Judul: Kinerja Model Deret Waktu berbasis Richard dan Logistik dalam Prediksi Kasus Baru COVID-19
Abstrak: A growth curve is a non-linear model to capture the growth process. Growth curves have been applied in many sectors, especially in the health sector. It can capture the growth of bacteria or cancer cells. Recently, the growth curve widely used to capture the future situation of the pandemic situation because of COVID-19 that spread all around the world. The Logistic growth curve is a growth curve that has characteristics such as taking very slowly at the early growth process, then rising sharply, and then slowing as it approaches the maximum carrying capacity. Another growth curve is the Richard growth curve which has a more flexible form than the Logistic growth curve to form the s-line in the cumulative curve. The first derivative of growth curves can be transformed into linear equations and hence, it can be modelled using time series analysis. This research interest is to evaluate performance of time series models based on Logistic and Richard growth curves in predicting new cases of COVID-19. This research used two types of data to evaluate the models. First, it is based on simulation methods to evaluate the bias, RMSE, and variance of the parameter estimates. Furthermore, the RMSEP of forecasting based on testing data is also calculated by means of simulation. The result showed that the Richard model tended to have a small bias and RMSE of parameter estimates when compared to Logistic model if the period of the training data was prolonged. However, if the data is generated from Logistic growth curve bias and RMSE of Logistic model are smaller than Richard model. These results were also shown by the RMSEP calculated based of the testing data. Regarding the evaluation of the variance of the parameter estimate, the result showed that Logistic growth curve tended to produce smaller variance when compared to the variance of Richard growth curve. The second evaluation is based on empirical dataset, in this case the weekly new cases of COVID-19 in Jakarta have been used. Using this empirical data the Richard growth curve performed better than the Logistic growth curve. This has been indicated by the RMSE and MAPE of predicting the training data. Moreover, this was also shown by RMSEP and MAPE calculated based on testing data in which the Richard growth curve tended to produce smaller values than the Logistic growth curve. This situation also has been shown by the 95% prediction interval of forecasting the testing data in which the prediction interval based on Richard model has included more testing data than the prediction interval based on Logistic model.
Keyword: Kurva Pertumbuhan, COVID-19, Peramalan, Kurva Pertumbuhan Richard, Kurva Pertumbuhan Logistik, Growth Curve, Forecasting, Richard Growth Curve, Logistic Growth Curve

Judul: Analisis dan Prediksi Pertumbuhan Jumlah Kasus COVID-19 di Pulau Jawa Menggunakan Metode Linear Regression dengan ARIMA Error
Abstrak: Coronavirus Disease 2019 (COVID-19) merupakan penyakit menular yang menyebabkan gangguan pada pernapasan dan radang paru-paru. COVID-19 ditetapkan sebagai pandemi pada 11 Maret 2020. Pulau Jawa merupakan pulau yang memiliki kasus terkonfirmasi positif COVID-19 terbanyak di Indonesia. Jumlah kasus terkonfirmasi positif COVID-19 di Pulau Jawa dapat disebabkan oleh beberapa faktor, salah satunya adalah mobilitas masyarakat. Penelitian ini melakukan analisis terkait dampak kebijakan yang diterapkan oleh pemerintah dalam menekan mobilitas masyarakat, yaitu Kebijakan Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM). Selain itu, fokus utama pada penelitian ini adalah melakukan analisis prediktif jumlah kasus terkonfirmasi positif COVID-19 menggunakan Linear Regression dengan ARIMA error. Dengan menggunakan Linear Regression dengan ARIMA error dan prediktor berupa data mobilitas didapatkan nilai MAPE pada model yang buat berkisar antara 10,90% hingga 22,93% tiap provinsinya kecuali Jawa Tengah yang memiliki nilai MAPE sebesar 47,02%., Coronavirus Disease 2019 (COVID-19) is an infectious disease that causes respiratory problems and inflammation of the lungs. COVID-19 was declared a pandemic on March 11, 2020. The island of Java is the island that has the most positive confirmed cases of COVID-19 in Indonesia. The number of positive confirmed cases of COVID-19 in Java can be caused by several factors, one of which is community mobility. This study analyzes the impact of policies implemented by the government in suppressing community mobility, namely the Policy for the Enforcement of Restrictions on Community Activities (PPKM). In addition, the main focus of this study is to carry out a predictive analysis of the number of positive confirmed cases of COVID-19 using Linear Regression with ARIMA error. By using linear regression with ARIMA error and employing the mobility predictors, we obtained a MAPE value within the range of 10,90% to 22,93% for each province, with the exception of Jawa Tengah which had a MAPE of 47,02%.
Keyword: ARIMA error, COVID-19, linear regression, PPKM

Judul: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4