Analisis dan Implementasi Web Usage Mining Menggunakan Algoritma Graph Partitioning (Studi Kasus : Tuneeca Online Store)

ABSTRAKSI: Peningkatan aktivitas kunjungan terhadap website menghasilkan data yang cukup banyak mengenai user dan interaksinya dengan website yang disimpan dalam web server log. Informasi yang bisa diperoleh salah satunya adalah pola navigasi user. Pola navigasi user menggambarkan aktivitas apa saja yang dilakukan user selama mengakses suatu website. Memahami pola navigasi user dalam mengakses suatu website dapat berguna untuk memahami tingkah laku user dalam mengakses websitetersebut. Sehingga dapat digunakan sebagai acuan dalam perbaikan kualitas website dan menjamin kepuasan user ketika menggunakan website tersebut.Pada ranah e-commerce, pola navigasi user dapat digunakan sebagai acuan untuk menentukan strategi bisnis berdasarkan tingkah laku user yang diperoleh. Dalam tugas akhir ini, web server logdari tuneeca online storeakan diproses dengan mengimplementasikan salah satu metode dalamweb usage mining yaitu clustering.Web usage mining merupakan salah satu pengaplikasian teknik data mining yang dapat digunakan untuk menemukan pola navigasi user. Data log tersebut akan melalui tahap preprocessing, kemudian dilakukan clustering terhadap page dengan menggunakan algoritma graph partitioning. Hasil penelitian menunjukkan bahwa penentuan parameter nilai minimum bobot mempengaruhi jumlah klaster yang dihasilkan serta nilai visit coherence yang diperoleh. Performansi dari algoritma graph partitioning cukup baik dalam membentuk klaster pola navigasi berdasarkan tingginya nilai modularization qualityyang diperoleh. Pola navigasi user yang dihasilkan dapat digunakan sebagai acuan untuk rekomendasi pengembangan web dari tuneeca online store.KATA KUNCI: web usage mining, pola navigasi user,web server log, graph partitioning, visit coherence, modularization qualityABSTRACT: Increased activity of a visit to the website generates huge enough data about users and their interaction with a website that is stored in the web server logs. One of the information that can be obtained is user navigation patterns. User navigation patternsgenerated, could give an overview about what users actually do and need when access the website. Understanding the user navigation patternscan be useful for understanding user behavior in accessing the website. So it can be used as a reference in improving the quality of the website and ensure user satisfaction when using the website. In the domain of e-commerce, user navigation patterns can be used as a reference for determining a business strategy based on user behavior is obtained. In this final project, the web server logs of tuneeca online storewill be processed by implementing clustering, one of web usage mining methods.Web usage mining is one of the application of data mining techniques that can be used to discover the user navigation patterns. The log data will be going through the preprocessing stage, then performed clustering to the pages by using graph partitioning algorithm. The result shows that determining the minimum weight value affects the number of clusters produced and the visit coherence value obtained. Performance of graph partitioning algorithm is quite good in forming clusters of navigation patterns based on high value of modularization quality obtained. User navigation patterns generated can be used as a reference for the recommendation of web development Tuneeca online store.KEYWORD: web usage mining, user navigation patterns, web server log, graph partitioning, visit coherence, modularization qualit

Development of bambangan (Mangifera pajang) carbonated drink

Mangifera pajang Kostermans or bambangan is a popular fruit among Sabahan due to its health and economic values. However, the fruit is not fully commercialized since it is usually been used as traditional cuisine by local people. Thus, development of bambangan fruit into carbonated drink was conducted to produce new product concept. The objectives of this study were to conceptualize, formulate, evaluate consumer acceptance, and determine physicochemical properties and nutritional composition of the accepted product. Method used in conceptualising the product was based on questionnaire. The consumer acceptance was evaluated based on descriptive and affective tests with four product formulations tested. The physicochemical properties on carbon dioxide volume, colour, pH, total acidity, total soluble solid (TSS) and viscosity were highlighted, meanwhile nutritional composition on fat, protein, carbohydrates and energy content were determined. About 77% respondents gave positive feedback, and 69% respondents decided this product is within their budget. The formulation of 5% bambangan pulp, 70% water, 25% sugar and 0.2% citric acid was highly accepted in descriptive and affective tests with 4.4 and 6.39 mean scores, respectively. The physicochemical properties and nutritional composition of the acceptance product were in optimum value except for colour, total acidity and TSS. Overall, this study showed that the product has high potential to be commercialized as new product concept, and heritage of indigenous people can be preserved when this fruit is known regionally

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

Energy efficient mining on a quantum-enabled blockchain using light

We outline a quantum-enabled blockchain architecture based on a consortium of quantum servers. The network is hybridised, utilising digital systems for sharing and processing classical information combined with a fibre--optic infrastructure and quantum devices for transmitting and processing quantum information. We deliver an energy efficient interactive mining protocol enacted between clients and servers which uses quantum information encoded in light and removes the need for trust in network infrastructure. Instead, clients on the network need only trust the transparent network code, and that their devices adhere to the rules of quantum physics. To demonstrate the energy efficiency of the mining protocol, we elaborate upon the results of two previous experiments (one performed over 1km of optical fibre) as applied to this work. Finally, we address some key vulnerabilities, explore open questions, and observe forward--compatibility with the quantum internet and quantum computing technologies.Comment: 25 pages, 5 figure