Judul: Clustering Analysis of Cities/Regencies in Java Island Based on Open Unemplyment Rates Before and After Covid-19 with Generalized LASSO
Abstrak: Covid-19 memberikan dampak yang sangat signifikan terhadap dunia, termasuk Indonesia. Pemerintahan Indonesia menerapkan peraturan pembatasan sosial berskala besar (PSBB) di seluruh Indonesia yang menyebabkan titik terendah pertumbuhan ekonomi Indonesia sejak tahun 1998. Tingkat pengangguran terbuka (TPT) tinggi disebabkan oleh turunnya perekonomian Indonesia, terutama di Pulau Jawa. Lima dari enam provinsi di Pulau Jawa memiliki TPT terbesar di Indonesia sebagai dampak pandemi Covid-19. Mencari tahu dimana TPT tertinggi sangat penting untuk mencegah bertambahnya angka tersebut. Hal ini dapat dilakukan dengan penggerombolan Generalized LASSO. Metode ini menambahkan matriks penalti D untuk menggambarkan struktur ketetanggaan kabupaten di Pulau Jawa. Dua metode akan digunakan untuk membentuk matriks D, struktur ketetanggaan biasa dan dengan melakukan penggerombolan dengan KNN terlebih dahulu. Hasil penelitian menunjukkan bahwa pembentukan matriks D dengan struktur ketetanggaan menghasilkan penggerombolan terbaik dan mudah untuk diintepretasikan. Enam gerombol terbentuk pada data TPT 2019, tujuh gerombol pada TPT 2021, dan empat gerombol untuk selisih antara TPT 2021 dengan TPT 2019. Penggerombolan juga mengungkapkan bahwa bagian barat Pulau Jawa memiliki TPT yang lebih tinggi dibandingkan dengan wilayah Jawa Tengah dan Jawa Timur. Akan tetapi wilayah tersebut lebih tahan terhadap goncangan pandemi Covid-19., Covid-19 has impacted the economy of the world harshly, including Indonesia. Government of Indonesia implemented strict social distancing mandates nationwide, resulting in a drop of economic growth that has never been seen since the 1998 economic crash. This led to high rise of open unemployment rate, especially in Java Island. Five out of six provinces in Java Island have the highest increase of open unemployment rate due to Covid-19. Finding out where the highest unemployment rates are located and how they are distributed is imperative to prevent the open unemployment rate from going any higher and reduce the number itself. This can be done through Generalized LASSO. This method adds a penalty through the D incidence matrix, therefore able to sketch the structure of neighboring regencies. Two methods are used to form the D incidence matrix, neighboring structures and by KNN clustering beforehand. Results show that using the neighboring structures give the best clustering results to interpret. Six clusters are formed for the 2019 data, seven clusters for 2021, and four the delta. The clusters show that the western part of Java Island have higher unemployment rates than the east but are more resilient to the Covid-19 pandemic.
Keyword: Covid-19, Generalized LASSO, Java Island, open unemployment rate, pandemic

Judul: Generalized LASSO Regression dalamIdentifikasi Peubah yang Memengaruhi Tingkat Kemiskinan di Pulau Jawa Tahun2020
Abstrak: Sustainable Development Goals (SDGs) adalah program lanjutan dari Millenium Development Goals (MGDs). Tujuan pertama SDGs adalah mewujudkan hidup tanpa kemiskinan (no poverty). Kemiskinan selalu menjadi prioritas pembangunan. Kemiskinan dapat dipengaruhi oleh peubah yang bersifat positif atau negatif. Bersifat positif apabila peningkatan peubah tersebut dapat meningkatkan kemiskinan dan sebaliknya. Pulau Jawa merupakan pulau dengan jumlah penduduk terbanyak, yaitu mencakup 56% dari total penduduk di Indonesia. Jumlah penduduk miskin di Pulau Jawa mencakup 54% dari total penduduk miskin Indonesia tahun 2020. Generalized LASSO Regression digunakan untuk menyeleksi peubah yang berpengaruh sekaligus melakukan klasterisasi untuk melihat pengaruh setiap peubah terhadap daerah. Metode ini sangat baik karena memiliki dua fungsi yang dapat dijalankan bersamaan. Hasil analisis menggunakan metode ini menyatakan bahwa peubah yang berpengaruh terhadap persentase kemiskinan di Pulau Jawa adalah Produk Domestik Regional Bruto (PDRB), Harapan Lama Sekolah (HLS), dan Rataan Lama Sekolah (RLS). Tidak ada peubah yang memiliki dampak yang sama terhadap seluruh daerah (provinsi) di Pulau Jawa. Peubah RLS memiliki dampak yang sama terhadap lima provinsi kecuali Provinsi D.I. Yogyakarta sedangkan dua peubah lainnya membentuk dua kelompok daerah. Ketiga peubah tersebut memengaruhi persentase kemiskinan secara negatif di seluruh daerah (provinsi)., The Sustainable Development Goals (SDGs) are a continuation of the Millennium Development Goals (MGDs). The first goal of the SDGs is to realize a life without poverty (no poverty). Poverty has always been a development priority. Poverty can be influenced by positive or negative variables. It is positive if the increase in this variable can increase poverty and vice versa. Java Island is the island with the largest population, which covers 56% of the total population in Indonesia. The number of poor people in Java covers 54% of the total poor population in Indonesia in 2020. Generalized LASSO Regression is used to select influential variables as well as clustering to see the effect of each variable on the region. This method is very good because it has two functions that can be executed simultaneously. The results of the analysis using this method state that the variables that affect the percentage of poverty in Java are Gross Regional Domestic Product (GRDP), Expected Years of Schooling (EYS), and the Average Length of Schooling (ALS). There is no variable that has the same impact on all regions (provinces) on the Java island. The ALS variable has the same impact on five provinces except for the Province of D.I. Yogyakarta while the other two variables form two regional groups. These three factors negatively affect the percentage of poverty across the regions (provinces).
Keyword: generalized LASSO, java island, poverty

Judul: Sintesis Flavonol melalui 2’-Hidroksikalkon
Abstrak: Flavonol is a class of flavonoids commonly found in fruits and vegetables. Flavonol is very useful, especially in food sector and health. In this study, flavonol unsubstituted was successfully synthesized from an intermediate material 2’-hydroxychalcone. The 2’-hydroxychalcone was synthesized by Claisen-Schmidt condensation method between o-hydroxyacetophenone and benzaldehyde using 4 different catalysts, namely KOH 60%, solid pellet of KOH-grinding technique, BF3-Et2O, and NaH in DMF. The KOH 60% was the best catalyst, giving the highest yield (57%). Afterward, the flavonol was synthesized in 1-stage oxidation cyclization of o-hydroxyacetophenone using Algar-Flynn-Oyamada (AFO) reaction, giving 76%. The flavonol total yield from 2’-hydroxychalcone was 43%. All synthesized products were characterized by their melting points and their spectra. The success of the synthesis flavonol opens the way to synthesis of useful flavonol derivatives such as quercetin and kaempferol
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