Pengaruh Layanan Bimbingan Kelompok Terhadap Motivasi Berprestasi Siswa Sekolah Menengah Pertama

Penelitian ini meneliti tentang pengaruh layanan bimbingan kelompok terhadap motivasi berprestasi siswa kelas VIII SMP Negeri 4 Kota Gorontalo. Jenis penelitian ini adalah Pre-experimental design dengan rancangan One-Group Pretest-Postest Design. Subjek penelitian dalam penelitian ini adalah siswa kelas VIII SMP Negeri 4 Kota Gorontalo yang berjumlah 15 orang siswa. Teknik pengambilan sampel menggunakan Purposive Sampling. Hasil penelitian diperoleh nilai rata-rata pretest 182,47 dan nilai rata-rata posttest 221,67. Hasil pengujian hipotesis diperoleh thitung > ttabel yaitu 8,948>2,98. Dapat disimpulkan hipotesis dalam penelitian ini terdapat pengaruh layanan bimbingan  kelompok terhadap motivasi berprestasi siswa di SMP Negeri 4 Kota Gorontalo

Enriching Business Process Models with Decision Rules

Making the right decisions in time is one of the key tasks in every business. In this context, decision theory fosters decision-making based on well-defined decision rules. The latter evaluate a given set of input parameters and utilize evidenced data in order to determine an optimal alternative out of a given set of choices. In particular, decision rules are relevant in the context business processes as well. Contemporary process modeling languages, however, have not incorporated decision theory yet, but mainly consider rather simple, guard-based decisions that refer to process-relevant data. To remedy this drawback, this paper introduces an approach that allows embedding decision problems in business process models and applying decision rules to deal with them. As a major benefit, it becomes possible to automatically determine optimal execution paths during run time

Construction simulation using parallel computing environments

The paper describes and compares alternative approaches to implementing construction simulation models within a multiprocessor computing environment. Both parallel-algorithmic and neural network based methods of simulating construction processes are considered, and compared with the conventional serial-algorithmic approach. The lines along which a simulation algorithm can be divided into tasks for parallel execution on a multiprocessor are first discussed, and the merits of each approach are identified. This is followed by a brief discourse on neural networks, their application to construction simulation, and the way in which such an implementation can be implemented within a multiprocessing environment. The merits and demerits of all approaches are discussed with particular reference to a model of an excavation system. A case study comparing the speed at which each implementation can process a simulation shows the neural approach to operate approximately two orders of magnitude faster than the alternatives. The paper concludes with an indication of future research to be conducted in this field.