HUBUNGAN ANTARA KUALITAS KEHIDUPAN KERJA DENGAN KOMITMEN ORGANISASI PADA KARYAWAN PT. KUSUMA ABADI DI JAKARTA

Penelitian ini bertujuan untuk mengetahui apakah terdapat hubungan antara kualitas kehidupan kerja dengan komitmen organisasi pada karyawan PT. Kusuma Abadi di Jakarta. Penelitian ini dilakukan selama tiga bulan terhitung mulai bulan April 2015 sampai dengan bulan Juni 2015. Metode penelitian yang digunakan adalah metode survei dengan pendekatan korelasional. Populasi dalam penelitian ini adalah seluruh karyawan PT. Kusuma Abadi di Jakarta yang berjumlah 104 karyawan. Berdasarkan tabel populasi dan sampel dengan taraf kesalah 5% maka diperoleh sampel sebanyak 78 karyawan dengan menggunakan teknik acak proporsional (Proporsional random sampling). Data dari variabel X tentang kualitas kehidupan kerja dan variabel Y tentang komitmen organisasi berbentuk kuisioner. Uji persyaratan analisis yang dilakukan adalah dengan mencari persamaan regresi yang didapat adalah Ŷ = 10,587 + 0,586 X. Hasil uji normalitas liliefors menghasilkan Lhitung = 0,084 dan Ltabel= 0,100 pada taraf signifikansi (a) = 0.05 untuk jumlah sampel (n) 78 . Karena Lhitung= (0,084) < Ltabel = (0,100) maka variabel X dan Y berdistribusi normal. Pengujian hipotesis dengan uji keberartian regresi menghasilkan Fhitung (39,99) > Ftabel (3,98), yang berarti persamaan regresi tersebut signifikan. uji kelinieran regresi menghasilkan Fhitung (1,08) < Ftabel (1,76) sehingga Fhitung< Ftabel maka dapat disimpulkan bahwa model persamaan regresi adalah linier. Uji koefisien korelasi product moment menghasilkan rhitung = 0,587. Uji-t menghasilkan thitung (6,32) > ttabel (1,68). Hasil penelitian tersebut menyimpulkan bahwa terdapat hubungan positif antara Kualitas Kehidupan Kerja dengan komitmen organisasi pada karyawan. Dengan uji koefisien determinasi atau penentu diperoleh hasil 34,48% Komitmen Organisasi ditentukan oleh kualitas kehidupan kerja

Rhythmic pattern modeling for beat and downbeat tracking in musical audio

ABSTRACT Rhythmic patterns are an important structural element in music. This paper investigates the use of rhythmic pattern modeling to infer metrical structure in musical audio recordings. We present a Hidden Markov Model (HMM) based system that simultaneously extracts beats, downbeats, tempo, meter, and rhythmic patterns. Our model builds upon the basic structure proposed by Whiteley et. a

Compositional hierarchical model for music information retrieval

In recent years, deep architectures, most commonly based on neural networks, have advanced the state of the art in many research areas. Due to the popularity and the success of deep neural-networks, other deep architectures, including compositional models, have been put aside from mainstream research. This dissertation presents the compositional hierarchical model as a novel deep architecture for music processing. Our main motivation was to develop and explore an alternative non-neural deep architecture for music processing which would be transparent, meaning that the encoded knowledge would be interpretable, trained in an unsupervised manner and on small datasets, and useful as a feature extractor for classification tasks, as well as a transparent model for unsupervised pattern discovery. We base our work on compositional models, as compositionality is inherent in music. The proposed compositional hierarchical model learns a multi-layer hierarchical representation of the analyzed music signals in an unsupervised manner. It provides transparent insights into the learned concepts and their structure. It can be used as a feature extractor—its output can be used for classification tasks using existing machine learning techniques. Moreover, the model’s transparency enables an interpretation of the learned concepts, so the model can be used for analysis (exploration of the learned hierarchy) or discovery-oriented (inferring the hierarchy) tasks, which is difficult with most neural network based architectures. The proposed model uses relative coding of the learned concepts, which eliminates the need for large annotated training datasets that are essential in deep architectures with a large number of parameters. Relative coding contributes to slim models, which are fast to execute and have low memory requirements. The model also incorporates several biologically-inspired mechanisms that are modeled according to the mechanisms that exists at the lower levels of human perception (e.g. lateral inhibition in the human ear) and that significantly affect perception. The proposed model is evaluated on several music information retrieval tasks and its results are compared to the current state of the art. The dissertation is structured as follows. In the first chapter we present the motivation for the development of the new model. In the second chapter we elaborate on the related work in music information retrieval and review other compositional and transparent models. Chapter three introduces a thorough description of the proposed model. The model structure, its learning and inference methods are explained, as well as the incorporated biologically-inspired mechanisms. The model is then applied to several different music domains, which are divided according to the type of input data. In this we follow the timeline of the development and the implementation of the model. In chapter four, we present the model’s application to audio recordings, specifically for two tasks: automatic chord estimation and multiple fundamental frequency estimation. In chapter five, we present the model’s application to symbolic music representations. We concentrate on pattern discovery, emphasizing the model’s ability to tackle such problems. We also evaluate the model as a feature generator for tune family classification. Finally, in chapter six, we show the latest progress in developing the model for representing rhythm and show that it exhibits a high degree of robustness in extracting high-level rhythmic structures from music signals. We conclude the dissertation by summarizing our work and the results, elaborating on forthcoming work in the development of the model and its future applications