ANALISIS RASIO KEUANGAN PADA PT ARTIMA INDUSTRI INDONESIA

Laporan akhir ini disusun untuk memenuhi syarat agar dapat menyelesaikan pendidikan Diploma III pada Jurusan Akuntansi di Politeknik Negeri Sriwijaya. Laporan ini bertujuan untuk mengetahui Kinerja Keuangan Perusahaan pada PT Artima Industri Indonesia Berdasarkan rasio keuangan. Parameternya terdiri atas 4 (empat) rasio yaitu rasio likuiditas, rasio solvabilitas, rasio aktivitas, dan rasio profitabilitas. Laporan ini merupakan studi kasus melalu deskriptif kuantitatif. Setelah penulis melakukan perhitungan maka dapat disimpulkan bahwa berdasarkan rasio likuiditas tahun 2017, 2018, dan 2019 perusahan dalam keadan likuid, rasio solvabilitas tahun 2017, 2018, dan 2019 dalam keadaan baik perusahaan mampu membayar seluruh kewajibannya tanpa dibebani oleh banyak utang, rasio aktivitas tahun 2017, 2018, dan 2019 masih berada dibawah standar industri menunjukan bahwa perusahaan belum efektif dalam mengelola aktiva yang dimiliki, serta rasio profitabilitas dari tahun 2017 sampai dengan tahun 2019 masih berada dibawah standar industri. Dari kesimpulan tersebut, sebaiknya perusahaan dapat mengendalikan aset lancar dengan melakukan penagihan piutang dan meningkatkan uang kas agar dapat membayar kewajiban tepat waktu dan melakukan promosi untuk meningkatkan penjualan dan mendapatkan laba bagi perusahaan

Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorde

Peptidomimetic and Non- Peptidomimetic Derivatives as Possible SARS-CoV-2 Main Protease (Mpro) Inhibitors

To design novel inhibitors of the SARS-CoV-2 main protease (Mpro), we investigated the binding mode of the recently reported α-ketoamide inhibitors of this enzyme. Following, we utilized in-silico screening to identify 168 peptidomimetic and non-peptidomimetic compounds that are high probability Mpro binding candidates. The compounds were synthesized in 5 to 10 mg for initial screening for their potential inhibition of Mpro using Fluorescence Resonance Energy Transfer (FRET) assay. The study was conducted using the main protease, MBP-tagged (SARS-CoV-2) Assay Kit (BPS Bioscience, #79955-2), and the fluorescence due to enzymatic cleavage of substrate measured using BMG LABTECH CLARIOstar™, a fluorescent microplate reader, with an excited/emission wavelength of 360 nm/460 nm, respectively. The FRET assay showed 29 compounds to exhibit lower fluorescence compared to the positive control, indicating inhibitory activity, with three of the compounds exhibiting over 50% enzymatic inhibition. The assay average scores were plotted as dose inhibition curves using variable parameter nonlinear regression to calculate the IC50 values. To design more potent inhibitors, an in-silico molecular docking simulation using the SARS-CoV-2 Mpro crystal structure was conducted to investigate on a molecular level the key binding residues at the active site, as well as the possible binding modes and affinity of the lead inhibitors. Additionally, an in-silico study of the compounds\u27 molecular properties and physicochemical profiles was performed to predict their pharmacokinetic properties and assess their suitability as potential orally active drug candidates.https://scholarscompass.vcu.edu/gradposters/1139/thumbnail.jp

Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators

The file attached to this record is the Publisher's final version. Open access article.Stock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for different combinations of input variables and different neural network designs. The study seeks to investigate the optimal artificial neural networks (ANN) parameters and settings that enhance the performance of the NARX model. Therefore, extensive parameter trails were studied for various combinations of input variables and NARX neural network configurations. The proposed model is further enhanced by preprocessing and optimising the NARX model’s input and output parameters. The prediction performance is assessed based on the mean squared error (MSE), R-squared, and hit rate. The performance of the proposed model is compared with other models, and it is shown that the utilisation of technical indicators with the NARX neural network improves the accuracy of one-step-ahead prediction for CIMB stock in Malaysia. The performance of the proposed model is further improved by optimising the input data and neural network parameters. The improved prediction of stock prices could help investors increase their returns from investment in stock markets

A triangular MIMO array antenna with a double negative metamaterial superstrate to enhance bandwidth and gain

Multiple-input-multiple-output (MIMO) array antenna integrated with the double negative metamaterial superstrate is presented. The triangular metamaterial unit cell is designed by combining two triangular elements positioned in complementary on the same plane at different sizes. Such design with more gaps is used to excite rooms for more capacitance effects to shift the resonance frequency thus enlarging the bandwidth of the MIMO antenna. The unit cell is arranged in 7 × 7 periodic array created a superstrate metamaterial plane where the Cstray exists in parallel between the two consecutive cells. It is found that the existence of Cstray and gaps for each unit cells significantly influenced the bandwidth of the MIMO antenna. The higher value of the capacitance will lead to the negativity of permittivity. The superstrate plane is then located on top of the 4 × 2 MIMO with a gap of 5 mm. The integration resulted in improving the bandwidth to 12.45% (5.65-6.4GHz) compared to only 3.49% bandwidth (5.91-6.12GHz) of the MIMO antenna itself. Moreover, the negative permeability characteristic is created by a strong magnetic field between the complementary unit cells to have 14.05-dBi peak gain. Besides that, the proposed antenna managed to minimize the mutual coupling and improve the mean effective gain, envelope correlation coefficient, and multiplexing efficiency

Growth factor concentrations and their placental mRNA expression are modulated in gestational diabetes mellitus: possible interactions with macrosomia

<p>Abstract</p> <p>Background</p> <p>Gestational diabetes mellitus (GDM) is a form of diabetes that occurs during pregnancy. GDM is a well known risk factor for foetal overgrowth, termed macrosomia which is influenced by maternal hypergycemia and endocrine status through placental circulation. The study was undertaken to investigate the implication of growth factors and their receptors in GDM and macrosomia, and to discuss the role of the materno-foeto-placental axis in the <it>in-utero </it>regulation of foetal growth.</p> <p>Methods</p> <p>30 women with GDM and their 30 macrosomic babies (4.75 ± 0.15 kg), and 30 healthy age-matched pregnant women and their 30 newborns (3.50 ± 0.10 kg) were recruited in the present study. Serum concentrations of GH and growth factors, <it>i.e</it>., IGF-I, IGF-BP3, FGF-2, EGF and PDGF-B were determined by ELISA. The expression of mRNA encoding for GH, IGF-I, IGF-BP3, FGF-2, PDGF-B and EGF, and their receptors, <it>i.e</it>., GHR, IGF-IR, FGF-2R, EGFR and PDGFR-β were quantified by using RT-qPCR.</p> <p>Results</p> <p>The serum concentrations of IGF-I, IGF-BP3, EGF, FGF-2 and PDGF-B were higher in GDM women and their macrosomic babies as compared to their respective controls. The placental mRNA expression of the growth factors was either upregulated (FGF-2 or PDGF-B) or remained unaltered (IGF-I and EGF) in the placenta of GDM women. The mRNA expression of three growth factor receptors, <it>i.e</it>., IGF-IR, EGFR and PDGFR-β, was upregulated in the placenta of GDM women. Interestingly, serum concentrations of GH were downregulated in the GDM women and their macrosomic offspring. Besides, the expression of mRNAs encoding for GHR was higher, but that encoding for GH was lower, in the placenta of GDM women than control women.</p> <p>Conclusions</p> <p>Our results demonstrate that growth factors might be implicated in GDM and, in part, in the pathology of macrosomia via materno-foeto-placental axis.</p

The spine in Paget’s disease

Paget’s disease (PD) is a chronic metabolically active bone disease, characterized by a disturbance in bone modelling and remodelling due to an increase in osteoblastic and osteoclastic activity. The vertebra is the second most commonly affected site. This article reviews the various spinal pathomechanisms and osseous dynamics involved in producing the varied imaging appearances and their clinical relevance. Advanced imaging of osseous, articular and bone marrow manifestations of PD in all the vertebral components are presented. Pagetic changes often result in clinical symptoms including back pain, spinal stenosis and neural dysfunction. Various pathological complications due to PD involvement result in these clinical symptoms. Recognition of the imaging manifestations of spinal PD and the potential complications that cause the clinical symptoms enables accurate assessment of patients prior to appropriate management

Improved Bald Eagle Search Optimization With Deep Learning-Based Cervical Cancer Detection and Classification

Cervical cancer (CC) is the fourth most popular cancer affecting women worldwide. Mortality and incidence rates can be consistently enhancing, particularly in emerging countries, because of the lack of screening services, lack of awareness, and restricted qualified experts. CC has screened utilizing human papillomavirus (HPV) test, Papanicolaou (Pap) test, histopathology test, and visual inspection after application of acetic acid (VIA). Intra- and Inter-observer variability can take place in the manual analysis method, resulting in misdiagnosis. Previous studies have exploited either deep learning (DL) or machine learning (ML) approaches, the preceding one could not be efficient as it needs segmentation and attaining hand-crafted features that utilize critical stage. Artificial Intelligence (AI) based computer-aided diagnoses (CAD) methods are generally explored for identifying CC for enhancing the standard testing method. This manuscript offers an Improved Bald Eagle Search Optimization with Deep Learning based Cervical Cancer Detection and Classification (IBESODL-CCDC) algorithm. The drive of the IBESODL-CCDC algorithm lies in the automated classification and detection of CC. In the presented IBESODL-CCDC technique, a contrast enhancement process takes place to enhance the image qualities. In addition, the IBESODL-CCDC technique utilizes a modified LeNet model for the feature extraction model. For CC detection, the IBESODL-CCDC technique applies an attention-based long short-term memory (ALSTM) network. A wide-ranging experiment was applied to validate the greater outcome of the IBESODL-CCDC technique. The experimental values highlight the remarkable performance of the IBESODL-CCDC algorithm with other recent systems