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Electronic Medical Record (EMR) has now become a trend in the world of health care. Lot of obstacles and barriers that interfere implementation of EMR. This paper discussed the eleven factors suspected to affect the implementation of EMR, with a case study hospital in Banyumas. By using logistic regression analysis obtained eight of the eleven factors that significantly affect the implementation of RME. The eight factors are financial factors, Human Resources (HR), the process of changing, psychological factors, legal factors, the time factor, organizational factors, and ICT trends. Technology, infrastructure, and social is a factor that does not significantly affect the implementation of EMR in hospital. ICT trends are new factors that significantly affect the implementation of EMR in hosital. Keywords : EMR, logistic regression, Banyumas, ICT trends, health care ABSTRAKSalah satu penggunaan Teknologi Informasi dan Komunikasi (TIK) dalam dunia kesehatan yang telah menjadi tren dalam dunia pelayanan kesehatan secara global adalah Rekam Medis Elektronik (RME). Pada bagian lain berbagai macam kendala dan hambatan yang berpotensi untuk dapat mengganggu terwujudnya program tersebut tidaklah sedikit. Pada makalah ini dibahas sebelas faktor yang diduga mempengaruhi implementasi RME, dengan mengambil studi kasus Rumah Sakit (RS) di Kabupaten Banyumas. Dengan menggunakan analisis regresi logistik didapatkan delapan dari sebelas faktor yang signifikan berpengaruh terhadap implementasi RME. Adapun kedelapan faktor tersebut adalah: faktor finansial, faktor Sumber Daya Manusia (SDM), faktor proses perubahan, faktor psikologi, faktor legal, faktor waktu, faktor organisasi, dan tren TIK. Faktor teknologi, infrastruktur, dan sosial adalah faktor yang tidak signifikan berpengaruh terhadap implementasi RME di RS. Faktor tren TIK merupakan faktor baru yang secara signifikan berpengaruh terhadap implementasi RME di RS.

The existence of identical objects in a data set is a necessity. This paper proposes a new indicator and procedure to obtain the initial medoids. The new algorithm guarantees no empty groups and identical objects in the same group, either in the initial or final groups. We use six real data sets to evaluate the proposed method and compare the results of other methods in terms of adjusted Rand index and clustering accuracy. The experiment results show that the performance of the proposed method is comparable with other methods

Environmental quality is an essential aspect of the world's sustainability. This paper presents a combination of several methods to obtain a profile of provinces in Indonesia according to the environmental quality indices. We apply the block-based k-medoids algorithm, Ward's method, and the Hartigan index to determine the number of clusters. Based on Hartigan index suggested classifying 34 provinces in Indonesia into four groups. A comparison of the mean vector of four groups using multivariate analysis of variance concluded that three areas have the best environment quality index and eight regions require serious attention to improve their environmental quality methods.