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All Journal MATHunesa: Jurnal Ilmiah Matematika Journal of Statistics and Data Science
Putri, Henida Ratna Ayu
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Integration Cluster and Path Analysis Based on Science Data in Revealing Stunting Incidents Marchamah, Mamlu’atul; Fernandes, Adji Achmad Rinaldo; Solimun; Wardhani, Ni Wayan Surya; Putri, Henida Ratna Ayu
Journal of Statistics and Data Science Vol. 1 No. 2 (2022)
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jsds.v1i2.23570

Abstract

The purpose of this research is to utilize big data to explore the factors that influence the prevalence of stunting in Wajak Regency, model these factors using integrated cluster analysis and` path analysis model, and develop an information system for stunting incidence modeling. This study uses a descriptive and explanative approach, namely using Discourse Network Analysis, cluster analysis, path analysis, and integration of cluster and path analysis. The sample of this research is children under five in Wajak District who were selected using stratified random sampling. The distance measure that has the highest model goodness value in modeling using the integration of cluster analysis with path analysis is the Mahalanobis distance measure. The cluster analysis with Mahalanobis distance produces 3 clusters where cluster one is a toddler who has a low stunting category, cluster two is a group of toddlers who has a moderate stunting category, and cluster three is a group of toddlers who has a high stunting category. The originality of this study is the application of Discourse Network Analysis analysis to obtain new variables followed by a comparison of three distances namely euclidean, manhattan, and mahalanobis in modeling using cluster integration and parametric paths.
PENAMBAHAN METODE NEURAL NETWORK DALAM PEMODELAN GSTAR-SUR UNTUK MENGATASI KASUS NON LINIER PADA PERAMALAN DATA CURAH HUJAN Iriany, Atiek; Fernandes, Adji Achmad Rinaldo; Efendi, Achmad; Putri, Henida Ratna Ayu; Ariyanto, Danang; Ngabu, Wigbertus
MATHunesa: Jurnal Ilmiah Matematika Vol. 12 No. 1 (2024)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v12n1.p226-236

Abstract

Salah satu model peramalan yang dapat yang menggabungkan unsur spasial (spatial) dan temporal (time) adalah Generalized Space Time Autoregressive (GSTAR). Pendugaan parameter yang digunakan adalah Seemingly Unrelated Regression (SUR). Peramalan iklim pada tanaman hortikultura pada masa kini sulit untuk diprediksi karena memiliki pola dan karakteristik yang sulit diidentifikasi dan dapat disebut aktivitas non linier. Unsur non linier ini dapat ditangkap oleh metode neural network. Penelitian ini ingin mengetahui hasil peramalan curah hujan pada 6 wilayah di Tengger menggunakan model GSTAR dengan pendugaan parameter menggunakan metode SUR dan digabungkan dengan neural network agar hasil peramalan yang lebih akurat. Data yang digunakan dalam penelitian ini adalah data curah hujan enam lokasi di wilayah Tengger, yakni Ngadirejo, Puspo, Wonokitri, Argosari, Ngadas, dan Wonokerto. Model yang tepat dalam melakukan peramalan pada data curah hujan pada 6 lokasi Tengger adalah model GSTAR (1,2,3,4,5,6,7,36(1)) Backpropagation Neural Network (96-120-6).
Co-Authors Achmad Efendi Atiek Iriany Danang Ariyanto Fernandes, Adji Achmad Rinaldo Marchamah, Mamlu’atul Ni Wayan Surya Wardhani Solimun Wigbertus Ngabu