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: Analisis Time Series Clustering Berdasarkan Faktor Sosial, Ekonomi, Demografi, dan Pemodelan ARIMA untuk Prediksi Jumlah Kasus Aktif COVID-19 di Indonesia
Abstrak: Pandemi COVID-19 menjadi ancaman kesehatan bagi penduduk dunia. Penelitian terdahulu telah melakukan time series clustering untuk mengelompokkan wilayah terdampak berdasarkan jumlah kasus aktif COVID-19, namun belum banyak yang menganalisis hubungan antara faktor sosial, ekonomi, dan demografi terhadap hasil cluster-nya. Oleh karena itu, penelitian ini melakukan time series clustering terhadap jumlah kasus aktif COVID-19 di Indonesia kemudian menganalisis faktor sosial, ekonomi, dan demografi pada setiap cluster untuk mengetahui faktor pendorong yang membedakan pola penyebarannya. Teknik time series clustering yang digunakan pada penelitian ini berhasil membagi 34 provinsi menjadi tiga cluster dan menunjukkan bahwa cluster yang lebih padat penduduk dan aktif secara ekonomi cenderung mengalami lonjakan kasus aktif lebih cepat dengan jumlah kasus tinggi. Selain itu, dilakukan pemodelan ARIMA pada setiap cluster untuk memprediksi jumlah kasus aktif COVID-19. Pemodelan ARIMA menghasilkan tiga model dengan nilai Mean of Percentage Error (MAPE) sebesar 10,38%; 6,33%; 7,73% untuk cluster 0, 1, dan 2 secara berturut-turut.
Keyword: ARIMA, COVID-19, time series clustering

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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