Jurnal Aplikasi Statistika & Komputasi Statistik
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.
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"Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus"
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<![CDATA[Development of Application Providing Public’s Perspectives on Official Statistical Indicators ]]>
<![CDATA[Novi Kanadia]]>;
<![CDATA[Siti Mariyah]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.391
<![CDATA[BPS-Statistics Indonesia, as an official data producer, puts data quality as a top priority. Public acceptance and trust in data reflect data reliability which is one of the data quality indicators. The existing survey that collects user acceptance, trust, and perspective to data produced by BPS can only reach data users in a limited number. Perspectives from a large number of data users cannot be captured. This research aims to build an application that collects and provides users’ perspectives and sentiment to official statistics sourced from online news. One feature in this application is Named Entity Recognition, which extracts public perspectives in entities such as names, organizations, statistical indicators, quotes or opinions, etc. This application objectively measures the sentiment of news discussing or citing statistical indicators. This application also facilitates BPS to do social network analysis to understand the relationships between fellow data users for each statistical indicator. The implicit goal is to effectively provide insights into how frequently society uses and refers to statistical indicators produced by BPS in any domain and their perspective on data. All models and features provided in this application have been evaluated based on standard performance metrics.]]>
<![CDATA[Mobility-Covid-19 Impact Quadrant : Quantitative Approach to Analyze Community Responses to Covid-19 Pandemic ]]>
<![CDATA[Usman Bustaman]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.387
<![CDATA[There are two premises regarding the relationship between mobility and COVID-19, namely: (1) mobility affects the spread of COVID-19, or (2) the spread of COVID-19 affects mobility. This paper further explores both premises to analyze community responses to COVID-19 pandemic using Google Mobility Index (mobility) and the COVID-19 Spread Risk Index (risk) of Indonesia. Cross-correlogram of both indices is examined to determine optimum values called Risk Detection Time (Rdt). A scatter plot of Rdt and its correlation coefficient resulted Mobility-COVID-19 Impact Quadrant which maps the community responses into four zones based on quadrant ‘conscious–competence’ framework. The results confirmed both premises: (1) risk can be triggered by mobility in the previous few days, or (2) mobility can represent the community responses to risk information in the previous few days. Regarding the mobility restriction implemented in Indonesia, the analysis shows that the community responses leaped from Learning zone in PSBB period (15/03/2020 to 31/05/2020) to the Recovery zone in the New Normal and PPKM period (01/06/2020 to 02/07/2021). However, the policy was late responded so that the recovery target did not go as expected and brought the community into Fear and Uncertainty zone in the Emergency PPKM period (starting from 03/07/2021).]]>
<![CDATA[Adoption of Agriculture Mechanization on Paddy Farmers in Indonesia: Demographic Determinants, Internet Access Influence, and The Impact of Adoption on The Yield ]]>
<![CDATA[Kadir Kadir]]>;
<![CDATA[O. R. Prasetyo]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.392
<![CDATA[This study aims to identify the demographic determinants of the agriculture mechanization adoption on paddy farmers in Indonesia and the impact of internet use by farmers on the probability of being adopters. Besides, it also analyses the difference in the average paddy yield cultivated by adopters and non-adopters to determine the adoption impact on agricultural productivity. We found that farmers' level of education and age significantly impacts the probability of being adopters. However, the magnitude of the age impact tends to be diminishing with the increase in age. The probability of being adopters is affected significantly by gender, region, and the farm scale. Male farmers tend to be more likely of being adopters than their female counterparts. Farmers in Java have a slightly higher probability of being adopters than farmers outside Java. Adopting agriculture mechanization also has a positive association with the farm scale, where the larger the farm scale, the more likely the farmers are to be adopters. Our study also found that internet use positively and significantly impacts the farmers' probability of being adopters. Moreover, our study also confirmed a strong indication that the mechanization adoption affects the paddy yield positively indicated by the higher paddy yield average of adopters than non-adopters. Therefore, boosting the adoption of mechanization must be done, for instance, by attracting young people with high education to get involved in agriculture and up-scaling the implementation of tools and agricultural machinery assistance facilitated by the government.]]>
<![CDATA[Klasifikasi Tutupan Lahan Berdasarkan Random Forest Algorithm Menggunakan Cloud Computing Platform ]]>
<![CDATA[Hady Suryono]]>;
<![CDATA[Arif Handoyo Marsuhandi]]>;
<![CDATA[Setia Pramana]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.383
<![CDATA[Statistik pertanian merupakan salah satu data yang vital di dunia dan memiliki kontribusi besar terhadap pencapaian tujuan program Sustainable Development Goals (SDGs). Dalam SDGs, perhatian terhadap ketahanan pangan difokuskan pada indikator kunci kedua yaitu nol kelaparan (SDG 2). Ketersediaan data tutupan lahan yang akurat diperlukan sebagai data dasar untuk luasan baku sawah yang akan digunakan untuk mengukur tingkat ketahanan pangan. Pemetaan tanaman membutuhkan pemrosesan dan pengelolaan data citra satelit dengan volume yang sangat besar dan tidak terstruktur yang mengarah pada permasalahan Geo Big Data dan menuntut teknologi dan sumber daya baru yang mampu menangani citra satelit dalam jumlah besar. Secara khusus, munculnya sumber daya cloud computing, seperti Google Earth Engine telah mengatasi masalah Geo Big Data ini. Kami menggunakan algoritma Random Forest (RF) pada platform Google Earth Engine (GEE) di Kota Jakarta Utara pada tahun 2019 untuk mengklasifikasikan tutupan lahan. Hasil penelitian menunjukkan bahwa overall accuracy (OA)]]>
<![CDATA[Digital Divide and A Spatial Investigation of Convergence in ICT Development Across Provinces in Indonesia ]]>
<![CDATA[Herdina Dwi Ramadhanti]]>;
<![CDATA[Erni Tri Astuti]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.388
<![CDATA[In the digital era, technology plays a much bigger role all over the world including in Indonesia. In fact, Indonesia’s ICT Development Index (IDI) has improved over time. However, it is noticeable that disparities among regions in regards to ICT development are still wide resulting in digital divide. This paper aims to examine the convergence of regions with regard to ICT development and analyze the key factors which drive the convergence process using annual data from 2015 to 2019. Spatial panel regression method is used to test for absolute and conditional beta convergence. The results reveal that there is evidence for the existence of absolute and conditional convergence in regards to ICT development in Indonesia. Regarding conditional convergence, the convergence process takes a shorter time to reach half-time convergence by taking into account several control variables consisting of output in ICT sector, FDI, and government allocation on education. In addition, the significant spatial correlation among provinces shows that the growth of neighboring provinces’ ICT development also contributes to the convergence process. This finding implies that the digital divide has declined and the factors mentioned above are important instruments to help bridge the digital divide.]]>
<![CDATA[Female Worker Problems : Skill Mismatch Versus Working Hours Mismatch ]]>
<![CDATA[Jamalludin Jamalludin]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.393
<![CDATA[Female workers have a double burden, between work in the office and at home. Skill mismatch and working hours mismatch exacerbate the problems faced by female workers. This study aims to analyze the relationship between skill mismatch and working hours mismatch with the job satisfaction of female workers. This study used secondary data from Happiness Level Measurement Survey (SPTK) 2017. Indonesia Statistics office organized SPTK2017 at all Indonesian provinces with 72.317 respondents. Respondents in SPTK2017 are the head of the household or his/her couple. The unit of analysis in this study was female workers with a total of 21,805 observations.The analytical method used is descriptive analysis and multiple linear regression. The descriptive findings show that as many as 22.1 percent of female workers with skills mismatch and 27.85 percent of female workers working hours mismatch are not satisfied with the work they are doing. The regression findings show that skill mismatch and working hours mismatch is negatively related to women's job satisfaction. Working hours mismatch has the strongest relationship to women's job satisfaction among other variables in the model.]]>
<![CDATA[Penerapan Regresi Generalized Poisson Pada Valuasi Ekonomi Objek Agrowisata: Studi Kasus Taman Bunga X di Kabupaten Pandeglang Provinsi Banten ]]>
<![CDATA[Moch Suryana]]>;
<![CDATA[Weksi Budiaji]]>;
<![CDATA[Setiawan Sariyoga]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.384
<![CDATA[Valuasi ekonomi objek wisata sangat penting agar objek wisata dapat dikembangkan dengan tepat. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi permintaan wisata dan menduga nilai ekonomi dari agrowisata. Sampel diambil dari 72 responden pengunjung agrowisata secara purposif. Faktor-faktor yang diukur adalah biaya perjalanan, pendapatan, jarak, usia, tingkat pendidikan, dan persepsi pengunjung terhadap objek wisata. Permintaan wisata dari agrowisata yang diukur dengan jumlah kunjungan dimodelkan menggunakan enam Regresi Generalized Poisson. Regresi terbaik dipilih berdasarkan kriteria informasi Akaike (AIC). Hasil penelitian menunjukkan bahwa permintaan wisata dipengarahui oleh jarak pengunjung dari tempat asal. Koefisien biaya perjalanan pada regresi Generalized Possion digunakan untuk menghitung nilai surplus konsumen yang menghasilkan Rp 284.900, - per kunjungan. Nilai ekonomi objek agrowisata adalah Rp 2.165.240.000, - per tahun dihitung dari Februari 2020 sampai dengan Januari 2021 yang mengindikasikan bahwa objek agrowisata sangat penting untuk dijaga.]]>
<![CDATA[Cluster Analysis Of Covid-19 Impact On Poverty In Indonesia Using Self-Organizing Map Algorithm ]]>
<![CDATA[Ika Nur Laily Fitriana]]>;
<![CDATA[Mohammad Okky Mabruri]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.389
<![CDATA[The increase in poverty rates caused by the COVID-19 pandemic requires immediate attention from policymakers. Each province in Indonesia has unique characteristics of poverty, and as a result, each province's response to COVID-19's impact on poverty is unique. As a result, a provincial cluster analysis based on the similarity of poverty characteristics is necessary to identify provinces that require increased vigilance. The purpose of this study is to cluster Indonesian provinces according to their similarity in terms of poverty impact before and during COVID-19. The impact of poverty prior to and during COVID-19 is quantified by comparing 2021 (during COVID-19) to 2019 (before COVID-19). We discovered that the COVID-19 has a significant impact on poverty. Hybrid SOM-Kmeans with three clusters is the optimal method for producing the smallest Davies-Bouldin Index. COVID-19 has high, moderate, and low impact on poverty, respectively. Cluster 1 is a cluster with a significant impact on poverty in a province where tourism is the primary industry. Due to sluggish tourism, the community's purchasing power is diminished, thereby increasing poverty. Cluster 3, namely Papua, has a low impact due to its primary sector characteristics in the mining sector.]]>
<![CDATA[Financial Inclusion Effect on Core Poverty During The Pandemic Period in East Java ]]>
<![CDATA[Abdus Salam]]>;
<![CDATA[Hermanto Hermanto]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.394
<![CDATA[Poverty is increasing globally at this time especially the impact of the Covid-19 pandemic. Meanwhile, the banking sector has a role to play in controlling the growing poverty, one of which is through financial inclusion. In some areas, the financial inclusion index has improved, indicated by the number of people who have switched from traditional savings to financial institution savings and have also reached the infrastructure of conventional commercial banks, but the fact has not yet reached the “usefulness of banking services”. So that financial inclusion is not really felt by the community. This study uses data from the March 2020 National Socio-Economic Survey (Susenas) and Podes 2020 to determine the effect of financial inclusion on core poverty. From the results of the logit regression estimation, it was found that financial inclusion had a strong effect on the status of core poverty in households when the COVID-19 pandemic hit in East Java (2020). Ownership of bank accounts has a strong and negative effect on poverty. The farther the distance to banking facilities, the greater the chance of experiencing poverty. Access to formal credit has the opportunity to move away from poverty. Education remains a key variable if you want to be free from poverty. The added value of farming households is still not optimal in increasing welfare, because they still have the opportunity to experience poverty. Access to information technology in households can increase opportunities to be more prosperous. From these results, the government should seek to increase public participation in having a bank account through banking literacy, through formal education, adding infrastructure for financial institutions evenly to villages, expanding access to formal credit services, and adding cellular/internet network infrastructure up to suburbs (rural/remote areas).]]>
<![CDATA[Estimasi Produktivitas Padi Level Kecamatan di Kabupaten Tulungagung Menggunakan Geoadditive SAE ]]>
<![CDATA[Garinca Firgiana Santoso]]>;
<![CDATA[Siti Muchlisoh]]>
<![CDATA[ Jurnal Aplikasi Statistika & Komputasi Statistik Vol 12 No 3 (2020): Jurnal Aplikasi Statistika dan Komputasi Statistik Edisi Khusus ]]>
Publisher : <![CDATA[Pusat Penelitian dan Pengabdian kepada Masyarakat Politeknik Statistika STIS ]]>
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DOI: 10.34123/jurnalasks.v14i1.385
<![CDATA[Paddy productivity data is one of the benchmarks for the government to audit the success of local food self-sufficiency program. Paddy productivity data is used to calculate paddy production in a region. Local government needs paddy production data at sub-district level to identify local food supply for the population. However, the estimation of paddy production data at sub-district level is constrained by the absence of paddy productivity data at sub-district level. BPS presents the data at regency level only. This research aims to estimate paddy productivity at sub-district level in Tulungagung Regency in 2019 using geoadditive small area estimation, evaluate the accuracy of the estimation using Root Mean Square Error (RMSE) and Relative Standard Error (RSE), and identify the rice surplus-deficit at sub-district level. Analysis method being used was inferential analysis using indirect estimation by geoadditive SAE. The estimation showed that the highest paddy productivity was in Pucanglaban Sub-district (8,8648 ton/ha), while the lowest paddy productivity in Pagerwojo Sub-district (3,6576 ton/ha). The use of geoadditive SAE gave more precision to the estimation because it produced smaller RMSE and RSE than direct estimation method. The estimation also showed that major sub-districts of Tulungagung Regency experienced surplus in rice during 2019, but there were also six sub-districts which suffered deficit in rice.]]>
