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All Journal Jurnal Saintika Unpam : Jurnal Sains dan Matematika Unpam PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND OFFICIAL STATISTICS
Yuniar Putri Awaliyah Risky
Politeknik Statistika STIS
Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Physics

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PREDIKSI KASUS COVID-19 MELALUI ANALISIS DATA GOOGLE TREND DI INDONESIA: PENDEKATAN METODE LONG SHORT TERM MEMORY (LSTM) Lisa Widyarsi; Ivana Yoselin Purba Siboro; Peterson Hamonangan Immanuel Sihotang; Satria Dirgantara; Yakobus Natanael Tarigan; Yuniar Putri Awaliyah Risky; Rani Nooraeni
JURNAL SAINTIKA UNPAM Vol 3, No 2 (2021)
Publisher : Program Studi Matematika FMIPA Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jsmu.v3i2.7786

Abstract

Salah satu faktor yang diperlukan untuk menekan angka kasus COVID-19 adalah tingginya perhatian atau atensi masyarakat. Hal tersebut terlihat dari intensitas pencarian informasi publik mengenai COVID-19 di platform online bernama Google Trend. Makalah ini bertujuan untuk mendeskripsikan kondisi wabah COVID-19 di masyarakat dengan menggunakan data Google Trend dan memprediksi kasus COVID-19 baik dengan metode nowcasting maupun forecasting dengan menggabungkan data atensi publik dari Google Trend dengan data resmi pertumbuhan COVID-19 di Indonesia. Data yang digunakan berupa data time series harian dari tanggal 1 April hingga 30 September 2020. Metode Regresi Linear Berganda juga digunakan untuk membandingkan hasil prediksi dengan LSTM. Hasil regresi time series menghasilkan RMSE 1060,80. Selain metode analisis time series, prediksi penambahan kasus COVID-19 juga dilakukan menggunakan metode LSTM dengan empat skenario, di mana skenario pertama menghasilkan RMSE 526,59, skenario kedua menghasilkan RMSE 528,81, skenario ketiga menghasilkan RMSE 528,81. RMSE 483,25 dan skenario terakhir menghasilkan RMSE 482,21. Prediksi menggunakan metode LSTM dengan scnario keempat menghasilkan RMSE, sehingga metode LSTM merupakan metode keempat dengan prediksi yang cukup baik.
The Effect of the Digital Economy on Indecent Work in Indonesia 2019 Yuniar Putri Awaliyah Risky; Nucke Widowati Kusumo Projo
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.v2021i1.88

Abstract

The emergence of the digital economy is indicated to affect the employment sector. The job opportunities created by the digital economy provide an opportunity for workers to work in poor jobs, full of risks and indecent works. This study aims: first, to describe the economic digital and indecent work conditions in Indonesia. Second, to investigate the direct influence of infrastructure and digital media on the digital economy. Third, to examine the direct impact of the digital economy on indecent work. The data used is secondary data with observations from 34 provinces sourced from BPS and other ministries. Using the SEM-PLS analysis method, the results show that infrastructure and digital media positively impact the digital economy. Similarly, the digital economy, reflected by e-commerce sellers and buyers, has a positive and significant relationship to indecent work as reflected by Employment Excessive Working Time (EEWT), Precarious Employment Rate (PER), and non-union workers. It can be said that the increase in the digital economy influences the conditions of indecent work.
Co-Authors Ivana Yoselin Purba Siboro Lisa Widyarsi Nooraeni, Rani Nucke Widowati Kusumo Projo Peterson Hamonangan Immanuel Sihotang Satria Dirgantara Yakobus Natanael Tarigan