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: Generalized LASSO in Region-based Modeling Based on Variables Affecting Population Migration in Java Island
Abstrak: Pulau Jawa terus menghadapi tantangan dan tekanan yang signifikan terkait dengan migrasi internal. Kebutuhan sosial dan ekonomi yang tidak tercukupi mendorong para migran untuk bermigrasi. Penelitian ini menggunakan metode LASSO dalam melakukan seleksi peubah untuk mengatahui peubah yang memengaruhi migrasi penduduk di Pulau Jawa tahun 2022. Untuk memahami dinamika migrasi secara spesisifik di tingkat regional, metode generalized LASSO digunakan. Metode ini memodelkan migrasi berdasarkan peubah yang telah dipilih dan mengidentifikasi dampaknya pada berbagai wilayah. Hal ini memungkinkan pengambilan keputusan yang lebih tepat dalam perencanaan kebijakan di setiap wilayah. Model LASSO dibangun dengan nilai λ=0,031 dan terpilih peubah yang memengaruhi migrasi penduduk meliputi indeks gini, angka harapan hidup, rata-rata lama sekolah, tingkat pengangguran terbuka, upah minimum, indeks pembangunan gender, indeks ketimpangan gender, dan indeks risiko bencana. Dalam model generalized LASSO yang diterapkan untuk mempelajari dinamika migrasi penduduk di Pulau Jawa, model dibangun dengan nilai λ= 0,425 dan matriks D berukuran 54×54. Terungkap bahwa Jawa Tengah dan DI Yogyakarta menunjukkan pengaruh yang sama dalam 7 dari 8 peubah, menandakan pola migrasi yang serupa. Peubah-peubah dengan pengaruh yang sama membentuk tiga kelompok wilayah di Pulau Jawa, yaitu barat, tengah, dan timur., Java Island continues to face significant challenges and pressures related to internal migration. Insufficient social and economic needs encourage migrants to migrate. This study uses the LASSO method in selecting variables to determine the variables that affect population migration in Java Island in 2022. To understand migration dynamics specifically at the regional level, the generalized LASSO method is used. This method models migration based on the selected variables and identifies its impact on different regions. This enables more informed decision-making in policy planning in each region. The LASSO model was built with a value of λ=0.031 and selected variables that affect population migration include gini ratio, life expectancy, average years of schooling, open unemployment rate, minimum wage, gender development index, gender inequality index, and disaster risk index. In the generalized LASSO model applied to study the dynamics of population migration in Java, the model was built with a value of λ = 0.425 and a D matrix of 54×54. It was revealed that Central Java and Special Region of Yogyakarta showed similar influence in 7 out of 8 variables, indicating similar migration patterns. The variables with similar influence form three regional groups in Java Island, namely west, center, and east.
Keyword: generalized LASSO, migrasi penduduk, provinsi, pulau jawa

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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