.14 and cluster 2 is in the less EQI category with an AQI value of 60.04. For the 2020 passive sampler data, cluster 1 is included in the good EQI category with a AQI value of 80.68 and cluster 2 is in the less EQI category with a AQI value of 61.53.Keywords: Cluster analysis, k-medoid, CLARA, AQI, Silhouette Coefficient","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123327057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autokorelasi Pada Pembentukan Grafik Kendali Komponen Utama","authors":"N. Rasyid, Dhian Eka Wijaya, Dian Firmayasari, Harianto Harianto, Muh. Isbar Pratama","doi":"10.35580/jmathcos.v5i2.37734","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.37734","url":null,"abstract":"Pembentukan grafik kendali untuk data berautokorelasi tidak dapat dilakukan. Penelitian ini bertujuan untuk menganalisis pengaruh data berautokorelasi pada pembentukan grafik kendali komponen utama Penelitian ini menggunakan metode studi kasus yang dilakukan pada simulasi data dengan dua variabel dan penerapannya pada data unsur iklim dikota Makassar yang terdiri atas temperatur udara, penyinaran matahari, kelembaban  udara, dan  kecepatan angin. Untuk menganalisis pengaruh  data berautokorelasi dilakukan : (1) pembentukan struktur matriks variansi-kovariansi dari data berautokorelasi; (2) pembentukan grafik kendali komponen utama berdasarkan nilai eigen terbesar; dan (3) studi kasus simulasi dengan data dua variabel. Hasil penelitian menunjukkan bahwa jika data berautokorelasi negatif dan nilainya dari -0,9-(-0,5), batas kendalinya akan melebar dan jika nilainya dari -0,5-(-0,1), batas kendali akan menyempit.","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penyelesaian Persamaan Panas Dimensi Satu dengan Metode Beda Hingga Skema Eksplisit","authors":"Wahidah Sanusi, Syafruddin Side, Muh. Isbar Pratama, Fitriyani Fitriyani","doi":"10.35580/jmathcos.v5i2.33560","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.33560","url":null,"abstract":"Penelitian ini merupakan peneltian murni berupa kajian teori yang bertujuan untuk mengetahui penyelesaian persamaan panas dimensi satu dengan menggunakan metode beda hingga skema eksplisit dan mengetahui simulasi persamaan panas dimensi satu. Metode beda hingga skema eksplisit adalah suatu metode alternatif yang digunakan untuk menyelesaiakan persamaan differensial parsial. Langkah pertama pada penelitian ini yaitu membangun dan menganalisis persamaaan panas dimensi satu. Selanjutnya mendiskritisasi persamaan panas dimensi satu dengan menggunakan turunan numerik. Kemudian menyelesaikan persamaan panas dimensi satu dengan menggunakan skema eksplisit. Terakhir, menggunakan program Matlab untuk melakukan simulasi penyelesaian persamaan panas dimensi satu. Hasil simulasi menunjukkan bahwa adanya perubahan suhu dari suhu yang tinggi ke suhu yang lebih rendah yang dipengaruhi oleh waktu karena adanya proses perpindahan panas.Kata Kunci: Persamaan Panas, Metode Beda Hingga, Skema Eksplisit.This research is a pure research in the form of a theoretical study that aims to determine the solution of the one-dimensional heat equation using the finite difference method explicit scheme and to know the simulation of the one-dimensional heat equation. The explicit schema finite difference method is an alternative method used to solve partial differential equations. The first step in this research is to build and analyze the one-dimensional heat equation. Next, discretize the one-dimensional heat equation by using numerical derivatives. Then solve the one-dimensional heat equation using an explicit schema. Finally, using the Matlab program to simulate the solution of the one-dimensional heat equation. The simulation results show that there is a change in temperature from a high temperature to a lower temperature which is influenced by time due to the heat transfer processKeywords: Heat Equation, Finite Difference Method, Explicit Schematic","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128253913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On The Boundedness Properties of the Generalized Fractional Integrals on the Generalized Weighted Morrey Spaces","authors":"Yusuf Ramadana","doi":"10.35580/jmathcos.v5i2.36040","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i2.36040","url":null,"abstract":"Morrey Spaces were first introduced by C.B. Morrey in 1938. Morrey space can be considered as a generalization of the Lebesgue spaces. Morrey spaces were then generalized become the generalized Morrey spaces, the weighted Morrey spaces, and the generalized weighted Morrey spaces. One of the studies on Morrey spaces is the boundedness of certain operators on the spaces. One of the operators is the fractional integral. The boundedness of fractional integrals on the classical Morrey spaces, the weighted Morrey spaces, the generalized Morrey spaces, and the generalized weighted Morrey spaces had been known. One of the extensions of fractional integrals is generalized fractional integral. The operator was bounded on the generalized Morrey spaces. The purpose of this study is to investigate the boundedness of generalized fractional integrals on the generalized weighted Morrey spaces. The weight used is Muckenhoupt class. The results obtained show that the generalized fractional integral is bounded from generalized weighted Morrey space to another generalized weighted Morrey space under some assumptions. The main result obtained then implies the boundedness of the generalized fractional maximal operator on generalized weighted Morrey spaces under the same assumptions.","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126396538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pemodelan Spasial Bayesian dalam Menentukan Faktor yang Mempengaruhi Kejadian Stunting di Provinsi Sulawesi Selatan","authors":"A. Aswi, S. Sukarna","doi":"10.35580/jmathcos.v5i1.33499","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i1.33499","url":null,"abstract":"Indonesia merupakan negara dengan prevalensi balita stunting yang tinggi. Salah satu provinsi di Indonesia yang memiliki kasus stunting yang cukup tinggi adalah Provinsi Sulawesi Selatan. Penelitian mengenai kasus stunting dan faktor penyebabnya telah dilakukan. Namun, penelitian tersebut belum mengimplementasikan model Bayesian spasial Conditional Autoregressive (CAR). Penelitian ini bertujuan untuk mengetahui faktor yang mempengaruhi kejadian stunting di Provinsi Sulawesi Selatan dengan mengimplementasikan berbagai model Bayesian spasial CAR Leroux tanpa kovariat dan dengan memasukkan kovariat dalam model. Hasil penelitian menunjukkan bahwa model terbaik dalam memodelkan kasus stunting di Provinsi Sulawesi Selatan tahun 2020 adalah model Bayesian spasial CAR Leroux dengan hyperprior Inverse-Gamma IG(0,5;0,0005) dengan memasukkan kovariat persentase kemiskinan dan persentase balita 0-59 bulan gizi kurang. Persentase kemiskinan dan persentase balita 0-59 bulan gizi kurang berpengaruh positif terhadap kejadian stunting. Semakin tinggi persentase kemiskinan dan persentase balita 0-59 bulan dengan gizi kurang di suatu wilayah, semakin tinggi risiko stunting di wilayah tersebut. 50% kabupaten/kota di Provinsi Sulawesi Selatan berada dalam kategori risiko tinggi stunting. Kota Parepare merupakan kota dengan nilai risiko relatif (RR) tertinggi stunting, diikuti oleh Kabupaten Toraja dan Enrekang. Sebaliknya, Kabupaten Wajo merupakan kabupaten dengan RR terendah, diikuti oleh Kabupaten Luwu Timur dan Bone.Kata Kunci: Stunting, Bayesian, spasial CAR, Leroux  Indonesia is a country with a high prevalence of stunting. One of the provinces in Indonesia that has a fairly high number of stunting cases is South Sulawesi Province. Research on stunting cases and their causes has been done. However, these researches have not implemented the Bayesian Spatial Conditional Autoregressive (CAR) model. This study aims to determine the factors that influence the incidence of stunting in South Sulawesi Province by implementing various Bayesian spatial CAR Leroux models with and without covariates included in the model. The results showed that the best model for modeling stunting cases in South Sulawesi Province in 2020 is the Bayesian spatial CAR Leroux model with hyperprior Inverse-Gamma IG (0.5;0.0005) by including the covariates of the percentage of poverty and the percentage of children under five 0-59 months of malnutrition. The percentage of poverty and the percentage of children under five 0-59 months of malnutrition have a positive effect on the incidence of stunting. The higher the percentage of poverty and the percentage of children aged 0-59 months with malnutrition in an area, the higher the risk of stunting in that area. 50% of districts/cities in South Sulawesi Province are in the high-risk category of stunting. Parepare City is the city with the highest Relative Risk (RR) value for stunting, followed by Toraja and Enrekang Regencies. On the other hand, Waj","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133937942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Faktor-Faktor Yang Mempengaruhi Inflasi Di Pulau Sumatera Menggunakan Metode Regresi Data Panel","authors":"Kartika Anjalya, Sufri Sufri, Gusmi Kholijah","doi":"10.35580/jmathcos.v5i1.32088","DOIUrl":"https://doi.org/10.35580/jmathcos.v5i1.32088","url":null,"abstract":"Abstrak. Inflasi merupakan dilema yang menghantui perekonomian setiap negara, apalagi bagi negara-negara berkembang di dunia. Inflasi adalah suatu keadaan perekonomian dimana harga-harga secara umum mengalami kenaikan secara terus menerus dalam waktu yang panjang. Beberapa indikator yang dianggap mempengaruhi inflasi, yaitu indeks harga konsumen, produk domestik regional bruto, upah minimum kabupaten/kota, dan pertumbuhan ekonomi. Salah satu metode yang digunakan dalam menganalisis faktor-faktor yang mempengaruhi inflasi di Pulau Sumatera adalah Metode Regresi Data Panel yaitu analisis untuk memodelkan pengaruh variabel bebas terhadap variabel terikat selama periode waktu tertentu dengan suatu observasi sebagai objek dalam penelitian. Penelitian ini menyampaikan bahwa model regresi terbaik yang diperoleh Fixed Effect Model (FEM). Model tersebut menyampaikan secara parsial hanya variabel indeks harga konsumen dan pertumbuhan ekonomi yang paling signifikan mempengaruhi laju inflasi di Pulau Sumatera. Namun seluruh variabel yaitu indeks harga konsumen, produk domestik regional bruto, upah minimum kabupaten/kota, dan pertumbuhan ekonomi dalam model FEM secara bersama-sama atau simultan mampu menjelaskan laju inflasi di Pulau Sumatera sebesar 58,19%, sisanya 41.81% dijelaskan oleh variabel lain diluar model yang tidak diteliti.Kata Kunci: Inflasi, Data Panel, Model.","PeriodicalId":363413,"journal":{"name":"Journal of Mathematics Computations and Statistics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129169812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}