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The digital signature image is a digital pattern with highly variable features. The pattern recognition of digital signature images aims to build a specific characteristic capable of representing a considerable pattern variation while maintaining the boundary conditions of authentication. The feature as an attribute that describes the characteristics of a pattern becomes a determinant factor of reliability of a method of recognizing digital signature image pattern for Handwritten Signature Verification (HSV). To construct HSV required two types of signature samples that are the original signature samples used as training samples and the guess signature samples (consist of valid and imposter signature) which are used as test samples. This study proposes two unique features of 16-Bits Binary Chain to Decimal (16BCD) and Virtual Center of Gravity (VCG). The 16BCD feature obtained from image segmentation with a 4x4 pixel region. All pixels in each region of the segmentation result rearranged into a 16-bit binary chain. The VCG feature is a virtual representation of the Original Signature Pattern (OSP) gravity center against Pattern Space and Background. The verification mechanism uses criteria: the percent of acceptable correlation coefficients for the acceptable feature of 16BCD feature, Mean Absolute Error (MAE) against 16BCD, and the percent deviation of acceptable distance to the VCG feature prototype. Verification test results obtained Acceptance Rate (AR) 80% (which states the percentage of HSV success based on a number of original signature samples) with an efficiency of 90% (which states the percentage of success of HSV in distinguishing valid or forgery signature based on a sample of guessing signatures).

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PT. Adimitra Baratama Nusantara merupakan perusahaan batu bara di Sangasanga yang memberikan bantuan dana beasiswa kepada siswa siswi yang ada di Sangasanga. Banyaknya pendaftar peserta beasiswa membuat pihak perusahaan kesulitan dalam menangani pengolahan data, sehingga diperlukan perangkat lunak untuk mempermudah pengolahan data tersebut. Penelitian ini dilakukan dengan tujuan membangun suatu model keputusan untuk menentukan penerima beasiswa menggunakan metode SAW agar proses seleksi calon penerima beasiswa dapat terselesaikan dengan tepat, cepat, dan lebih terprosedural. Penentuan penerima beasiswa ditentukan dari beberapa kriteria antara lain; penghasilan orang tua, jumlah anggota keluarga tidak bekerja, daya listrik rumah, nilai rata-rata raport, kelas, dan jumlah extrakurikuler yang diikuti. Kemudian untuk merancang sebuah aplikasi diperlukan beberapa tahap yaitu dengan membuat Context Diagram, Data Flow Diagram, dan Entity Relationship Diagram dan menerapkannya ke dalam suatu software/program yang akan dibangun menggunakan bahasa pemrograman visual basic 6 beserta database microsoft access. Model keputusan calon penerima beasiswa pada PT. Adimitra Baratama Nusantara dengan Metode Simple Additive Weighting (SAW)  ini menghasilkan 10 pendaftar terpilih sebagai penerima beasiswa dan 20 pendaftar dinyatakan gagal dalam proses seleksi

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The bank is a type of company that acts as the executor of monetary policy and as a guarantor of the stability of the financial system of a country. Total assets are an important aspect for a bank to generate net income. Return on Assets (ROA) is a profitability ratio to measure the ability of a bank in generating profits with all investments owned. This study predicts the total assets of the largest banks in Indonesia, referring to the Indonesia Stock Exchange data from 2005 to 2016. The time series data model used is Autoregressive (AR) model and Multi Input Single Output (MISO) Autoregressive with exogenous input (ARX) model. Adaptive Artificial Neural Network Backpropagation (Adaptive ANN-BP) is used as an approximation model of both models.

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