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<![CDATA[Visualisasi dan Diseminasi Kerangka Induk BS2020 Berbasis Web ]]>
<![CDATA[Sukron Al Amin]]>;
<![CDATA[Waris Marsisno]]>
<![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.v2021i1.913
<![CDATA[The Master Framework of the 2020 Census Block (BS2020) is the result of mapping activities conducted by the Central Statistics Agency (BPS) in 2019. This master framework contains a collection of census blocks in a statistical working area (wilkerstat) with the attributes of load, dominant load, and type of census block. The geographic information system (GIS) used by BPS currently cannot display digital maps down to the smallest unit of analysis level, namely the census block and local environmental unit (SLS). The purpose of this research is to build a web-based BS2020 GIS that has the ability to visualize and disseminate digital maps up to the Census Block and SLS levels. System development is carried out using the Rapid Application Development (RAD) method. The GIS for Visualization and Dissemination of the BS2020 Master Framework has been successfully built. The results of the Black Box testing show that all scenarios have run as expected. Meanwhile, from the user assessment using the Scale Usability System method, a score of 77.95 was obtained which indicates that the system has been functioning and can be well received by users.]]>
<![CDATA[Penerapan Klasifikasi Kueri untuk Meningkatkan Efektivitas Mesin Pencari ]]>
<![CDATA[Handy Geraldy]]>;
<![CDATA[Lutfi Rahmatuti Maghfiroh]]>
<![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.v2021i1.914
<![CDATA[As a data provider, BPS provides services for the public to access BPS data. One of these services is the search service on the BPS website. However, the search services have not met consumer expectations. One of the efforts that can be done to meet consumer expectations is to increase search effectiveness to make it more relevant to the user's intent. Therefore, this study aims to build a query classification function on a search engine and test whether this function can improve search effectiveness. The query classification function is built using a machine learning model. We compare five algorithms, those are SVM, random forest, Gradient Boosting, KNN, dan Naive Bayes. Of the five algorithms used, the best model is obtained from the SVM algorithm. Then, the function is implemented on a search engine whose effectiveness is measured based on precision and recall values. As a result, the query classification function can narrow the search results to a particular query, thereby increasing the precision value. However, the query classification function does not affect the recall value.]]>
<![CDATA[Penerapan Regresi Robust pada Pengeluaran Listrik Rumah Tangga Miskin di Perkotaan dan Perdesaan Kawasan Timur Indonesia Tahun 2020 ]]>
<![CDATA[Vina Tri Yasmi]]>;
<![CDATA[Lia Yuliana]]>
<![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.v2021i1.923
<![CDATA[The Eastern Region of Indonesia (EI) has a lower electrification ratio than the Western Region of Indonesia. This means that there are more households in EI that do not have access to electricity which is also in line with the high percentage of poverty in EI. The percentage of poverty in rural areas is also higher and access to electricity is more difficult than in urban areas. The electricity subsidy budget targeting poor households also continues to increase even though the targeted subsidy policy was implemented in 2017. Therefore, this study aims to determine the socio-economic characteristics and electricity consumption of poor households in urban and rural EI in 2020, as well as analyze socio-economic characteristics that affect the electricity expenditure of poor households in urban and rural EI in 2020. The results with robust regression indicate that the variables that had a positive significant effect on the electricity expenditure of poor households in urban and rural areas are higher education of household head, number of household members, floor area, income per capita, electronic ownership, and the significant negative is residence ownership. The secondary education of household head are only significant in rural areas, while those that are not significant in urban and rural areas are the age of household head and working activity of household head.]]>
<![CDATA[Analisis Pengaruh PDB dan Rasio Keuangan terhadap Harga Saham Perusahaan Big-Cap Sektor Properti yang Terdaftar di BEI pada Masa Pandemi Tahun 2020 ]]>
<![CDATA[Monica Seftaviani Sijabat]]>;
<![CDATA[Lia Yuliana]]>
<![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.v2021i1.925
<![CDATA[The COVID-19 pandemic that occurred in Indonesia had a major impact on the capital market. The stock prices of all sectors experienced significant fluctuations. The property sector is the sector with the lowest share price in 2020. This can be seen from the movement of the property sector index which experienced a downward trend. The movement of the sector index is influenced by the movement of stock prices of large-cap companies (Big-Cap), where the average share price of Big-Cap companies tends to decrease compared to the fourth quarter of 2019. This study aims to determine what variables affect prices. Big-Cap company shares in the property sector in 2020. Panel data regression analysis was used to determine the effect of GDP, CR, DER, ROE, and PBV on the share price of Big-Cap companies in the property sector. The results showed that DER and ROE had a negative effect, while PBV had a positive effect on the stock price of Big-Cap companies in the property sector significantly. Variables CR and GDP have no significant effect on the share price of Big-Cap companies in the property sector. Therefore, investors who invest in shares of Big-Cap companies in the property sector need to consider the value of financial ratios, especially DER, ROE, and PBV before making investment decisions.]]>
<![CDATA[Penerapan Empirical Best Linear Unbiased Prediction (EBLUP) pada Pendugaan Tingkat Pengangguran Terbuka Level Kecamatan di Provinsi Banten ]]>
<![CDATA[Apriliansyah -]]>;
<![CDATA[Ika Yuni Wulansari]]>
<![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.v2021i1.927
<![CDATA[Pengangguran merupakan masalah yang kompleks baik disebabkan maupun memberikan dampak terhadap banyak faktor. Di Indonesia, indikator pengangguran diukur melalui tingkat pengangguran terbuka (TPT). Indikator ini dikumpulkan melalui Survei Angkatan Kerja Nasional (Sakernas). Banten merupakan provinsi dengan TPT tertinggi serta selalu masuk dalam lima besar TPT tertinggi di Indonesia sejak tahun 2016 hingga 2018. Sebagai upaya percepatan penurunan angka pengangguran, perlu adanya informasi sampai ke level terkecil. Namun, sampel yang digunakan oleh Sakernas tidak mencukupi untuk pendugaan TPT secara langsung. Tujuan dari penelitian ini adalah menghasilkan penduga tingkat pengangguran terbuka (TPT) level kecamatan dengan presisi yang lebih baik melalui Small Area Estimation (SAE). Metode SAE yang digunakan adalah Empirical Best Linear Unbiased Prediction (EBLUP). Data yang digunakan adalah indikator penyusun TPT dari Sakernas 2018 dan variabel penyerta yang berasal dari Podes 2018 Provinsi Banten. Hasil dari penelitian ini menunjukkan bahwa terdapat sepuluh variabel penyerta yang berkorelasi dengan TPT. Kemudian, nilai RRMSE menunjukkan bahwa EBLUP meningkatkan presisi pendugaan jika dibandingkan dengan penduga langsung. Terdapat dua kecamatan yang memiliki hasil TPT sangat tinggi yaitu kecamatan Curugbitung dan Koroncong. Hasil estimasi EBLUP pada TPT level kecamatan ini dapat digunakan oleh pemerintah daerah untuk menurunkan TPT secara lebih terfokus dan lebih tepat sasaran, utamanya pada kecamatan-kecamatan dengan TPT yang tinggi.]]>
<![CDATA[Penerapan Regresi Logistik Biner pada Status Kesejahteraan Rumah Tangga di Provinsi Bali Tahun 2020 ]]>
<![CDATA[Vinanda Sonya Permatasari]]>;
<![CDATA[Lia Yuliana]]>
<![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.v2021i1.943
<![CDATA[Welfare is a target of all countries including Indonesia. Actions to achieve prosperity can be started by evaluating the region and following in the footsteps of other prosperous regions. Researchers chose Bali as a region that can be used as a role model in the field of welfare because of its success in achieving prosperity and reducing poverty. This study aims to determine the general description of household welfare and the effect of household characteristics on household welfare in Bali in 2020. The research unit of analysis used was households with working and temporarily unemployed household heads. The analytical method used is descriptive and binary logistic regression analysis. The results are the number of household members, gender of head of household, age of head of household, last education of household head, household business field, and main occupation sector of the household head have a significant effect on welfare in Bali in 2020.]]>
<![CDATA[Analisis Simultan Neraca Pembayaran Indonesia Tahun 2009-2020 ]]>
<![CDATA[Rulfi Annas Salamah]]>;
<![CDATA[Krismanti Tri Wahyuni]]>
<![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.v2021i1.953
<![CDATA[Sejak tahun 1970, Indonesia melibatkan diri dalam perdagangan internasional. Seluruh transaksi ekonomi antara penduduk dan non penduduk tercatat dalam neraca pembayaran Indonesia (NPI). Dari tahun 2014 hingga 2020 tw-II NPI cukup berfluktuasi, bahkan transaksi berjalan selalu mengalami defisit. Untuk mengatasi defisit diperlukan dolar lebih banyak lagi sehingga nilai tukar rupiah terdepresiasi. Penelitian ini bertujuan untuk menganalisis faktor-faktor yang mempengaruhi kinerja NPI pada tahun 2009-2020 melalui model persamaan simultan. Model persamaan simultan diperlukan untuk melihat hubungan antara NPI dan nilai tukar rupiah. Hasil penelitian menunjukkan adanya hubungan simultan antara NPI dan nilai tukar, namun NPI tidak berpengaruh signifikan terhadap nilai tukar. NPI dipengaruhi signifikan oleh nilai tukar, net export, neraca modal dan finansial, serta NPI triwulan sebelumnya, sedangkan suku bunga dan dummy Covid-19 tidak berpengaruh signifikan. Variabel yang berpengaruh signifikan terhadap nilai tukar adalah nilai tukar triwulan sebelumnya. Peningkatan nilai ekspor dan kestabilan nilai tukar rupiah diperlukan untuk memperbaiki kinerja NPI.]]>
<![CDATA[Model Spatial Autoregressive Pengangguran Provinsi Kepulauan Bangka Belitung ]]>
<![CDATA[Ridho Ilahi]]>
<![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.v2021i1.955
<![CDATA[Pengangguran merupakan masalah utama yang dihadapi oleh Provinsi Kepulauan Bangka Belitung. Keberadaan dependensi spasial menyebabkan pengangguran kabupaten/kota dipengaruhi oleh pengangguran kabupaten/kota yang berdekatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi pengangguran regional di Provinsi Kepulauan Bangka Belitung menggunakan data panel dengan memasukkan unsur dependensi spasial. Adanya kedekatan secara geografis/spasial dan kedekatan secara ekonomi antar kabupaten/kota memungkinkan terjadinya spillover antar daerah melalui proses migrasi, transfer pengetahuan dan penyebaran informasi, sehingga muncul dependensi spasial pengangguran antar kabupaten/kota. Penelitian ini menggunakan Model Spatial Autoregressive dengan kombinasi bobot spasial queen contiguity dan jalur transportasi untuk menggambarkan arus migrasi tenaga kerja. Penelitian ini menghasilkan suatu temuan, yaitu pertumbuhan ekonomi dan dependensi spasial berpengaruh signifikan terhadap pengangguran. Adanya dependensi spasial menyebabkan Indeks Pembangunan Manusia tidak berpengaruh signifikan dan koefisien Tingkat Partispasi Angkatan Kerja berlawanan arah. Penelitian ini bermanfaat bagi pemerintah provinsi maupun kabupaten/kota dalam menyusun kebijakan untuk mengatasi masalah pengangguran regional.]]>
<![CDATA[Analisis Faktor-Faktor yang Mempengaruhi Produk Domestik Regional Bruto Berdasarkan Kondisi Infrastruktur di Jawa Timur Menggunakan Regresi Data Panel ]]>
<![CDATA[Rosa Viana Nur Addini]]>;
<![CDATA[Dwi Endah Kusrini]]>
<![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.v2021i1.956
<![CDATA[Kinerja ekonomi yang kuat dikaitkan dengan infrastruktur yang ada di suatu negara atau wilayah. Infrastruktur merupakan aspek penting, mesin penggerak perekonomian dan penopang utama unsur-unsur sistem sosial ekonomi dalam masyarakat. Tujuan dari penelitian ini untuk mengetahui karakteristik data dan faktor-faktor mana saja yang mempengaruhi Produk Domestik Regional Bruto (PDRB) berdasarkan kondisi infrastruktur di Jawa Timur pada tahun 2016 hingga 2019 menggunakan statistika deskriptif dan regresi data panel. Hasil analisis terpilih model terbaik REM (Random Effect Model) dengan variabel infrastruktur kesehatan, infrastruktur air, dan infrastruktur hotel, penginapan, dan restoran dengan pengaruh positif signifikan terhadap PDRB Jawa Timur dengan koefisien determinasi sebesar 72,35%, dimana model mampu menjelaskan variasi PDRB di Jawa Timur sebesar 72,35%, sedangkan 27,65% dijelaskan oleh variabel lain yang belum termasuk dalam model.]]>
<![CDATA[Dampak Minyak Dunia, Energi Terbarukan, dan Kebijakan Moneter Terhadap Laju Pertumbuhan Ekonomi di Indonesia Tahun 1987-2019 ]]>
<![CDATA[Andi Nur Fauziyah Syafriany]]>;
<![CDATA[Ika Yuni Wulansari]]>
<![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.v2021i1.960
<![CDATA[One of the indicators that can determine the progress of a country is economic growth rate. Indonesia as one of developing country has a volatile economic growth rate from year to year. This thing can be affect by some factors in some aspects of life such energy, production factors, and monetary. Therefore, the goal of this study is know the general description of Indonesia’s economic growth rate 1987-2019 and the factors that can affect Indonesia’s economic growth rate, both in the long and short term. This study use Error Correction Mechanism method. The result of this study is world oil production, exchange rate of Rupiah to United Stated Dollar, and interest rate significantly influenced Indonesia economic growth rate in long term. Besides that, gross fixed capital formation, exchange rate of Rupiah to United Stated Dollar, interest rate, and Error Correction Term (ECT) significantly influenced Indonesia economic growth in short term.]]>
