KARAKTERISTIK PERUSAHAAN, GEJOLAK EKONOMI DAN CORPORATE DISTRESS PADA PERUSAHAAN JASA NON KEUANGAN DI INDONESIA (1998 - 2019

Penelitian ini bertujuan untuk menganalisis dan mengetahui karakteristik perusahaan yang dapat mempengaruhi prediksi corporate distress dan juga mengethaui dampak gejolak ekonomi terhadap prediksi corporate distress pada perusahaan jasa non keuangan di Indonesia dari tahun 1998 – 2019. Jenis penelitian yang dilakukan adalah penelitian kuantitatif dimana objek penelitian adalah perusahaan jasa non keuangan di Indonesia dengan jumlah populasi sebanyak 326 perusahaan yang terdaftar per Februari 2020. Tekhnik pengambilan sampel dengan purposive sampling yang memiliki kriteria (1) Perusahaan terdaftar di Bursa Efek Indonesian dari tahun 1998 – 2019, (2) Memiliki laporan keuangan dan tahunan yang lengkap dari tahun 1998 -2019 dan (3) Perusahaan aktif memperdagangkan sahamnya selama periode 1998-2019,perusahaan tidak pernah delisting dari bursa dalam peride 1998-2019. dari kriteria tersebut didapatkan sampel sebanyak 66 perusahaan. penelitian menggunakan data panel dengan analisis regresi data panel dan analisis komperatif non parametrik. Temuan penelitian ini mengungkapkan bahwa dalam memprediksi corporate distress dengan model Zmijewsky di dapat selama tahun 1998 sampai tahun 2019 terjadi fluktuasi perusahaan yang mengalami corporate distress dan dengan estimasi data panel didapat variabel Pengalaman perusahaan yang di proksi dengan umur berpengaruh secara signifikan terhadap corporate distress, serta gejolak ekonomi yang di proksi dengan pertumbuhan ekonomi dalam penelitian ini juga berpengaruh secara signifikan terhadap prediksi corporae distress. Terdapat perbedaan karakteristik pada setiap masa gejolak ekonomi dari hasil yang didapat bahwa pada masa-masa ekonomi mengalami penurunan karaktersistik yang berpengaruh adalah jumlah tenaga kerja, sedangkan pada saat ekonomi bertumbuh atau recovery karakteristik perusahan yang berpengaruh Pengalaman perusahaan dan ukuran perusahaan Kata Kunci : Karakteristik Perusahaan, Model X-Score, Corporate distress, gejolak ekonomi

2019 Program

University of Missouri-St. Louis Undergraduate Research Symposium Progra

COMPUTATIONAL MODELING OF CLIMATE ATTRIBUTES AND CONDITION DETERIORATION OF CONCRETE HIGHWAY PAVEMENTS

An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. Road and highway infrastructures performance in any country is impacted by load repetitions and it is further compromised by climate attributes and extreme weather events. Damages to roads and bridges are among the infrastructure failures that have occurred during these extreme events. If maintenance and rehabilitation are not done promptly, the damages to the road caused by heavy traffic and extreme climate may lead to life-threatening conditions for road users. A disruption in any one system affects the performance of others. For example, damages in road and bridge infrastructure will delay the recovery operation after a disaster. In 2018, a total of 331 natural disaster occurrences were reported worldwide, which resulted in 14,385 deaths. From 1900 to 2000, in 119 years, 14,854 natural disaster occurrences were reported which caused 32,651,605 deaths. Natural disaster occurrences like hurricanes, floods, droughts, landslides, etc. may be influenced by specific climate mechanisms like El Niño and Southern Oscillation (ENSO). Several climate attributes models were developed in this research employing Auto-Regressive Integrated Moving Average (ARIMA) methodology. The sea surface temperature data were analyzed and a prediction model was developed to predict future ENSO years. The model successfully predicted the 2018-2019 El Niño year. The model prediction showed that the next El Niño years will be 2021-22 and 2025-26. The model prediction also shows that the next La Niña year will be 2028-29. Global mean sea level (GMSL) data were analyzed and a prediction model was developed. The predicted annual rate of change in GMSL is 0.6 mm/year from 2013 to 2050. But a higher annual rate of change (1.4 mm/year) is predicted from 2031 to 2050. Northern hemisphere (Arctic) sea ice extent and southern hemisphere (Antarctic) sea ice extent data were investigated and two different models were developed. The model prediction shows that the total loss of northern hemisphere sea ice extent in 2050 will be 1.66 million km2. But the total gain of southern hemisphere sea ice extent will be 1.24 million km2. The net change of global sea ice extent will be -0.24 million km2, which indicates a loss of sea ice. The model predictions of the climate attributes can be used to understand and assess the future climate change in different climate zones worldwide. This understanding of climate changes and future predictions of climate attributes will help to develop climate adaptation strategies and better prepare the communities for extreme weather-related natural disaster occurrences. The condition deterioration progression of infrastructures, such as roads and bridges, is caused by load repetitions, as well as climate attributes and extreme weather. Pavements undergo maintenance and rehabilitations periodically to provide a smooth riding experience to the riders. Previous researches never considered maintenance and rehabilitation action history in the development of the condition deterioration model. This research considered the maintenance and rehabilitation history in the development and implementation of pavement condition deterioration models. The development of the IRI prediction model using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) considered the Long Term Pavement Performance (LTPP) climatic region, pavement structural properties, and traffic. The developed models are more objective, incorporate important input variables that are easily available, and are easy to implement in decision making. The concrete highway pavement IRI deterioration prediction models were developed and evaluated in this research for LTPP datasets of 1,482 for JPCP, 577 for JRCP, and 575 for CRCP. Comparatively, the AASHTO MEPDG performance equations were developed using fewer test sections. Three performance models were developed for output variable, IRI (outside wheel path) (m/km) for Jointed Plain Concrete Pavement (JPCP), Jointed Reinforced Concrete Pavement (JRCP), and Continuously Reinforced Concrete Pavement (CRCP). The input variables are similar for all the models. An in-depth study of M&R history collected from the LTPP database for all concrete pavement produced several CN_Code. The best models were found with the CN_Code developed based on the IRI value improvement and the type of M&R action and this variable is a continuous variable where number increment indicates the frequency of M&R action provided in the pavement section. The models’ final structure and accuracy statistics can be summarized as: JPCP (13-19-1; ANN R2 =0.94 and MLR R2 =0.49), JRCP (11-19-1; ANN R2 =0.95 and MLR R2 =0.58), and CRCP (14-19-1; ANN R2 =0.95 and MLR R2 =0.83). The ANN models show better accuracy in predicting pavement performance compare to the multiple regression models for all types of concrete pavements. The developed IRI prediction models can successfully characterize the behavior (i.e. the increase of IRI values with time and decrease of IRI value after maintenance and rehabilitation). The ANN models can be used to provide future M&R action by changing CN_Code frequency and the model successfully distinguishes the behavior of IRI (i.e. decrease of IRI after M&R action and increase of IRI with time as CESAL increases). The developed condition deterioration models for concrete highway pavement present a significant improvement on the models currently used in the mechanistic-empirical pavement design method. It is recommended to implement the pavement condition deterioration model developed in this research for life-cycle asset management and M&R programs

Developmental Origins of the Human Hypothalamic-Pituitary-Adrenal Axis

Introduction: The developmental origins of disease or fetal programming model predicts that intrauterine exposures have life-long consequences for physical and psychological health. Prenatal programming of the fetal hypothalamic-pituitary-adrenal (HPA) axis is proposed as a primary mechanism by which early experiences are linked to later disease risk. Areas covered: This review describes the development of the fetal HPA axis, which is determined by an intricately timed cascade of endocrine events during gestation and is regulated by an integrated maternal-placental-fetal steroidogenic unit. Mechanisms by which stress-induced elevations in hormones of maternal, fetal, or placental origin influence the structure and function of the emerging fetal HPA axis are discussed. Recent prospective studies documenting persisting associations between prenatal stress exposures and altered postnatal HPA axis function are summarized, with effects observed beginning in infancy into adulthood. Expert commentary: The results of these studies are synthesized, and potential moderating factors are discussed. Promising areas of further research highlighted include epigenetic mechanisms and interactions between pre and postnatal influences

ANALISIS PREDIKSI KEBANGKRUTAN (FINANCIAL DISTRESS) DENGAN PERBANDINGAN MODEL ALTMAN, ZMIJEWSKI DAN GROVER

The purpose of this study is to analyze bankruptcy predictions (Financial Distress) with a Comparison of the Altman Model (Z-Score), Zmijewski (X-Score) and Grover (G-Score) (Empirical Study in Manufacturing Companies in the Basic Industrial and Chemical Sector Sectors registered in IDX 2015-2018). This study uses non-parametric statistical data analysis with Kruskal Wallis different test. The research sample is 37 companies manufacturing basic and chemical industrial sectors which are listed on the Indonesia Stock Exchange (IDX) for the 2015-2018 period. The data used in this study are secondary data derived from the company's financial statements. The research sample was selected by purposive sampling technique with predetermined criteria. Hypothesis test results showed there are differences in predictions between the Altman model (Z-Score), Zmijewski (X-Score) and Grover (G-Score) in predicting bankruptcy (financial distress) of manufacturing companies in the basic and chemical industry sectors listed on the Indonesia Stock Exchange period 2015-2018. The Grover (G-Score) model is the most accurate prediction model with an accuracy level of 85.14%. While the Altman model (Z-Score) has an accuracy rate of 77.70% and the Zmijewski model (X-Score) of 79.73%.Keywords: Financial Distress, Altman Model, Zmijewski Model and Grover Model

Influence of Maternal Prior Life Adversity on the Psycho-Neuroendocrine-Immune Profile During Pregnancy

Pregnancy is accompanied by a multitude of physical and psychological changes. Adaptation to these changes through reduced anxiety and attenuated stress responsiveness is necessary across gestation and into the postpartum period for optimal maternal-infant health. In contrast, exposure to higher amounts of stressors during pregnancy can disrupt neuroendocrine-immune processes required for successful pregnancy outcomes. Evolving evidence demonstrates that exposure to adversity early in life has long-lasting effects on stress response systems that alter stress reactivity during adulthood. Given this evidence, it is posited that women who experience greater pre-pregnancy adversity during their childhood are at greater risk for negative maternal-infant health sequelae. Thus, the purpose of this study is to investigate the relationship between maternal childhood adversity and the psychological-neuroendocrine-immune profile during pregnancy. In addition, maternal risk and protective factors posited to moderate this profile were examined. Lastly, the relationship among maternal childhood adversity, maternal PNI profile during pregnancy, and neonatal outcomes were explored. The findings can contribute to improved approaches to identify and stratify risk for adverse maternal-infant health outcomes, as well as guide the development of early intervention programs and health policy for women who are pregnant or who plan to become pregnant. This is significant because the well-being of mothers and infants determines the health of the next generation. Improving maternal-infant well-being can markedly reduce public health challenges and ultimately reduce health care costs across the lifespan (U.S. Department of Health and Human Service, 2011)

The doctoral research abstracts. Vol:11 2017 / Institute of Graduate Studies, UiTM

Foreword: Congratulation to IGS on the continuous effort to publish the 11th issue of the Doctoral Research Abstracts which highlights the research in various disciplines from science and technology, business and administration to social science and humanities. This research abstract issue features the abstracts from 91 PhD doctorates who will receive their scrolls in this 86th UiTM momentous convocation ceremony. This is a special year for the Institute of Graduate Studies where we are celebrating our 20th anniversary. The 20th anniversary is celebrated with pride with an increase in the number of PhD graduates. In this 86th convocation, the number of PhD graduates has increased by 30% compared to the previous convocation. Each research produces an innovation and this year, 91 research innovations have been successfully recognized to have made contributions to the body of knowledge. This is in line with this year UiTM theme that is “Inovasi Melonjak Persaingan Global (Innovation Soars Global Competition)”. Embarking on PhD research may not have been an easy decision for many of you. It often comes at a point in life when the decision to further one’s studies is challenged by the comfort of status quo. I would like it to be known that you have most certainly done UiTM proud by journeying through the scholarly world with its endless challenges and obstacles, and by persevering right till the very end. Again, congratulations to all PhD graduates. As you leave the university as alumni we hope a new relationship will be fostered between you and UiTM to ensure UiTM soars to greater heights. I wish you all the best in your future endeavor. Keep UiTM close to your heart and be our ambassadors wherever you go. / Prof Emeritus Dato’ Dr Hassan Said Vice Chancellor Universiti Teknologi MAR