Judul: A development of mobile recommendation systems by spatial data ecotourism
Abstrak: Recommender System (RSS) is one of the techniques of Customer Relationship Management (CRM) strategy, in the form of software tools and techniques that can provide recommendations to user. This system is a model of an application from the observation of the situation and the wishes of the user. RSS can provide advice to the user through the process and decision support systems can provide explanations and advice that can help users find what they need or preferably item made in the form of a system. Therefore, this system needs a method of making appropriate recommendations in order to provide appropriate recommendations to user desire, as well as helping to facilitate the user for making the right decision. This research has two main objectives, which are: 1) to develop a mechanism and the algorithm recommender system using the CARS method, 2) build a mobile application recommender system for eco-tourism object location based on user profile. While the scopes of this research are: 1) the method used in this study is CARS method, 2) the application is made on the Android mobile environment. The data will be used in this research are spatial data. Spatial data is a reference to the position data, objects and relationships among the earth in space. Spatial data used in this study is vector data that provide position information based on the data points by taking the case of ecotourism object in Bogor. Ecotourism is one of the tourism sectors that can help the economy. Ecotourism is a form of tourism that focuses on rural culture as a tourist attraction. Dynamic application for ecotourism RSS is not existed at this time. Based on the existing results, information about ecotourism is sites and blogs that have static information. The application of RSS applications in mobile devices has not been much developer who develops applications for this particular case study ecotourism is becoming challenge in this study. Other challenges were conducted in this research is how to determine the appropriate method for the manufacture of mobile applications based on RSS which has dynamic user profiles. Mobile devices are now widely used in various circles of society and the development of mobile applications for the software and hardware equipment continues to move dynamically in accordance with the movement of increasing need of user. The method in this application is context-aware recommender systems (CARS). Therefore, with this mobile-based recommender system can help promote ecotourism and simplify recommendations for traveler user with location information, direction and other information about ecotourism. Besides that, mobile-based recommender system is a more dynamic, iterative, efficiently, fast and can recommend information objectively.
Keyword: context aware recommendation, ecotourism, recommendation systems, spatial data, user profile

Judul: Pemodelan Hybrid Tourism Recommendation Menggunakan Hidden Markov Model dan Text Mining Berbasis Data Sosial Media
Abstrak: Dalam bidang pariwisata, sistem rekomendasi dapat membantu pengguna mempercepat proses pengambilan keputusan untuk memilih destinasi tempat wisata. Salah satu sistem rekomendasi wisata yang sudah dikembangkan yaitu mobile ecotourism. Rekomendasi yang dihasilkan berbasis jarak dan pemetaan data statis. Sistem mobile ecotourism mengalami masalah seperti pada sistem rekomendasi tradisional yaitu atribut penentu hasil rekomendasi yang sebagian besar masih dalam data statis dan memiliki ketergantungan terhadap data rating. Penelitian ini bertujuan membangun model rekomendasi untuk mengatasi masalah yang terdapat pada sistem rekomendasi mobile ecotourism. Pada penelitian ini mengusulkan dua model rekomendasi hybrid yaitu model rekomendasi berbasis spasial dan berbasis text mining. Dua model ini menggunakan data media sosial. Data media sosial tumbuh secara cukup signifikan dan alami. Pemodelan rekomendasi berbasis spasial memberikan rekomendasi berdasarkan jarak dan analisis user mobility behavior. Pemodelan ini melibatkan data histori check-in dari location based social network. Data histori check-in dimodelkan berdasarkan Hidden Markov yang merepresentasikan user mobillity behavior yaitu perilaku dari mobilitas pengguna saat berpindah dari satu lokasi ke lokasi lain. Asumsi yang digunakan yaitu kunjungan pengguna ke suatu lokasi dipengaruhi oleh posisi current location dan waktu kunjungan. Pada Hidden Markov Model perpindahan pengguna dari suatu lokasi ke lokasi lain dimodelkan dalam state observasi dan waktu kunjungan ke suatu lokasi dimodelkan sebagai hidden state. Pemodelan ini menghasilkan daftar lokasi yang dekat dengan lokasi pengguna dan sesuai dengan perilaku pengguna. Model ini sudah diimplementasikan ke dalam bentuk web. Hasil evaluasi model dengan mean average precision menunjukkan nilai sebesar 0.78 yang merepresentasikan bahwa hasil rekomendasi cukup relevan. Pemetaan data statis antara hobi dan lokasi wisata diselesaikan dengan proses ekstraksi teks pada kumpulan data media sosial objek wisata dan pengguna untuk membangun suatu pemetaan yang bersifat dinamis. Text mining dilakukan untuk mengekstraksi teks sehingga didapatkan penciri dari objek wisata tersebut. Penelitian ini berfokus pada ekstraksi teks berbahasa Indonesia. Rekomendasi dibangun berdasarkan vector space model antara term wisata dan kemunculan term tersebut dalam posting pengguna di jejaring sosial. Pada pemodelan ini juga dilakukan agregasi rekomendasi dengan advice seeking sebagai representasi proses rekomendasi di dunia nyata. Model ini baru dibangun dengan menggunakan bahasa pemrograman R. Hasil dari pemodelan ini kurang baik karena term wisata masih didominasi kata-kata yang tidak representatif.
Keyword: hidden markov model, media sosial, sistem rekomendasi wisata hybrid, text mining, user mobility behavior

Judul: Pengaruh Ph Terhadap IgG Anti H5N1 Asal Kolostrum Sapi Yang Divaksin Dengan Vaksin Avian Influenza H5N1
Abstrak: The aims of this study is to determine the stability of IgG anti-H5 from bovine colostrum (BC) after exposure to acid and basic pH. Two samples of BC, whole colostrum and whey colostrum incubated at pH 4, 5, 9, and 10 for 30 and 60 minutes. Haemagglutination Inhibition (HI) test is used to determine IgG titer after incubation. IgG titers decreased after exposure to acid or basic pH. Acid exposure (pH 5) decreased antibodi titer of whole colostrum as much as 3,75% and 16,25% after incubation 30 and 60 minutes respectively. Antibody titer to pH 4 also decreased until 12,5% and 16,5% after for 30 and 60 minutes respectively. In whey colostrum, both pH 4 and pH 5 reduce 18,75% antibody titer. On other hand, IgG titers is decreased until 100% at basic pH (pH 9-10). In conclusion, IgG from BC is degraded in acidic and alkaline conditions.
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