Judul: Analisis dan Desain Algoritme Paralel Branch and Bound Reverse Skyline pada Pencarian Pelanggan Potensial untuk Bundling Product.
Abstrak: Kemajuan teknologi membuat kegiatan belanja menjadi lebih mudah dilakukan, yaitu dengan berbelanja secara online. Data aktivitas transaksi pelanggan yang terekam pada saat berbelanja online merupakan sumber informasi yang potensial bagi manajer Customer Relationship Management (CRM) untuk mengembangkan strategi penjualan dan meningkatkan loyalitas pelanggan. Strategi penjualan diperlukan untuk mengidentifikasi produk yang diminati pelanggan dan calon pelanggan untuk bundling product tertentu. Strategi tersebut umumnya didasarkan pada Market Basket Analysis (MBA). MBA dapat menggabungkan produk dalam bundling, dan menawarkan secara personal kepada setiap pelanggan dengan menggunakan pola transaksi belanja pelanggan. Strategi ini belum mempertimbangkan kriteria preferensi produk pelanggan. Penerapan skyline menghasilkan himpunan produk yang paling menarik dan disukai berdasarkan semua preferensi pelanggan. Sedangkan, untuk mencari himpunan pelanggan potensial yang tertarik pada produk-produk tertentu, reverse skyline dapat digunakan untuk meningkatkan strategi pemasaran bundling product. Branch and Bound Reverse Skyline (BBRS) adalah algoritme untuk mencari reverse skyline yang dapat diterapkan untuk mendapatkan pelanggan potensial dari bundling product. Namun demikian, waktu komputasi BBRS ini akan meningkat secara linier seiring dengan pertambahan jumlah pelanggan yang bertransaksi dan pertambahan jumlah produk yang di-bundling. Oleh karena itu, penelitian ini mengusulkan Recency, Frequency, dan Monetary (RFM) untuk mengurangi calon pelanggan potensial dari bundling product sebagai data masukan bagi algoritme BBRS. RFM dapat mengelompokkan pelanggan yang paling berpotensi melakukan pembelian lagi di masa mendatang. Selanjutnya, BBRS dapat diterapkan untuk menemukan pelanggan paling potensial yang menyukai setiap produk dalam paket bundling. Penelitian ini menggunakan paralel BBRS untuk mengatasi peningkatan waktu komputasi saat terjadi penambahan jumlah produk dalam bundling. Hasil percobaan menunjukkan bahwa waktu komputasi paralel BBRS yang dihasilkan pada penelitian ini memiliki peningkatan kecepatan paling tinggi hingga mencapai 2.1 kali lebih cepat daripada BBRS tanpa paralel, terutama pada dataset yang berukuran besar. Dengan demikian, penerapan RFM dan paralel BBRS dapat digunakan untuk pencarian grup pelanggan terbaik pada kasus pemasaran bundling product untuk mendukung strategi pemasaran bundling product yang lebih efisien waktu dan biaya, juga berpotensi efektif memasarkan produk dengan tepat sasaran.
Keyword: analisis RFM, BBRS, bundling product, CRM, paralel BBRS, reverse skyline

Judul: Preferensi layanan pelanggan berbasis CRM (Customer Relationship Management) dengan pendekatan apriori dan bond
Abstrak: Market basket analysis discovers association pattern in retail transaction data. Association rules mining is a method of data mining that aims to find a set of items that frequently occur together. Apriori algorithm is an algorithm to find pattern of association that is used for market basket analysis (MBA). Cross-selling is a part of the CRM (customer relationship management), which sells the main products with additional products recommended. Cross-selling should be analyzed based on transaction data by using data mining concepts. The analysis process involves the extraction of information from customer transaction that includes what products they buy, customer buying behavior etc. RFM analysis (Recency Frequency Monetory) is a marketing technique to analyze customer buying behavior. Mining association rules based on steps of RFM measurement, analyzes relationship and customer loyalty on products to get good recommendations to increase the company's revenue . Apriori algorithm is usually used for the MBA. In this study, besides a’priori we add a minimum measurement bond correlation, which shows the close relationship between products. With the addition of the bond measure, it would be easier to select products for cross-selling and reduce processing time. Result of this research is a hypothesis cross-selling marketing strategy that is customer service preferences; providing good recommendations to improve customer loyalty and increase the company's revenue . The proposed method is applied to sales transactions of spare parts of central AC company as dataset in the case. Results of this study showed 9 rules which can be used to determine the cross-selling sales strategy, taking into account the highest value of improvement (lift), confidence, bond and support. Recommendations for cross-selling strategy is composed by sets of rules measure from four parameters above. The proposed products for cross-selling based on the results of association rules mining and measurement bond are oil filter, maintenance kit, filter kit, separator kit, G roto inject oil, air filter, oil separator and chemical. The results also indicate the relationship between products and to get the best customer loyalty recommendation to the company. Results of the analysis is customers with the highest RFM that give more benefits for the company. Company is advised to maintain customer satisfaction and customer loyalty, to offer cooperation for service contracts, and to offer reselling (customers involved in selling products). It is recommended to maximize RFM analysis by taking into account geographical segmentation of the customers.
Keyword: association rules mining, bond measure, CRM (customer relationship management), cross-selling, data mining

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
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