Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%.

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Employee performance assessment is a very important task that has to be performed by Human Resource Department in a certain institution, because it would be useful for the policy holders to help them making a decision in promoting or not their employees. This paper delivered the analysis of using Multi Layer Perceptron (MLP) to predict the performance of security unit personnels which has been trained by a formal institution. The data that was used in this research were collected from PT. Garuda Merah Indonesia, that is a company that has role to train and to educate people who wants to be a security personnel. The data consist of 175 record with 10 attributes which include the assessment from aspects of cognitive, personality, and skill. MLP predicted the security personnel performances into three categories, i.e. “Good”, “Enough”, and “Fail”. The 10 folds Cross Validation technique was also used in testing phase to measure its performance comprehensively with the output of the best accuracy was 97,75%.

Panjangnya antrean di sebuah loket penyedia layanan tentunya membuat para pengguna layanan tidak nyaman, bosan, bahkan kelelahan selama menunggu. Selain itu, waktu yang terbuang pun cukup banyak selama menunggu antrean. Ketidakpastian waktu dalam menunggu giliran pun seringkali menimbulkan masalah bagi pengguna layanan yang seringnya berujung pada tidak terlayaninya para pengantre karena jam layanan sudah berakhir. Hal ini pula yang terjadi pada beberapa layanan administrasi di Universitas Teknologi Yogyakarta, di mana mahasiswa harus antre panjang di loket-loket bank yang ada di kampus ataupun loket Bagian Keuangan sekadar untuk membayarkan uang kuliah atau melakukan validasi pembayaran. Oleh karena itu, pada artikel ini, kami membahas mengenai pengembangan aplikasi berbasis Android yang berfungsi untuk membantu penyedia layanan dalam mengelola antrean, serta memudahkan para pengantre dalam mengambil nomor antrean dan mendapatkan estimasi waktu menunggu giliran untuk mendapatkan layanan. Aplikasi tersebut dikembangkan menggunakan Android Studio dengan memanfaatkan basis data Firebase milik Google untuk menunjang kemudahan pengembangan aplikasi. Untuk mengukur kelayakan penggunaan aplikasi, maka pengujian pada beberapa perangkat berbeda pun telah dilakukan.

In this paper, we discuss the implementation of Machine Learning (ML) to predict the victory of candidates in Regional Elections in Indonesia based on data taken from General Election Commission (KPU). The data consist of composition of political parties that support each candidate. The purpose of this research is to develop a Machine Learning model based on verified data provided by official institution to predict the victory of each candidate in a Regional Election instead of using social media data as in previous studies. The prediction itself simply a classification task between two classes, i.e. ‘win’ and ‘lose’. Several Machine Learning algorithms were applied to find the best model, i.e. k-Nearest Neighbors, Naïve Bayes Classifier, Decision Tree (C4.5), and Neural Networks (Multilayer Perceptron) where each of them was validated using 10-fold Cross Validation techniques. The selection of these algorithms aims to observe how the data works on different Machine Learning approaches. Besides, this research also aims to find the best combination of features that can lead to gain the highest performance. We found in this research that Neural Networks with Multilayer Perceptron is the best model with 74.20% of accuracy.