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<![CDATA[Determinan Demografi Penggunaan Internet Petani Padi di Indonesia dan Kaitannya Dengan Produktivitas ]]>
<![CDATA[Kadir Kadir]]>;
<![CDATA[Octavia Rizky Prasetyo]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.807
<![CDATA[This study aims to identify the demographic determinants of internet use on paddy farmers in Indonesia and the link of the usage to the paddy yield cultivated by the farmers. A logistic regression model implemented on the enumeration results of the Intercensal of Agricultural Survey (SUTAS) carried out by Statistics Indonesia (BPS) in 2018 found that internet use among the paddy farmers is a phenomenon of young farmers, well educated, men and residents of Java Island. Unfortunately, paddy farmers with those combined demographic characteristics only represent a small fraction of the total paddy farmers in Indonesia. This study also pointed out that there is a statistically significant difference in the yield of paddy cultivated by farmers using the internet and non-users. The average yield of internet users is significantly higher than that of non-internet users. This finding is a strong indication that internet use by paddy farmers could have a significant impact on the increase of paddy yield cultivated.]]>
<![CDATA[Faktor-Faktor yang Memengaruhi Pembangunan Manusia ]]>
<![CDATA[Rizq Taufiq Bahtiar Razendrya]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.810
<![CDATA[Pembangunan menjadi hal yang sangat penting terutama bagi negara yang sedang berkembang. Upaya peningkatan kualitas penduduk sebagai sumber daya pembangunan, baik dari sektor kesehatan, pendidikan, dan ekonomi, sehingga berdampak positif pada peningkatan partisipasi penduduk dalam pembangunan. Pada penelitian ini digunakan analisis faktor eksploratori dengan tujuan untuk mengetahui faktor-faktor yang memberikan kontribusi terhadap pembangunan manusia di Provinsi Nusa Tenggara Timur. Dengan hasil penelitian yang diperoleh adalah bahwa sektor ekonomi dilihat dari PDRB dan pengeluaran perkapita memiliki pengaruh paling besar terhadap pembangunan manusia, kemudian diikuti oleh sektor pendidikan yang didasarkan dari rata-rata lama sekolah dan harapan lama sekolah, dan sektor kesehatan yang berdasarkan umur harapan hidup menjadi yang paling kecil pengaruhnya terhadap pembangunan manusia di Provinsi Nusa Tenggara Timur. Berdasarkan hasil ini, pemerintah dapat melakukan pengeluaran anggaran daerah untuk sektor ekonomi dan usaha agar pengeluaran perkapita dan PDRB mengalami peningkatan, yang nantinya diharapkan bisa meningkatkan kualitas pembangunan manusia di Provinsi NTT.]]>
<![CDATA[Efek Berita mengenai COVID-19 terhadap Harga Saham di Indonesia ]]>
<![CDATA[Saraswati Dyah Pramuji]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.815
<![CDATA[Information from the media can be one of the considerations for investors in investing stocks, so the role of the media is very meaningful in determining stock price movements. This study aims to analyze the long and short term influence of media coverage related to COVID-19 on stock prices in Indonesia during the economic recovery from April to May 2021 using Error Correction Mechanism (ECM). The results show that the infodemic index variable and the number of deaths due to COVID-19 have a negative effect on stock prices in the long term. The media hype index variable and the number of deaths due to COVID-19 have a negative effect on stock prices in the short term. Meanwhile, the sentiment index and panic index variables have no effect on stock prices, both in the long and short term. It is known that the number of deaths due to COVID-19 has a negative effect on stock prices in the long and short term, so that the proper handling of COVID-19 to lower the death rates can improve the investment climate in this economic recovery period.]]>
<![CDATA[Nowcasting Jumlah Penumpang Kereta Api di Indonesia Menggunakan Indeks Google Trends ]]>
<![CDATA[Parma Dwi Widy Oktama]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.820
<![CDATA[Jumlah penumpang kereta api merupakan salah satu indikator strategis di sektor transportasi. Rilis data yang dilakukan BPS dengan jeda waktu tiga puluh hari sejak berakhirnya bulan amatan, menjadikan kondisi indikator ini tidak diketahui secara real time. Dengan trend penggunaan mesin pencari di internet yang semakin masif dewasa ini, penelitian ini bertujuan untuk mengkaji kemungkinan pemanfaatan Indeks Google Trends (GT) untuk melakukan nowcasting sehingga didapatkan kondisi indikator lebih awal. Hasil penelitian menunjukkan bahwa indeks GT dan official statistics yang dirilis BPS memiliki korelasi kuat dan kesamaan pola pergerakan dari waktu ke waktu. Penerapan Indeks GT ke dalam pemodelan runtun waktu menggunakan time series regression dan SARIMAX mampu mendekati nilai aktual variabel respon dengan cukup baik. Selain itu terbukti bahwa pemanfaatan indeks GT dapat meningkatkan akurasi peramalan model. Dengan time series regression, hasil nowcasting jumlah penumpang kereta api di Indonesia pada bulan Agustus 2021 adalah sebesar 9,03 juta orang.]]>
<![CDATA[Pengaruh Tingkat Minat Penelusuran Antar Penyedia Transportasi Online di Google Trends ]]>
<![CDATA[Vania Orva Nur L]]>;
<![CDATA[Paramita Dewanti]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.822
<![CDATA[Transportasi online adalah transportasi umum yang penggunaannya menggunakan aplikasi berbasis android dengan tujuan yang lebih efisien dalam hal waktu tempuh dan tarif yang jauh lebih murah. Transportasi online mempunyai banyak kelebihan dibandingkan dengan transportasi konvensional. Tarif yang jauh lebih murah menjadi alasan utama jenis transportasi ini banyak diminati. Hingga saat ini, transportasi online mengalami kemajuan yang sangat pesat di Indonesia. Hampir di semua tempat, transportasi online tersebut dapat dengan mudah ditemui dan juga siap digunakan dengan cara menggunakan aplikasi berbasis android agar bisa menikmati pelayanan transportasi tersebut. Sejak diakuisisnya Uber oleh Grab sejak awal tahun 2018, hanya ada dua penyedia jasa transportasi online yang merajai pasar di Indonesia yaitu Gojek dan Grab. Keduanya saling bersaing untuk menjaring banyak konsumen dengan mengeluarkan banyak promo kepada para penggunanya. Guna melihat pengaruh tingkat minat penelusuran “Grab” terhadap pengaruh tingkat minat penelusuran “Gojek” di Google Trends maka dipergunakanlah model Fungsi Transfer. Model fungsi transfer merupakan salah satu model time series yaitu gabungan pendekatan regresi dan ARIMA pada Analisis Deret Waktu (ADW). Berdasarkan model fungsi transfer dapat diketahui jika tingkat minat penelusuran “Grab” dan “Gojek” di Google Trends tidak saling mepengaruhi satu dengan lainnya.]]>
<![CDATA[SEM-PLS Untuk Analisis Struktur Kemiskinan Pada Masa Pandemi Covid-19 ]]>
<![CDATA[Rahma Rahma Nuryanti]]>;
<![CDATA[Tulus Soebagijo]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.836
<![CDATA[The Covid-19 pandemic has had various impacts, especially on the economic aspect. This difficult economic condition causes people's income to decrease, and causes the number of poor people to increase. The number of poor people increased by 1.28 million people in 2020. East Java Province is a province that has a poverty rate (10.20 percent) slightly higher than the national (10.19 percent) in 2020. This is due to the impact of the pandemic. leading to job losses and increased poverty rates. This study will analyze the structure of poverty in East Java Province in 2020. The purpose of this study is to see the structure of poverty in East Java Province in 2020. The analytical method used in this study is component-based Structural Equation Modeling (SEM), namely Partial Least Square. (PLS). In the structural equation model there are 4 significant paths, namely the effect of health variables on the education variable, the effect of health and education variables on the economy, and the influence of economic variables on the poverty variable. The results of the Grouping Analysis with Finite Mixture Partial Least Square based on the Akaike Information Criterion (AICk), Consistent Akaike Information Criterion (CAICk) and Bayesian Information Criterion (BICk) and Normal Entrophy (EN) criteria, the best results were 2 segments. So that from 38 regencies/cities in East Java Province, they can be grouped into 2 segments. The First Segment is 91.9 percent of the total regencies/cities, and the Second Segment is 8.1 percent of the total regencies/cities in the East Java region. The regencies/cities in the second segment are Situbondo Regency, Nganjuk Regency and Kediri City. Meanwhile, 35 other regencies/cities are in the first segment.]]>
<![CDATA[Analisis Kondisi Pemberdayaan Gender di Indonesia Tahun 2020 dengan Agglomerative Hierarchical Clustering dan Biplot ]]>
<![CDATA[Dina Salsabila]]>;
<![CDATA[Muhammad Yunus Hendrawan]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.839
<![CDATA[Pemberdayaan gender menjadi salah satu agenda penting dalam tujuan pembangunan berkelanjutan, baik dalam level nasional maupun internasional. Penelitian ini bertujuan untuk mengetahui kondisi pemberdayaan gender di Indonesia tahun 2020. Data yang digunakan adalah Indeks Pemberdayaan Gender (IDG) dan data tiga komponen pembentuk IDG yang bersumber dari BPS. Penelitian ini melakukan clustering IDG di Indonesia dengan metode agglomerative hierarchical clustering dan biplot. Dari hasil clustering IDG didapatkan tiga klaster IDG tahun 2020 berdasarkan kesamaan karakteristik komponen pembentuk IDG. Klaster pertama terdiri dari 3 provinsi yang memiliki nilai komponen IDG lebih rendah jika dibandingkan dengan kelompok lain, terutama dari sisi komponen keterwakilan perempuan dalam parlemen. Klaster kedua terdiri dari 5 provinsi yang memiliki nilai tinggi pada komponen keterwakilan perempuan dalam parlemen. Adapun klaster ketiga terdiri dari 26 provinsi yang memiliki nilai sedang pada ketiga komponen IDG. Hasil clustering ini dapat menjadi pertimbangan bagi pemerintah dalam menentukan kebijakan terkait pemberdayaan gender diIndonesia.]]>
<![CDATA[Indeks Komposit Pekerjaan Layak di Indonesia Pada Era Pandemi COVID-19 ]]>
<![CDATA[Katamso Noto Santoso]]>;
<![CDATA[Suryo Adi Rakhmawan]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.840
<![CDATA[The pandemic has an impact on various levels of socio-economic life, including employment. The impact of reducing working hours, temporarily not working, unemployment, or being out of the labour force is felt by formal and informal workers. Moreover, informal workers have become a vulnerable workforce. These problems make it necessary to know how the conditions of employment in Indonesia are during the pandemic. This study calculates the Decent Work Index (DWI) during the pandemic in Indonesia, which aims to find out the description of the provinces in Indonesia so that relevant parties can carry out planning to careful evaluation. This study found that several areas still have low DWI using factor analysis. This means that the central and local governments in the related areas should be more sensitive and responsive to the work worthiness of the population, starting from focusing on variables that have a significant impact.]]>
<![CDATA[Penggunaan Data Google Trends untuk Peramalan Tingkat Pengangguran Terbuka di Tingkat Nasional dan Regional di Provinsi Jawa Barat ]]>
<![CDATA[Lisa Widyarsi]]>;
<![CDATA[Hardius Usman]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.842
<![CDATA[Unemployment rate data is needed by the government as a basis for making and evaluating policies, especially in times of crisis such as the COVID-19 pandemic. However, official statistics produced by BPS show lag time in presentation and limited survey frequency. Therefore, data with more frequency is needed and presented in real-time as a complement to official statistics, by utilizing big data, one of which is the Google Trends Index. This study aims to utilize Google Trends data to predict the Open Unemployment Rate in Indonesia and West Java. This study uses the ARIMAX method with a research period of February 2005-February 2020. The results of this study found that the Google Trends index can be used to predict the Open Unemployment Rate at the national and regional levels in West Java with good accuracy.]]>
<![CDATA[Linkages Perekonomian Bali ]]>
<![CDATA[Rizky Zulkarnain]]>;
<![CDATA[Nasiyatul Ulfah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2021i1.845
<![CDATA[Several studies have been conducted to analyze multiplier effect of Bali economy. However, those studies merely focus on intersectoral linkages using Input-Output (IO) model, whereas regional production activities could be interdependent through various externalities and supply-chain networks. This study aims to analyze Bali economy by considering interregional linkage as well as intersectoral linkage. This study employs Inter Regional Input Output (IRIO) model. The IRIO table (17 industries x 34 provinces) was acquired from Statistics Indonesia (BPS). The results showed that there were several key sectors in Bali economy: Electricity and Gas, Transportation and Storage, Information and Communication, and Business Activities. Electricity and Gas had highest intersectoral linkage and output multiplier. Moreover, interregional analysis showed that final demand shock in Bali had significant impact to provinces in Java, especially East Java. On the other side, Bali economy was influenced by final demand shock in West Nusa Tenggara.]]>
