Judul: Penggerombolan Debitur Berdasarkan Karakteristik Nasabah dengan Analisis Gerombol Laten
Abstrak: Bapak Dr. Ir. I Made Sumertajaya, M.Si. Analisis gerombol adalah suatu metode untuk mengklasifikasikan obyek atau suatu kasus ke dalam suatu kelompok yang relatif homogen, yang disebut gerombol (cluster), dimana keragaman obyek dalam satu gerombol lebih kecil dibandingkan keragaman obyek antar gerombol. Metode penggerombolan yang biasa digunakan adalah penggerombolan berbasiskan jarak. Obyek dengan skala pengukuran kategorik maupun campuran kategorik tidak dapat digerombolkan dengan metode penggerombolan biasa. Metode yang digunakan untuk data bertipe kategorik atau campuran kontinu (numerik) dan kategorik, adalah Analisis Gerombol Laten (Latent Cluster Analysis). Analisis gerombol laten digunakan untuk menggerombolkan debitur pada data kredit bank X. Peubah yang digunakan dalam penelitian ini berjumlah 9 peubah yang terdiri dari peubah bertipe kategorik dan kontinu. Peubah-peubahnya adalah Besar Angsuran, Status Tempat Tinggal, Pendidikan, Jenis Kelamin, Status Pernikahan, Pekerjaan, Lama Pinjaman, Usia, dan Banyaknya Tanggungan. Gerombol optimal yang dihasilkan sebanyak 8 gerombol berdasarkan nilai Bayesian Information Criterion (BIC) terkecil. Peubah Usia, Status Tempat Tinggal, Pendidikan, Jenis Kelamin, Status Pernikahan, Banyaknya Tanggungan, dan Besar Angsuran adalah peubah pembentuk gerombol 1. Peubah Pekerjaan adalah peubah pembentuk gerombol 3 dan peubah Lama Pinjaman adalah peubah pembentuk gerombol 5.
Keyword: latent cluster analysis, BIC, Credit

Judul: Analisis Gerombol k-rataan untuk Segmentasi Nasabah Pengguna Kartu Kredit Studi Kasus Bank "XXX"
Abstrak: Nowadays, the number of credit card customer and provider has increasing. It makes the competition among providers tighter. Customer characteristic which is different between each other, make credit card provider give different promotion to hold their customers. One of method in customer relationship management is k-means cluster analysis. In this method customer divide into a group based on their similar characteristic. Data in this research show bank "XXX" credit card use transaction record in any merchants. The result of this research tells that there are two groups of customer. Characteristic of the first group is total transaction 64 million a year, and the other group is 56 million a year. The other characteristic of group 1 are age between 34 years and 42 years, occupation BUMN/BUMD, education Bachelor and saving deposit between 1 milion and 10 milion. Group 2 characteristics are age between 34 years and 42 years, occupation non-PNS/BUMN/BUMD, education High School and saving deposit less than 1 milion. The promotion strategy that could give to the customer is discount the thr related group.
Keyword: multiple correspondence analysis., k-means cluster analysis, credit card

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.
Keyword: