Judul: Aplikasi Mobile untuk Cross Selling pada Customer Relationship Management di Agroindustri Ban
Abstrak: CRM activities are used to identify customer service needs in order to get more detail information. Good CRM could be implemented by applying it on a mobile-based application. This research was using Association Rules with Apriori algorithm. Steps of the research is pre-processing data to determine frequent item sets (data that often occur together) using apriori algorithm, analyzing association rules using support, confidence and improvement. Next step is determining marketing strategy which is next item deals (next sequential product) may be purchased by customer based on the rules that have been obtained. Last step is forecast analysis to determine the time whenever items and next sequential items will be purchased by the customers using Moving Average formula. These application were built in Java and implemented in Android platform version 2.2 or above. Default support and confidence values are 20% and 70% and can be manually adjusted. The advantages of the application are users can use it anywhere and anytime as long as they bring their mobile device with the cross selling application installed
Keyword: mobile, Android, next sequential product, customer relationship management, apriori algorithm, association rules

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: Karakteristik Anatomi Skelet Tubuh Buaya Senyulong (Tomistoma schlegelii)
Abstrak: Buaya senyulong (Tomistoma schlegelii) merupakan buaya besar yang rentan punah dan hanya dapat ditemukan di rawa dataran rendah, danau dan sungai di Sumatera bagian timur, Kalimantan dan Jawa Barat (Indonesia), Serawak, Semenanjung Malaysia (Malaysia) serta Brunei Darussalam. Tujuan dari penelitian ini adalah mempelajari karakteristik anatomi skelet tubuh buaya senyulong yang dikaitkan dengan fungsi dan perilakunya. Penelitian ini menggunakan satu set preparat skelet tubuh buaya senyulong. Parameter penelitian meliputi pengamatan bentuk bagian skelet yang khas, mengukur panjang dan lebar tulang, serta dibandingkan dengan skelet tubuh jenis buaya lain, biawak air (Varanus salvator) maupun literatur yang terkait dengan sistem skelet tubuh hewan. Karakteristik anatomi skelet tubuh buaya senyulong relatif mirip pada jenis buaya lain. Beberapa karakteristik yang ditemukan yaitu, proatlas pada os atlas, os axis hanya memiliki zygapophysis caudalis, bagian lateral dari ossa vertebrae cervicales yang dilengkapi dengan cervical ribs, memiliki jumlah costae sternalis yang banyak, rangkaian ossa gastralia pada abdomen, ossa vertebrae lumbales memiliki zygapophysis cranialis dengan sudut >90o dari sumbu tubuh. Selain itu, os vertebrae caudalis I memiliki caput cranial dan caudal, ossa vertebrae caudales memiliki processus transversus yang semakin memendek ke arah caudal. Di medioventral tulang ini ditemukan ossa chevron dengan hemal arch sampai menjelang akhir ossa vertebrae caudalis. Skelet tubuh buaya senyulong memiliki hubungan yang kokoh dan kompak untuk menunjang fleksibilitas tubuh untuk berjalan, berenang dan berburu mangsa
Keyword: anatomi, buaya senyulong, skelet tubuh