Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.

Data mining is the process of finding patterns or interesting information in selected data by using a particular technique or method. Utilization of data mining one of which is forecasting. Various forecasting methods have progressed along with technological developments. Support Vector Regression (SVR) is one of the forecasting methods that can be used to predict inflation. The level of accuracy of forecasting is determined by the precision of parameter selection for SVR. Determination of these parameters can be done by optimization, to obtain optimal forecasting of SVR method. The optimization technique used is Weight Attribute Particle Swarm Optimization (WAPSO). The use of WAPSO can find optimal SVR parameters, so as to improve the accuracy of forecasting. The purpose of this research is to implement SVR and SVR-WAPSO to predict the inflation rate based on Consumer Price Index (CPI) and to know the level of accuracy. The data used in this study is CPI Semarang City period January 2010-February 2018. Implementation experiments using Netbeans 8.2 gives results, SVR method has an accuracy of 94.654%. SVR-WAPSO method has an accuracy of 97.459%. Thus, the SVR-WAPSO method can increase the accuracy of 2,805% of a single SVR method for inflation rate forecasting. This research can be used as a reference for the next researcher can make improvements in determining the range of SVR parameters to get the value of each parameter more effective and efficient to get more optimal accuracy.

AbstrakProses penjadwalan di Universitas Negeri Semarang yang sedemikian rumit menghasilkan data penjadwalan yang tersimpan di dalam database Sikadu (Sistem Informasi Akademik Terpadu) berupa keterkaitan antara data dosen, mahasiswa, dan mata kuliah. Namun, data ini tidak diintegrasikan secara langsung ke dalam aplikasi/sistem e-learning yang disediakan oleh Unnes, mengakibatkan adanya proses/kegiatan yang tidak perlu sebelum dapat menggunakan aplikasi e-learning. Dengan fakta bahwa data penjadwalan dapat diakses secara online, dapat dirancang aplikasi pendukung e-lecture dengan memanfaatkan data tersebut. Pertama-tama, dirancang web service yang akan menyajikan akses aman ke dalam data Sikadu. Lalu, dirancang database e-lecture yang akan memanfaatkan web service yang telah dibuat tersebut.Data akan disajikan dalam interface yang dibuat dengan HTML, bermesin PHP. Dosen dan mahasiswa dapat menggunakan akses login yang sama dengan Sikadu untuk dapat langsung memanfaatkan aplikasi ini. Dengan adanya aplikasi ini, proses perkuliahan meliputi sharing bahan ajar, pemberian tugas/aktivitas kuliah, integrasi pengumpulan tugas, koreksi nilai tugas, pembatasan waktu pengumpulan tugas secara tegas (tersistem) dan lain sebagainya dapat dilakukan secara mudah dan efisien.

Expert Systems is a computer systems that has been entered the base knowledge and a set of rules used to solve problems like an expert. Methods that can be used in the expert systems which is Naïve Bayes and Certainty Factor. Naïve Bayes method can handle quantitative calculations and discreate data and only requires a little research data to estimate the parameters needed in the clasification and Certainty Factor which is suitable for measuring something whether it is certain or not in diagnosing. Diabetes is one of the most frequent diseases suffered in Indonesia. The purpose of this research is implementation expert systems used Naïve Bayes and Certainty Factor in diagnosing diabetes and knowing the level of accuracyof the systems. Data that is used by researchers as much 100 data medical record, obtained from the medical record RSUD Bendan Kota Pekalongan. The variabels used in this research is age, gender, the symptoms of the desease diabetes and result diagnose desease from expert. The accuracy rate of this system derived from the scenario distribution data 70 training data and 30 testing data that is equal to 100% according to the doctor's diagnosis.

ERP was built to simplify existing business processes so that business processes became more effective and efficient. The term “business” also refers to individuals or organizations organized efforts and activities to produce added value, unexceptionally in education. Higher education institutions have an education system that must be accredited as an assessment of the institution’s performance. Based on the accreditation assessment criteria carried out by BAN-PT, there are at least nine assessment criteria in which almost all universities have their data in their system. This paper discusses collecting the right data to acquire data from various systems within the university into an integrated accreditation system. The data transactions between various university information systems and accreditation systems implementing the Application Programming Interface (API) to make the data acquisition process more effective. The results showed an increase in accreditation data collection speed, and assessors could see quickly and clearly through the data collected in the accreditation system.