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"ABSTRAKKecelakaan lalu lintas adalah peristiwa yang terjadi secara tidak sengaja dan di luar dugaan. Di Indonesia, angka kecelakaan terus mengalami peningkatan setiap tahun dan merugikan lebih dari satu milyar per tahun serta memakan korban lebih dari 800.000 jiwa dalam periode 2014-2018. Jalan Tol Cikopo-Palimanan adalah ruas terpanjang dari jaringan tol Trans-Jawa yang mengkoneksikan pulau Jawa dari Pelabuhan Merak, Banten hingga Pelabuhan Ketapang, Jawa Timur. Dalam langkah pencegahan dan penurunan kecelakaan, diperlukan strategi untuk mengidentifikasi faktor-faktor kecelakaan. Data mining adalah metode pencarian informasi untuk data berjumlah besar. Metode data mining yang digunakan adalah clustering untuk mengurangi heterogenitas data dan association untuk mengidentifikasi hubungan antara faktor kecelakaan. Penelitian ini menemukan ada tiga belas cluster kecelakaan yang kemudian setiap cluster dianalisis menggunakan metode apriori algorithm dengan parameter minimum support 20% dan nilai lift 1.

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ABSTRACT

Traffic accidents are events that occur accidentally and unexpectedly. In Indonesia, the number of accidents continues to increase every year and costs more than one billion per year and claimed more than 800,000 lives in the 2014-2018. Cikopo-Palimanan Toll Road is the longest section of the Tol Trans-Jawa road network that connects Pelabuhan Merak, Banten Pelabuhan Ketapang, East Java. In order to prevent and decrease number of accidents, a strategy is needed to identify accident factors. Data mining is a method of finding information from large amounts of data. Data mining methods used in this study are clustering to reduce data heterogeneity and association to identify the relationship between accident factors. This study found thirteen accident clusters and each cluster was analyzed using apriori algorithm method with a minimum support parameter of 20% and a lift value of 1."

"ABSTRAK

Dalam rangka memperluas pasar konsumen, perusahaan perlu memperhatikan kepuasan konsumen yang akan berdampak pada keberlanjutan kegiatan pembelian produk. Market basket analysis dilakukan untuk melihat pola pembelian konsumen dengan cara mengidentifikasi asosiasi dari berbagai produk yang diletakkan konsumen pada keranjang belanja. Penelitian ini dilakukan untuk melihat pengaruh jenis gerai terhadap pola pembelian konsumen. Data yang digunakan pada penelitian ini merupakan basis transaksi pelanggan. Data tersebut diolah menggunakan teknik data mining dan salah satu algoritma association rule, yaitu apriori. Hasil dari penelitian ini menunjukkan adanya perbedaan pola pembelian konsumen pada setiap jenis gerai.

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ABSTRACT

In order to expand the current market, companies need to pay attention to customer satisfaction that will affect the sustainability of product purchasing activities. Market basket analysis is done to extract consumer buying behavior by identifying the associations of various products that consumers put on the shopping cart. This research was conducted to see whether outlet type affects consumer buying behavior. The data used in this study was taken from customer transactions database. The data was processed using data mining techniques and association rule algorithm, which is apriori. The results of this study show that there are differences in consumer buying behavior on each type of outlet."

"ABSTRAKLibrary X is an academic library in Jakarta, Indonesia. Library X has provided Online Public Access Catalog (OPAC) as a tool to provide information related to the collection. However, sometimes the information appears does not show high relevancy. One way to solve this problem is to develop user need based-book recommendation system. The purpose of this study is to create personalization model of book recommendations in Library X.Data Collection Method. The method used in this study was association rule mining using Apriori algorithm. Results and Discussions. The results showed that the book relationships for the minimum support was 0.1% and the minimum confidence was 10% and generated 42 association rules. It is noted that 657 (Accounting) and 658 (Management) are found to support for 2.6% with the confidence level for 14%.Conclusions. Book recommendation is formulated by selecting the rule with maximum support and confidence. The recommendation system is designed to be integrated to web application and users e-mail."