Judul: Analisis Kelompok Rate Premi Asuransi Kredit Perdagangan Domestik dengan Metode Regresi Logistik Ordinal
Abstrak: Perdagangan domestik merupakan salah satu penopang utama ekonomi nasional. Dengan berkembangnya teknologi dan informasi saat ini, alur perdagangan menjadi lebih mudah. Namun, terdapat beberapa risiko dalam perdagangan domestik, salah satunya adalah risiko gagal bayar. Risiko tersebut dapat diatasi dengan mengalihkannya kepada perusahaan asuransi melalui produk asuransi kredit perdagangan domestik. Perusahaan asuransi harus menentukan rate premi yang akan digunakan secara tepat agar perusahaan asuransi tidak mengalami kerugian yang mendalam. Analisis kelompok rate premi dilakukan agar mendapatkan model serta faktor-faktor yang dapat menentukan kelompok rate premi secara tepat. Analisis yang dapat digunakan yaitu regresi logistik ordinal. Pendugaan parameter pada model regresi logistik ordinal dilakukan dengan menggunakan metode Maximum Likelihood Estimator. Faktor-faktor yang memengaruhi penentuan kelompok rate premi dalam penelitian kali ini yaitu tenor pembayaran, kredit limit pusat dan jenis komoditas. Model regresi logistik ordinal yang didapat berbentuk dua model logit. Nilai ketepatan klasifikasi model yang didapat sebesar 57.45%. Domestic trade is one of the main pillars of the national economy. With the development of technology and information today, the trade flow has become smoother. However, there are still some risks in domestic trade, which one of it is the risk of default payment. This risk can be overcome by transferring it to insurance companies through domestic trade credit insurance. The insurance company must determine the premium rate that will be utilized so the insurance company does not suffer a significant loss. The premium rate group analysis is carried out in order to obtain the model and the factors that could precisely determine the premium rate group. The analysis that is used is ordinal logistic regression. Parameter estimation in the ordinal logistic regression model is performed using the Maximum Likelihood Estimator method. The factors that influence the determination of the premium rate group in this study are the payment period, central credit limit, and the commodity type. The ordinal logistic regression model obained is in the form of two logit model. The value of the classification accuracy of the model obtained is 57.45%.
Keyword: domestic trade credit insurance, ordinal logistic regression, premium rate group

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: Studi Taksonomi Katak Subgenus Papurana (Lesson, 1831) dari Halmahera (Anura, Ranidae, Hylarana), Studi Taksonomi Katak Subgenus Papurana (Lesson, 1831) dari Halmahera (Anura, Ranidae, Hylarana
Abstrak: I reevaluated taxonomic of subgenus Papurana position from Halmahera based on morphological and molecular analyses. My results show that two separate species occur on the Halmahera Island, each nested within two distinct clades (hereafter papua clade and celebensis clade), one of which corresponds to H. moluccana (celebensis clade) and the other to an unidentified species (papua clade). By p-distance ranging from 6.9 to 11.5% on the 16S rRNA gene, the unidentified species (Hylarana sp.) is genetically different from all congeners of the papua clade. The Hylarana sp. can be distinguished from all congeners by following combination of charactes: vomer teeth in two oblique rows with narrow inter-vomer distance; snout rounded dorsally; dorsum with few scattered cone-shaped tubercles that are black with white tips; distinct skin folds (ridges) on the dorsal side of the thigh coinciding with dark brown cross bars; a marbled pattern on the ventral side of the thigh and yellowish groin. I also present new data for H. daemeli, H. volkerjane, and H. arfaki from the western region of Papua. Furthermore, I found that H. celebensis has numerous mtDNA lineages, indicating cryptic diversity within the celebensis clade. The discovery of two separate species on Halmahera necessitates more research into the biogeographic history of Hylarana in Northern Moluccas.
Keyword: frog, Halmahera, Hylarana, morphology, Papurana, phylogenetic