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All Journal Eksakta : Berkala Ilmiah Bidang MIPA Limits: Journal of Mathematics and Its Applications
Maiyastri
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Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Clustering Provinces in Indonesia Based on The Main Food Crop Production Using The Spatial Fuzzy C-Means Yozza, Hazmira; Aldi Mukhlis; Maiyastri
EKSAKTA: Berkala Ilmiah Bidang MIPA Vol. 25 No. 04 (2024): Eksakta : Berkala Ilmiah Bidang MIPA (E-ISSN : 2549-7464)
Publisher : Faculty of Mathematics and Natural Sciences (FMIPA), Universitas Negeri Padang, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/eksakta/vol25-iss04/385

Abstract

Agriculture plays a strategic role in achieving food security in Indonesia. However, the production of major food crops in Indonesia shows uneven distribution, which may affect efforts to achieve food self-sufficiency. This study aims to cluster 34 provinces in Indonesia based on the total production of seven major food crops (rice, corn, soybean, mung bean, peanut, cassava, and sweet potato) using the Spatial Fuzzy C-Means (sFCM) method. Cluster validation using Modified Partition Coefficient (MPC) and Partition Entropy (PE) shows that the clustering results have high membership clarity and low entropy, making them relevant for spatial data analysis. The findings highlight the unequal distribution of food crop production and provide policy recommendations, where the first cluster can be optimized as a national food production hub, while the second cluster requires interventions based on infrastructure, technology, and redistribution policies.  This research makes an important contribution in providing a data-driven scientific basis for food production equity planning. The sFCM method used demonstrates effective capabilities in analyzing data with spatial elements, supporting more inclusive policies for the improvement of national food security and the achievement of sustainable development goals in Indonesia. 
Komparasi Model Pertambahan Tinggi Badan Balita Stunting Dengan Metode Regresi Kuantil dan Regresi Kuantil Bayesian Ferra Yanuar; Cintya Mukti; Maiyastri
Limits: Journal of Mathematics and Its Applications Vol. 20 No. 2 (2023): Limits: Journal of Mathematics and Its Applications Volume 20 Nomor 2 Edisi Ju
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

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Abstract

Penelitian ini bertujuan untuk mengkonstruksi model pertambahan tinggi badan balita stunting di Kabupaten Solok dengan menggunakan metode regresi kuantil dan metode regresi kuantil Bayesian. Metode regresi kuantil merupakan metode pendugaan parameter yang dilakukan dengan membagi data menjadi dua bagian yang tidak harus simetri ketika dicurigai terdapat perbedaan nilai estimator pada kuantil-kuantil tertentu. Metode regresi kuantil Bayesian merupakan metode pendugaan parameter yang menggabungkan konsep analisis kuantil ke dalam pendekatan Bayesian. Pada pendekatan Bayesian, digunakan distribusi Asymmetric Laplace Distribution (ALD) dalam pembentukan fungsi likelihood sebagai dasar dari pembentukan distribusi posterior yang digunakan dalam proses pendugaan parameter. Data penelitian yang digunakan adalah data 950 balita stunting di Kabupaten Solok pada bulan Agustus 2021 dan bulan Februari 2022. Pada penelitian ini diperoleh bahwa metode regresi kuantil Bayesian menghasilkan model dugaan yang lebih baik daripada metode regresi kuantil. Kriteria kebaikan model adalah berdasarkan lebar selang kepercayaan 95% terpendek dan nilai terbesar. Faktor-faktor yang signifikan dalam mempengaruhi pertambahan tinggi badan balita stunting di Kabupaten Solok adalah jenis kelamin, berat badan lahir, ASI eksklusif, dan imunisasi.
Co-Authors Aldi Mukhlis Cintya Mukti Hazmira Yozza Yanuar, Ferra