A phenomenon of coronavirus became a big deal around the world at the end of December 2019. To find out how deadly the disease is, we can use the Case Fatality Rate (CFR), which provides the ratio number of deaths due to covid-19 between founded cases number of covid-19. However, studies to see the relationship between the number of cases and the number of deaths caused by covid-19 in Indonesia rarely done. Time Series analysis that can see how the relationship between the number of cases and the number of deaths due to covid-19 in Indonesia is Vector Autoregressive Integrated Moving Average analysis (VARIMA). Data used in this model must be qualified the stationary. For that reason, the transformation using differencing and logarithm on data must be performed to resolve non-stationary. The result shows the model that fulfilled all assumptions and had the smallest AICC value is VARIMA (1,1,1). The model shows the number of cases influenced by the number of cases and the number of deaths in the previous period. The same condition applies to the number of deaths affected by the number of deaths and the number of cases from the preceding period.
Predict the number of foreign tourist's visit Indonesia. In 2020 the Ministry of Tourism and Creative Economy has targeted the number of foreign tourists visiting Indonesia as many as 17 million visits. However, the number of foreign tourist visits decreased cumulatively (January- July 2020) by 64.64 per cent compared to the same period in 2019. Based on these conditions, it is significant to make accurate predictions to see if the target will be achieved or not in the future. One of the prediction methods used is Seasonal ARIMA (Autoregressive Integrated Moving Average). This model predicted the predictable number of foreign tourists visit in 2020 forecast to pass the target
Tingkat pengangguran terbuka di Jawa Barat Pada Tahun 2019 mencapai 7,99% menurut Badan Pusat Statistik (BPS) angka ini merupakan angka tertinggi di Indonesia. Permasalahan tingginya tingkat pengangguran tentunya akan berdampak kepada aspek perekonomian yang mengakibatkan tidak maksimalnya tingkat kemakmuran (Amalia, 2019) sehingga, diperlukan penanganan yang tepat untuk mengatasi permasalahan ini. Akan tetapi penelitian yang sudah dilakukan masih mengarah pada hasil yang bersifat global tanpa mempertimbangkan keberagaman karakteristik di setiap daerah. Sebagaimana kita ketahui, bawasanya setiap daerah memiliki karakteristik yang berbeda sehingga analisis berdasarakn pada pemodelan regresi global kurang tepat. Adanya efek spasial pada Tingkat Pengangguran terbuka mengakibatkan kemungkinan terjadinya keragaman spasial. Analisis GWR yang merupakan perluasan dari regresi global mampu mengakomodir permasalahan tersebut. Namun, analisis ini masih memiliki kelemahan salah satunya apabila terjadi multikoliniritas, pemodelan yang dilakukan dengan GWR kurang optimal.Geographically Weighted Lasso (GWL) merupakn teknik yang menggunakan pendekatan Lasso dalam model GWR untuk mengatasi masalah multikolinieritas disamping itu, model GWL juga dapat sekaligus menyeleksi variabel yang tidak signifikan dengan cara menyusutkan nilai koefisien regresi sampai ke nol. Sehingga variabel-variabel dengan koefisien regesi nol tidak berpengaruh signifikan (Wheeler D 2009). Dalam penelitian ini diperoleh bahwa signifikansi variabel yang mempengaruhi tingkat pengangguran terbuka disetiap daerah berbeda-beda dimana variabel Angkatan kerja yang tidak berijazah, Angka Putus Sekolah, Lowongan Kerja, dan Kepadatan Penduduk memberikan pengaruh yang signifikan pada sebagian besar kota dan kabupaten di Jawa Barat. Sedangkan kabupaten Bandung hanya dipengaruhi signifikan oleh variabel IPM. Pemodelan dengan menggunakan GWL memberikan koefisien determinansi yang lebih tinggi dibandingkan dengan model regresi global dan GWR.
Penelitian ini bertujuan menganalisis kemiskinan menurut variasi wilayah dengan pendekatan spasial melalui penerapan metode Geographically Weighted Lasso (GWL). Studi kasus yang diambil adalah Sumatera Utara, salah satu provinsi dengan tingkat kemiskinan tertinggi di Indonesia. Data penelitian bersifat sekunder yang berasal dari publikasi dan laman BPS. Hasil penelitian menunjukkan metode GWL mampu mengatasi multikolinieritas lokal dan heterogenitas data spasial. Sebesar 85,93 persen kemiskinan di Sumatera Utara dapat dijelaskan oleh seluruh variabel prediktor. Variabel yang signifikan adalah persentase rumah tangga dengan luas lantai kurang dari 8 m2, tingkat setengah pengangguran, dan persentase pekerja informal. Pemodelan kemiskinan dengan metode GWL mampu meningkatkan ketepatan estimasi parameter sehingga program pengentasan kemiskinan di Sumatera Utara akan lebih efektif jika disesuaikan dengan karateristikmasing-masing daerah.
One of the health issues listed in the Sustainable Development Goals (SDGs) is to end the tuberculosis epidemic in 2030. Indonesia is the country with the third-highest number of tuberculosis cases in the world after India and China in 2018. Aims of this study to model the number of tuberculosis cases in each province in Indonesia, depending on the characteristics of each region. Geographically Weighted Lasso (GWL) is a method used to overcome the local multicollinearity that appears in the Geographically Weighted Regression (GWR) model. By using this method, each region will have a different regression model according to its respective characteristics. There is local multicollinearity (VIF> 10) in each explanatory variable used. Banten, West Java, South Kalimantan, East Kalimantan, East Nusa Tenggara and Papua Province are provinces where all research variables affect the number of tuberculosis cases. The variable that has the most significant effect on the number of tuberculosis cases in each region in Indonesia is the number of health centers. Therefore, to end the number of tuberculosis cases, the government should increase the number of health centers and improve the health service.
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