IMPLEMENTASI ALGORITMA APRIORI DALAM MENENTUKAN POLA PEMBELIAN (CAP N CHRIS CAFÉ & RESTO JEPARA) BERBASIS WEB

With the rapid advancement of technology at this time, there are many sales transactions received every day. The increase in sales certainly brings good. However, with the existence of sales activities, the sales data for the food menu is getting more and more. And the data only serves as an archive only.This study aims to create a website-based application with the Apriori Algorithm by utilizing sales transaction data, by determining the relationship between items from sales data, in this case the food or beverage ordered so that consumer purchasing patterns can be found. The results of research conducted from data from 16 transactions obtained 4 association rules with a minimum support of 3 and a minimum confidence of 60.Dengan adanya kemajuan teknologi yang pesat pada saat ini membuat banyaknya jumlah transaksi penjualan yang diterima setiap harinya. Jumlah kenaikan penjualan tentunya membawa kebaikan. Namun dengan adanya kegiatan penjualan membuat data penjualan menu makanan semakin lama semakin tambah banyak. Dan data tersebut hanya berfungsi sebagai arsip saja.Penelitian ini bertujuan untuk membuat aplikasi berbasis website dengan  Algoritma Apriori dengan memanfaatkan data transaksi penjualan, dengan menetukan hubungan antar item dari data penjualan, dalam hal ini adalah makanan atau minuman yang dipesan sehingga dapat ditemukan pola pembelian konsumen. Hasil penelitian yang dilakukan dari data 16 transaksi didapatkan 4 aturan asosiasi dengan minimun support 3 dan minimun confidence 6

EsPRESSo: Efficient Privacy-Preserving Evaluation of Sample Set Similarity

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information sharing and retrieval. One interesting open problem in this context involves two parties that need to assess the similarity of their datasets, but are reluctant to disclose their actual content. This paper presents an efficient and provably-secure construction supporting the privacy-preserving evaluation of sample set similarity, where similarity is measured as the Jaccard index. We present two protocols: the first securely computes the (Jaccard) similarity of two sets, and the second approximates it, using MinHash techniques, with lower complexities. We show that our novel protocols are attractive in many compelling applications, including document/multimedia similarity, biometric authentication, and genetic tests. In the process, we demonstrate that our constructions are appreciably more efficient than prior work.Comment: A preliminary version of this paper was published in the Proceedings of the 7th ESORICS International Workshop on Digital Privacy Management (DPM 2012). This is the full version, appearing in the Journal of Computer Securit

Evaluating espresso coffee quality by means of time-series feature engineering

Espresso quality attracts the interest of many stakeholders: from consumers to local business activities, from coffee-machine vendors to international coffee industries. So far, it has been mostly addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a datadriven analysis exploiting time-series feature engineering.We analyze a real-world dataset of espresso brewing by professional coffee-making machines. The novelty of the proposed work is provided by the focus on the brewing time series, from which we propose to engineer features able to improve previous data-driven metrics determining the quality of the espresso. Thanks to the exploitation of the proposed features, better quality-evaluation predictions are achieved with respect to previous data-driven approaches that relied solely on metrics describing each brewing as a whole (e.g., average flow, total amount of water). Yet, the engineered features are simple to compute and add a very limited workload to the coffee-machine sensor-data collection device, hence being suitable for large-scale IoT installations on-board of professional coffee machines, such as those typically installed in consumer-oriented business activities, shops, and workplaces. To the best of the authors' knowledge, this is the first attempt to perform a data-driven analysis of real-world espresso-brewing time series. Presented results yield to three-fold improvements in classification accuracy of high-quality espresso coffees with respect to current data-driven approaches (from 30% to 100%), exploiting simple threshold-based quality evaluations, defined in the newly proposed feature space