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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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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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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
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