Statistics based privacy-aware recommender systems make suggestions more powerful by extracting knowledge from the log of social contacts interactions, but unfortunately, they are static-moreover, advice from local experts effective in finding specific business categories in a particular area. We propose a dynamic recommender algorithm based on a lazy random walk that recommends toprank shopping places to potentially interested visitors. We consider local authority and topical authority. The algorithm tested on FourSquare shopping data sets of 5 cities in Indonesia with k-steps={5,7,9} (lazy) random walks and compared the results with other state-of-the-art ranking techniques. The results show that it can reach high score precisions (0.5, 0.37, and 0.26 respectively on p@1, p@3, and p@5 for k=5). The algorithm also shows scalability concerning execution time. The advantage of dynamicity is the database used to power the recommender system; no need to be very frequently updated to produce a good recommendation.
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ABSTRAK Angka Kematian Ibu (AKI) dan Angka Kematian Bayi (AKB) merupakan salah satu indikator penting dalam menilai tingkat derajat kesehatan masyarakat di suatu negara. Berdasarkan data Dinas Kesehatan Provinsi Bengkulu tahun 2012 hingga 2015, AKI dan AKB di Provinsi Bengkulu masih diatas rata-rata nasional. K-Means Clustering merupakan salah satu metode pengelompokan non hirarki yang bertujuan mengelompokkan objek sedemikian hingga jarak-jarak tiap objek ke pusat kelompok di dalam satu kelompok adalah minimum. Penelitian ini bertujuan (1) Merancang dan membangun Sistem Informasi Geografis untuk memetakan angka kematian ibu dan bayi di setiap Kota/Kabupaten di Provinsi Bengkulu menggunakan metode K-Means Clustering, (2) Mengetahui perbedaan dan status pengelompokkan angka kematian ibu dan bayi di setiap Kota/Kabupaten di Provinsi Bengkulu. Hasil penelitian yang diperoleh yaitu (1) Penelitian ini berhasil memetakan angka kematian ibu dan bayi dalam 3 kelompok, yaitu rendah, sedang dan tinggi (2) berhasil menerapkan metode K-Means Clustering (3) Persentasi AKI berdasarkan kota/kabupaten di Provinsi Bengkulu, sebagai berikut: 15% kota/kabupaten berada di tingkat rendah, 65% berada di tingkat sedang dan 20% berada di tingkat tinggi. Sedangkan persentasi AKB-nya 32,5% kota/kabupaten berada di tingkat rendah, 60% berada di tingkat sedang dan 7,5% berada di tingkat tinggi. Secara keseluruhan dapat dikatakan bahwa tingkat AKI/AKB di Provinsi Bengkulu masih belum memuaskan, yaitu < 15% AKI dan < 32,5% AKB.. ABSTRACT Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR) is one important indicator in assessing the degree of public health in a country. Based on data from Bengkulu Provincial Health Office in 2012 until 2015, MMR and IMR in Bengkulu is still above the national average. K-Means Clustering is one of the non-hierarchical clustering method that aims to group objects so that the distance from the object to the center of each group in the group is the minimum. This study aims to (1) Designing and building a Geographic Information System to map the mortality rate of mothers and babies in each City/Regency in Bengkulu using K-Means Clustering, (2) Know the difference and status grouping of maternal and infant deaths in each city/regency in Bengkulu. The results obtained are: (1) This research has mapped the mortality rate of mothers and infants into three groups: low, medium and high (2) successfully applied the method of K-Means Clustering (3) Percentage of AKI city/regency in Bengkulu, as follows: 15% city/regency is at a low level, 65% were in the middle level and 20% are at a high level. While his AKB percentage 32.5% city/regency is at a low level, 60% were in the moderate and 7.5% were at high levels. Overall it can be said that the rate of MMR / IMR in Bengkulu Province is not too satisfied in term of healty service management that is < 15% MMR and < 32.5% IMR. How to Cite : Aditya, K.B. Setiawan, Y. Puspitaningrum, D. (2017). SISTEM INFORMASI GEOGRAFIS PEMETAAN FAKTOR-FAKTOR YANG MEMPENGARUHI ANGKA KEMATIAN IBU (AKI) DAN ANGKA KEMATIAN BAYI (AKB) DENGAN METODE K-MEANS CLUSTERING (STUDI KASUS: PROVINSI BENGKULU). Jurnal Teknik Informatika, 10(1), 59-66. doi:10.15408/jti.v10i1.6817Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i1.6817
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