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"Instansi keuangan di Indonesia termasuk bank memiliki kewajiban dalam melaporkandata debitur yang dimiliki dalam bentuk pelaporan Sistem Layanan Informasi Keuangan (SLIK) ke pihak regulator. Berdasarkan hasil wawancara menyebutkan bahwa pelaporan data debitur SLIK yang dilaporkan oleh Bank XYZ masih menerima sanksi administrasi dari regulator. Penelitian ini bertujuan untuk mengemukakan prioritas strategi yang dapat meningkatkan kualitas data pelaporan dan meminimalisir sanksi administrasi dari regulator. Penelitian dilakukan menggunakan metode Quality Function Deployment (QFD) yang menggabungkan kebutuhan pengguna dan kebutuhan teknis yang didapat berdasarkan hasil wawancara dengan Subject Matter Expert (SME) SLIK yang ada dalam Bank XYZ. Kebutuhan pengguna dan kebutuhan teknis tersebut digambarkan dalam bentuk House of Quality (HoQ) untuk selanjutnya hasil penelitian didapat berdasarkan skema yang ada dalam QFD. Selain QFD, penelitian ini juga menggunakan Analytic Hierarchy Process (AHP) untuk menentukan bobot serta prioritas dari dimensi kualitas data beserta kebutuhan pengguna. Geometric Mean juga digunakan dalam penelitian ini untuk menghitung ratarata dari nilai kuesioner. Dengan QFD, prioritas strategi dapat dihasilkan sekaligus tujuan dan hasil penelitian didapatkan demi meningkatkan kualitas data pelaporan SLIK. Hasil penelitian ini mengemukakan strategi berupa kebutuhan pengguna dengan urutan prioritasnya dari yang terpenting yaitu pengawasan kualitas data input di setiap cabang, sistem untuk melakukan validasi data SLIK, aplikasi DQM mengakomodir kualitas data kredit SLIK, parameter ceklis yang wajib dipenuhi setiap ada penambahan requirement baru SLIK, prosedur preventive sebelum proses pelaporan, parameter valid untuk menentukan alamat nasabah yang memiliki lebih dari satu value, kebijakan dan prosedur untuk perbaikan data SLIK secara manual, meningkatkan kecepatan proses data SLIK. Strategi lain berupa prioritas dari kebutuhan teknis yang perlu diadakan ataupun ditingkatkan kualitasnya berdasarkan yang terpenting yaitu proses audit cabang, pembuatan kebijakan, prosedur, parameter ceklis, utilisasi dan optimasi server, pengembangan aplikasi DQM, dan pembuatan sistem validasi data.

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Financial institution in Indonesia included Bank have an obligation of reporting the debtor data had in form of Sistem Layanan Informasi Keuangan (SLIK) reporting to the regulator side. Based on interview result which is mentioned that the reporting of SLIK debtor data has been reported by Bank XYZ still received an administrative punishment from regulator. The objective of this research is to conclude strategy priority which could improve the quality of reporting data and minimalize an administrative punishment from regulator. This research used Quality Function Deployment (QFD) method which is combined user needs and technical needs resulted from interview with Subject Matter Expert (SME) of SLIK in Bank XYZ. Those user needs and technical needs described in form of House of Quality (HoQ) and the next is the result of this research resulted based on the schema on QFD. Beside QFD, this research is also used Analytic Hierarchy Process (AHP) to decide the weight and the priority from both of data quality dimensions and user needs. Geometric Mean is also used in this research to calculate the average of questionnaire value. With QFD, the strategy priority could resulted and also the objective of the research is achieved for improving the data quality of SLIK reporting. The result of this research proposed strategies that is user needs with their priority based on the most important is the controlling data input quality in every branches, the system to do validate SLIK data, the application of DQM which accomodates the quality of SLIK credit data, the to do list parameter which has to be fulfilled in every adding of SLIK new requirement, the preventive procedures before doing reporting process, the valid parameters to decide customer address which has more than one record, the policy and procedures to fix SLIK data manually, improving the quickness of SLIK data processes. The other strategies is the priority of technical needs which has to be done or improve those qualities based on the most important is branches audit process, the making of policies, procedures, to do lists, the utilization and optimation of server, the development of DQM application, and the making of validating data system."

"Saat ini credit scoring calon nasabah produk Kreasi Pegadaian masih menggunakan scorecard konvensional berupa pembobotan pertanyaan. Model credit scoring tersebut dibangun berdasarkan pengalaman pakar (expert scorecard) dan kemungkinan ada unsur subjektivitas dalam penilaian kelayakan kredit. Untuk mengatasi masalah tersebut, penelitian ini membangun model credit scoring dengan pendekatan data mining menggunakan data riwayat kredit nasabah produk Kreasi (data driven scoring) menggunakan algoritma klasifikasi, diantaranya: support vector machine (SVM), naïve bayes, decision tree dan neural network. Pengembangan model dilakukan dengan menggunakan metodologi CRISP-DM (The Cross Industry Standard Process for Data Mining). Model dibangun dengan kriteria tanpa penggunaan feature selection dan dengan feature selection. Teknik SMOTE (Synthetic Minority Over Sampling Technique) dan Oversampling dipilih untuk menyeimbangkan class data. Dari hasil evaluasi kinerja model menunjukan model SVM dengan feature selection dan penyeimbangan class menggunakan teknik Oversampling dipilih sebagai model dengan kinerja terbaik.

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Currently, the credit worthiness of Pegadaian prospective customers still uses a conventional scorecard in the form of weighting questions. The model is built based on expert experience which is called expert scorecard. There might be an element of subjectivity in credit assessment. To resolve that problem, in this research data mining classification techniques are used to build credit scoring models. There are four classification algorithms, namely SVM (Support Vector Machine), Naïve Bayes, Decision Tree and Neural Network as a classification algorithm. Modelling uses the historical customer credit data of Pegadaian Kreasi product. CRISP-DM (The Cross Industry Standard Process for Data Mining) is used as a development methodology. Modeling is done with two criteria, by considering the use of feature selection and without feature selection. The SMOTE (Synthetic Minority Over Sampling Technique) and Oversampling techniques are chosen to balance the class data. The result of this research shows the SVM model with feature selection and data balancing using the Oversampling technique was chosen as the model with the best performance."