Judul: CART Method for Credit Payment Collectibility Analysis of PT. N.
Abstrak: Kredit merupakan salah satu bentuk bantuan keuangan yang diberikan baik oleh perbankan maupun non perbankan kepada perorangan atau badan usaha yang harus dikembalikan dalam jangka waktu tertentu. PT. N merupakan perusahaan atau badan usaha milik pemerintah daerah (pemda) Kabupaten Siak, Provinsi Riau yang bergerak di bidang pemberian kredit, yaitu Kredit Pembiayaan Modal Kerja (KPMK). PT. N sebagai pihak pemberi kredit memiliki resiko kredit. Resiko kredit akan semakin besar seiring dengan semakin banyaknya kredit yang disalurkan. Resiko kredit pada dasarnya dapat diperkecil dengan pemilihan nasabah yang tepat. Permasalahan untuk memilih nasabah yang tepat ini dapat diselesaikan dengan menggunakan teknik data mining. Salah satu metode klasifikasi dalam data mining adalah CART (Classification and Regression Tree). Peubah yang berpengaruh adalah lama angsuran, besar pinjaman, profesi, sektor ekonomi, dan pekerjaan pasangan. Metode ini memiliki tingkat keakuratan prediksi sebesar 71.5%., Credit is a form of financial assistance given by both the banking and non banking to individuals or entities that should be returned within a specified period. PT . N is a company which is owned by local governments of Siak, Riau province engaged in the provision of credit, namely Kredit Pembiayaan Modal Kerja (KPMK). PT . N as the lender has credit risk. Credit risk will be greater as more and more loans distributed. Credit risk can be minimized by basically selecting the right customers. The problem to select the right customers can be solved using data mining techniques. One method of classification in data mining is the CART (Classification and Regression Tree). Variables that influence are the number of installments, the loan amount, professions, economic sectors ,and partner’s professions. This method has a prediction accuracy rate of 71.5 %. Keywords : CART, collectibility, KPMK
Keyword: CART, kolektibilitas, KPMK

Judul: Analisis Regresi Logistik dan CART untuk Credit Scoring dengan Penanganan Kelas Tak Seimbang
Abstrak: Tidak adanya jaminan pada suatu jenis kredit akan membuat risiko kredit (gagal bayar) yang dihadapi oleh pihak bank semakin tinggi. Penerapan prinsip kehati-hatian oleh bank diterapkan dengan mengelola portofolio kredit yang dimiliki, sehingga potensi risiko yang terjadi dapat diukur dan dikontrol dalam suatu model. Credit scoring menggambarkan seberapa besar kemungkinan debitur akan macet dengan pembayaran. Pada penelitian ini dibandingkan pemodelan credit scoring antara analisis regresi logistik dan Classification and Regression Trees (CART) dalam mengklasifikasikan debitur yang nantinya dapat digunakan oleh bank untuk membuat atau mengevaluasi kebijakan terkait kredit. Permasalahan klasifikasi pada ketidakseimbangan kelas salah satunya dapat ditangani dengan Synthetic Minority Oversampling Technique (SMOTE). Hasil penelitian ini menunjukkan bahwa analisis Regresi Logistik dengan penanganan SMOTE menghasilkan kinerja yang lebih baik karena memiliki nilai sensitifitas yang lebih tinggi daripada analisis CART dan tidak terdapat perbedaan nilai terlalu besar pada nilai Area Under Curve (AUC). Peubah yang berpengaruh signifikan dalam klasifikasi debitur (lancar, macet) adalah tingkat pendidikan, status kepemilikan rumah, dan pendapatan., The absence of collateral for a type of credit will increase the bank's credit risk (failed to pay). Banks apply the precautionary principle by managing their credit portfolios so that potential hazards that occur can be measured and controlled in a model. Credit scoring describes how likely a debtor will fail with payments. This study aimed to compare logistic regression analysis and Classification and Regression Trees (CART) in classifying debtors to evaluate credit policies. One of the problems in classification is unbalanced data. Synthetic Minority Oversampling Technique (SMOTE) is a technique to handle the unbalanced problem in classification. The results show that the logistic regression model with SMOTE has higher sensitivity than the CART model, and there was no difference in Area Under Curve (AUC). The variables that have significant effects on the classification of debtors (good, bad) are level of education, homeownership status, and income.
Keyword: CART, Credit Scoring, Logistic Regression, Credit Risk, SMOTE

Judul: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity