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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Statistika Universitas Muhammadiyah Semarang AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Kreativitas PKM Jurnal Aplikasi Statistika & Komputasi Statistik METIK JURNAL Variance : Journal of Statistics and Its Applications Jurnal Varian Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Mandalika Mathematics and Educations Journal Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Journal of Mathematics Education and Science Mathematics and Applications (MAp) Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research Journal of Research Applications in Community Services Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Progressive Physics Journal Eksponensial Journal of Mathematics, Computation and Statistics (JMATHCOS) IJASDS: Indonesian Journal of Applied Statistics and Data Science

Memi Nor Hayati

Universitas Mulawarman

Author-ID : 278791

Religion Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Analisis Cluster Pada Data Kategorik dan Numerik dengan Pendekatan Cluster Ensemble: Studi Kasus: Puskesmas di Provinsi Kalimantan Timur Kondisi Desember 2017 Lestari, Nur Aini Ayu; Hayati, Memi Nor; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis used to process categorical and numerical data at once is Cluster Ensemble algorithm Based on Mixed Data Clustering (algCEBMDC), which is a cluster algorithm with an ensemble cluster approach. The method used for numerical data is Agglomerative Nesting (AGNES) algorithm and for categorical data is the RObust Clustering using linK (ROCK) algorithm. The best clustering method and the optimum number of clusters in the AGNES algorithm is selected based on the maximum Pseudo-F value and the minimum icdrate value. The optimum number of clusters in the ROCK algorithm is selected using the minimum value of ratio . The purpose of this study was to make a group of 179 Puskesmas in East Kalimantan on December 2017. Based on the results of the analysis, obtained 5 optimum cluster for numerical clustering with the AGNES algorithm and 2 optimum cluster for categorical clustering data with the ROCK algorithm. Final cluster for mixed data clustering obtained 2 optimum cluster at a threshold of 0.2 and 0.3 with value of ratio is . The first cluster consists of 83 Puskesmas and cluster two of 96 Puskesmas.

Penerapan Metode Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Kesejahteraan Rakyat Tahun 2020 Nurmin, Deviyana; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 13 No. 2 (2022)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Clustering is a method of grouping data into several clusters or groups so that data in one cluster has a high level of similarity and data between clusters has a low level of similarity. The clustering method used in this research is Fuzzy C-Means (FCM). FCM is a data grouping technique in which the existence of each data point in a cluster is determined by the degree of membership. To optimize the grouping results, it is necessary to validate the number of clusters using Partition Coefficient (PC). The purpose of this study is to obtain optimal grouping results from the FCM method using the PC validity indices from the people's welfare indicator data in 56 regencies/cities on the island of Kalimantan in 2020. Based on the results of the analysis, the conclusion is that the optimal number of clusters is three clusters. The first cluster consists of 24 regencies/cities on the island of Kalimantan, the second cluster consists of 17 regencies/cities on the island of Kalimantan, and the third cluster consists of 15 regencies/cities on the island of Kalimantan.

Pencegahan Penyakit Kusta di Lingkungan Hutan Tropis Lembab Kalimantan Melalui Pemodelan Geographically Weighted Poisson Regression Wati, Fatma; Suyitno, Suyitno; Hayati, Memi Nor
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v12i1.756

Geographically Weighted Poisson Regression (GWPR) model is a regression model developed from Poisson regression which is applied to spatial data. Parameter estimation of the GWPR model is done at each observation location using spatial weighting. This study goal is to obtain the GWPR model and the factors influencing the number of leprosy cases in each regency(municipality) on Kalimantan Island in 2018. Spatial weighting was obtained by using the adaptive bisquare kernel function and optimal bandwidth was determined by using Generalized Cross-Validation (GCV) criteria. The data of this study was secondary data namely the number of leprosy cases in 56 regency on Kalimantan Island in 2018. The parameter estimation method of GWPR model is Maximum Likelihood Estimation (MLE). The results of analysis showed that maximum likelihood estimator is obtained by using the Newton-Raphson iterative method and the factors affecting the number of leprosy cases in each regency were different and locally. The factors influencing locally were the number of health facilities, the number of health workers, the number of male population and population density.

Penerapan Algoritma K-Medoids pada Pengelompokan Wilayah Desa atau Kelurahan di Kabupaten Kutai Kartanegara: Studi Kasus : Data Hasil Pendataan Potensi Desa (PODES) Tahun 2018 Ibrahim, Rizky Nur; Hayati, Memi Nor; Amijaya, Fidia Deny Tisna
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v11i2.658

Kutai Kartanegara Regency (Kukar) was recorded as the largest contributor to the poor population in East Kalimantan (Kaltim) Province in 2017, so that appropriate strategies are needed to solve proverty problems. The development strategy is prioritized for the regions with the largest number of poor people. Identification is conducted based on facilities, infrastructures, access, social, population and economy is provided in the Village Potential data (PODES). K-Medoids is a grouping method that uses representative objects as a central point, which can be used to find out the characteristics of a region. This research is aimed to find out the optimal cluster formed by choosing the largest value of Silhouette Coefficient (SC) from the grouping of villages / political district in Kukar Regency using PODES data in 2018. Clusters that will be formed in this research are 2 clusters, 3 clusters, 4 clusters and 5 clusters. Based on the analysis, it can be seen that the value of SC 2 cluster is 0.430, the value of SC 3 cluster is 0.174, the value of SC 4 cluster is 0.175 and the value of SC 5 cluster is 0.196. So that the largest SC or optimal cluster values obtained in the grouping of 2 clusters with a SC value of 0.430. Cluster 1 consists of 186 villages / political dsitrict and cluster 2 consists of 46 villages / political district.

Analisis Spasial Persebaran Jumlah Kasus Malaria di Kalimantan Timur Menggunakan Indeks Moran dan Local Indicator Spatial of Autocorrelation Hadisti, Zahrah Dhafina; Hayati, Memi Nor; Fauziyah, Meirinda
EKSPONENSIAL Vol. 15 No. 1 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i1.1232

Spatial analysis is an analysis that considers the location and distance of an object in the research data. Moran’s index is one of the spatial methods used to analyze spatial autocorrelation globally. Furthermore, there is the Local Indicator of Spatial Autocorrelation (LISA) method which is used to analyze spatial autocorrelation locally. This study aims to determine whether there is spatial autocorrelation and determine the distribution pattern formed in the data on the average number of malaria cases in East Kalimantan based on the regency/city during 2018-2022. The results showed that based on the Moran index globally, there was no spatial autocorrelation in the average number of malaria cases in East Kalimantan in 2018-2022. The type of spatial pattern in the distribution of malaria cases in East Kalimantan is a clustering pattern indicated by the clustering of malaria cases in each district/city in East Kalimantan. Furthermore, the results of spatial autocorrelation using LISA show that locally there is spatial autocorrelation in several districts/cities in East Kalimantan, namely Paser, Kutai Timur, Kutai Barat and Penajam Paser Utara.

Klasifikasi Status Hipertensi Pasien UPTD Puskesmas Sempaja, Kota Samarinda Menggunakan Metode K-Nearest Neighbor Soraya, Raihana; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 14 No. 2 (2023)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v14i2.1009

Data mining is a method of selecting, exploring and modeling large amount of data to find knowledge and clear patterns or interesting relation of the data and useful in the process of data analysis. In data mining there are several techniques that have different function and one of them is classification tehcnique. The classification process itself is the process of finding patterns or differences between classes or data that can be used to predict object classes whose class labels are unknown. K-nearest neighbor (K-NN) is one of the methods in classification algorithm. This study discusses the classification using K-NN algorithm which is applied to the data hypertension status. The aim is to find out the optimal neighborliness value (K) accuracy value and the best propotion of the data hypertension status. The data used is the data of patients UPTD health center Sempaja, Samarinda city from February to May 2022 with dependent variabel is hypertension status and uses 4 independent variables, age, gender, diabetes mellitus and heart disease. Based on the research that has been done, obtained an accuracy value of 62,60% with K = 5 in the best proportion of the data is 70%:30%.

Pengujian Hipotesis Parameter Model Mixed Geographically Weighted Regression Data Indeks Pembangunan Manusia di Kalimantan Tahun 2016 Utami, Riska Putri; Suyitno, Suyitno; Hayati, Memi Nor
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Mixed Geographically Weighted Regression (MGWR) model is a Geographically Weighted Regression (GWR) model with some parameters are global (have the same value) and several other parameters are local (have different values) for each observation location. The purpose of this study is to obtain a MGWR model on the Human Development Index (HDI) data and find out the factors that influence the HDI of each district (city) in the provinces of East Kalimantan, Central Kalimantan and South Kalimantan in 2016. The parameter estimation method is carried out through two stages (backshift), namely local parameter estimation by using the Weighted Least Square (WLS) method and global parameter estimation by using the Ordinary Least Square (OLS) method. Spatial weighting on local parameter estimation is obtained by using an adaptive Bisquare weighting functions, where optimum bandwidth determination uses Generalized Cross-Validation (GCV) criterion. Based on the result of MGWR parameter testing, it was concluded that the school enrollment rates (SMP) affected the HDI of all districts (cities) in East Kalimantan, Central Kalimantan and South Kalimantan, while the population density affects the HDI only in a few districts (cities), namely East Kutai, Balikpapan, Samarinda and Bontang.

Pengelompokan Data Kategorik Dengan Algoritma Robust Clustering Using Links: Studi Kasus: PT. Prudential Life Jalan MT. Haryono Samarinda Dewi, Isma; Syaripuddin, Syaripuddin; Hayati, Memi Nor
EKSPONENSIAL Vol. 11 No. 2 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Cluster analysis is a technique of data mining that is used to group data based on the similarity of attributes of data objects. The problem that is often encountered in cluster analysis is the data on a categorical scale. Categorical scale data grouping can be done using the ROCK (RObust Clustering using linKs) algorithm. The ROCK algorithm is included in the of agglomerative hierarchical clustering algorithms in cluster analysis. This algorithm introduces a concept called neighbors and links in grouping data. Categorical data grouping with ROCK algorithm is done in three steps. The first step is counting similarities. The second step is determining the neighbors and the last is calculating the links between the observation objects. The value of the link is affected by θ. The optimum number of clusters in the ROCK algorithm is selected using a minimum ratio value of . The purpose of this study is to group 100 data of insurance customers of PT. Prudential Life Samarinda in 2018. Based on the analysis results, obtained that the optimum group is at θ = 0.1 with a ratio value of is 0.1371. The optimum number of groups formed is 2 clusters. The first group consisted of 42 customers and the second group consisted of 58 customers.

Model Spatial Autoregressive Moving Average (SARMA) pada Data Jumlah Kejadian Demam Berdarah Dengue (DBD) di Provinsi Kalimantan Timur dan Tengah Tahun 2016 Sari, Devi Nur Endah; Hayati, Memi Nor; Wahyuningsih, Sri
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Spatial Autoregressive Moving Average (SARMA) is a spatial regression model that uses the regional approach. The weighting matrix used is an adjacency matrix which is based on the intersection between observed locations. This study was conducted to determine the SARMA model and the factors that influence the number of cases of dengue hemorrhagic fever (DHF) in the provinces of East Kalimantan and Central Kalimantan in 2016. Based on the results of the Moran's Index test, there is a spatial autocorrelation on the number of dengue events in East Kalimantan Province and Central Kalimantan in 2016. The Lagrange Multiplier (LM) test has a spatial lag on the dependent variable and the error variable, which is a parameter and that is significant to the significance level . Based on the results of SARMA modeling that the factors that influence the number of dengue events in the provinces of East Kalimantan and Central Kalimantan in 2016 are the percentage of population density, the percentage of healthy houses, and the percentage of puskesmas.

Metode Hierarchical Density-Based Spatial Clustering of Application with Noise (HDBSCAN) Pada Wilayah Desa/Kelurahan Tertinggal di Kabupaten Kutai Kartanegara: (Studi Kasus : Data Hasil Pendataan Potensi Desa (PODES) Tahun 2018) Wahyuni, Nanda Anggun; Hayati, Memi Nor; Rizki, Nanda Arista
EKSPONENSIAL Vol. 12 No. 1 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

The underdeveloped areas are generally the districts which are relatively underdeveloped compared to other regions on a national scale. Determination of underdeveloped villages is often done in order to determine the distribution of government assistance so that assistance can be distributed appropriately. The identification is based on facilities, infrastructure, access, social, population and economy provided in the Village Potential data (PODES). The concept of grouping based on regional or spatial is done to find out certain characteristics in an area. HDBSCAN is a grouping concept with a parameter called Mpts. The purpose of this study is to know the number of clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method. The Mpts parameters that is used in this study is from 2 to 6. Based on the results of the analysis, the clusters formed in the grouping of underdeveloped villages / urban areas in Kutai Kartanegara Regency using the HDBSCAN method, were 3 clusters. Cluster 0 consists of 19 villages / urban areas , cluster 1 consists of 4 villages / urban areas and cluster 2 consists of 61 villages / urban areas. Based on the analysis, villages / urban areas included in cluster 1 could be the main target of the government in providing assistance and development of regional facilities / infrastructure.

Co-Authors - Purhadi Abda Abda Alifta Ainurrochmah Amanah Saeroni Anak Agung Gede Sugianthara Andi M. Ade Satriya Anjani Anjani Annabaa Aulia, Muzizah Asnita, Asnita Astuti, Putri Sri Cahyaningsih, Ariyanti Candra Dewi, Ni Luh Ayu Casuarina, Indah Putri Damayanti, Elok Dani, Andrea Tri Rian Darnah Darnah Darnah, Darnah Desi Yuniarti Deviyana Nurmin Dewi, Isma Diani, Milda Alfitri Dini Elizabeth Dwi Husnul Mubiin Edy Fahrin Emi Harmianti Eric Sapto Raharjo Fatma wati Fauzia, Rina Fauziyah, Meirinda Fidia Deny Tisna Amijaya Goenjatoro, Rito Hadisti, Zahrah Dhafina Hadistii, Zahrah Dhafiinia Hidayatullah, Aji Syarif Hisintus Suban Hurint Ibrahim, Rizky Nur Iim Masfian Nur Ika Purnamasari Ika Purnamasari Ika Puspita, Ika Ineu Sintia Julia Julia Julnita Bidangan Karima, Nabila Al Kartika Ramadani Khairun Nida Khasanah, Lisa Dwi Nurul Krisna Rendi Awalludin Lestari, Nur Aini Ayu Lili Widyastuti Lupinda, Indah Cahyani M. Fathurahman Mahmuda, Siti Marsandy, Aldwin Falah Hasan Masrawanti Masrawanti Meiliyani Siringoringo Messakh, Gerald Claudio Mochammad Imron Awalludin Muhammad Jainudin Nabilla, Maghrisa Ayu Nana Nirwana Nanda Arista Rizki Nida, Khairun Ningsih, Eva Lestari Nohe, Darnah Andi Nur - Azizah Nur Annisa Fitri Nur Azizah Nur Fajar Apriyani Nurmalia Purwita Yuriantari Nurmin, Deviyana Nurul Hidayah Oroh, Chiko Zet Paradilla, Yunda Sasha Pratama Yuly Nugraha Pratiwi, Reni Purhadi - Putri Ayu Dwi Lestari, Putri Ayu Dwi Putri, Nurlia Sucianti Rahmah, Putri Aulia Rahmaulidyah, Fatihah Noor Ramadani, Kartika Riska Veronika Rito Goejantoro, Rito Ronald Tediwibawa Safitri, Ranita Nur Sari, Devi Nur Endah Sa’diyah, Lita Vindiyatus Sekar Nur Utami Sembiring, Rinawati Sifriyani, Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Siti Mahmuda Siti Rahmah Binaiya Soraya, Raihana Sri Wahyuningsih Sri Wahyuningsih Sri Wahyuningsih Suerni, Widya - Sumartini Sumartini Surya Prangga Suyitno Suyitno Suyitno Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Syamsiar, Syamsiar Syaripuddin Syaripuddin Tiara Nur Hikmaulida Tiara Nurul Ma’ala Utami, Riska Putri Verawaty Bettyani Sitorus Wahyuni, Nanda Anggun Yuki Novia Nasution, Yuki Novia Yuniarti, Desi

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