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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics Jurnal Master Pariwisata (JUMPA) Jurnal Sistem Cerdas Jurnal Mantik
Yohanssen Pratama
Institut Teknologi Del
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Face recognition for presence system by using residual networks-50 architecture Yohanssen Pratama; Lit Malem Ginting; Emma Hannisa Laurencia Nainggolan; Ade Erispra Rismanda
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5488-5496

Abstract

Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.
Deep convolutional neural network for hand sign language recognition using model E Yohanssen Pratama; Ester Marbun; Yonatan Parapat; Anastasya Manullang
Bulletin of Electrical Engineering and Informatics Vol 9, No 5: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (723.859 KB) | DOI: 10.11591/eei.v9i5.2027

Abstract

An image processing system that based computer vision has received many attentions from science and technology expert. Research on image processing is needed in the development of human-computer interactions such as hand recognition or gesture recognition for people with hearing impairments and deaf people. In this research we try to collect the hand gesture data and used a simple deep neural network architecture that we called model E to recognize the actual hand gestured. The dataset that we used is collected from kaggle.com and in the form of ASL (American Sign Language) datasets. We doing accuracy comparison with another existing model such as AlexNet to see how robust our model. We find that by adjusting kernel size and number of epoch for each model also give a different result. After comparing with AlexNet model we find that our model E is perform better with 96.82% accuracy.
Detection roasting level of Lintong coffee beans by using euclidean distance Yohanssen Pratama; I Gde Eka Dirgayussa; Paian Fernando Simarmata; Mia Hotmaria Tambunan
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3153

Abstract

Coffee roasting is the process by which raw coffee beans (green beans) are roasted until they reach a certain roast level. In general, the roast level of roasted coffee beans is divided into 3 levels, namely the roast level of light, medium and dark. One way to find out the roast level of roasted coffee beans is to see the color change of the coffee beans. However, it is very difficult to know the exact color conditions of each roast level of roasted coffee beans and this can be overcome by build an automatic coffee roasting equipment. In this research, an automatic coffee roaster was done with a system that is able to control the roasting temperature and stirring of coffee beans. This tool can also monitor the change in color of the coffee beans during the roasting process. The system that has been implemented can detect color changes and classify the level of dark roast of roasted coffee beans using the Euclidean distance algorithm. The Euclidean distance give a threshold to classified the roast level. The system accuracy for predicting coffee beans color at the level of dark roast is 90% and 80% for overall.
Literasi Media Digital Pada Komunitas Pariwisata di Kawasan Danau Toba Yohanssen Pratama; Arnaldo Marulitua Sinaga; Riyanthi Angrainy Sianturi; Verawaty Situmorang
JURNAL MASTER PARIWISATA Volume 08, Nomor 01, Juli 2021
Publisher : Magister Tourism Study, Faculty of Tourism, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JUMPA.2021.v08.i01.p13

Abstract

Lake Toba region is included in one of the 10 priority tourism destination locations. To answer the challenges of changing world tourism trends that utilize digital technology, several strategies must be carried out. In digitizing the process, it is necessary to involve the local community in formulating regulations, strengthening value chain partnerships between businesses in the tourism sector, increasing motivation and enabling the community through various kinds of assistance and training. Therefore, research is conducted that has the aim to develop the tourism potential of the Lake Toba Region through digitalization with the concept of community empowerment. As the output of this research, a community will be formed in each Lake Toba District, so that based on this community various development policies and strategies can be formulated in overcoming problems and developing the tourism sector in the Lake Toba region. Keywords: Community Based Tourism, Digitalization, Lake Toba, Local community, Literacy
Evaluation of Digital Capabilities and Digital Marketing Practices of Micro, Small, and Medium Business in Toba Regency Verawaty Situmorang; Mariana Simanjuntak; Parmonangan Togatorop; Yohanssen Pratama
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The era of disruption is marked by the presence of various innovations, technologies, platforms, and new business models. This era opens up opportunities for Micro, Small, and Medium Enterprise (MSME) to grow. Initial observations that aimed at Toba MSME actors show that the use of digitalization for marketing is still very low. This prompted to conduct the research with purpose to find out the possibility of how the implementation of MBKM can help accelerate the digitization of Toba MSMEs. The data collection method was carried out by surveying students, lecturers and staff as interested parties in MBKM, and continued with Focus Group Discussions with Toba MSME actors. Through this study, an evaluation of the digital capabilities and implementation of e-marketing of Toba MSME actors was carried out in order to produce further recommendations and obtaining the results in the form of an overview of Toba MSME digital capabilities based on aspects of ‘digital innovation, digital technology, digital orientation, digital capability, and marketing capability’. From this research we found that the most common obstacles faced by Toba MSME actors are the limited digital literacy of MSME actors, limited capital, lack of understanding of marketing concepts and practices, and limited number of human resources. These limitations are very likely to be resolved with the MBKM program so that the acceleration of the Toba MSME digital transformation can be carried out.
Analisis Watermarking Menggunakan Metode Discrete Cosine Transform (DCT) dan Discrete Fourier Transform (DFT) Mukhammad Solikhin; Yohanssen Pratama; Purnama Pasaribu; Josua Rumahorbo; Bona Simanullang
Jurnal Sistem Cerdas Vol. 5 No. 3 (2022)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v5i3.192

Abstract

Digital image watermarking is the insertion of watermarks into digital image media. Several types of watermarking methods used are Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT). Both of these watermarking methods work in the frequency domain (transform). Digital image watermarking using the frequency domain is carried out on the frequency coefficient. This study used 30 digital image data as material for digital image watermaking analysis with 10 data each in binary, grayscale and color digital images in jpg, png and bmp formats. Digital images in the binary and grayscale domains are conversions from digital images in the true color (RGB) domain. Digital image watermarking includes three main processes, namely embedding the watermarked image on the original digital image, extracting the watermarked image and measuring the correlation between the two digital images. Correlation aims to measure two variables that have the same relationship. The technology used in this research work is MATLAB (Matrix Laboratory) as a high-performance programming language for computing in solving problems with solutions expressed in mathematical notation. The results of the discussion prove that the watermarking process in terms of color, for DCT, RGB is better and binary is better for DFT. And the watermaking process, in terms of the type of watermark inserted, for both DCT and DFT, a good watermark is an invisible watermark.
Corn Plant Disease Classification Based on Leaf using Residual Networks-9 Architecture Tegar Arifin Prasetyo; Victor Lambok Desrony; Henny Flora Panjaitan; Romauli Sianipar; Yohanssen Pratama
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2908-2920

Abstract

Classification on corn plants is used to classify leaf of corn plants that are healthy and have diseases consisting of Northern Leaf Blight, Common Rust and Gray Leaf Spot. Convolutional Neural Network (CNN) is one of algorithms from the branch of deep learning that utilizes artificial neural networks to produce accurate results in classifying an image. In this study, ResNet-9 architecture implemented to build the best model CNN for classification corn plant diseases. After that we doing comparisons of epochs have been carried out to obtain the best model, including comparisons of epochs of 5, 25, 55, 75 and 100. After the epoch comparison, the highest accuracy value was obtained in the 100 epoch experiment so that in this study 100 epochs were used in model formation. The number of datasets used is 9145 data which is divided into two, there are training data (80%) and testing data (20%). In this study, three hyperparameter tuning experiments were carried out and the results of hyperparameter tuning experiments where num_workers is 4 and batch_size is 32. This classification obtained an accuracy rate of 99% and the model is implemented into a web interface.
Image preprocessing and hyperparameter optimization on pretrained model MobileNetV2 in white blood cell image classification Parmonangan R. Togatorop; Yohanssen Pratama; Astri Monica Sianturi; Mega Sari Pasaribu; Pebri Sangmajadi Sinaga
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1210-1223

Abstract

White blood cells play a role in maintaining the immune system which consists of several types such as neutrophils, lymphocytes, monocytes, eosinophils and basophils. MobileNetV2 is one of the pretrained convolutional neural network (CNN) models that provides excellent advantages and performance in classifying images. In this research was conducted to find out how to apply optimization hyperparameters and the impact of image processing on white blood cell image classification using MobileNetV2, so that it is expected to find a combination of preprocessing and combination of hyperparameter values that can produce the highest accuracy value. To maximize the classification process, before classifying the image, several stages of image preprocessing are carried out, namely cropping, grayscale, resizing and augmentation. Hyperparameter tuning was carried out for an experiment to improve model performance. The three main parameters used in hyperparameter tuning are learning rate, batch size, and number of epochs. Performance optimization model performance will be measured using accuracy, sensitivity, specificity and using a confusion matrix. Based on the experimental results in this study, it shows that the best learning rate value is 0.00001, the best batch size value is 32, and the best epoch value is 250.
Co-Authors Ade Erispra Rismanda Anastasya Manullang Astri Monica Sianturi Bona Simanullang Emma Hannisa Laurencia Nainggolan Ester Marbun Henny Flora Panjaitan I Gde Eka Dirgayussa Josua Rumahorbo Lit Malem Ginting Mariana Simanjuntak Mega Sari Pasaribu Mia Hotmaria Tambunan Mukhammad Solikhin Paian Fernando Simarmata Parmonangan R. Togatorop Pebri Sangmajadi Sinaga Prasetyo, Tegar Arifin Purnama Pasaribu Romauli Sianipar Sianturi, Riyanthi Angrainy Sinaga, Arnaldo Marulitua Verawaty Situmorang Victor Lambok Desrony Yonatan Parapat