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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 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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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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Klasterisasi Prevalensi Stunting Menggunakan K-Prototype pada Data Campuran Marsandy, Aldwin Falah Hasan; Hayati, Memi Nor; Fauziyah, Meirinda
METIK JURNAL Vol 8 No 2 (2024): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v8i2.824

Abstract

Cluster analysis is a statistical method for grouping objects based on the similar characteristics of each object. One of the algorithms used in cluster analysis is K-Prototype, which was developed to handle mixed data, namely numerical and categorical data. The validation method used to determine the optimal number of clusters in K-Prototype cluster analysis is the Elbow method. The aim of the research is to determine the optimal number of clusters and optimal cluster results on the prevalence of stunting and indicators that influence the prevalence of stunting in Indonesia in 2022. The results of the research show that the optimal number of clusters produced is 4 clusters, using the Elbow graph the WCSS (Within Cluster Sum Square) value is obtained. optimal is 65.83. Cluster 1 consists of 2 provinces, cluster 2 consists of 7 provinces, cluster 3 consists of 10 provinces, and cluster 4 consists of 15 provinces.
Prediksi Ketepatan Klasifikasi Status Predikat Lulusan Program Sarjana FMIPA Universitas Mulawarman Menggunakan Regresi Logistik Biner dan Neural Networks Khasanah, Lisa Dwi Nurul; Fathurahman, M.; Hayati, Memi Nor
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1301

Abstract

Classification is a learning technique for identifying categorical groups from a data set whose group member categories are known. Several methods that can be used in classification include binary logistic regression and neural networks. This research aims to compare the prediction results for the accuracy of the classification of predicate status for graduates of the FMIPA Mulawarman University undergraduate program in 2021. In the binary logistic regression method, the model parameters are estimated using the maximum likelihood estimation and Fisher scoring iteration methods. The neural networks used the backpropagation algorithm. The results of the research show that the classification accuracy using the confusion matrix obtained with binary logistic regression and neural networks is the same, namely 87.5%.
Penerapan Algoritma Divisive Analysis dalam Pengelompokan Provinsi di Indonesia Berdasarkan Prevalensi Stunting Suyono, Ari Krisna; Hayati, Memi Nor; Siringoringo, Meiliyani; Prangga, Surya; Fathurahman, M.
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1341

Abstract

Cluster analysis is an analysis that aims to group data (objects) based only on the information contained in the data that describes objects and the relationships between the objects. Divisive analysis is a clustering method using a top-down approach which starts by placing all objects into one cluster or what is called a hierarchical root and then dividing the cluster root into several smaller clusters. This research aimed to group 34 provinces in Indonesia into 2,3, and 4 clusters based on stunting prevalence data and factors causing stunting in 2022 using a divisive analysis algorithm. The results showed that for 2 clusters, cluster 1 consisted of 32 provinces with low stunting prevalence, and cluster 2 consisted of 2 provinces with high stunting prevalence. For 3 clusters, cluster 1 consisted of 26 provinces with moderate stunting prevalence, cluster 2 consisted of 6 provinces with low stunting prevalence, and cluster 3 consisted of 2 provinces with high stunting prevalence. For 4 clusters, cluster 1 consisted of 21 provinces with moderate stunting prevalence, cluster 2 consisted of 5 provinces with low stunting prevalence, cluster 3 consisted of 6 provinces with high stunting prevalence, and cluster 4 consisted of 2 provinces with very high stunting prevalence.
Selection of Optimum Exponential Smoothing Parameters with Golden Section to Forecast Rainfall in East Kutai Regency Sa’diyah, Lita Vindiyatus; Wahyuningsih, Sri; Hayati, Memi Nor
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1269

Abstract

The exponential smoothing method is one method that can be used to forecast time series data by smoothing the data. In this research, the method used is exponential smoothing with one smoothing parameter from Brown. The data used is the amount of rainfall in East Kutai for the period January 2017 to December 2021. The purpose of this study was to obtain the optimum parameter value of the exponential smoothing method using the golden section method to obtain MAPE values and obtain forecasting results for the amount of rainfall in East Kutai Regency for the period January to March 2022. From the results of the analysis, smoothing parameters was obtained optimum in Double Exponential Smoothing (DES) of 0.3924052 and Triple Exponential Smoothing (TES) of 0.1995108. The results showed that forecasting the amount of rainfall with the DES method had a MAPE of 37.9061200% and the TES method had a MAPE of 39.4323800%. The DES method is a better method than the TES method to forecast the amount of rainfall in East Kutai Regency.
Pengelompokan Kabupaten/Kota di Kalimantan Berdasarkan Indikator Pendidikan Menggunakan Metode K-Means dengan Optimasi Principal Component Analysis Putri, Nurlia Sucianti; Hayati, Memi Nor; Goejantoro, Rito
EKSPONENSIAL Vol. 15 No. 2 (2024): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v15i2.1373

Abstract

Cluster analysis is used to group several objects based on similarities within the group. There are many methods included in cluster analysis, including k-means. K-means is a non-hierarchical cluster analysis method. The assumption that needs to be considered in cluster analysis is that there is no strong correlation between research variables. An alternative that can be done to deal with variables that are strongly correlated is to use Principal Component Analysis (PCA). This research aims to group districts/cities in Kalimantan based on education indicators in 2022 using k-means with PCA optimization, as well as finding out the optimal cluster based on the smallest Davies Bouldin Index (DBI) value. Based on the results of the analysis, from 11 research variables two main components were formed. From these two main components, new data transformations are produced which are then used in grouping districts/cities in Kalimantan based on education indicators using the k-means methods. The analysis results, it was found that the optimal cluster with k-means grouping was 5 clusters with a DBI value of 0.835. Cluster 1 has 8 regencies/cities, cluster 2 has 16 regencies/cities, cluster 6 has 5 regencies/cities, cluster 4 has 21 regencies/cities, and cluster 5 has 5 regencies/cities.
PERBANDINGAN KLASIFIKASI ALGORITMA C5.0 DENGAN CLASSIFICATION AND REGRESSION TREE (STUDI KASUS: DATA SOSIAL KEPALA KELUARGA MASYARAKAT DESA TELUK BARU KECAMATAN MUARA ANCALONG TAHUN 2019) Pratiwi, Reni; Hayati, Memi Nor; Prangga, Surya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 14 No 2 (2020): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.946 KB) | DOI: 10.30598/barekengvol14iss2pp267-278

Abstract

Decision tree is a algorithm used as a reasoning procedure to get answers from problems are entered. Many methods can be used in decision trees, including the C5.0 algorithm and Classification and Regression Tree (CART). This research aims to determine the classification results of the C5.0 and CART algorithms and to determine the comparison of the accuracy classification results from these two methods. The variables used in this research are the average monthly income (Y), employment (X1), number of family members (X2), last education (X3) and gender (X4). After analyzing the results obtained that the accuracy rate of C5.0 algorithm is 79,17% while the accuracy rate of CART is 84,63%. So it can be said that the CART method is a better method in classifying the average income of the people of Teluk Baru Village in Muara Ancalong District in 2019 compared to the C5.0 algorithm method
A PENERAPAN METODE SUBTRACTIVE FUZZY C-MEANS PADA TINGKAT PARTISIPASI PENDIDIKAN JENJANG SEKOLAH MENENGAH ATAS/SEDERAJAT DI KABUPATEN/KOTA PULAU KALIMANTAN TAHUN 2018 Suerni, Widya -; Hayati, Memi Nor; Goejantoro, Rito
VARIANCE: Journal of Statistics and Its Applications Vol 2 No 2 (2020): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol2iss2page63-74

Abstract

Cluster analysis is a data exploration method uses to obtain hidden characteristics by forming data clusters. One of the cluster analysis methods is Subtractive Fuzzy C-Means (SFCM). SFCM is a combination of Subtractive Clustering and Fuzzy C-Means methods. The SFCM method has the advantages of not requiring many iterations and the results obtained are more stable and accurate than the FCM and SC methods. This study aims to determine the result of clustering on the enrollment rate data for Senior High School (SHS) / equivalent. The data used were the enrollment rate data for high school / equivalent level in the Regency / City of Kalimantan Island in 2018 using three variables, namely the Crude Participation Rate (CPR), the School Participation Rate (SPR) and the Net Enrollment Rate (NER). Based on the three validity indices, namely Partition Coefficient Index (PCI) Validity Index, Modified Partition Coefficient Index (MPCI), and Xie & Beni Index (XBI) in the SFCM method, the optimal cluster were two clusters. Keywords: clustering, education, Subtractive Fuzzy C-Means
Klasifikasi Naïve Bayes Pada Data Status Kesejahteraan Rumah Tangga Penerima Manfaat di Kecamatan Samarinda Ilir Tahun 2023 Lupinda, Indah Cahyani; Goejantoro, Rito; Hayati, Memi Nor; Hidayatullah, Aji Syarif
EKSPONENSIAL Vol. 16 No. 1 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/eksponensial.v16i1.1489

Abstract

Data mining is the process of extracting useful information and patterns from very large amounts of data. Based on the task or work performed, data mining is divided into cluster analysis, association analysis, anomaly detection, and predictive modeling. Predictive modeling consists of two types, namely regression and classification. Classification is a method for determining the membership of an object in a class based on available data. There are several methods for classification, one of which is naïve Bayes with the advantages of being easy to build and having good performance. This research aims to determine the results of the accuracy of the naïve Bayes classification on data on the welfare status of beneficiary households in Samarinda Ilir District in 2023. Based on the research results, it can be seen that the accuracy level of the naïve Bayes classification on this data is 0.8316 or 83.16%. The results of accuracy measurements show that the naïve Bayes classification of this data has a fairly high level of accuracy.
Klasterisasi Prevalensi Stunting Menggunakan K-Prototype pada Data Campuran Marsandy, Aldwin Falah Hasan; Hayati, Memi Nor; Fauziyah, Meirinda
METIK JURNAL (AKREDITASI SINTA 3) Vol. 8 No. 2 (2024): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v8i2.824

Abstract

Cluster analysis is a statistical method for grouping objects based on the similar characteristics of each object. One of the algorithms used in cluster analysis is K-Prototype, which was developed to handle mixed data, namely numerical and categorical data. The validation method used to determine the optimal number of clusters in K-Prototype cluster analysis is the Elbow method. The aim of the research is to determine the optimal number of clusters and optimal cluster results on the prevalence of stunting and indicators that influence the prevalence of stunting in Indonesia in 2022. The results of the research show that the optimal number of clusters produced is 4 clusters, using the Elbow graph the WCSS (Within Cluster Sum Square) value is obtained. optimal is 65.83. Cluster 1 consists of 2 provinces, cluster 2 consists of 7 provinces, cluster 3 consists of 10 provinces, and cluster 4 consists of 15 provinces.
Pemodelan GWR Menggunakan Fungsi Pembobot Adaptive Box-Car Pada Angka Kesakitan DBD di Pulau Kalimantan Tahun 2023 Candra Dewi, Ni Luh Ayu; Hayati, Memi Nor; Fauziyah, Meirinda
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 7 No. 01 (2025)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm355

Abstract

Demam Berdarah Dengue (DBD) merupakan penyakit yang disebabkan oleh penyebaran virus dengue yang berkaitan dengan karakteristik suatu wilayah yang berbeda-beda. GWR merupakan pemodelan yang mempertimbangkan adanya aspek lokasi yang berbeda-beda sehingga akan menghasilkan penduga parameter yang bersifat lokal di setiap lokasi pengamatan. Penelitian ini bertujuan untuk mendapatkan model GWR dan faktor-faktor yang berpengaruh signifikan terhadap angka kesakitan DBD di kabupaten/kota di Pulau Kalimantan Tahun 2023. Penaksiran parameter model GWR menggunakan metode Weighted Least Square (WLS) dengan fungsi kernel adaptive box-car sebagai pembobot spasial dan nilai bandwidth optimum ditentukan menggunakan kriteria Cross-Validation (CV). Hasil penelitian mendapatkan nilai koefisien determinasi model GWR sebesar 51,04%, yang nilai koefisien determinasinya lebih besar dibandingkan regresi linier berganda. Hasil estimasi parameter model GWR didapatkan model yang nilai koefisien determinasinya berbeda-beda di setiap lokasi pengamatan. Faktor-faktor yang berpengaruh signifikan adalah ketinggian di atas permukaan laut, ketidaktersediaan fasilitas buang air besar, dan jarak ke Ibu Kota Provinsi
Co-Authors - Purhadi Abda Abda 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 Diani, Milda Alfitri 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 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