Analisis Likuiditas Saham Sektor Perbankan di BEI Menggunakan Analisis Intervensi dan Autoregressive Conditional Duration

Tax Amnesty merupakan Undang-Undang yang menjadi isu hangat 2016  dan berakhir pada Maret 2017. Kebijakan Tax Amnesty mengharuskan pihak bank menjadi pihak penerima dana repatriasi. Terkait hal itu, berdasarkan isu ekonomi finansial 2015 hingga 2016 terdapat saham bank yang selalu diburu oleh investor karena saham yang likuid. Saham perbankan yang paling dipertimbangkan untuk diperdagangkan yaitu Bank Central Asia (BBCA), Bank Mandiri (BMRI), Bank Rakyat Indonesia (BBRI), dan Bank Negara Indonesia (BBNI) karena masuk dalam kelompok saham LQ45. Tujuan penelitian ini adalah mengetahui gambaran data volume, mengetahui efek adanya intervensi akibat Tax Amnesty, dan mendapat kesimpulan mengenai likuiditas saham sebelum dan selama Tax Amnesty dari model ACD. Model ACD merupakan model alternatif lain di luar intervensi. Analisis intervensi yang dilakukan menunjukkan bahwa terdapat efek intervensi diberlakukannya Tax Amnesty pada volume saham perusahaan BMRI dan BBNI, namun tidak pada BBCA dan BBRI. Model intervensi yang terbentuk belum memenuhi distribusi normal. Model ACD menghasilkan bahwa volume transaksi lebih likuid dilihat dari durasi yang tinggi pada periode Tax Amnesty. Durasi menunjukkan kejadian volume transaksi yang rendah, jadi bila nilai durasi tinggi maka volume transaksi rendah jarang terjadi. Hanya saja, pada saham BBRI tidak dapat dibandingkan sebelum Tax Amnesty dan setelah Tax Amnesty karena data tidak terdapat efek ACD dilihat dari parameter konstanta saja yang signifikan dalam model

The Performance of Ramsey Test, White Test and Terasvirta Test in Detecting Nonlinearity

The objective of this research is to compare Ramsey test, White test and Terasvirta test in the identification of nonlinearity. Ramsey test is a test based on the regression specification error test. While White test and Terasvirta test are based on neural network models. The difference between White test and Terasvirta test is in determining its weight, White test based on random sampling, while Terasvirta test based on Taylor expansion. Simulation studies are carried out with various scenarios in each test by generating linear models, linear models with outliers and nonlinear models. The results of the simulation study showed that Terasvirta test had better power than Ramsey test and White test in detecting nonlinearity. Terasvirta test is also more sensitive to the presence of outliers in linear models

Number of Foreign Tourist Arrival Forecasting Using Percentile Error Bootstrap Based on VARIMA Model

Forecasting number of foreign tourist arrivals is important to improve the policies in the tourism sector. Better accuracy of forecast would help the government and investor to make operational, tactical, and strategic decisions. Data used in this research are monthly number of foreign tourist arrivals taken from Indonesia Central Bureau of Statistics. Multivariate forecasting at Soekarno-Hatta, Juanda, and Adi Sumarmo arrival gates was conducted using VARIMA ([12],1,0) (0,1,0)12 model. However, the longer step ahead to forecast, the larger variance error of corresponding models. As a result, the prediction interval become wider. This research computed the prediction interval using percentile error bootstrap based on VARIMA models that produced more precise forecast

Analisis Perangkingan Perguruan Tinggi Negeri Berbadan Hukum (PTN-BH) di Indonesia Berdasarkan Indikator Publikasi Penelitian pada Lembaga Internasional

Pemerintah menargetkan PTN-BH untuk masuk dalam rangking 500 perguruan tinggi terbaik dunia. Salah satu hal yang menjadi pertimbangan dalam perangkingan perguruan tinggi secara internasional adalah indikator publikasi penelitian pada Scopus maupun Google Scholar. Oleh karena itu pada penelitian ini, dilakukan evaluasi terhadap kondisi eksisting publikasi penelitian terindeks Scopus dan Google Scholar seluruh PTN-BH menggunakan pemodelan faktor-faktor yang mempengaruhi jumlah sitasi dan indeks-h publikasi dengan regresi kuantil rekursif. Regresi kuantil digunakan sebagai alternatif metode untuk menangani distribusi data yang tidak seragam dan pemodelan rekursif digunakan karena adanya hubungan searah antara jumlah sitasi dan indeks-h publikasi. Dari hasil analisis regresi kuantil rekursif tersebut didapatkan kesimpulan bahwa jurnal Scopus Q1 memberikan dampak yang paling tinggi terhadap pertambahan jumlah sitasi Scopus di seluruh PTN-BH pada semua jenis kuantil dan jumlah publikasi jurnal Q1 (X2) yang sama memberikan pengaruh yang berbeda terhadap pertambahan indeks-h Scopus, yaitu 0,253 untuk kuantil 0,1, 0,382 untuk kuantil 0,5, serta 0,352 untuk kuantil 0,9

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T2 based PCA will be compared with conventional T2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T2 control chart, the T2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.

Comparison between VAR, GSTAR, FFNN-VAR and FFNN-GSTAR Models for Forecasting Oil Production

Monthly data about oil production at several drilling wells is an example of spatio-temporal data. The aim of this research is to propose nonlinear spatio-temporal model, i.e. Feedforward Neural Network - Vector Autoregressive (FFNN-VAR) and FFNN - Generalized Space-Time Autoregressive (FFNN-GSTAR), and compare their forecast accuracy to linear spatio-temporal model, i.e. VAR and GSTAR. These spatio-temporal models are proposed and applied for forecasting monthly oil production data at three drilling wells in East Java, Indonesia. There are 60 observations that be divided to two parts, i.e. the first 50 observations for training data and the last 10 observations for testing data. The results show that FFNN-GSTAR(1 1 ) and FFNN-VAR(1) as nonlinear spatio-temporal models tend to give more accurate forecast than VAR(1) and GSTAR(1 1 ) as linear spatio-temporal models. Moreover, further research about nonlinear spatio-temporal models based on neural networks and GSTAR is needed for developing new hybrid models that could improve the forecast accuracy

A Comprehensive Analysis of Neutrosophic Bonferroni Mean Operator

The Neutrosophic Bonferroni operator is a novel operator that we provide in this paper. Then the arithmetic operations for Neutrosophic Bonferroni operator is developed which tells the existence of Neutrosophic Bonferroni operator. Then its properties were discussed with special cases. To group decision-making issues with several attributes, arithmetic ranking operations and the Neutrosophic approach are used. The result is compared with the existing methodology. The suggested approach will more accurately give the decision maker the ideal attribute than the existing system does. Neutrophic multicriteria is a method of decision-making that makes use of ambiguity to integrate various criteria or factors—often with imprecise or ambiguous data—to reach a result. The neutrosophic multicriteria analysis enables the assessment of subjective and qualitative factors, which can assist in resolving conflicting goals and preferences. In Neutrosophic Multi-Attribute Group Decision Making (NMAGDM) problems, all the data supplied by the decision makers (DMs) is expressed in single-value Neutrosophic triangular and trapezoidal numbers, which are studied in this work and can improve the flexibility and precision of capturing uncertainty and aggregating preferences

PENGUJIAN LAGRANGE MULTIPLIER PADA SPESIFIKASI SPATIAL MODEL PERTUMBUHAN EKONOMI INDONESIA

Beberapa model ekonometrika didasari pada teknik asimtotik dan terdapat tiga prinsip untuk pembangunan tes hipotesis parametrik. Pengujian tersebut diantaranya : (i) metode Wald, (ii) metode maximum likelihood ratio (LR) dan (iii) metode Lagrange Multiplier (LM). Terdapat uji diagnostik untuk penilaian model yang disebabkan dependensi spatial dan heterogenitas spatial sebagai aplikasi dari prinsip Lagrange Multiplier. Tujuan dari paper ini adalah mempertimbangkan penggunaan uji Lagrange Multiplier untuk menyusun spesifikasi model spatial pertumbuhan ekonomi di Indonesia. Data yang digunakan  adalah  produk  domestic  regional  bruto  (PDRB)  untuk  masing- masing provinsi serta faktor-faktor yang mempengaruhinya bersumber dari Badan Pusat Statistik Republik Indonesia (BPS RI) tahun 2017. Berdasarkan hasil  pengujian  LM  mengindikasikan  bahwa  parameter  rho  dan  lamda (SARMA) berpengaruh signifikan. Dengan demikian, spesifikasi model spatial terbaik adalah model yang menambahkan parameter rho dan lamda, seperti model spatial SAC dan SAC mixed.Keywords:   Lagrange   Multiplier,   Uji   Diagnostik  Spatial,   Spatial   Model, pertumbuhan ekonomi, infrastruktur transportasi

Comparison between hybrid quantile regression neural network and autoregressive integrated moving average with exogenous variable for forecasting of currency inflow and outflow in bank Indonesia

Some problems arise in time series analysis are nonlinearity and heteroscedasticity. Methods that can be used to analyze such problems are neural network and quantile regression. There are a lot of studies and developments on both methods, but the study that focuses on the performances of combination of these two methods applied in real case are still limited. Therefore, this study performed a comparison between hybrid Quantile Regression Neural Network (QRNN) and Autoregressive Integrated Moving Average with Exogenous Variable (ARIMAX). Both methods were employed to model the currency inflow and outflow from Bank Indonesia in Nusa Tenggara Timur province. Based on the empirical result, the hybrid QRNN method provided better forecasting for currency outflow whereas the ARIMAX resulted in better forecasting for the inflow