Peringkat Obligasi Ditinjau dari Produktivitas dan Penerapan Corporate Governance Perception Index (CGPI).

Penelitian ini bertujuan untuk menganalisis pengaruh produktivitas dan penerapan corporate governance perception index (CGPI) terhadap peringkat obligasi Perusahaan di Bursa Efek Indonesia (BEI) periode 2012–2015. Objek dalam penelitian berupa Perusahaan penerbit obligasi yang terdaftar di BEI selama periode penelitian. Populasi yang digunakan dalam penelitian ini berupa obligasi korporasi (corporate bond) yang terdaftar di BEI selama tahun penelitian. Untuk penentuan sampel menggunakan metode non probability sampling dengan menggunakan purposive sampling, kriteria dalam penentuan sampel penelitian diantaranya, Perusahaan penerbit obligasi di BEI yang telah mendapatkan skor CGPI dari IICG dan obligasi korporasi di BEI yang diperingkat oleh PT PEFINDO. Jumlah sampel sesuai kriteria yang ditentukan sebanyak 39 sampel. Metode penelitian menggunakan analisis ordinal logistik regresi. Berdasarkan hasil penelitian, produktivitas yang diproksikan dengan perputaran aktiva tidak memiliki pengaruh terhadap peringkat obligasi, dan penerapan corporate governance perception index (CGPI) yang diproksikan dengan skor CGPI yang didapat Perusahaan memiliki pengaruh terhadap peringkat obligasi

Pembinaan nilai budaya melalui permainan rakyat daerah sulawesi utara

Naskah ini merupakan hasi I proyek tahun anggaran 1996/1 997 yang penelitiannya dilakukan oleh Burhanudin Domili dkk, staf peneliti Balar Kajian Sejarah dan Nilai Tradisional Manado. Nilai-nilai budaya yang ingin kita bina dan kembangkan adalah nilai-nilai luhur yang Pancasilais dan yang udah diperinci dalam Pedoman Penghayatan dan Pengamalan Pancasila

Compressive and flexural strength of concrete containing palm oil biomass clinker and polypropylene fibres

This paper presents the effects of using palm oil biomass (POB) clinker with polypropylene (PP) fibres in concrete on its compressive and flexural strength performances. Due to infrastructural development works, the use of concrete in the construction industry has been increased. Simultaneously, it raises the demand natural sand, which causes depletion of natural resources. While considering the environmental and economic benefits, the utilization of industrial waste by-products in concrete will be the alternative solution of the problem. Among the waste products, one of such waste by-product is the palm oil biomass clinker, which is a waste product from burning processes of palm oil fibres. Therefore, it is important to utilize palm oil biomass clinker as partial replacement of fine aggregates in concrete. Considering the facts, an experimental study was conducted to find out the potential usage of palm oil fibres in concrete. In this study, total 48 number of specimens were cast to evaluate the compressive and flexural strength performances. Polypropylene fibre was added in concrete at the rate of 0.2%, 0.4% and 0.6%, and sand was replaced at a constant rate of 10% with palm oil biomass clinker. The flexural strength of concrete was noticed in the range of 2.25 MPa and 2.29 MPa, whereas, the higher value of flexural strength was recorded with 0.4% polypropylene fibre addition. Hence, these results show that the strength performances of concrete containing POB clinker could be improved with the addition of polypropylene fibre

Self-Supervised Clustering on Image-Subtracted Data with Deep-Embedded Self-Organizing Map

Developing an effective automatic classifier to separate genuine sources from artifacts is essential for transient follow-ups in wide-field optical surveys. The identification of transient detections from the subtraction artifacts after the image differencing process is a key step in such classifiers, known as real-bogus classification problem. We apply a self-supervised machine learning model, the deep-embedded self-organizing map (DESOM) to this "real-bogus" classification problem. DESOM combines an autoencoder and a self-organizing map to perform clustering in order to distinguish between real and bogus detections, based on their dimensionality-reduced representations. We use 32x32 normalized detection thumbnails as the input of DESOM. We demonstrate different model training approaches, and find that our best DESOM classifier shows a missed detection rate of 6.6% with a false positive rate of 1.5%. DESOM offers a more nuanced way to fine-tune the decision boundary identifying likely real detections when used in combination with other types of classifiers, for example built on neural networks or decision trees. We also discuss other potential usages of DESOM and its limitations

Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

The past few decades have seen the burgeoning of wide field, high cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy, however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO), whose primary science objective is the optical follow-up of Gravitational Wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near real-time to fully exploit the time-domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this "off-the-shelf" pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to sub-pixel levels, and that measured L-band photometries are accurate to ∼50 mmag at mL∼16 and ∼200 mmag at mL∼18. These values compare favourably to those obtained using GOTO's primary, in-house pipeline, GOTOPHOTO, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic "obs package" that others can build-upon should they wish to use the LSST Science Pipelines to process data from other facilities

Searching for electromagnetic counterparts to gravitational-wave merger events with the prototype Gravitational-wave Optical Transient Observer (GOTO-4)

We report the results of optical follow-up observations of 29 gravitational-wave (GW) triggers during the first half of the LIGO–Virgo Collaboration (LVC) O3 run with the Gravitational-wave Optical Transient Observer (GOTO) in its prototype 4-telescope configuration (GOTO-4). While no viable electromagnetic (EM) counterpart candidate was identified, we estimate our 3D (volumetric) coverage using test light curves of on- and off-axis gamma-ray bursts and kilonovae. In cases where the source region was observable immediately, GOTO-4 was able to respond to a GW alert in less than a minute. The average time of first observation was 8.79 h after receiving an alert (9.90 h after trigger). A mean of 732.3 square degrees were tiled per event, representing on average 45.3 per cent of the LVC probability map, or 70.3 per cent of the observable probability. This coverage will further improve as the facility scales up alongside the localization performance of the evolving GW detector network. Even in its 4-telescope prototype configuration, GOTO is capable of detecting AT2017gfo-like kilonovae beyond 200 Mpc in favourable observing conditions. We cannot currently place meaningful EM limits on the population of distant (⁠D^L=1.3 Gpc) binary black hole mergers because our test models are too faint to recover at this distance. However, as GOTO is upgraded towards its full 32-telescope, 2 node (La Palma & Australia) configuration, it is expected to be sufficiently sensitive to cover the predicted O4 binary neutron star merger volume, and will be able to respond to both northern and southern triggers

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We presentan approach for creating a training set by using all detections in the science images to be thesample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21-by-21pixel stamps centered at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95% prediction accuracy on the real detections at a false alarm rate of 1%

Machine learning for transient recognition in difference imaging with minimum sampling effort

The amount of observational data produced by time-domain astronomy is exponentially increasing. Human inspection alone is not an effective way to identify genuine transients from the data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We present an approach for creating a training set by using all detections in the science images to be the sample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21 x 21 pixel stamps centred at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95 per cent prediction accuracy on the real detections at a false alarm rate of 1 per cent