Proceedings of The International Conference on Data Science and Official Statistics最新文献_第4页

Analysis of Spotify's Audio Features Trends using Time Series Decomposition and Vector Autoregressive (VAR) Model 利用时间序列分解和向量自回归 (VAR) 模型分析 Spotify 的音频特征趋势

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.375

Daffa Adra Ghifari Machmudin, Mila Novita, Gianinna Ardaneswari

{"title":"Analysis of Spotify's Audio Features Trends using Time Series Decomposition and Vector Autoregressive (VAR) Model","authors":"Daffa Adra Ghifari Machmudin, Mila Novita, Gianinna Ardaneswari","doi":"10.34123/icdsos.v2023i1.375","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.375","url":null,"abstract":"Streaming is the most popular music consumption method of the current times. As the biggest streaming platform based on subscriber number, Spotify stores miscellaneous information regarding the music in the platform, including audio features. Spotify’s audio features are descriptions of songs features in form of variables such as danceability, duration, and tempo. These features are accessible via Application Programming Interface (API). On the other hand, Spotify also publishes their own charts consisting of 200 most streamed songs on the platform (based on regions) which are updated daily. By combining Spotify’s song charts and the songs’ respective audio features, this research conducted analysis on musical trends using time series modelling. First, the combined data is decomposed to extract the trend features. Second, a Vector Autoregressive (VAR) model is built and followed by forecasting of the audio features. Lastly, the performance of forecasted values and the actual observations is evaluated. As a result, this research has proven that musical trends can be forecasted in the future for a short period by using VAR model with relatively low error.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Implementation of Machine Learning and Its Interpretation for Mapping Social Welfare Policy in Indonesia 印度尼西亚社会福利政策制图中机器学习的实施及其解读

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.383

Aldo Leofiro Irfiansyah, Ari Rismansyah, Novia Permatasari, Isnaeni Noviyanti, Atqo Mardiyanto, Ade Koswara

{"title":"Implementation of Machine Learning and Its Interpretation for Mapping Social Welfare Policy in Indonesia","authors":"Aldo Leofiro Irfiansyah, Ari Rismansyah, Novia Permatasari, Isnaeni Noviyanti, Atqo Mardiyanto, Ade Koswara","doi":"10.34123/icdsos.v2023i1.383","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.383","url":null,"abstract":"This research leverages data from the 2022 Early Socio-Economic Registration (Regsosek) activity to develop a machine learning model capable of predicting family expenditure levels based on the Proxy Mean Test (PMT) with high accuracy. By integrating the SHAP (SHapley Additive exPlanations) method for model interpretation, we identify the contributions of socio-economic features to expenditure predictions and link them to relevant social assistance programs. We compare two regions, Kulonprogo Regency and Yogyakarta City, representing varying poverty levels, and identify unique characteristics influencing family welfare in each area. The results highlight that effective policy interventions must be tailored to the unique characteristics of each region and family, taking into account dimensions such as housing, education, income, and community expenditures. This research provides valuable insights for policymakers, demonstrating that successful poverty alleviation policies are data-driven and adaptable to the diverse socio-economic realities across regions.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"83 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formulation of Kumaraswamy Generalized Inverse Lomax Distribution 库马拉斯瓦米广义反洛马克斯分布的计算公式

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.416

Andrew Bony Nabasar Manurung, S. Nurrohmah, Ida Fithriani

{"title":"Formulation of Kumaraswamy Generalized Inverse Lomax Distribution","authors":"Andrew Bony Nabasar Manurung, S. Nurrohmah, Ida Fithriani","doi":"10.34123/icdsos.v2023i1.416","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.416","url":null,"abstract":"Lifetime data is a type of data that consists of a waiting time until an event occurs and modelled by numerous distributions. One of its characteristics that is interesting to be studied is the hazard function due to the flexibility that it has compared to other characteristics of distribution. Inverse Lomax (IL) distribution is one of the distributions considered to have advantages in modelling hazard shape and extended in several ways to address the problem of non-monotone hazard which is often encountered in real life data. However, it needs to be extended to another family of distribution to increase its modelling potential and Kumaraswamy Generalized (KG) family of distribution is used as it adds two more parameters to the distribution. The newly developed distribution is called the Kumaraswamy Generalized Inverse Lomax (KGIL) distribution. The main characteristics of KGIL distribution will be derived, such as cumulative distribution function (cdf), probability density function (pdf), hazard function, and survival function. Maximum likelihood method will also be used to estimate the parameters. The application of the new model is based on head-and-neck cancer lifetime data set. The modelling results show that the KGIL distribution is the best to capture important details of the data set considered","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139142384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine 利用谷歌地球引擎为精准农业数据集整理多模态卫星图像

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.399

Bagus Setyawan Wijaya, Rinaldi Munir, N. P. Utama

{"title":"Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine","authors":"Bagus Setyawan Wijaya, Rinaldi Munir, N. P. Utama","doi":"10.34123/icdsos.v2023i1.399","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.399","url":null,"abstract":"In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one of which is paddy. This paper tries to describe the vegetation indices, climate, and soil index features related to paddy plants and curates a collection of satellite imagery on the Google Earth Engine (GEE). This paper reveals how GEE can be used to collect and process multimodal satellite imagery to form a precision agriculture dataset. The objective of this study is to establish a comprehensive precision agriculture dataset by leveraging multimodal satellite imagery to monitor paddy crops. The data collected as a dataset originates from 306 locations in Karawang Regency, Indonesia, during the 2019-2020 period. In the first step, we identify the relevant features essential for paddy crop analysis. Subsequently, we carefully select image collections within GEE based on these features. Afterward, we perform data acquisition and necessary preprocessing through the Google Colab environment. The results showed that satellite imagery from Sentinel-2 outperforms Landsat 8 in terms of spatial and temporal resolution. Apart from that, the generated dataset successfully captures the growth patterns of paddy plants.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"138 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139145668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time-Series Clustering of the Regencies Hotel Room Occupancy Rate in Indonesia after the COVID-19 Pandemic COVID-19 大流行后印度尼西亚各地区酒店客房入住率的时间序列聚类分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.387

Ladisa Busaina, Setia Pramana, Satria Bagus Panuntun

引用次数: 0

SPATIAL ANALYSIS OF FIRE OCCURRENCE IN JAKARTA, INDONESIA 印度尼西亚雅加达火灾发生的空间分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.275

Ika Rosantiningsih, Chotib Chotib

{"title":"SPATIAL ANALYSIS OF FIRE OCCURRENCE IN JAKARTA, INDONESIA","authors":"Ika Rosantiningsih, Chotib Chotib","doi":"10.34123/icdsos.v2023i1.275","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.275","url":null,"abstract":"The occurrence of fire incidents in urban villages of Jakarta Special Capital Region significantly impacted losses, necessitating prevention and handling efforts. Therefore, this study aims to analyze the spatial influence of social and physical variables (independent variables) such as sex ratio, vulnerable age population, number of buildings, and size of slum areas on fires (dependent variable) in Jakarta Special Capital Region. The analysis area includes five municipalities of Jakarta Special Capital Region. Secondary data were obtained from Central Agency of Statistics of Jakarta Special Capital Region, maps from the official site jakartasatu.jakarta.go.id, and publication data from Government of Jakarta Special Capital Region for 2020. Furthermore, the quantitative approach in descriptive and inferential analysis, determined using Microsoft Excel and GeoDa version 1.20.0.10, was used to evaluate the spatial relationships between adjacent sub-districts. Although the regression data processing results using GeoDa were significant, the spatial regression results with Lagrange Multiplier (LM) Lag and Lagrange Multiplier (LM) error > 0.05 were insignificant and significant when using the parameter 0.1. This means fire symptoms in Jakarta Special Capital Region do not have a spatial effect, contrary to the clustering observed between dependent and independent variables using Morans'I and Scatter Plots. The results of this study can aid the Jakarta provincial government in preventing and handling potential fires by restructuring slum areas to minimize the likelihood of such incidents.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping 设计和实施交互式可视化仪表板，用于监测洪水脆弱性和绘制地图

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.362

W. R. Azizah, Arie Wahyu Wijayanto

{"title":"Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping","authors":"W. R. Azizah, Arie Wahyu Wijayanto","doi":"10.34123/icdsos.v2023i1.362","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.362","url":null,"abstract":"This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, \"show me where I am\", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139143653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modeling Coastal Area Change Analysis of Coastal Urban Areas at Semarang City, Indonesia 印度尼西亚三宝垄市沿海城市地区变化模型分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.367

Renata De La Rosa Manik, Arie Wahyu Wijayanto

{"title":"Modeling Coastal Area Change Analysis of Coastal Urban Areas at Semarang City, Indonesia","authors":"Renata De La Rosa Manik, Arie Wahyu Wijayanto","doi":"10.34123/icdsos.v2023i1.367","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.367","url":null,"abstract":"A coastal area is defined as the boundary between land and sea. Coastal urban areas are susceptible to various hazards that are becoming more severe, such as flooding, erosion, and subsidence due to a mix of man-made and natural factors, including urbanization and climate change. Regardless of the high importance of coastal area monitoring, conducting field surveys is expensive, time-consuming, and geographically limited to non-remote regions. Semarang City is one of the cities in Indonesia that is at risk of changes in its coastline and causes various natural problems. This research aims to estimate changes in the coastal land area in Semarang City. In observing the phenomenon of changes in area in coastal areas in Semarang City, remote sensing technology with Sentinel-2 satellite imagery was used. This research implements and compares the Random Forest (RF) and Support Vector Machine (SVM) machine learning methods in building classification models. From the results of land area in 2019, 2021, and 2023 with the best classification model, namely SVM, information was obtained on an increase in coastal area of 387.94 ha in 2021, then a change in area decrease of 417.32 ha in 2023.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status 利用地理信息系统监测省级粮食安全状况的粒状粮食安全指数地图的地理可视化仪表板

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2023-12-29 DOI: 10.34123/icdsos.v2023i1.364

Dwi Karunia Syaputri, Bony Parulian Josaphat, Arie Wahyu Wijayanto

{"title":"A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status","authors":"Dwi Karunia Syaputri, Bony Parulian Josaphat, Arie Wahyu Wijayanto","doi":"10.34123/icdsos.v2023i1.364","DOIUrl":"https://doi.org/10.34123/icdsos.v2023i1.364","url":null,"abstract":"This study aims to build a web-based interactive geovisualization dashboard from a granular food security index map using satellite imagery and other geospatial big data. The map dashboard is built using a two-dimensional (2D) data visualization approach. Making a two-dimensional map using QuantumGIS (QGIS) tools, displayed in the form of WebGIS with the plugin used \"Qgis2web\" based on javascript leaflets. Once included in WebGIS, interactive visualizations are displayed on websites with interfaces based on hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). The dashboard map is equipped with interactive features such as legend, click grid, zoom, show me where I am, measure distance, and search. Therefore, the dashboard map can be used to monitor the food security index, search for food security index areas, as well as geographical identification of food security index areas which are useful for supporting the analysis of decision-making or policies by the government regarding food security strategies.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"21 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139147330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wages of Workers Spatial Analysis in Indonesia Region 2019 2019年印尼地区工人工资空间分析

Proceedings of The International Conference on Data Science and Official Statistics Pub Date : 2022-01-04 DOI: 10.34123/icdsos.v2021i1.66

M. Maghfirah, O. B. Samosir

{"title":"Wages of Workers Spatial Analysis in Indonesia Region 2019","authors":"M. Maghfirah, O. B. Samosir","doi":"10.34123/icdsos.v2021i1.66","DOIUrl":"https://doi.org/10.34123/icdsos.v2021i1.66","url":null,"abstract":"The wage inequality of workers in Indonesia is one of the main problems and concerns that are important to be addressed by the government. The determination of the regional minimum wage by the local government has not been able to solve the problem of inequality. On a larger scale, the wage inequality of workers can affect the stability of the national economy. Research on the spatial analysis of workers' wages is very important to be carried out as a basis for making appropriate policies by the government. In this study, we have succeeded in analyzing the dependence and spatial relationship of a region with the wages of its workers and have identified the factors that affect the wages of workers in a region. The result reveals the spatial dependences are detected among districts, followed by the spatial clusters and spatial outliers through global and local spatial autocorrelation. Applying two spatial autoregressive models, spatial autoregressive lag model (SAL) and spatial autoregressive error model (SEM), SAL confirmed that there are 4 significant independent variables with a level of 10 percent and have a positive relationship, namely education, age, internet, and sex ratio variables. And SEM confirmed that there are significants 5 significant independent variables with a level of 10 percent and have a positive relationship, namely education, age, technology, internet, and sex ratio variables. As the policy implication, since regional inequality in term of wage is still a major issue, it will be a call for better coordination and cooperation within and between regions.","PeriodicalId":151043,"journal":{"name":"Proceedings of The International Conference on Data Science and Official Statistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124887019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0