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All Journal Indonesian Journal of Electronics and Instrumentation Systems IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Agfianto Eko Putra
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Estimation of Average Car Speed Using the Haar-Like Feature and Correlation Tracker Method Muhammad Dzulfikar Fauzi; Agfianto Eko Putra; Wahyono Wahyono
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 4 (2020): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.57262

Abstract

The speed of a car traveling on the road can generally be estimated by using a speed gun. Efforts are needed to use CCTV (closed circuit television) as a tool that can be used to estimate the speed of the car so as to ease the burden on the road operator to estimate the speed of the car. This study discusses the estimated average speed of the car with the Haar-like Feature method used to detect the car, then the detection results are tracked using Correlatin Tracker to track the movement of objects that have been detected and calculate the distance of movement from the car, so that the speed of the car detected in video can be estimated. The results of the estimated average speed compared with the results of taking speed with a speed gun so that an error is obtained by MAE testing of 5,55 km / hour and the resulting standard deviation is 4,61 km / hour, thus it can be concluded that the system is made valid and can be used by road organizers to monitor the average speed of a car.
Mobile-based Primate Image Recognition using CNN Nuruddin Wiranda; Agfianto Eko Putra
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.65640

Abstract

Six out of 25 species of primates most endangered are in Indonesia. Six of these primates are namely Orangutan, Lutung, Bekantan, Tarsius tumpara, Kukang, and Simakobu. Three of the six primates live mostly on the island of Borneo. One form of preservation of primate treasures found in Kalimantan is by conducting studies on primate identification. In this study, an android app was developed using the CNN method to identify primate species in Kalimantan wetlands. CNN is used to extract spatial features from primate images to be very efficient for image identification problems. The data set used in this study is ImageNets, while the model used is MobileNets. The application was tested using two scenarios, namely using photos and video recordings. Photos were taken directly, then reduced to a resolution of 256 x 256. Then, videos were taken in approximately 10 to 30 seconds with two megapixel camera resolution. The results obtained was an average accuracy of 93.6% when using photos and 79% when using video recordings. After calculating the accuracy, the usability test using SUS was performed. Based on the SUS results, it is known that the application developed is feasible to use.
Deteksi Onset Gamelan Bebasis DWPT dan BLSTM Hisyam Mustofa; Agfianto Eko Putra
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 13, No 1 (2023): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijeis.79534

Abstract

Gamelan consists of various kinds of instruments that have different characteristics. Each has characteristics in terms of the basic frequency, amplitude, signal envelope, and different ways of playing it, resulting in differences in the sustain power of the signal. These characteristics cause the problem of vanishing gradient in the Elman Network model which was used in previous studies in studying the onset detection in the Saron instrument signal which has an average interval of more than 0.6 seconds. This study uses BLSTM (Bidirectional Long Short Term Memory) as a model for training and Wavelet Packet Transformation to design a psychoacoustic critical bandwidth as a model for feature extraction. For the peak picking method, this study uses a fixed threshold method with a value of 0.25. The use of the BLSTM model supported by the Wavelet Packet Transform is expected to overcome the vanishing gradient that exists in a simple RNN architecture. The model was tested based on 3 evaluation parameters, namely precision, recall and F-Measure. Based on the test scenario carried out, the model can overcome the vanishing gradient problem on the Saron instrument which has an average interval between onset of 600 ms. Out of a total of 428 onsets on the Saron instrument, the model successfully detected 426 correctly, with 4 incorrectly detected onsets and 2 undetected onsets. A thorough evaluation for each of the precision, recall, and F1-Measure algorithms obtained 0.975, 0.945 and 0.960.
Co-Authors Fauzi, Muhammad Dzulfikar Hisyam Mustofa Wahyono Wahyono Wiranda, Nuruddin