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INDONESIA
Jurnal Aplikasi Statistika & Komputasi Statistik
Published by Politeknik Statistika STIS
ISSN : 20864132     EISSN : 26151367     DOI : -
Core Subject : Science, Education,
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics
Redaksi menerima karya ilmiah atau artikel penelitian mengenai kajian teori statistika dan komputasi statistik pada bidang ekonomi dan sosial dan kependudukan, serta teknologi informasi. Redaksi berhak menyunting tulisan tanpa mengubah makna subtansi tulisan. Isi jurnal Aplikasi Statistika dan Komputasi Statistik dapat dikutip dengan menyebutkan sumbernya.
Arjuna Subject : -
Articles 12 Documents
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Search results for , issue "Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus" : 12 Documents clear
Classification of Village Development Index at Regency/Municipality Level Using Bayesian Network Approach with K-Means Discretization Nasiya Alifah Utami; Arie Wahyu Wijayanto
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i1.390

Abstract

Village development has been one of the most important targets of government policies in Indonesia in order to fully optimize its potential. Under Law 06 Year 2014 on Villages, local governments from regency/municipality level to village level are required to understand their respective village potentials in order to increase the village potentials in their regions. In this paper, we build and analyze the Bayesian network methods to classify the village development index at regency/municipality and gain a better understanding of the causal relationships between independent variables of the village potential status. Using a web scraping method of information retrieval, data are collected from the Ministry of Village, Development of Disadvantaged Regions, and Transmigration (Kemendesa) website, and Village Development Evaluation (Indeks Pembangunan Desa—IPD) of Statistics Indonesia (BPS) publication in 2018 data. Further, we combine the discretization using the K-Means clustering method to handle the continuous nature of retrieved data. An extensive comparison of different learning structures of the Bayesian Network is performed, which includes the learning structure of Naive Bayes, Maximum Spanning Tree with weighted Spearman correlation coefficient, Hill Climbing search, and Tabu Search during the construction of Bayesian networks. For fairness evaluation, all constructed models are built using 80% data as a training set and the remaining 20% as a testing set. The results show that Bayesian network approach can be applied in village development index status classification where the construction using maximum spanning tree with K-Means data discretization gain the best performance of 90.69% accuracy.
The Effects of Price, Income, and Household Characteristics on Ultra-Processed Food Consumption In Jakarta, Indonesia Atika Putri Syatira; Ekaria Ekaria
Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus
Publisher : Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/jurnalasks.v14i1.386

Abstract

During the 2010s, ultra-processed food consumption in Indonesia increases and leads to high rates of obesity and chronic non-communicable diseases. DKI Jakarta has the highest ultra-processed food consumption and obesity prevalence in Indonesia. Therefore, raw data from “Core” and “Consumption and Expenditure” modules of March 2019 Susenas (Indonesia National Socioeconomic Survey) are analysed to examine ultra-processed food consumption and how economic factors and household characteristics affect it in Jakarta. The analysis is conducted using M-estimation robust regression due to a large number of influential outliers in the data. The research sample is divided into three classes based on daily per capita expenditure. The results show that ultra-processed food consumption increases with income class. Higher ultra-processed food consumption occurs in households that pay higher price for ultra-processed food, have higher per capita income, have more children or adolescents, and have working female household head or wife. For Class 3 households, formal sector households consume more ultra-processed food than informal sector households. While for Class 1 households, households with female household head or wife with senior high school degree or above consume more ultra-processed food than households with female household head or wife with junior high school degree or below.

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