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All Journal Prosiding Seminar Nasional Official Statistics PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Salwa Rizqina Putri
Politeknik Statistika STIS
Humanities Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Information Dashboard Penjualan Produk Fashion Muslim di DKI Jakarta Sebagai Pendukung Strategi Pemulihan Ekonomi Nasional Muhammad Fachry Nazuli; Nasiya Alifah Utami; Salwa Rizqina Putri
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (808.569 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1009

Abstract

Untuk memaksimalkan potensi penjualan produk fashion muslim di DKI Jakarta melalui e-commerce sebagai salah satu strategi peningkatan aktivitas dunia usaha yang dicanangkan oleh pemerintah dalam Pemulihan Ekonomi Nasional (PEN), stakeholder, dalam hal ini pemerintah dan pelaku usaha, perlu mengetahui pola penjualan produk fashion muslim. Penelitian ini bertujuan untuk menganalisis secara deskriptif penjualan produk fashion muslim di DKI Jakarta melalui e-commerce dan membangun information dashboard menggunakan javascript untuk visualisasi data serta json untuk menyimpan data. Data yang digunakan adalah data penjualan produk fashion muslim di wilayah DKI Jakarta pada 19 April-11 Mei 2021 yang diambil dengan metode web-scraping pada website Tokopedia. Information dashboard pada penelitian ini berhasil dibangun dengan performa baik. Secara umum, penjualan produk fashion muslim cenderung tinggi di awal dan di pertengahan bulan Ramadhan. Wilayah DKI Jakarta dengan kontribusi penjualan produk fashion muslim terbanyak adalah Jakarta Pusat, sedangkan wilayah dengan omset penjualan produk fashion muslim tertinggi adalah Jakarta Barat.
Penerapan Bayesian Network dalam Memodelkan Kondisi Ekonomi Hijau Indonesia di Era Pandemi Berdasarkan Big Data Salwa Rizqina Putri; Thosan Girisona Suganda; Setia Pramana
Seminar Nasional Official Statistics Vol 2021 No 1 (2021): Seminar Nasional Official Statistics 2021
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (681.519 KB) | DOI: 10.34123/semnasoffstat.v2021i1.1023

Abstract

To support Indonesia's green economic growth, further analysis is needed regarding economic activity during the pandemic and its relationship to environmental conditions. This study aims to apply the Bayesian Network approach in modeling Indonesia's green economy conditions during the pandemic based on variables that are allegedly influential, such as economic activity, air quality, population mobility levels, and positive cases of COVID-19 obtained through big data. The Bayesian Network model that was constructed manually with the Maximum Spanning Tree algorithm was chosen as the best model with an average 5-cross validation accuracy in predicting four classes of GRDP is 0.83. The best model chosen shows that Indonesia's economic conditions in the pandemic era are directly influenced by the intensity of night light (NTL) which shows economic activity, air quality (AQI), and positive cases of COVID-19. Analysis of parameter learning shows that the economic growth of the Indonesian provinces still tends not to be in line with the maintenance of air quality so that efforts to achieve a green economy condition still have to be improved.
Learning Bayesian Network for Rainfall Prediction Modeling in Urban Area using Remote Sensing Satellite Data (Case Study: Jakarta, Indonesia) Salwa Rizqina Putri; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2021 No. 1 (2021): Proceedings of 2021 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2021i1.37

Abstract

Rainfall modeling is one of the most critical factors in agricultural monitoring and statistics, transportation schedules, and urban flood prevention. Weather anomaly during the dry season in urban coastal areas of tropical countries such as Jakarta, Indonesia has become a challenging issue that causes unexpected changes in rain patterns. In this paper, we propose the Bayesian Network (BN) approach to model the probabilistic nature of rain patterns in urban areas and causal relationships among its predictor variables. Rain occurrences are predicted using temperature, relative humidity, mean-sea level (MSL) pressure, cloud cover, and precipitation variables. Data are obtained from the remote sensing sources of the National Oceanic and Atmospheric Administration (NOAA) satellite in Jakarta 2020-2021. We compare both of the score-based, i.e., Hill Climbing (HC), and hybrid structure learning algorithms of Bayesian Network including the techniques of Max-Min Hill Climbing (MMHC), General 2-Phase Restricted Maximization (RSMAX2), and Hybrid-Hybrid Parents & Children (H2PC). Further, we also compare the performance of score-based model (Hill Climbing) under five different popular scorings: Bayesian Information Criterion (BIC), K2, Log-Likelihood, Bayesian Dirichlet Equivalent (BDE), and Akaike Information Criterion (AIC) methods. The main contributions of this study are as follows: (1) insights that the hybrid structure learning algorithms of Bayesian Network models are either superior in performance or at least comparable to its score-based counterparts (2) our proposed best performed Bayesian Network model that is able to predict the rain occurrences in Jakarta with a promising overall accuracy of more than 81 percent.
A Land cover change analysis of buffer areas in New Capital City of Nusantara, Indonesia: A cellular automata approach on satellite imageries data Maria Shawna Cinnamon Claire; Salwa Rizqina Putri; Arie Wahyu Wijayanto
Proceedings of The International Conference on Data Science and Official Statistics Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official St
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34123/icdsos.v2023i1.338

Abstract

The proposed plan to move Indonesia's capital city to the New Capital City of Nusantara in East Kalimantan Province undoubtedly requires careful efforts to ensure food supply for the population. Population migration to the new capital may pose a food security challenge. To address this fundamental issue, one of the most crucial approaches is to establish buffer areas that can support the food needs of the new capital. The currently existing official Area Sampling Frame survey conducted by the government to assess food vulnerability faced several limitations, including weather conditions, field terrain variations, and high cost. In this study, we propose the utilization of remote sensing satellite imagery data in buffer areas to analyze changes and predict future land cover, which can provide valuable data for assessing food availability. We investigate the integration of a Cellular Automata method with the two most popular analytical methods of classical Logistic Regression and data-driven Artificial Neural Networks, known as CA-LR and CA-ANN, to identify and map land cover changes in the new capital buffer zones. Our findings reveal that both combined methods, CA-LR and CA-ANN, yield fairly promising results, with correctness and kappa statistic values exceeding 80%. Prediction results indicate that buffer areas are predominantly covered by trees, while built-up areas are still limited. The flooded vegetation cover, including rice fields, is predicted to decrease by 2024. This should be a matter of concern for stakeholders, considering the construction of the new capital city is still ongoing and the number of migrants is expected to keep rising.
Co-Authors Arie Wahyu Wijayanto Maria Shawna Cinnamon Claire Muhammad Fachry Nazuli Nasiya Alifah Utami Pramana, Setia Thosan Girisona Suganda