Judul: Analisis Subsidi Perumahan untuk Masyarakat Berpenghasilan Rendah Menggunakan Metode Generalized LASSO di Pulau Jawa
Abstrak: Pada tahun 2021 terdapat 12,7 juta masyarakat berpenghasilan rendah yang tidak memiliki rumah di Indonesia. Sebanyak 49% masyarakat berpenghasilan rendah berada di Pulau Jawa. Hal ini mengakibatkan pemerintah membuat kebijakan berupa subsidi KPR perumahan untuk memudahkan masyarakat untuk memiliki rumah. Eksplorasi data dan penentuan wilayah di Pulau Jawa terkait kondisi permintaan kepemilikan rumah menjadi tujuan utama penelitian ini. Metode yang digunakan antara lain metode statistika deskriptif untuk mengetahui sebaran masyarakat yang menerima subsidi KPR, metode heatmap clustering untuk mengetahui pengelompokkan pendapatan masyarakat penerima rumah subsidi dan metode Generalized LASSO dengan kedekatan wilayah dan K-Nearest Neighbor untuk menggerombolkan wilayah berdasarkan permintaan kepemilikan rumah. Hasil dari penelitian ini diperoleh bahwa mayoritas masyarakat dengan angka penerimaan subsidi KPR tertinggi terdapat pada Kabupaten Bekasi dan Kabupaten Bogor. Selain itu, gerombol pendapatan masyarakat yang paling tinggi menerima rumah subsidi berada pada kisaran pendapatan 3,9 sampai 5,9 juta rupiah. Serta, terbentuk sebanyak tujuh gerombol wilayah di Pulau Jawa berdasarkan hasil terbaik pada K-Nearest Neighbor dengan K = 2 pada kasus penggerombolan permintaan kepemilikan rumah dengan metode Generalized LASSO., In 2021, there were 12.7 million low-income people who did not own a home in Indonesia. As many as 49% of low-income people live in Java. This resulted in the government making a policy in the form of housing mortgage subsidies to make it easier for people to own houses. Data exploration and regional determination in Java related to the condition of demand for home ownership are the main objectives of this study. The methods used include descriptive statistical methods to determine the distribution of people who receive mortgage subsidies, heatmap clustering methods to determine the income grouping of people receiving subsidized houses and the Generalized LASSO method with regional proximity and K-Nearest Neighbor to group areas based on requests for home ownership. The results of this study found that the majority of people with the highest number of mortgage subsidy receipts were in Bekasi Regency and Bogor Regency. In addition, the highest income group of people receiving subsidized houses is in the income range of 3.9 to 5.9 million rupiah. Also, as many as seven regional clusters were formed in Java Island based on the best results on K-Nearest Neighbor with K = 2 in the case of clustering requests for home ownership by the method Generalized LASSO.
Keyword: Generalized LASSO, Low-income community, Subsidized housing

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: Color Characteristic of Butterfly Pea (Clitoria ternatea L.) Anthocyanin Extracts and Brilliant Blue
Abstrak: Butterfly pea (Clitoria ternatea L.) is one of potential anthocyanin sources which can be use as a food colorant. Anthocyanin extracts of butterfly pea was evaluated under different concentration, pH, tinctorial strength, and storage conditions and compared to synthetic colorant, brilliant blue. The structure of the largest molecule of anthocyanin in butterfly pea extracts, ternatin A1, made the extracts had a wide range of color spectrum from red in pH 1-2, purple to blue in pH 3-7, and yellow green in pH 8-14. The color of brilliant blue solution showed high stability from pH 1-12. The good stability of brilliant blue was also showed by the score of color density and degradation index.Chromaticity parameters of the samples were evaluated with the score of lightness, chroma, and hue. The hue score of anthocyanin extracts of butterfly pea at pH 4,5 and 7 showed blue to purple while brilliant blue showed blue color at same pH. When both of them were added to yogurt drink (pH 4,5) and rice (pH 7), the food added with anthocyanin extracts became blue-purple and brilliant blue gave bright blue color to the food.
Keyword: chromaticity parameters., tinctorial strength, pH, brilliant blue, anthocyanin extract