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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 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) Eksponensial Journal of Mathematics, Computation and Statistics (JMATHCOS) IJASDS: Indonesian Journal of Applied Statistics and Data Science

Memi Nor Hayati

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Author-ID : 278791

Religion Arts Humanities 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 Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Analisis Diagram Kontrol Fuzzy U: Studi Kasus: Kecacatan Produk Kayu Lapis (Plywood) di PT. Segara Timber Mangkujenang, Samarinda Provinsi Kalimantan Timur Tahun 2018 Fauzia, Rina; Yuniarti, Desi; Hayati, Memi Nor
EKSPONENSIAL Vol. 11 No. 1 (2020)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Fuzzy in general means that an element can be classified into two sets simultaneously. Fuzzy control diagrams are very suitable to be used for observations that produce information (data) that is uncertain, unclear and based on one's subjectivity. This study was applied to data on plywood products in PT. Segara Timber, Samarinda, East Kalimantan Province in 2018. The purpose of this study is to get the results of the decision fuzzy u control diagram. Based on the results of the use of the fuzzy control diagram u produce the most found decisions are rather in control that is equal to 26 observations, while the second most is rather out of control that is equal to 22 observations, and out of control that is equal to 14 and in control of 5 out of 67 observation.

Penerapan Metode K-Means Dalam Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator Pendidikan Messakh, Gerald Claudio; Hayati, Memi Nor; Sifriyani, Sifriyani
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.v14i2.1103

Cluster analysis is an analysis that aims to classify data based on the similarity of spesific characteristics. Based on the structure, cluster analysis is divided into two, namely hierarchical and non-hierarchical methods. One of the non-hierarchical methods used in this study is K-Means. K-Means is a partition-based non-hierarchical data grouping method. This purpose of this study is to obtain the best results of grouping regencies/cities on the island of Kalimantan based on education indicators using the K-Means method based on the smallest ratio of standard deviation. Based on the results of the analysis, it can be concluded that the best grouping results based on the smallest ratio of standard deviation is 0.6052 which produces optimal clusters of 2 clusters with the first cluster consisting of 14 Regencies/Cities while the second cluster consists of 42 Regencies/Cities on Kalimantan Island

Pengklasifikasian Status Gizi Balita di Puskesmas Sempaja Samarinda menggunakan Probabilistic Neural Network (PNN) Tahun 2019 Lestari, Putri Ayu Dwi; Hayati, Memi Nor; Nasution, Yuki Novia
EKSPONENSIAL Vol. 12 No. 2 (2021)
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Probabilistic Neural Network (PNN) is a model in Artificial Neural Networks (ANN) that is used for classification. PNN depends on the smoothing parameter (α). PNN has the advantage of being able to value of problems that previously existed in the back propagation method of ANN. The PNN method in this study was applied to the nutritional status of toddlers. Assessment of the nutritional status of toddlers can be determined through measurements of the human body known as anthropometry. Parameters for determining nutritional status based on anthropometry are age, weight and height. Therefore, in this study, a classification of the nutritional status of children under five is carried out to determine whether the toddler is experiencing good nutrition or poor nutrition. It was found that PNN with the best classification accuracy rate on the nutritional status of toddlers, namely the proportion of training data and testing data of 50%: 50% with α = 1, with accuracy results between training data and training data of 85% and accuracy results between data testing of the training data by 70%.

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.

Co-Authors - Purhadi Alifta Ainurrochmah 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 Deviyana Nurmin Dewi, Isma Fatma wati Fauzia, Rina Fauziyah, Meirinda Fidia Deny Tisna Amijaya Goenjatoro, Rito Hadisti, Zahrah Dhafina Hadistii, Zahrah Dhafiinia Hidayatullah, Aji Syarif Ibrahim, Rizky Nur Ika Purnamasari Ika Purnamasari Ika Puspita, Ika Julnita Bidangan Karima, Nabila Al Khasanah, Lisa Dwi Nurul Krisna Rendi Awalludin Lestari, Nur Aini Ayu Lupinda, Indah Cahyani M. Fathurahman Mahmuda, Siti Marsandy, Aldwin Falah Hasan Meiliyani Siringoringo Messakh, Gerald Claudio Mochammad Imron Awalludin Nabilla, Maghrisa Ayu Nana Nirwana Nanda Arista Rizki Nida, Khairun Ningsih, Eva Lestari Nohe, Darnah Andi Nur Annisa Fitri Nur Azizah Nurmin, Deviyana Oroh, Chiko Zet Paradilla, Yunda Sasha Pratiwi, Reni Purhadi - Putri Ayu Dwi Lestari, Putri Ayu Dwi Putri, Nurlia Sucianti Rahmah, Putri Aulia Rahmaulidyah, Fatihah Noor Ramadani, Kartika Rito Goejantoro, Rito Safitri, Ranita Nur Sari, Devi Nur Endah Sa’diyah, Lita Vindiyatus Sembiring, Rinawati Sifriyani, Sifriyani Sinaga, Julia Oriana Siringoringo, Meiliyani Soraya, Raihana Sri Wahyuningsih Suerni, Widya - Surya Prangga Suyitno Suyitno Suyitno Suyitno Suyitno Suyono, Ari Krisna Syamsiar, Syamsiar Syaripuddin Syaripuddin Utami, Riska Putri Wahyuni, Nanda Anggun Yuki Novia Nasution, Yuki Novia Yuniarti, Desi

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