ANALISIS PENGARUH SEKTOR INDUSTRI KECIL, INDUSTRI MENENGAH DAN INDUSTRI BESAR TERHADAP PENDAPATAN ASLI DAERAH KOTA SURABAYA TAHUN 2014-2018

Hero Maulana Ananda Putra 2021, 201410180311132, Universitas Muhammadiyah Malang, Fakultas Ekonomi Dan Bisnis, Program Studi Ekonomi Pembangunan, ANALISIS PENGARUH SEKTOR INDUSTRI KECIL, INDUSTRI MENENGAH DAN INDUSTRI BESAR TERHADAP PENDAPATAN ASLI DAERAH KOTA SURABAYA TAHUN 2014-2018 Pendapatan Asli Daerah (PAD) sebagai salah satu indikator dari kemandirian otonomi daerah dalam menggali potensi untuk meningkatkan sumber-sumber penerimaan dan besarnya kontribusi pengeluaran pemerintah daerah terhadap pertumbuhan ekonomi daerah seharusnya sebagai sebuah peluang yang dapat dimanfaatkan secara optimal untuk mendorong perekonomian daerah. Pendapatan Asli Daerah (PAD) yang semakin besar, maka semakin mandiri daerah tersebut dalam mengambil keputusan dan kebijakan pembangunan. Kabupaten maupun kota di Indonesia memiliki kewenangan yang lebih luas untuk membangun daerahnya sendiri setelah diberlakukannya otonomi daerah. Otonomi daerah diatur dalam Undang-Undang No. 32 Tahun 2004 dan kemudian diamandemen menjadi Undang-Undang No. 23 Tahun 2014 tentang Pemerintahan Daerah, yang mana keberadaan pemerintahan daerah mempunyai hak penuh untuk kewenangan dan kewajiban mengatur daerahnya sendiri secara mandiri, termasuk keuangan daerahnya sendiri yang diatur oleh Undang-Undang No. 33 Tahun 2004 tentang Perimbangan Keuangan antara Pemerintah Pusat dan Pemerintah Daerah. Kewenangan otonomi yang luas mewajibkan pemerintah daerah meningkatkan pelayanan serta kesejahteraan masyarakat secara demokratis, adil, merata dan berkesinambungan (Halim, 2016). Berdasarkan hasil pembahasan penelitian yang telah dilakukan, maka dapat disimpulkan sebagai berikut: 1. Nilai Produksi Industri Kecil berpengaruh positif signifikan terhadap Pendapatan Asli Daerah (PAD). Hal ini menunjukkan bahwa ketika terjadi peningkatan pada PDRB Industri, khususnya nilai produksi industri kecil akan meningkatkan Pendapatan Asli Daerah (PAD) Kota Surabaya. 2. Nilai Produksi Industri Menengah berpengaruh positif signifikan terhadap Pendapatan Asli Daerah (PAD). Hal ini menunjukkan bahwa ketika terjadi peningkatan pada PDRB Industri, khususnya nilai produksi industri menegah akan meningkatkan Pendapatan Asli Daerah (PAD) Kota Surabaya. 3. Nilai Produksi Industri Besar berpengaruh positif signifikan terhadap Pendapatan Asli Daerah (PAD). Hal ini menunjukkan bahwa ketika terjadi peningkatan pada PDRB Industri, khususnya nilai produksi industri besar akan meningkatkan Pendapatan Asli Daerah (PAD) Kota Surabaya

Robust Multiple Signal Classification via Probability Measure Transformation

In this paper, we introduce a new framework for robust multiple signal classification (MUSIC). The proposed framework, called robust measure-transformed (MT) MUSIC, is based on applying a transform to the probability distribution of the received signals, i.e., transformation of the probability measure defined on the observation space. In robust MT-MUSIC, the sample covariance is replaced by the empirical MT-covariance. By judicious choice of the transform we show that: 1) the resulting empirical MT-covariance is B-robust, with bounded influence function that takes negligible values for large norm outliers, and 2) under the assumption of spherically contoured noise distribution, the noise subspace can be determined from the eigendecomposition of the MT-covariance. Furthermore, we derive a new robust measure-transformed minimum description length (MDL) criterion for estimating the number of signals, and extend the MT-MUSIC framework to the case of coherent signals. The proposed approach is illustrated in simulation examples that show its advantages as compared to other robust MUSIC and MDL generalizations

On Measure Transformed Canonical Correlation Analysis

In this paper linear canonical correlation analysis (LCCA) is generalized by applying a structured transform to the joint probability distribution of the considered pair of random vectors, i.e., a transformation of the joint probability measure defined on their joint observation space. This framework, called measure transformed canonical correlation analysis (MTCCA), applies LCCA to the data after transformation of the joint probability measure. We show that judicious choice of the transform leads to a modified canonical correlation analysis, which, in contrast to LCCA, is capable of detecting non-linear relationships between the considered pair of random vectors. Unlike kernel canonical correlation analysis, where the transformation is applied to the random vectors, in MTCCA the transformation is applied to their joint probability distribution. This results in performance advantages and reduced implementation complexity. The proposed approach is illustrated for graphical model selection in simulated data having non-linear dependencies, and for measuring long-term associations between companies traded in the NASDAQ and NYSE stock markets

A SYSTEMIC FUNCTIONAL ANALYSIS ON JAVANESE POLITENESS: TAKING SPEECH LEVEL INTO MOOD STRUCTURE

Speech level is an important aspect in Javanese grammar. It is just like, among others, tenses in English. Thus, the involvement of speech level in any study of Javanese grammar is highly necessary. On the other hand, speech level must also be studied the grammatical point of view. So far, however, there are very limited numbers—if any does really exist—of grammatical study on Javanese speech level. Most major studies on Javanese speech level are of sociolinguistics, lexical taxonomy or grouping, and prescriptive analysis. It is probably due to the idea of speech level as merely a social phenomenon has been taken for granted. Therefore, taking the speech level system into a grammatical analysis seems hardly possible. It is assumed that the seemingly impossible attempt comes only to the formal approach of the grammar study tradition for it has neglected the social aspect. Hence, it is necessary to look for an alternative grammatical approach which is able to cope with the speech level both grammatically and socially. A particular approach of grammar which involves social context is systemic functional grammar (SFG). SFG proposes that language has three kinds of functional component. One of them is the interpersonal function. This function sees language as an interaction between addresser and addressee—language is used for enacting participants‘ roles and relation among them. The interpersonal function is expressed through a particular grammatical structure, namely mood structure. This article is going present a demonstration of systemic functional analysis on Javanese speech level by taking it into the mood structure analysis. In addition, this paper aims for two kinds of potential significance. First, it could be an adequate description of Javanese speech level grammaticalization. Second, it can be a typological supplement for SFG in dealing with languages which apply a speech level system

Foundational principles for large scale inference: Illustrations through correlation mining

When can reliable inference be drawn in the "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, with implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics the dataset is often variable-rich but sample-starved: a regime where the number $n$ of acquired samples (statistical replicates) is far fewer than the number $p$ of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data." Sample complexity however has received relatively less attention, especially in the setting when the sample size $n$ is fixed, and the dimension $p$ grows without bound. To address this gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where the variable dimension is fixed and the sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa-scale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables that are of interest. We demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks