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Osteoarthritis (OA) is a degenerative bone disease that begin by loss of articulary cartilage (joint). The disease most commonly affects the elder or adult people. Elderly people experiencing degenerative processes in which decreasing organ function. The organ degeneration causes several diseases resulting the patients to receive multi drug for the treatment and causing polypharmacy, thus will increase the risk of drug interactions. This study was conducted to determine the treatment for osteoarthritis and drug interactions that occur in patients with osteoarthritis. The method used was total sampling using the descriptive-retrospective approach. The sample was elderly osteoarthritis patients in outpatient installation which were treated in RSD dr. H Koesnadi Bondowoso during January to December 2013. In this study we found that the main treatment for osteoarthritis was non steroidal anti inflammatory drug (NSAID) that use to relief pain. From 108 samples of elderly osteoarthritis patients, we found that 17 patients (15.74%) indicating the potential for drug interactions.   Keywords: osteoarthritis, outpatient age above 60 years, NSAID, drug interaction

Penelitian ini bertujuan mengetahui pengaruh kompetensi, independensi, dan profesionalisme auditor internal pemerintah terhadap kualitas audit. Sampel penelitian terdiri atas 54 auditor internal pemerintah. Kuesioner diuji validitas dan uji reliabilitas sebelum penelitian. Uji prasyarat analisis yang digunakan adalah uji normalitas, uji linieritas, uji mulitikolonieritas dan uji heteroskedastisitas. Analisis data yang digunakan adalah analisis regresi sederhana dan analisis regresi berganda. Hasil penelitian menunjukkan (1) Terdapat pengaruh positif signifikan  kompetensi  auditor  internal  pemerintah  terhadap  kualitas audit. (2) Terdapat pengaruh signifikan independensi auditor internal pemerintah terhadap kualitas audit. (3) Terdapat pengaruh positif profesionalisme auditor internal pemerintah terhadap kualitas audit. (4) Terdapat pengaruh signifikan secara simultan kompetensi, independensi, profesionalisme auditor internal pemerintah terhadap kualitas audit.

<p class="Abstrak">Penggunaan ponsel sudah sangat erat dengan kehidupaan anak usia dini sehingga menimbulkan beberapa dampak negatif bagi anak usia dini terutama berkurangnya interaksi dengan dunia sekitarnya. Salah satu teknologi yang dapat dikembangkan pada ponsel adalah <em>computer vision. </em>Salah satu penggunaan <em>computer vision </em>adalah <em>object recognition</em> yang memberikan solusi untuk membantu mengenali objek.<em> </em>Pada penelitian ini dibangun sistem pengenalan objek benda di dalam rumah yang diaplikasikan pada ponsel yang diharapkan membantu anak usia dini mengenali benda disekitarnya. <em>MobileNet </em>merupakan salah satu <em>feature extraction</em> yang memiliki kinerja yang baik dan ringan digunakan pada perangkat ponsel. Arsitektur <em>MobileNet </em>terdiri dari <em>layer depthwise convolution </em>dan <em>layer pointwise </em><em>convolution </em>dalam mengekstraksi fitur<em>. </em>Percobaan ini juga menggunakan arsitektur <em>Single Shot Multibox Detector (SSD) </em>sebagai metode dalam mendeteksi objek<em>. Pre-trained model </em>dari dataset <em>COCO </em>digunakan pada eksperimen<em>,</em> untuk mengenali 20 jenis objek benda di dalam rumah. Dari hasil eksperimen, <em>MobileNetV2 </em>menghasilkan nilai <em>mean Average Precision (mAP)</em> yang lebih baik dibandingkan dengan <em>MobileNetV1 </em>dan<em> InceptionV2, </em>yaitu<em> </em>sebesar 99,34%.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"> <em>Mobile phone usage has been very close to early childhood life, so giving rise to some negative impact on early childhood, especially reduced interaction with the surrounding world. One of the technologies that can be developed on the cellphone is computer vision. One of the uses of computer vision is object recognition that provides solutions to help to recognize objects. This research builds a system for recognition objects inside in house that is developed on a cellphone that is expected to help early childhood recognize objects in the surrounding. MobileNet is one of feature extraction that has good performance and efficient use on a cellphone. MobileNet architecture consists of a depthwise convolution layer and pointwise convolution layer in extracting features. The experiment also uses the architecture of Single Shot Multibox Detector (SSD) as a method of detecting objects. We used MobileNet architecture as a pre-trained model that had previously been trained on COCO datasets, and implement transfer learning for 20 types of objects commonly found inside the house. The experimental result indicates that the mean Average Precision (mAP) of MobileNetV2 could exceed MobileNetV1 and InceptionV2 of 99.34%.</em></p>

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