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<![CDATA[Estimasi dan Komunikasi Data Penduduk Umur Sekolah Berdasarkan Proyeksi Penduduk Indonesia 2020-2050 Hasil Sensus Penduduk 2020 ]]>
<![CDATA[Prasojo, Ari Purwanto Sarwo]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2021
<![CDATA[Education inequality is a challenge in human development in Indonesia. Providing and communicating data on school-age population projections is important to achieve equitable quality education. This article estimates the school-age population based on Indonesia’s 2020-2050 population projections from the 2020 Population Census (SP2020) and develops an open-source interactive visualization application in the form of the R library “estPUSek” to explore these dynamics in 34 provinces. The estimation used the Sprague method, which proved accurate based on testing with Long Form Population Census 2020 (LF SP2020) data. The "estPUSek" application provides visualisations in the form of heat map plots, maps, dynamic tables, and time series plots, allowing for interprovincial and intertemporal analysis. This application is designed to facilitate discussions on the future dynamics of the school-age population, which is expected to support the development of better education programs.]]>
<![CDATA[Penerapan Stratified Sampling pada Klasifikasi Gambar Penyakit Daun Padi dengan Metode K-Nearest Neighbor ]]>
<![CDATA[Rizqi, Tasya Anisah]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2023
<![CDATA[Cultivation of rice plants, which is the staple food of Indonesian people, will experience a decline in yields in 2023, causing losses for farmers. Rice cultivation has obstacles such as disease which causes rice yields to decrease. This research aims to classify rice leaf disease using the KNN method by applying stratified sampling to RGB image extraction, so that farmers can easily maintain the stability of rice yields which decrease over time. The data used is rice leaf disease image data in .png format which consists of 3 classes, namely bacterial leaf blight, bacterial leaf streak, and brown leaf spot. The digital image processing used is an RGB image which is extracted and the average value of the red, green, and blue layers is taken. This research produces a superior KNN classificarion accuracy value on RGB images using stratified sampling of 73% compared to random sampling of 69%.]]>
<![CDATA[Pengaruh Degradasi Lahan Terhadap Keberlanjutan Pertanian Padi di Indonesia Hasil Survei Pertanian Terintegrasi (SITASI) 2021 ]]>
<![CDATA[Putri, Fitriani Aditya]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2024
<![CDATA[In realizing sustainable development in accordance with the second pillar of the 2045 Golden Indonesia Development related to food security and environmental stability, the current and future agricultural sector is faced with various challenges, one of which is land degradation. Land degradation can threaten agriculture in Indonesia. This study aims to examine the impact of land degradation on the sustainability of paddy farming in Indonesia. Based on data from the 2021 Integrated Agricultural Survey (SITASI), estimation results using multiple linear regression show that land degradation has an effect on paddy production in Indonesia. Land degradation in the form of soil erosion, soil infertility, and salinity of irrigated land can actually reduce paddy production in Indonesia. Therefore, the government's role is very important in preventing land degradation in Indonesia, such as implementing an environmentally friendly organic farming system and increasing the role of agricultural institutions. The hope is that agricultural land degradation can be prevented in order to maintain the sustainability of paddy farming in Indonesia in realizing food security by 2045.]]>
<![CDATA[Analisis Penawaran Tenaga Kerja di Provinsi Bali Studi Tahun 2019, 2020, dan 2022 ]]>
<![CDATA[Hartania, Made Sukma]]>;
<![CDATA[Cesarie, Herlinda Febrianti]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2026
<![CDATA[The Corona Virus Disease 2019 (COVID-19) pandemic has an impact on business and labor supply. This research aims to see changes in labor supply in 2019 (before pandemic), 2020 (during pandemic), and 2022 (after pandemic). Year 2021 is not included considering that economic conditions have not fully recovered from the pandemic. The data use is Bali Province’s The National Labour Force Survey (SAKERNAS). The analytical models used in this research are multiple linear regression. Study included the inverse Mills ratio variable to eliminate selective bias which occur as a result of non-random selection of individuals in the model. The results show that there were changes in labor supply in Bali Province. The impact of increasing income during the pandemic on working hours was recorded to be lower when compared to before and after the pandemic. Pandemic shows that the economic shock that occurred affected the labor supply in Bali Province.]]>
<![CDATA[Pemodelan Geographically Weighted Panel Regression (GWPR) pada Rumah Tangga yang Menempati Rumah Kumuh di Indonesia Tahun 2020—2022 ]]>
<![CDATA[Ninda, Faradilla Chairin]]>;
<![CDATA[Dinayanti, Lisma]]>;
<![CDATA[Berliana, Sarni Marniar]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2028
<![CDATA[Slum housing is a global problem, and it's on the 11th goal of the Sustainable Development Goals (SDGs). In Indonesia, the percentage of households occupying slum houses decreased significantly between 2020 and 2022. The average annual decrease is only 7.7%, down from 12.58% in 2019. The Theory of Slums and the Culture of Poverty concept definitively states that the emergence of slums is caused by social and economic characteristics. However, it is clear that spatial characteristics also influence slum housing due to its dispersed pattern. The regional autonomy policy also plays a role in regulating slum houses. To address these issues, we employ a method that can overcome spatial heterogeneity in panel data: Geographically Weighted Panel Regression (GWPR). The inferential analysis yielded 23 groups based on variables with significant influence. The predictor variables were open unemployment rate, life expectancy, average years of schooling, population growth rate, population density, proportion of informal employment, and percentage of poor people.]]>
<![CDATA[Pemanfaatan Machine Learning untuk Imputasi Data ]]>
<![CDATA[Lusmana, Putri Hera]]>;
<![CDATA[Paramudita, Ayu]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2032
<![CDATA[The challenge that is often faced in large-scale company-based surveys is non-response. Non-response is one of the causes of missing data. One way that can be used to deal with missing data is to perform data imputation. So far, data imputation in the Annual Survey of Manufacturing Industries (STPIM) has been carried out by using a combination of various approaches, including historical, auxiliary unit, and clerical imputation. These methods, however, tend to be inefficient and are unable to measure the outcomes’ accuracy. In light of these limitations, we aim to introduce an innovative approach in data imputation processing by utilizing machine learning. By comparing various machine learning methods, we obtained the results that K-nearest neighbors imputation is the best method in terms of accuracy in imputing output data in STPIM 2021. Meanwhile, in terms of computing performance, linear Support Vector Machine (SVM) gives the most efficient processing time.]]>
<![CDATA[Pembangunan Sistem Informasi Manajemen Agenda Kegiatan Berbasis Web di BPS Provinsi Nusa Tenggara Barat ]]>
<![CDATA[Dupayana, I Putu Agus Wahyu]]>;
<![CDATA[Anang, Yunarso]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2041
<![CDATA[The simplification of the organizational structure by BPS-Statistics Indonesia aims to form a simpler institution to support performance more effectively and efficiently. Challenges arise in the implementation of this simplification at Statistics of NTB Province, particularly with the division into several work teams leading to activity overlap. As a solution, it can be considered to develop an information system. The data collection method used involves interviews and observation, while system development follows the prototype model in the Software Development Life Cycle (SDLC). System evaluation was carried out using the Post-Study System Usability Questionnaire (PSSUQ). System development has gone through three iterations according to user needs and test results show the system is ready for use. After system integration on the server and system handover, an evaluation of system usability is carried out with PSSUQ. The evaluation results show that overall, the system provides good use for Statistics of NTB Province.]]>
<![CDATA[Penerapan Data Pipeline untuk Meningkatkan Efisiensi Penghitungan Indeks Perkembangan Harga (IPH) di Indonesia ]]>
<![CDATA[Sandyawan, Ignatius]]>;
<![CDATA[Rimawati, Yeni]]>;
<![CDATA[Suarjaya, I Made Oka]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2045
<![CDATA[The Price Change Index (PCI) is an indicator for monitor market price fluctuations. In 2022, Statistics Indonesia (BPS), worked with Ministry of Home Affairs (Kemendagri) as data steward and the Ministry of Trade (Kemendag) as a data collector, to calculate the PCI for 20 primary commodities across all cities in Indonesia. Weekly price data was collected by city trade departments and transferred to BPS for processing. Until late 2023, this process, done in Microsoft Excel, could take up to three days and was prone to errors. This research focuses on implementing a data pipeline for PCI calculations, automating tasks like data cleaning, index calculation, and visualization. Results showed the data pipeline reduced calculation time to just 16 minutes while maintaining consistency with manually obtained PCI values. The implementation has significantly improved time efficiency, minimized errors, and optimized resource use.]]>
<![CDATA[Aplikasi Regresi Data Panel Untuk Menganalisis Produktivitas Tenaga Kerja di Sulawesi Barat 2011-2023 ]]>
<![CDATA[Hidayat, Ahmad Sovi]]>;
<![CDATA[Yuniasih, Aisyah Fitri]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2047
<![CDATA[Economic development encompasses all efforts to enhance the economy of a country, thereby improving the welfare of its society. Economic growth can occur through increased productivity in generating the output. During 2011-2023, Sulawesi Island experienced the highest increase in labor productivity compared to other islands. However, West Sulawesi Province faced an issue where its labor productivity ranked 30th out of 34 provinces in 2023. This condition has implications for the low-income and welfare levels of its society. This study aims to analyze the general overview of labor productivity and analyze the factors affecting labor productivity in West Sulawesi from 2011-2023 using panel data regression. The study results show that labor productivity has gradually increased since 2011 but has started to slow down since 2020. The panel data regression analysis indicates that gross fixed capital formation, investment credit, the proportion of high school and above workforce, and health expenditure positively affect labor productivity.]]>
<![CDATA[Tipe Perkawinan dan Partisipasi Kerja Perempuan ]]>
<![CDATA[Jamalludin, Jamalludin]]>
<![CDATA[ Seminar Nasional Official Statistics Vol 2024 No 1 (2024): Seminar Nasional Official Statistics 2024 ]]>
Publisher : <![CDATA[Politeknik Statistika STIS ]]>
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DOI: 10.34123/semnasoffstat.v2024i1.2050
<![CDATA[Married women have many considerations when deciding to participate in the labor market. Gender norms that provide certain stereotypes often hinder married women from working. A woman's decision to work is closely related to the characteristics of her life partner. The type of marriage cannot be separated from individual preferences and applicable social norms. This paper analyzes the relationship between marriage type and women's work participation. 2017 SPTK data is the main data source in this research. The unit of analysis in this research is married women with 26,947 observations. The classification of marriage types in this research refers to educational assortative marriage, namely high-education homogamy, low-education homogamy, hypergamy, and hypogamy. Descriptive analysis shows that the majority of women have a low education homogamous marriage type, and participate actively in the labor market. Binary logit analysis shows that hypogamous marriages tend to provide the highest opportunities for women to enter the labor market. Higher education strengthens women's bargaining position in the household in making decisions to work.]]>
