Modifikasi Model Analisis Structural Equation Model (Sem) pada Reaksi Pasar di Perusahaan Bursa Efek Indonesia melalui Modification Indices

Pergerakan reaksi pasar dan rasio keuangan beserta Economic Value Added menjadi topik hangat terutama dengan berkembangnya pasar modal di tanah air. Melalui SEM, model yang dihasilkan mampu mengkontruks indikator-indikator rasio keuangan terhadap pergerakan saham. SEM merupakan pemodelan kuantitatif faktor-faktor yang menunjukkan hubungan sebab akibat antara beberapa faktor dependen dan independen melalui indikator-indikatornya. Analisis SEM merupakan kombinasi dari analisis faktor (Confirmatory Factor Analysis), analisis jalur (Path Analysis) dan analisis regresi. Untuk mendapatkan model yang lebih baik, maka analisis ini dipadukan dengan mengkorelasikan error berdasarkan Modification indices. Modification indices akan mengakibatkan terjadinya penurunan Chi-square serta terjadi Perubahan nilai CMINDF dan RMSEA menjadi semakin baik. Begitupula pada p-value, GFI, dan TLI. Sehingga dapat disimpulkan bahwa pengaruh korelasi antar measurement error dalam variabel rasio keuangan dalam variabel reaksi pasar dan antar variabel EVA mengakibatkan Perubahan yang signifikan pada kebaikan model. Kata Kunci: Reaksi pasar, modification indices, SE

Structural Data Recognition with Graph Model Boosting

This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbor method. In this paper, we propose strengthening the recognition performance of these models as well as their ability to capture structural variation. The proposed method constructs a large number of graph models and trains decision trees using the models. This paper makes two main contributions. The first is a novel graph model that can quickly perform calculations, which allows us to construct several models in a feasible amount of time. The second contribution is a novel approach to structural data recognition: graph model boosting. Comprehensive structural variations can be captured with a large number of graph models constructed in a boosting framework, and a sophisticated classifier can be formed by aggregating the decision trees. Consequently, we can carry out structural data recognition with powerful recognition capability in the face of comprehensive structural variation. The experiments shows that the proposed method achieves impressive results and outperforms existing methods on datasets of IAM graph database repository.Comment: 8 page

Structural Derivative Model for Tissue Radiation Response

By means of a recently-proposed metric or structural derivative, called scale-q-derivative approach, we formulate differential equation that models the cell death by a radiation exposure in tumor treatments. The considered independent variable here is the absorbed radiation dose D instead of usual time. The survival factor, Fs, for radiation damaged cell obtained here is in agreement with the literature on the maximum entropy principle, as it was recently shown and also exhibits an excellent agreement with the experimental data. Moreover, the well-known linear and quadratic models are obtained. With this approach, we give a step forward and suggest other expressions for survival factors that are dependent on the complex tumor structure.Comment: 6 pages, 2 collumn