Sistem Kamera Cerdas Untuk Deteksi Pelanggaran Marka Jalan

Penghitungan jumlah kendaraan dan deteksi pelanggaran rambu-rambu lalu lintas sejauh ini masih dilakukan sacara manual. Berkembangnya teknik pengolahan citra yang berasal dari sensor kamera mendorong adanya implementasi sistem pemantauan pelanggaran kendaraan pada area lampu lalu lintas dengan menggunakan kamera cerdas. Tujuan penelitian adalah mengembangkan sistem yang dapat mendeteksi dan menghitung jumlah kendaraan berupa mobil yang melanggar garis marka jalan. Metode pengolahan citra yang dikembagkan pada penelitian ini adalah mengubah citra video RGB hasil tangkapan kamera menjadi grayscale kemudian menerapkan teknik harr cascade untuk mendeteksi adanya pelanggaran garis marka jalan . Sistem yang dikembangkan mampu menghitung jumlah kendaraan yang melewati sensor kamera pada lajur jalan yang dipantau dan mendeteksi adanya pelanggaran pada marka jalan dengan akurasi deteksi sebesar 76 % dibandingkan dengan pengamatan secara langsung oleh mata manusia

Rancang Bangun Aplikasi Dekstop Untuk Pengelolaan Taman Pendidikan Al-qur'an

Taman Pendidikan Al-Qur'an (TPQ) as one of the community-based education requires an effective system and professional in administration and management. Problems that are found relating to the management of TPQ is still manual data management. This paper aims to provide a solution to manage the data that still manual can be completed quickly through a computer application program. The results of the design and implementation of the program is a computer application system that can be operated easily, can be used to manage the data of students, teachers and assets effectively in terms of time and operation, without error or bug, and can present a report of students, assets and financial flows quickly

Development of Empirical Mode Decomposition Based Neural Network for Power Quality Disturbances Classification

The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ disturbances into several intrinsic mode functions (IMF) components, which were extracted using statistical parameters and the Hilbert transformation. The statistical parameters consist of mean, root mean squared, range, standard deviation, kurtosis, crest factor, energy, and skewness, while the Hilbert transformation consists of instantaneous frequency and amplitude. The feature extraction results from both parameters were combined into a set of PQ disturbances and classified using Multi-Layer Feedforward Neural Networks (MLFNN). Training and testing were carried out on 3 feature datasets, namely statistical parameters, Hilbert transforms, and a combination of both as inputs from 3 different MLFNN architectures. The best results were obtained from the combined feature input on the network architecture with 2 layers of ten neurons, by 98.4 %, 97.75, and 97.4 % for precision, recall, and overall accuracy, respectively. The implemented method is used to classify PQ signals reliably for pure sinusoids, harmonics with sag and swell, as well as flicker with 100 % precisio