Judul: Negative Association Rule untuk Melihat Pola Keterkaitan Perilaku Konsumen pada Data Transaksi Pembelian
Abstrak: Association rule mining is one of the most popular data mining techniques to find associations among items in a set by mining necessary patterns in a large database. Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items or absent from transactions. The behavior of the customers is studied with reference to buying different products in a shopping store, the discovery of interesting patterns in this collection of data can lead to important marketing and management strategic decisions. Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other, in devising marketing strategies. This study used the combinations of minimum support of 10%, 30%, 50% and 70% and minimum confidence 10%, 30%, 50%, and 70%, also minimum interest 0.01, 0.001, 0.0001, and 0.00001. The result shows that the highest value is established by the occurence of rules if customers buy soap then customers will not buy soft drinks and otherwise if a customer buys a soft drink, the customer will not buy soap. Both rules have an interesting value 0.02012.
Keyword: purchasing transaction data., Negative association rule

Judul: Pembuatan Modul Delete pada Aplikasi Fuzzy Temporal Association Rule Mining untuk Data Transaksi
Abstrak: Transaction activities in supermarket produce large transaction data. It requires data mining techniques including association rule mining to extract patterns from the data. This research aims to implement the incremental updating technique to create association rules from expired data using fuzzy calendar on temporal database. The result is a deletion module which can find association rules without scanning the original entire database. The output are frequent itemsets and association rules in which some partitions are deleted from the original data set. The data used in this research are transaction data in a supermarket on period 1 March until 21 May 2004. The experiment was executed using support threshold values 20%, 30%, 40% and confidence threshold values 65%, 70%, 75% with early week or early year as the fuzzy calendar. By applying the deletion module the research obtains results that association rules generation are effective and efficient which means the process can produce interesting association rules in a relatively short time. For five partitions deleted data with support threshold 30% and confidence threshold 70%, one frequent itemset is generated and there is one association rule: 30(snack) → 80(milk). The execution time to generate association rules with deletion module is 13.984 seconds and the execution time without deletion module is 40.891 seconds with support threshold 40% and confidence threshold 75% based on the assumption in deletion module that frequent itemsets generated from the original data set are already provided.
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Judul: Performa Ayam Kampung yang Divaksinasi Tetelo di Desa Karacak
Abstrak: Ayam Kampung nlemp~lnyai daya adaptasi yang cukup baik untuk bisa hidup di lingkungan pedesaan maupun perkotaan. Ayam lokal ini tidak tnungkin dapat dipisahkan dari kehidupan masyarakat karena cukup berperan sebagai tabungan dan pemenuhan gizi keluarga. Produktivitas yang lambat dan tingkat kematian akibat penyakit merupakan kendala yang sering dihadapi dalam pengemballgall ayam Kampulig di pedesaan.
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