Judul: Classification and study of the performance of customer economic enterprises village savings and loan (eev-sl) at pisang berebus village
Abstrak: Usaha Ekonomi Desa Simpan Pinjam (UED-SP) merupakan suatu lembaga yang bergerak di bidang simpan pinjam dan merupakan milik masyarakat desa. UED-SP ini salah satunya berdiri di Desa Pisang Berebus. UED-SP memiliki resiko kredit macet yang dapat menyebabkan kerugian. Resiko kredit macet pada UED-SP dapat dihindari dengan mengetahui kriteria nasabah yang akan mengalami kredit macet dalam pembayaran dan kapan akan terjadinya kredit macet. Analisis regresi logistik biner, pohon klasifikasi CART, dan analisis daya tahan dapat digunakan untuk mengetahui kriteria nasabah yang mengalami kredit macet. Analisis daya tahan juga dapat digunakan untuk mengetahui waktu terjadinya kemacetan. Hasil penelitian menunjukkan bahwa riwayat peminjaman menjadi faktor yang paling dominan yang mempengaruhi kredit macet. Model yang dihasilkan cukup baik digunakan untuk melakukan pendugaan dengan persentase klasfikasi benar sebesar 66.6%. Persentase nasabah dengan status lancar yang telah menyelesaikan pembayaran kredit sampai angsuran ke-18 sebesar 60%. Resiko terjadinya kredit macet bagi nasabah dengan status macet relatif sama untuk setiap kali angsurannya, Economic Enterprises Village Savings and Loan (EEV-SL) is an institution that is engaged in the savings and loan and belong to village communities. EEV-SL is one of them standing in Pisang Berebus Village. EEV-SL has a non performing loan risk that could cause loss. The risk of non performing loan in EEV-SL can be avoided by knowing the criteria that customers will be non performing loan and when it will be happen. Binary logistic regression analysis, CART, and survival analysis can be used to determine criteria for customers who have non performing loan. Survival analysis can used to find out when it will be happen. The result of research show that history of loan became the most dominant factor affecting non performing loan. The result of model is quite well used to estimate with correct classification rate about 66.6%. Performance of customer payment with the perform status completed payment on the 18th payment about 60%. Customers with a non perform status has same risk for non performing loan on each payment. Keywords: bad credit, CART, logistic regression, survival analysis
Keyword: analisis daya tahan, kredit macet, CART, regresi logistik

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: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
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