Judul: Aplikasi Regression Tree pada Prestasi Siswa SMA ALAtiqiyah Sukabumi
Abstrak: Regression Tree (Regression Trees) is a non-parametric technique. This technique is used to explore the large and complex data. Complex dimensions can be formed large data, mixed data types, such as continuous and categorical, either nominal or ordinal. One application of regression tree is in the field of education, including to determine the factors that affect student’s achievement (academic ability) students that is the results of National Examination (UN). There are many factors influenced student’s achievement, those are internal and external factors. There are four factors that most influenced on student’s achievement, those are environment where the student lived, the average score of student’s report, final test, and gender.
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Judul: Pohon Regresi untuk Analisis Hasil Ujian Nasional Berbasis Komputer (UNBK) pada Level Siswa
Abstrak: Ujian nasional merupakan kontrol kualitas pendidikan secara nasional. Oleh karena itu, perlu untuk diketahui faktor-faktor apa saja yang memengaruhi hasil ujian nasional terutama dari segi siswa. Data yang digunakan adalah data hasil survei angket ujian nasional berbasis komputer (UNBK) terhadap siswa SMA di Indonesia pada tahun 2018. Survei angket tersebut memuat 35 pertanyaan. Selanjutnya pertanyaan-pertanyaan tersebut dianalisis dengan metode Classification and Regression Tree (CART) yang membentuk pohon regresi dari data. Hasil pohon regresi menunjukkan bahwa faktor-faktor yang memengaruhi hasil UNBK siswa SMA di Indonesia adalah peubah penggunaan komputer di rumah, penggunaan internet dalam satu minggu, kepemilikan komputer pribadi, lama waktu mengenal komputer, penghasilan orangtua dalam satu bulan, pendidikan terakhir ibu dan ayah, motivasi mengikuti UNBK dan penggunaan alat transportasi mobil ke sekolah. Tingkat keragaman nilai rata-rata UNBK yang dapat dijelaskan oleh peubah-peubah penjelas yang dimasukkan ke dalam pohon regresi adalah sebesar 31.15%.
Keyword: pohon regresi, SMA, UNBK

Judul: Extraction and Characteristic of Collagen and Nanoparticle Collagen from the Skin of Stingray Fish (Pastinachus solocirostris) as Ingredient of Cosmetics.
Abstrak: Isolation of collagen from the skin of stingray fish (Pastinachus solocirostris) has been successfully conducted with three stages: first soaked in 0.05 M NaOH with a sample/solution ratio of 1:10 (w/v) for 6 hour, second soaked in 0.1 M acetic acid with a sample/solution ratio of 1:6 (w/v) for 2 hour, and third extraction with destilated water at temperature 40 C for 2 hour with a yield of 14.475 % (wet base). Nanoparticle collagen was made with desolvation method using etanol as a desolvating agent with a collagen solution/etanol ratio of 1:1. Yield of nanoparticle collagen was 9.64% (wet base) with particle size of 185.24 nm. Collagen and nanoparticle collagen is in the form of white powder with whiteness degree 72.48% and 60.03%, respectively. Protein is the major component in collagen and nanoparticle collagen (86.97% and 86.18%) with trace amount of ash (0.17% and 0.13%) and fat (0.19% and 0.25%), respectively. Based on sodium dodecyl sulfate-polyacrylamide gel electrophoresis patterns, amino acid composition, and fourier transform infrared (FTIR) spectra of both collagen and nanoparticle collagen were characterized as type I collagens. Nanostructural analysis of collagen and nanoparticle collagen by Scanning Electron Microscopy (SEM) shows that both were separate aggregates. Other characteristics of collagen and nanoparticle collagen were: Tmax of 165.88 C and 156.63 C; Viscosity 292.33 cP dan 110 cP; pH 5.00 dan 4.93; respectively. Collagen has highest solubility at pH 7 and the isoelectric point at pH 6, 9, and 10. Therefore, nanoparticle collagen has highest solubility at pH 3 dan and the isoelectric point at pH 6. Collagen and nanoparticle collagen from skin of stingray fish (Pastinachus solocirostris) has a characteristic for a potential use as a cosmetic ingredient.
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