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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
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 CREDIT SCORING TERHADAP STATUS PEMBAYARAN BARANG ELEKTRONIK DAN FURNITURE MENGGUNAKAN BOOTSTRAP AGGREGATING K-NEAREST NEIGHBOR Astuti, Putri Sri; Hayati, Memi Nor; Goejantoro, Rito
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 4 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (556.631 KB) | DOI: 10.30598/barekengvol15iss4pp735-744

Abstract

Classification is the process of grouping objects that have the same characteristics into several categories. This study applies a combination of classification algorithms, namely Bootstrap Aggregating K-Nearest Neighbor in credit scoring analysis. The aim is to classify the credit payment status of electronic goods and furniture at PT KB Finansia Multi Finance in 2020 and determine the level of accuracy produced. Credit payment status is grouped into 2 categories, namely smoothly and not smoothly. There are 7 independent variables that are used to describe the characteristics of the debtor, namely age, number of dependents, length of stay, years of service, income, amount of payment, and payment period. The application of the classification algorithm at the credit scoring analysis is expected to assist creditors in making decisions to accept or reject credit applications from prospective debtors. The results showed that the accuracy obtained from the Bootstrap Aggregating K-Nearest Neighbor algorithm with a proportion of 90:10, m=80%, C=73, and K=5 was the best, which was 92.308%.
IMPLEMENTATION OF THE FUZZY GUSTAFSON-KESSEL METHOD ON GROUPING DISTRICTS/CITIES IN KALIMANTAN ISLAND BASED ON POVERTY ISSUES FACTORS Paradilla, Yunda Sasha; Hayati, Memi Nor; Sifriyani, Sifriyani
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (364.419 KB) | DOI: 10.30598/barekengvol17iss1pp0125-0134

Abstract

Cluster analysis is an analysis that is useful in summarizing data by grouping objects based on certain similarity characteristics. One of the group analysis is Fuzzy Gustafson-Kessel (FGK) which is the development of the Fuzzy C-Means (FCM) method. The FGK method has a good way in adjusting the form of cluster membership function correctly for a data. This study aims to determine the results of the optimal number of groups based on the Partition Coefficient (PC) and Classification Entropy (CE) validity indexes and to find out the results of grouping 56 districts/cities on the island of Kalimantan based on poverty issue factors in 2021. The optimal number of groups using the FGK method based on the validity indexes of PC and CE are two groups. The first group and the second group each consist of 28 districts/cities in Kalimantan Island.
PENERAPAN SPATIAL DURBIN MODEL PADA DATA PENYAKIT MALARIA DI INDONESIA Nabilla, Maghrisa Ayu; Hayati, Memi Nor; Sifriyani, Sifriyani
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 19 No. 2 (2025): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v19i2.20334

Abstract

The Spatial Durbin Model (SDM) is a special case of the Spatial Autoregressive (SAR) model, involving the addition of spatial lag effects of both the dependent and independent variables. The parameter estimation used in this study is the maximum likelihood estimator. Parameter estimation for the SDM is performed at each observation location using spatial weighting. The spatial weights are calculated based on queen contiguity and customized contiguity weighting methods. This study aims to obtain the SDM and identify the factors influencing the number of malaria cases in Indonesia in 2023. The Lagrange Multiplier (LM) test indicates that there is a spatial lag in the dependent variable, with the parameter ρ being significant at a significance level of α = 0.1. Based on the results of the SDM analysis, it was found that the factors directly influencing the number of malaria cases in Indonesia in 2023 are the percentage of poor population, number of medical personnel and the percentage of households with access to adequate drinking water services. Meanwhile, the factors that have an indirect or spatial lag effect are the open unemployment rate and the percentage of poor population.
Pendampingan Desain Infografis dengan Statistika dan Sains Data Bagi Siswa/Siswi MAN 1 Kota Samarinda Muhammad Fathurahman; Dani, Andrea Tri Rian; Fauziyah, Meirinda; Darnah; Goenjatoro, Rito; Hayati, Memi Nor; Prangga, Surya; Siringoringo, Meiliyani; Oroh, Chiko Zet
Journal of Research Applications in Community Service Vol. 4 No. 3 (2025): Journal of Research Applications in Community Service
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/jarcoms.v4i3.5158

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk memberikan pendampingan desain infografis yang mengintegrasikan ilmu statistika dan sains data serta meingkatkan literasi data bagi siswa dan siswi MAN 1 Kota Samarinda. Dalam era digital yang ditandai dengan kemudahan akses informasi, masih terdapat kekurangan pemahaman di kalangan siswa mengenai pemanfaatan teknologi, khususnya dalam desain infografis berbasis statistika dan sains data. Infografis merupakan alat yang efektif untuk menyajikan informasi secara visual yang membantu mempercepat pemahaman data kompleks menjadi lebih mudah dipahami. Aplikasi Canva dipilih sebagai platform dalam pendampingan ini karena kemudahan penggunaannya, yang memungkinkan siswa untuk berkreasi secara mandiri. Berdasarkan hasil tes awal, siswa belum memanfaatkan dengan optimal pengembangan ilmu data sains dalam pembuatan desain infografis. Oleh karena itu, kegiatan ini dirancang untuk memberikan pemahaman dan keterampilan praktis kepada peserta agar mereka dapat menggunakan teknologi visual dalam mengelola dan menyampaikan informasi berbasis data dengan lebih efektif dan inovatif. Melalui metode pengabdian ini, diharapkan terjadi peningkatan pemahaman dan keterampilan dalam penggunaan desain infografis serta pemanfaatan sains data literasi siswa yang dapat diterapkan dalam kegiatan belajar mengajar, terutama dalam pengolahan dan penyajian data statistik.
COMPARISON OF K-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFICATION METHODS FOR STATUS OF TODDLER NUTRITION DATA AT BAQA SAMARINDA SEBERANG COMMUNITY HEALTH CENTER Annabaa Aulia, Muzizah; Goejantoro, Rito; Hayati, Memi Nor
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 1 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.1.2025.1-13

Abstract

Classification is a job of assessing data objects to put them into a certain class from a number of available classes. The naïve Bayes method is a statistical classification that can be used to estimate the probability of membership in a class. Meanwhile, the K-Nearest Neighbor (K-NN) method is a supervised method used for classification. The aim of this research is to obtain classification results of the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center in 2022 using the naïve Bayes algorithm and the K-NN algorithm. Based on the calculation results for classification of the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center using accuracy calculations and confusion matrices, the highest accuracy was obtained using the naïve Bayes method of 82.15% and a Press's Q value of 168 with a training data proportion of 90%: testing data of 10%. Meanwhile, the results of accuracy calculations and the confusion matrix obtained the highest accuracy in the K-NN method of 90.57% at values 3-NN, 5-NN, 7-NN, 9-NN and Press's Q value of 187.65 with a training data proportion of 90% and testing data 10%. From the results of this analysis, it was concluded that the K-NN method worked better than the naïve Bayes method in classifying the nutritional status of toddlers at the Baqa Samarinda Seberang Community Health Center.
PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN DATA JUMLAH KEJADIAN DAN DAMPAK BENCANA BANJIR MENGGUNAKAN METODE FUZZY C-MEANS Hayati, Memi Nor; Goejantoro, Rito; Siringoringo, Meiliyani; Purnamasari , Ika; Yuniarti, Desi; Nida, Khairun; Messakh, Gerald Claudio
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 6 No. 01 (2024)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm167

Abstract

Cluster analysis is a technique used to find groups of similar data objects. The Fuzzy C-Means (FCM) method is a data grouping method where the existence of each data in a cluster is determined by the degree of membership. This study aims to determine the optimal number of clusters based on the Modified Partition Coefficient (MPC) validity index and to determine the optimal grouping results of 34 provinces in Indonesia based on data on the number of events and the impact of floods in 2017-2021. The optimal number of clusters using the FCM method is based on MPC value consists of 2 clusters, namely the first cluster consisting of 27 provinces in Indonesia and the second cluster consisting of 7 provinces in Indonesia.
Peramalan Nilai Tukar Petani Kalimantan Timur Menggunakan Metode Neural Network Rahmah, Putri Aulia; Hayati, Memi Nor; Cahyaningsih, Ariyanti
Indonesian Journal of Applied Statistics and Data Science Vol. 2 No. 1 (2025): Mei
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/ijasds.v2i1.5855

Abstract

The farmer exchange rate (NTP) is a significant indicator for measuring the purchasing power of Indonesian farmers, who are the main actors in the agricultural sector. This is because the agricultural sector is one of the main sectors in Indonesia, one of which is in East Kalimantan Province. This study aims to predict and forecast the NTP of East Kalimantan Province using the Neural Network (NN) method with the backpropagation algorithm. The data used is the NTP data of East Kalimantan Province for the period January 2020 to September 2024 obtained from the BPS of East Kalimantan Province. This study tested 5 NN architecture models with different numbers of layers in the hidden layer, namely 1, 2, 3, 4, and 5 layers in the hidden layer. The study was conducted using 1 input variable, a learning rate of 0.01, a maximum of 10,000 iterations, and a threshold of 0.5. Based on the training process that has been carried out, it was concluded that the best NN architecture that can be used to forecast the NTP of East Kalimantan Province is NN with 5 layers in the hidden layer with a MAPE of 2.087%.
Implementasi Metode Fuzzy Possibilistic C-Means pada Pengelompokan Provinsi di Indonesia Berdasarkan Data Jumlah Kejadian dan Dampak Bencana Banjir Nida, Khairun; Hayati, Memi Nor; Goejantoro, Rito
Journal of Mathematics Education and Science Vol. 7 No. 1 (2024): Journal of Mathematics Education and Science
Publisher : Universitas Nahdlatul Ulama Sunan Giri Bojonegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32665/james.v7i1.1919

Abstract

Analisis cluster merupakan salah satu teknik dalam data mining yang digunakan untuk menemukan kelompok objek data yang serupa. Metode Fuzzy Possibilistic C-Means (FPCM) adalah salah satu metode clustering yang merupakan pengembangan dari algoritma Fuzzy C-Means (FCM) dan Possibilistic C-Means (PCM) dengan menggunakan kelebihan dari pemodelan fuzzy dan possibilistic. Penelitian ini bertujuan untuk mengetahui jumlah cluster optimal berdasarkan indeks validitas Modified Partition Coefficient (MPC) serta mengetahui hasil pengelompokan optimal 34 Provinsi di Indonesia berdasarkan data jumlah kejadian dan dampak bencana banjir pada tahun 2017-2021. Menurut Badan Nasional Penanggulangan Bencana (BNPB) sejak tahun 2017 hingga 2021 jumlah bencana alam yang terjadi di Indonesia mencapai 18.658 kejadian di mana bencana banjir termasuk kategori bencana yang besar dengan persentase total kejadian 28% sejak tahun 2017 hingga 2021. Oleh sebab itu, perlu dilakukan pengelompokan Provinsi di Indonesia berdasarkan dampak bencana banjir sebagai upaya mitigasi dalam mengenali risiko bencana banjir. Jumlah cluster optimal dengan menggunakan metode FPCM berdasarkan indeks validitas MPC adalah sebanyak 2 cluster yaitu cluster pertama beranggotakan 19 Provinsi di Indonesia dan cluster  kedua beranggotakan 15 Provinsi di Indonesia. Cluster pertama didominasi oleh provinsi di Kepulauan Sumatera yang sebagian besar kawasannya terdiri dari dataran tinggi dan pegunungan, serta provinsi yang terletak di Kepulauan Papua dan Maluku yang memiliki jumlah penduduk lebih kecil dibandingkan dengan provinsi lain. Sementara pada cluster kedua didominasi oleh provinsi dengan jumlah pemukiman bantaran sungai yang cukup tinggi.
Clustering Regency in Kalimantan Island Based on People's Welfare Indicators Using Ward's Algorithm with Principal Component Analysis Optimization Ningsih, Eva Lestari; Mahmuda, Siti; Hayati, Memi Nor
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.5363

Abstract

Cluster analysis is used to group objects based on similar characteristics, so that objects in one cluster are more homogeneous than objects in other clusters. One method that is widely used in hierarchical clustering is Ward's algorithm. This method works by minimizing the sum of squared distances between objects in one cluster (within-cluster variance) to produce optimal clustering. However, one important assumption in using this method is that there is no high correlation between variables, or in other words, the data must be free from multicollinearity. Multicollinearity can cause distortion in distance calculation, resulting in less accurate clustering results. To overcome this problem, a Principal Component Analysis (PCA) approach is used to reduce the dimension and eliminate the correlation between variables by forming several mutually independent principal components. This research aims to cluster 56 districts/cities in Kalimantan Island based on 19 indicators of people's welfare in 2023, using Ward's algorithm optimized through PCA. Validation of clustering results is done using the Silhouette Coefficient value to assess the quality of clustering. This research method is a combination of Principal Component Analysis (PCA) and hierarchical clustering using Ward’s algorithm. PCA was applied to reduce 19 welfare-related indicators into four principal components that retained most of the essential information in the dataset. The clustering process based on these components resulted in two optimal clusters, as determined by a Silhouette Coefficient value of 0.651, which indicates a moderately strong cluster structure. The results of this research are that the first cluster consists of 47 districts/cities characterized by relatively low welfare levels, while the second cluster comprises 9 districts/cities with comparatively higher welfare conditions. These findings imply the existence of considerable disparities in welfare among regions on Kalimantan Island. The results can be used as a reference for policymakers in formulating more targeted and equitable development strategies
Fuzzy geographically weighted clustering pada pengelompokan kabupaten/kota di Kalimantan berdasarkan indikator indeks pembangunan manusia Diani, Milda Alfitri; Hayati, Memi Nor; Goejantoro, Rito
Mandalika Mathematics and Educations Journal Vol 7 No 4 (2025): Desember
Publisher : FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jm.v7i4.9977

Abstract

Fuzzy Geographically Weighted Clustering (FGWC) is a method of development of fuzzy clustering by considering geographical elements in the process of regional clustering. This study aims to identify the number and characteristics of optimal clusters formed using the FGWC algorithm with the validity index of the Partition Coefficient Index (PCI). The data analyzed includes HDI indicators for all regencies/cities on the island of Kalimantan in 2024 consisting of the variables Life Expectancy, Length of Schooling, Average School Length, Expenditure per Capita, Open Unemployment Rate, and Percentage of Poor Population. Based on the results of the study, the optimal number of clusters was obtained as many as 2 clusters with a PCI of 0.516. Cluster 1 consists of 18 regencies/cities covering 9 cities and 9 regencies with higher average values of HDI indicator variables, while cluster 2 consists of 38 regencies which are all dominated by inland areas with lower average values of HDI indicator variables.
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