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Abstrak :
Research upon performance procedure misconduct of the tender auction over winner determination as a form of collusion, corruption and nepotism in a government's Unit ?X? Technical Executor where unlawful conduct exixts, deprives both the community and the country. Such misconduct performed intentionally and it was compulsory, and such as this is hard to prove since all procedures were executed, unless the supervisory was maximally perform its principal duty and function. The research methodology here performed descriptively, provided that the research was in purpose to abtain or depict of such misconduct over the procedure of the tender auction deeply, including within, causal factor and people involved. Misconduct occurred according to the different Typology of Association Theory (Diiferential Association) by Edwin H. Sutherland that such performance procedure misconduct over tender aution in order to determine the winner as a form of collusion, corruption and nepotism within a Unit ?X? Technical Executor in the government may be included into a form of Collusion, Corruption and Nepotism crime and as a White Collar Crime. Tender auction procedure misconduct of determination the winner was a form of collusion, comrption and ncpotism, since it deprives the state?s finance, and imposes lesser trust, power and revenue, thus, hampering mentality of those who involved. And at this time being, it has never been investigated any better by the General Inspectorate neither the Development Finance Investigatory Administration. Prevention measures that should be perform that it need highly seriousness from the institution or administration who investigate the development performance and to provide fair sanction against the act, either to officials, the committee, technical managers, and contractors, making them learning their lesson and giving severe warning to those who intending to also misconduct.

Abstrak :
Lapangan-A merupakan lapangan minyak dan gas yang sudah diproduksi sejak tahun 1975. Selama ini, untuk melakukan perhitungan cadangan dan penempatan lokasi sumur yang baru, bertumpu pada model geologi yang merupakan model 3-dimensi, dengan sumber data dari data sumur dan beberapa penampang seismik 2-dimensi. Setelah kurang lebih 40 tahun berproduksi, dengan recovery factor dari minyak yang sudah diproduksi mencapai ~50%, perlu dilakukan terobosan-terobosan untuk meningkatkan dan meninjau recovery factor dengan jalan menemukan zona-zona minyak yang selama ini masih belum terproduksi secara optimal, baik disebabkan adanya kompartemenisasi akibat pensesaran atau perangkap stratigrafi yaitu lateral discontinuity akibat perbedaan facies. Untuk memetakan zona-zona dengan dengan kondisi pengurasan yang kurang optimal, dilakukan akuisisi seismik 3D pada akhir tahun 2011, dengan harapan dapat digunakan untuk membantu dalam prediksi penyebaran facies secara lateral dan juga memetakan hidrokarbon yang tersisa. Untuk memetakan distribusi hidrokarbon yang tersisa, dibangun ulang model 3-dimensi dengan mengintegrasikan data seismik, data geologi dan data produksi. Data seismik terdiri dari hasil interpretasi struktur geologi, atribut seismik yang menunjukan penyebaran batupasir dan peta anomali hidrokarbon dari hasil perhitungan AVO cubes. Data geologi berupa data tekanan dan kontak fluida dari sumur pengeboran, sedangkan data produksi yaitu kumulatif produksi hidrokarbon yang digunakan untuk memvalidasi interpretasi facies dan peta anomali. Dengan mengintegrasikan data geologi dan geofisika yang ada, diketahui terdapat beberapa beberapa area anomali hidrokarbon. Namun, di area selatan tingkat penurunan tekanan reservoirnya lebih kecil, sehingga dapat disimpulkan bahwa di area selatan masih terdapat prospek hidrokarbon yang belum terproduksi secara optimal. ......The A Field is oil and gas field that has already been produced since 1975. The existing model that is used to calculate the initial and actual reserves was based only on well data and some 2-D seismic lines. Having been massively produced for almost 40 years, the recovery factor for oil has been reach ~50%, indeed new methodology was required to improve the recovery factor. Improvement of the recovery factor might still be possible since there are several faults which can compartementalize the reserves and also some lateral barier due to different geological facies. New 3D seismic acquisition was completed in the end of 2011 and intended to identify the remaining hydrocarbon accumulation in the field. 3D geomodel that integrating both new structural interpretation and facies inputs from both seismic and well data was built. AVO cubes which are calculated based on pre-stacked data is used to identify and calibrating the remaining hidrocarbon especially in the un-calibrated area. Dynamic data from the well, which are pressure, contact levels and production history were used to justify the geological interpretation. By integrating both geological and geophysical data, and has been proved from pressure data, there is less pressure depletion in the southern area. Based on the data, it can be be used to locate the remaining potential hydrocarbon which is in the southern part of the study area.

Abstrak :
Penelitian dengan disain deskriptif korelasi dilatar belakangi ketidakpuasan perawat akibat lingkungan kerja. Tujuannya menguraikan lingkungan kerja, karakteristik individu dengan kepuasan kerja perawat RSUD Tugurejo Semarang. Populasi sebanyak 225, menggunakan total sampling, kriteria inklusi bekerja 6 bulan, tidak meninggalkan rumah sakit lebih 1 bulan, diuji dengan T Independen, chi squere, regresi logistik model prediksi. Ditemukan hubungan kualitas kepemimpinan, gaya manajemen, program dan kebijakan ketenagaan, otonomi, hubungan interdisiplin, dan pengembangan profesional dengan kepuasan kerja. Faktor paling dominan adalah program dan kebijakan ketenagaan setelah dikontrol kualitas kepemimpinan dan hubungan interdisiplin. Manajemen dapat meningkatkan program menciptakan lingkungan kerja positif, khususnya program dan kebijakaan ketenagaan. ......This study used a descriptive correlation design with background of nurses dissatisfaction with their working environment. It investigated the correlation between working environment, individual characteristics and job satisfaction of nurses at Tugurejo RSUD, Semarang. The population is 225 people using total sampling with inclusion criteria of working for six months, not leaving the hospital more than one month. It used independent T test, chi square and logistic regression prediction model. The result showed a relationship between leadership qualities, management style, programs and policies of personnel, autonomy, interdisciplinary relationships, professional development and job satisfaction. The most dominant factor was the programs and policies of personnel after being controlled the leadership quality and interdisciplinary relationships. Management can improve the program that creates a positive work environment, particularly programs and policies of personnel.

Abstrak :
Perkembangan teknologi yang dapat membantu pengukuran luas dan volume suatu objek semakin umum. Salah satu teknologi yang dapat membantu pengukuran luas dan volume suatu objek adalah sensor Light and Detection Ranging (LiDAR). Dalam konteks pengukuran luas dan volume suatu objek dimana objek tersebut adalah bangunan, sensor LiDAR dapat dibantu oleh deep learning dan clustering agar dapat mengidentifikasi bangunan yang nantinya dapat dihitung luas dan volume bangunan tersebut. Dataset yang digunakan pada penelitian ini adalah LiDAR Margonda, Depok dan LiDAR Dublin, Irlandia. Metode deep learning yang digunakan untuk melakukan segmentasi semantik adalah Dynamic Graph Convolutional Neural Network (DGCNN) dan algoritma yang digunakan untuk melakukan pelabelan bangunan adalah Pairwise Linkage Clustering. Penelitian ini juga bermaksud untuk memberikan perbandingan dengan Euclidean Clustering sebagai algoritma pelabelan bangunan. Segmentasi semantik dilakukan agar dapat membedakan objek bangunan dengan objek bukan bangunan sedangkan pelabelan bangunan dilakukan agar dapat memisahkan setiap objek bangunan. Secara hasil, penelitian ini berhasil menggunakan DGCNN sebagai metode segmentasi semantik dan Pairwise Linkage Clustering sebagai metode pelabelan bangunan. Evaluasi dilakukan menggunakan metrik Accuracy, Recall, Precision, F-score dan Intersection over Union untuk metode segmentasi semantik sedangkan metrik yang digunakan untuk pelabelan bangunan adalah Accuracy, Recall, Precision, dan F-Score. Pada dataset Margonda, Depok nilai akurasi yang didapatkan oleh DGCNN adalah 82% dan nilai akurasi yang didapatkan oleh Pairwise Linkage Clustering adalah 4.7% untuk Scale Cut-Off Distance 100, 28% untuk Scale Cut-Off Distance 200, 38% untuk Scale Cut-Off Distance 400, dan 28% untuk Scale Cut-Off Distance 800. Pada dataset Dublin, Irlandia nilai akurasi yang didapatkan oleh DGCNN adalah 86% dan nilai akurasi yang didapatkan oleh Pairwise Linkage Clustering adalah 10% untuk Scale Cut-Off Distance 100, 30% untuk Scale Cut-Off Distance 200, 40% untuk Scale Cut-Off Distance 400, dan 35% untuk Scale Cut-Off Distance 800. Dalam pelabelan bangunan, Pairwise Linkage Clustering berhasil memberikan hasil yang lebih baik daripada Euclidean Clustering pada dataset Margonda, Depok sedangkan Euclidean Clustering berhasil memberikan hasil yang lebih baik daripada Pairwise Linkage Clustering di dataset Dublin, Irlandia. ...... With development of technology becoming more advanced, technology that can help to measure area and volume of an object becomes more common. One of the technology that can help measure area and volume of an object is a sensor called Light and Detection Ranging (LiDAR). In the context of measuring area and volume of an object where the object is a building, LiDAR sensor can be helped by deep learning and clustering to identify building and then the building’s area and volume can be measured. In this research the dataset used are Margonda, Depok and Dublin, Irlandia. Deep learning method used to do semantic segmentation is Dynamic Graph Convolutional Neural Network (DGCNN) and the algorithm to do the clustering is Pairwise Linkage Clustering. This research is also intended to give comparison with Euclidean Clustering as an algorithm to do clustering. Semantic segmentation is done so the map can be separated as building object and non building object. Result wise, this research has succeeded to use DGCNN as a method to do semantic segmentation and Pairwise Linkage Clustering as a method to do clustering. Evaluation is done by using matrix such as Accuracy, Recall, Precision, F-Score and Intersection over Union for semantic segmentation while matrix used to evaluate the clustering algorithm is Accuracy, Recall, Precision, and F-Score. In Margonda, Depok dataset DGCNN has the accuracy score of 82% and the accuracy for Pairwise Linkage Clustering with cut off distance 100 is 4.7%, with cut off distance 200 is 28%, with cut off distance 400 is 38%, with cut off distance 800 is 28%. In Dublin, Irlandia dataset DGCNN has the accuracy score of 86% and the accuracy for Pairwise Linkage Clustering with cut off distance 100 is 10%, with cut off distance 200 is 30%, with cut off distance 400 is 40% and with cut off distance 800 is 35%. In the clustering part, Pairwise Linkage Clustering gives better result in Margonda, Depok dataset while Euclidean Clustering gives better result in Dublin, Irlandia dataset.