Judul: Comparison of ARIMA and Artificial Neural Networks in Forecasting the Number of Covid-19 Positive in DKI Jakarta
Abstrak: DKI Jakarta menjadi pusat penyebaran Covid-19. Hal ini ditunjukkan dari jumlah kumulatif positif Covid-19 di DKI Jakarta lebih tinggi dibandingkan dengan provinsi lain. Tingginya kasus di DKI Jakarta menjadi perhatian bagi semua kalangan, sehingga perlu dilakukan peramalan untuk meramalkan jumlah positif Covid-19 periode selanjutnya. Peramalan yang akurat dibutuhkkan untuk mendapatkan hasil yang lebih baik. Penelitian ini membandingkan metode Autoregressive Integrated Moving Average (ARIMA) dan Artificial Neural Networks (ANN) dalam peramalan jumlah positif Covid-19 di DKI Jakarta. Akurasi peramalan dihitung menggunakan nilai Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), dan korelasi. Hasil penelitian menunjukkan bahwa model terbaik untuk peramalan jumlah positif Covid-19 di DKI Jakarta adalah ARIMA(0,1,1) with drift dengan nilai MAPE sebesar 15,748, RMSE sebesar 268,808, dan korelasi antara nilai ramalan dengan nilai aktual sebesar 0,845. Peramalan menggunakan model ARIMA(0,1,1) with drift dan BP(3,10,1) menghasilkan ramalan terbaik pada panjang periode peramalan enam minggu ke depan. Kata kunci: ANN, ARIMA, Covid-19, peramalan, DKI Jakarta is the center of the spread of Covid-19. This is indicated by the higher cumulative number of Covid-19 positive in DKI Jakarta compared to other provinces. The high number of cases in DKI Jakarta is a concern for all groups, so it is necessary to do forecasting to predict the number of Covid-19 positive in the next period. Accurate forecasting is needed to get better results. This study compares the Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) methods in predicting the number of Covid-19 positive in DKI Jakarta. Forecasting accuracy is calculated using the value of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation. The results show that the best model for forecasting the number of Covid-19 positive in DKI Jakarta is ARIMA(0,1,1) with drift, with a MAPE value of 15.748, an RMSE of 268.808, and the correlation between the forecast value and the actual value of 0.845. Forecasting using ARIMA(0,1,1) with drift and BP(3,10,1) models produces the best forecast for the long forecasting period of the next six weeks. Keywords: ANN, ARIMA, Covid-19, forecast
Keyword: ANN, ARIMA, Covid-19, forecast

Judul: Time Series Clustering and Multi-Input Transfer Function Model for Covid-19 Incidences in Jakarta
Abstrak: The coronavirus, known as the Covid-19 virus, has become a global issue since this virus was first identified in December 2019 in Wuhan, China. This virus is not only disrupting the medical order but also affecting socio-economic conditions around the world. To date, only six countries worldwide have recorded zero cases of Covid-19. The massive spread of the virus caused 3.17 million people to be infected within 20 months, with 83,000 deaths. Indonesia is one of the countries with a very high spread of Covid-19 cases. The positive rate for Covid-19 cases in Indonesia in July 2021 reached 23% or almost five times the standard set by WHO. This condition has brought various handling reactions to curb the spread of Covid-19 in Indonesia, starting from the imposition of restrictions on community activities, accelerating the distribution of vaccinations, and scientifically data-based studies in handling Covid-19 in Indonesia. Seeing the very high rate of spread of Covid-19 cases in Indonesia, this does not escape the contribution of Covid-19 cases in 34 provinces. Several provinces recorded the highest cases, including Jakarta, West Java, Central Java, East Java, and South Sulawesi. Based on the Covid-19 handling task force data, among the five provinces, it was noted that Jakarta was the province with the highest Covid-19 incidence in Indonesia. The incidence of Covid-19 in DKI Jakarta reached 1.91% and contributed 25% for national cases. This situation is critical considering that DKI Jakarta is the center of the economy and should be a role model for other provinces regarding handling Covid-19 cases in Indonesia. The study of Covid-19 spread in Jakarta is necessarily considered due to the high mobilization of the community in Jakarta so that the policies that can be taken are right on target. The research carried out in this study was based on daily data on Covid-19 cases in DKI Jakarta, which were recorded at the sub-district level, even at the village level. Based on the daily data recording, this study aims to classify sub-districts in Jakarta based on the similarity in the distribution pattern of Covid-19 cases. The grouping of these sub-districts is carried out through a hierarchical time series cluster analysis. The hierarchical time-series clustering analysis was carried out by calculating the distance between two sub-districts from 44 sub-districts in Jakarta. Distance calculation is done using Dynamic Time Warping technique. Calculating the distance between two sub-districts will produce a distance matrix measuring 44×44 which is the basis for hierarchical clustering. The two districts with the minimum distance will be combined in a temporary cluster, and the distance matrix will be evaluated to 43×43. This process is carried out until only one cluster is formed. Evaluation of the distance matrix is done through the average linkage method. The results of this study classify 44 districts in DKI Jakarta into six optimal clusters based on the size of the Silhouette coefficient. The six clusters provide several unique characteristics that can be used as one of the keywords in formulating policies for handling Covid-19 cases in Jakarta. The characteristics formed include that cluster A has the
Keyword: ARIMA, Covid-19, Time Series Clustering, Transfer Function

Judul: Katalis Eterifikasi Gliserol Berbasis Poli–(asam stirena sulfonat) dari Limbah Styrofoam.
Abstrak: Styrofoam waste can be converted to poly–(sulfonate styrene acid) (PSSA) that can be used as catalyst. The objective of this research is to get PSSA that can catalyze the etherification of glycerol and benzyl alcohol. The styrofoam waste was converted to PSSA by reacting with H2SO4 98% (as sulfonation agent) and P2O5 (as sulfonation catalyst) at 40 °C for 6 hours. Characterizations of PSSA included functional groups, sulfonation levels, water swelling, and thermal stability. The yield of PSSA was 40-55%. The success of sulfonated polystyrene was shown by –SO3H group absorption at 1157.29 cm-1. Etherification of glycerol and benzyl alcohol using PSSA catalyst was conducted at 110 °C for 12 hours. The conversion in producing mono–benzyl ether glycerol was 8.10%, 1,3–dibenzyletherglycerol 2.60%, and dibenzyl ether 9.80%. Therefore, the PSSA can be used as etherification catalyst.
Keyword: benzyl alcohol, etherification, glycerol, polystyrene, PSSA