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: Prediction Model Number of COVID-19 Cases and Analysis Based on Demography, Social and Economy Using Long Short Term Memory (Case Study: East Java Province)
Abstrak: Coronavirus Disease 2019 atau (COVID-19) disebabkan oleh jenis virus corona baru yang dapat menimbulkan penyakit pernapasan. Penyakit ini ditemukan pertama kali di Wuhan, China, pada bulan Desember 2019. Virus ini ditularkan melalui cairan atau kontak langsung dan menginfeksi saluran pernapasan yang mengakibatkan pneumonia di sebagian besar kasus. Faktor demografi, sosial dan ekonomi merupakan faktor-faktor yang mendorong penyebaran penyakit menular. Antisipasi dan pengukuran dari berbagai faktor yang terlibat dalam penyakit menular dapat dimodelkan secara matematis dengan pemodelan matematika. Penelitian ini melakukan pemodelan prediksi kasus COVID-19 di Jawa Timur menggunakan Long Short Term Memory (LSTM). Berdasarkan hasil penelitian, kabupaten/kota di Jawa Timur terbagi menjadi lima cluster sesuai karakteristik demografi, sosial dan ekonominya. Jumlah kasus positif COVID-19 yang tinggi terdapat di cluster dengan PDRB yang tinggi. Hasil analisis regresi menunjukan bahwa atribut PDRB merupakan atribut yang berpengaruh signifikan terhadap jumlah terkonfirmasi COVID-19 di Jawa Timur. Penelitian ini memperkirakan jumlah terkonfirmasi positif COVID-19 masih terus naik hingga akhir Juli 2021., Coronavirus Disease 2019 or (COVID-19) is caused by a new type of coronavirus that can cause respiratory disease. This disease was first discovered in Wuhan, China, in December 2019. This virus is transmitted through fluids or direct contact and infects the respiratory tract resulting in pneumonia in most cases . Demographic, social and economic factors are factors that encourage the spread of infectious diseases. The anticipation and measurement of various factors involved in infectious diseases can be modeled mathematically. This research conducts predictive modeling of COVID-19 cases in East Java using Long Short Term Memory (LSTM). Based on the research results, districts/cities in East Java are divided into five clusters according to their demographic, social and economic characteristics. Many positive cases of COVID-19 are found in clusters with a high value of Gross Regional Domestic Product (GDP regional). The results of the regression analysis indicate that GDP regional attribute is an attribute that has a significant effect on the number of positive confirmed cases of COVID-19 in East Java. This study estimates that the addition of the positive number of COVID-19 will continue to rise until the end of July 2021.
Keyword: COVID-19, demographic, economic, long short term memory, prediction modeling, social

Judul: Degumming Lemak Tengkawang (Shorea spp.) dengan Asam Sitrat dan Asam Fosfat
Abstrak: Lemak tengkawang, yang diekstrak dari biji tengkawang, dapat dimanfaatkan antara lain sebagai bahan baku pengganti lemak coklat. Agar lemak tengkawang dapat digunakan untuk tujuan tersebut maka lemak tengkawang kasar yang baru diekstrak dari biji tengkawang hams dimurnikan terlebih dahulu. Proses degumming mempakan tahap awal dari proses pemumian lemak. Proses ini ditujukan untuk menghilangkan gum yang terdapat di dalam lemak. Gum yang terdapat di dalam lemak dapat menghambat proses pemumian berikutnya
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