Implication of Corporate Governance and Financial Performance on Firm Value

<p>This study seeks to examine the implications of corporate governance and financial performance on firm value. The population selected are all companies that follow CGPI (Corporate Governance Preception Index) scoring held by IICG (The Indonesian Institute for Corporate Governance) in 2015-2018. Sampling of the research is purposive sampling with samples criteria are public companies participate in CGPI scoring and publish their financial statements on Indonesia Stock Exchange’s website or publish on the official website of respective companies. The total population followed CGPI score was 137 companies, 55 of those are public companies and 5 samples are outlier data. The statistical test used in this research were descriptive statistics analysis and multiple regression analysis. This research results in findings that both simultaneously and partially there are implications for corporate governance and financial performance to firm value. Therefore, the implementation of corporate governance and financial information disclosure in companies is fundamental to realizing firm value.</p>

Handle outliers for Frechet distribution

This paper aims to handle outlier data for Frechet distribution. This study focused on two ways to deal with outliers. The rst way is to censor the ob- servation with the same percentage of outlier data. The second way is to trim outlier observations. A Monte Carlo simulation study is carried out to compare these ways in terms of estimate average, relative bias, and root mean square error (RMSE) using Mathematica-10.

Research on Outlier Detection for High-Dimensional Data Based on PPCLOF

Aiming at the “dimension disaster” problem encountered in the outlier detection of high-dimensional data, this paper uses the projection pursuit algorithm to perform non-linear dimensionality reduction on high-dimensional data by calculating the phase relationship between dimensions. According to the sample points obtained by dimensionality reduction, the LOF (Local Outlier Factor) algorithm is applied to calculate the outlier factor to obtain the relevant outlier data. In order to improve the calculation accuracy and efficiency of the LOF algorithm, clustering method is used to cut the outlier calculation data to reduce the amount of calculation. Experiments on real-world and artificial datasets, compared with the existing algorithms, demonstrated the effectiveness and efficiency of the proposed algorithm.

Robust linear modelling in ElectroEncephaloGraphy can be obtained using single weights reflecting each single trials’ dynamics

Being able to remove or weigh down the influence of outlier data is desirable for any statistical models. While Magnetic and ElectroEncephaloGraphic (MEEG) data used to average trials per condition, it is now becoming common practice to use information from all trials to build linear models. Individual trials can, however, have considerable weight and thus bias inferential results. Here, rather than looking for outliers independently at each data point, we apply the principal component projection (PCP) method at each channel, deriving a single weight per trial at each channel independently. Using both synthetic data and open EEG data, we show (1) that PCP is efficient at detecting a large variety of outlying trials; (2) how PCP derived weights can be implemented in the context of the general linear model with accurate control of type 1 family-wise error rate; and (3) that our PCP-based Weighted Least Square (WLS) approach leads to in increase in power at the group results comparable to a much slower Iterative Reweighted Least Squares (IRLS), although the weighting scheme is markedly different. Together, results show that WLS based on PCP weights derived upon whole trial profiles is an efficient method to weigh down the influence of outlier data in linear models.Data availabilityall data used are publicly available (CC0), all code (simulations and data analyzes) is also available online in the LIMO MEEG GitHub repository (MIT license).

Model Regresi Data Panel Pada Kasus Infeksi Saluran Pernapasan Akut (ISPA) di Provinsi Nusa Tenggara Timur

Acute respiratory infection (ARI) is an infectious desease cause by bacteria or viruses that attack the respiratory organs. This research aims to determine the best panel data regression model in the case of the factors that influence the number of patients with ARI in East Nusa Tenggara Province from 2014 to 2018. Response variable used is the number of ARI patients. Independent variables were observed among others, low birth weight, malnutrition, immunization, exclusive breastfeeding, and vitamin A in 22 districts or city in East Nusa Tenggara. The results showed that the Random Effect Models eliminate outlier data on response variable is a model that can describe the influence of independent variables on the number of patients with ARI in East Nusa Tenggara Province from 2014 to 2018. Variables that influence of ARI are malnutrition and exclusive breastfeeding with a coefficient of determination (R) of 9,2%.

Role of Artificial Intelligence of Things (AIoT) to Combat Pandemic COVID-19

COVID-19 is caused by virus called SARS-CoV-2, which was declared by the WHO as global pandemic. Since the outbreak, there has been a rush to explore Artificial Intelligence (AI) and Internet of Things (IoT) for diagnosing, predicting, and treating infections. At present, individual technologies, AI and IoT, play important roles yet do not impact individually against the pandemic because of constraints like lack of historical data and the existence of biased, noisy, and outlier data. To overcome, balance among data privacy, public health, and human-AI-IoT interaction is must. Artificial Intelligence of Things (AIoT) appears to be a more efficient technological solution that can play a significant role to control COVID-19. IoT devices produce huge data which are gathered and mined for actionable effects in AI. AI converts data into useful results which are utilized by IoT devices. AIoT entails AI through machine learning and decision making to IoT and renovates IoT to add data exchange and analytics to AI. In this chapter, AIoT will serve as a potential analytical tool to fight against the pandemic.

FIRM SIZE, PROFITABILITAS, LEVERAGE, JENIS PERUSAHAAN AUDIT, JENIS INDUSTRI, PENELITIAN DAN PENGEMBANGAN SEBAGAI DETERMINAN PENGUNGKAPAN MODAL INTELEKTUAL (ICD)

Penelitian ini bertujuan untuk menguji determinan (faktor-faktor penentu) pengungkapan modal intelektual. Firm size, profitabilitas, leverage, jenis perusahaan audit, jenis industri, penelitian dan pengembangan digunakan dalam penelitian ini sebagai determinan pengungkapan modal intelektual. Penelitian ini menggunakan populasi seluruh perusahaan yang tercatat di Bursa Efek Indonesia (BEI) tahun 2018. Penelitian ini menggunakan metode purposive sampling untuk menentukan sampel yang akan digunakan dan menghasilkan sampel akhir berjumlah 276 perusahaan yang meliputi berbagai sektor perusahaan. Setelah dilakukan outlier data menghasilkan 261 perusahaan yang dijadikan sampel. Data dalam penelitian ini dianalisis menggunakan IBM SPSS statistic. Uji asumsi klasik telah dilakukan sebelum uji analisis regresi. Hasil penelitian ini menunjukkan bahwa firm size, leverage, jenis perusahaan audit, jenis industri, penelitian dan pengembangan berpengaruh secara signifikan terhadap pengungkapan modal intelektual. Variabel profitabilitas menunjukkan hasil yang berbeda. Profitabilitas tidak berpengaruh terhadap pengungkapan modal intelektual. Penelitian ini juga membuktikan bahwa perusahaan yang diaudit oleh Kantor Akuntan Publik (KAP) Big Four menunjukkan pengungkapan modal intelektual yang lebih banyak. Perusahaan dengan kategori jenis industri (high-IC) intensive industries memiliki pengungkapan modal intelektual lebih banyak. Lebih lanjut, perusahaan yang melakukan penelitian dan pengembangan terbukti lebih banyak melakukan pengungkapan modal intelektual.

Pengelompokan Provinsi Di Indonesia Berdasarkan Indikator Mutu Pendidikan Sekolah Menengah Pertama Tahun 2016 – 2018 Menggunakan Model Based Clustering

Peran pendidikan dalam suatu bangsa bertujuan untuk membentuk sumber daya manusia yang terdidik, berkompeten dan berkarakter. Standar Nasional Pendidikan (SNP) ditetapkan sebagai acuan pengukuran mutu pendidikan di Indonesia, dengan melihat delapan aspek yaitu Standar Pendidikan dan Tenaga Kependidikan, Standar Sarana dan Prasarana, Standar Pengelolaan, Standar Pembiayaan Pendidikan, Standar Isi, Standar Proses, Standar Penilaian Pendidikan dan Standar Kompetensi Lulusan. Penelitian sebelumnya mengenai faktor dominan yang membentuk mutu pendidikan jenjang sekolah dasar dan menengah, menjelaskan bahwa terdapat lebih dari 50 indikator yang memuat data multivariat. Melalui indikator – indikator tersebut, penulis ingin mengukur mutu pendidikan dengan mengelompokan provinsi di Indonesia menggunakan metode Model Based Clustering. Metode ini sangat sesuai dengan data yang terindikasi kasus outlier, dengan pendekatan menggunakan jarak Mahalanobis dan jarak Robust akan diketahui di titik mana data tersebut mengalami outlier. Data yang terindikasi outlier menyebabkan tidak terpenuhinya asumsi normal multivariat, sehingga distribusi probabilitas yang sesuai adalah distribusi t multivariat. Penaksiran parameter menggunakan Maximum Likelihood Estimation terhadap Model Finite Mixture dengan t multivariat dipandang sebagai metode yang lebih robust terhadap data yang mengandung outlier. Model terbaik dari kelompok yang terbentuk akan dilihat melalui perhitungan ICL tertinggi