Judul: Pemodelan Regresi Lasso Berbasis Wilayah dalam Identifikasi Peubah yang Memengaruhi Pengangguran di Pulau Jawa 2022
Abstrak: Pengangguran merupakan salah satu permasalahan ekonomi yang sering dijumpai di negara Indonesia. Pada tahun 2022, Tingkat Pengangguran Terbuka (TPT) Indonesia sebesar 5,9% dan berada pada urutan ke-55 tertinggi di dunia. Tiga provinsi di Pulau Jawa memiliki nilai TPT di atas TPT Indonesia. Adanya kemungkinan ketidakmiripan karakteristik antar kabupaten/kota di dalam satu provinsi yang sama dapat memberikan hasil yang kurang dapat mewakili secara keseluruhan, sehingga diperlukan pembentukan cluster. Pembentukan cluster berdasarkan kabupaten/kota yang berdekatan menyebabkan di dalam cluster dapat terdiri dari beberapa provinsi. Penelitian ini bertujuan untuk mengetahui peubah yang memengaruhi TPT di Pulau Jawa 2022 dengan metode LASSO yang dapat melakukan seleksi peubah tanpa adanya pertimbangan cluster dan metode LASSO per cluster dan Generalized LASSO untuk melakukan pemodelan dengan mempertimbangkan cluster yang berdekatan. Hasil dari LASSO didapatkan bahwa hanya peubah TPAK dan UMK yang berpengaruh terhadap TPT di Pulau Jawa 2022, sedangkan hasil LASSO per cluster dan Generalized LASSO tidak terlalu berbeda jauh. Akan tetapi, pada Generalized LASSO dapat diketahui peubah dengan pengaruh yang sama antar cluster. Peubah yang berpengaruh terhadap TPT di Pulau Jawa 2022 berdasarkan hasil Generalized LASSO yaitu TPAK, kepadatan penduduk, IPM, UMK, dan PDRB., Unemployment is one of the most common economic problems in Indonesia. In 2022, Indonesia's Open Unemployment Rate (TPT) is 5.9% and ranked 55th highest in the world. Three provinces in Java Island have TPT above Indonesia's TPT. There is a possibility that the characteristics of dissimilarities between regencies/cities within the same province can produce results that are not representative as a whole, so that it is necessary to form clusters. The formation of clusters based on adjacent districts/cities means that a cluster can consist of several provinces. This study aims to determine the variables that affect TPT in Java Island with the LASSO which can select variables without considering clusters and the LASSO per cluster and Generalized LASSO for modeling by considering clusters. The results from LASSO that TPAK and UMK variables had an effect on TPT in Java Island, while the LASSO per cluster and Generalized LASSO results were not too different. Generalized LASSO it can be seen that the variables with the same effect between cluster. The variables that affect TPT in Java Island with Generalized LASSO are TPAK, population density, HDI, UMK, and GRDP.
Keyword: cluster, Jawa, Generalized LASSO, LASSO, Tingkat Pengangguran Terbuka

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: Sintesis dan Pencirian Kompleks Fe(III)-Kuersetin
Abstrak: Quercetin belongs to flavonols which is able to chelate metal ions. Quercetin may chelate metal ions at 3’,4’-catechol, 3- or 5-hydroxycromone sites. In this study, quercetin was complexed with Fe(III). The complex with Fe(III) ions from FeCl3·6H2O was more stable than the complex derived from Fe(NO3)3·9H2O. The quercetin was yellow in color and showed maximum wavelengths (λ􀭫􀭟􀭶) at 250 and 370 nm. The Fe(III)-quercetin complex from Fe(NO3)3·9H2O was yellowish green with 􀟣􀭫􀭟􀭶 at 280 and 420 nm, whereas that from FeCl3·6H2O was dark green with 􀟣􀭫􀭟􀭶 at 280 and 430 nm. The 􀟣􀭫􀭟􀭶 shift indicated that the complex was formed at 3-hydroxychromone site. The Fe(III)- quercetin complex was formed with 1:1 metal-ligand mole ratio based on atomic absorption spectrophotometry analysis and the X-ray diffractogram showed that the crystallinity of the complex was 37%.
Keyword: