Amir Mahmud Husein, Amir Mahmud
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DEEP NEURAL NETWORKS APPROACH FOR MONITORING VEHICLES ON THE HIGHWAY Husein, Amir Mahmud; Christopher, Christopher; Gracia, Andy; Brandlee, Rio; Hasibuan, Muhammad Haris
SinkrOn Vol 4 No 2 (2020): SinkrOn Volume 4 Number 2, April 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i2.10553

Abstract

Vehicle classification and detection aims to extract certain types of vehicle information from images or videos containing vehicles and is one of the important things in a smart transportation system. However, due to the different size of the vehicle, it became a challenge that directly and interested many researchers . In this paper, we compare YOLOv3's one-stage detection method with MobileNet-SSD for direct vehicle detection on a highway vehicle video dataset specifically recorded using two cellular devices on highway activities in Medan City, producing 42 videos, both methods evaluated based on Mean Average Precision (mAP) where YOLOv3 produces better accuracy of 81.9% compared to MobileNet-SSD at 67.9%, but the size of the resulting video file detection is greater. Mobilenet-SSD performs faster with smaller video output sizes, but it is difficult to detect small objects.
COMPARISON OF CELLULAR VIDEO QUALITY FOR OBJECT DETECTION USING NEURAL NETWORK CONVOLUTION Kevin, Kevin; Gunawan, Nico; Zagoto, Mariana Erfan Kristiani; Laurentius, Laurentius; Husein, Amir Mahmud
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10248

Abstract

Abstract? The purpose of this study is to compare the video quality between the Samsung HP camera and the Xiaomi HP camera. The object of study was UNPRI students who walked through the front yard of the UNPRI SEKIP campus. Here we test how accurate the camera's HP capture capacity is used to take the video. The method used to test this research is the Convolution Neural Network method. Object detection and recognition aim to detect and classify objects that can be applied to various fields such as face, human, pedestrian, vehicle detection (Pedoeem & Huang, 2018), besides the ability to find, identify, track and stabilize objects in various poses and important backgrounds in many real-time video applications. Object detection, tracking, alignment and stabilization have become very interesting fields of research in the vision and recognition of computer patterns due to the challenging nature of several slightly different objects such as object detection, where the algorithm must be precise enough to identify, track and center an object from the others
MESSAGE INSERTION USING THE CONVOLUTIONAL NEURAL NETWORK MODEL APPROACH Ambarwati, Lita; Sirait, Agrifa Darwanto; Tambun, Bella Siska; Purwanto, Eko Paskah Jeremia; Husein, Amir Mahmud
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10159

Abstract

One problem in computer vision that has long been sought for a solution is the classification of objects in the image in general. How to duplicate the ability of humans to understand image information, so that computers can recognize objects in the image as humans do. The feature engineering process used is generally very limited where it can only apply to certain datasets without the ability to generalize to any type of image. That is because various differences between images include differences in perspective, differences in scale, differences in lighting conditions, deformation of objects, and so on. Academics who have long struggled with this issue. The application of the Convolutional Neural Network (CNN) method for the insertion of messages in an image with the aim of securing the proposed message produces good security, from the test results, it can be concluded as follows The Convolutional Neural Network (CNN) method requires computing time to insert messages in a secret image. The model framework uses 2 (two) images with the aim of the cover image as input and the secret image where the secret image has been inserted a message so that the secret is not visible. The cover image that has been inserted a secret picture that contains the message looks not much different, but the file size of the secret picture has increased by 66%.
DRUG DEMAND PREDICTION MODEL USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) Husein, Amir Mahmud; Simarmata, Allwin M
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10238

Abstract

Drug planning is the process of activities in the selection of types, quantities, and prices in accordance with the needs and budget for the coming procurement period in order to avoid the occurrence of excess or emptiness of drug supplies when needed by patients. Management of planning that is not optimal drug needs will have a negative impact on hospitals, both medically and economically, because 50-60% of the total budget used for treatment and medical equipment, uncertainty of drug needs due to disease population and the number of patients can change according to conditions the volume of patient diagnostic data, thus requiring an automatic way to select drug needs according to disease progression. This study aims to create a prediction model for drug needs with the ANFIS method, the data analysis framework used is sourced from drug usage / sales data at the Royal Prima Hospital 2016-2017 by building a software that implements the ANFIS method. Stages of application testing are carried out by applying the previous year's data to predict the current year, namely the 2016 data for 2017 predictions, while the 2017 data for 2018 predictions. The data source will be used to analyze the ANFIS membership function that generates parameters for the ANFIS method in training and testing data. The results of the analysis of the ANFIS parameters will be updated to produce a small error value (close to 0), based on the value of Root Mean Square Error (RMSE), then an evaluation is carried out with a quantitative and qualitative analysis of the predicted results with the existing system.
ENHANCING THE QUALITY OF CELLULAR CAMERA VIDEO WITH CONVOLUTIONAL NEURAL NETWORK Tampubolon, Hotman Parsaoran; Sinurat, Watas; Gulo, Steven Eduard; Gulo, Befi Juniman; Husein, Amir Mahmud
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10239

Abstract

Abstrak? At present technological developments, especially in the field of computer vision, are showing significant performance such as the application of convolutional neural networks that have a very high degree of accuracy, for example improving video quality which recently has image restoration such as super resolution (VSR) thanks to deep learning with the aim of helping produce better visual videos. The use of video cameras for mobile devices is now increasingly highly developed. Nowadays mobile devices are experiencing a rapid increase in quality especially in cameras. However, physical limitations such as the small sensor size, compact lens and the lack of supporting hardware can prevent cellular devices from achieving good video camera quality results. For that many method approaches are applied, one of which is the CNN (Convolutional Neural Network) method. This method can improve the image of video recordings that have poor quality. Keywords?Convolutional neural network, computer vision, Improved video quality ;
DIGITAL SIGNS SECURITY SYSTEM USING AES-BLOWFISH-RSA HYBRID CRYPTOGRAPHY APPROACH HS, Christnatalis; Husein, Amir Mahmud
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10244

Abstract

Increasing application of digital signatures in legitimate authentication of administrative documents in both public and private environments is one of the points of concern, especially the issue of security and integrity of ownership of signatures. Digital signature is a mathematical scheme, which a unit to identify and prove the authenticity of the owner of the message or document. The study aims to analyze security patterns and identification of digital signatures on documents using the RSA-AES-Blowfish hybrid cryptographic method approach for securing digital signatures, while the Kohonen SOM method is applied to identify ownership recognition of signature images. The analysis framework used in this study is each signature will be stored in the form of a digital image file that has been encrypted using hybrid method of AES-Blowfish with the SHA 256 hash function. Process of forming private keys and public keys in the signature image using the RSA algorithm. Authentic verification of the use of digital signatures on the document has 2 (two) stages, the first stage is signature will be valid used on the document if the result of hashing the selected signature image is the same based on the private key and public key entered by the user, while the second stage identification is done using the Kohonen SOM method to validate the similarity of the chosen signature with the ownership of the signature.
APPLICATION OF DATA MINING FOR OPTIMAL DRUG INVENTORY IN A HOSPITAL Siringo-Ringo, Dewi Sahputri; Tambunan, Razana Baringin Daud; Yulizar, Dian; Daulay, Tri Agustina; Husein, Amir Mahmud
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10236

Abstract

The Hospital is a health care institution that conducts complete individual health services that provide inpatient, outpatient and emergency services. Drug inventory management is one thing that is very important for the survival of hospitals, management of the supply of medical equipment that is not optimal including medicines will have an impact on medical services as well as economically, because 70% of hospital revenue comes from drugs. In this study we propose data mining with a focus on contributions to the comparison of the K-Means and K-Nearest Neighbor (KNN) algorithms for disease classification, then the classification results are carried out mapping the correlation of diseases with drugs using Apriori, based on the results of testing the K-Means algorithm more accurately compared KNN in the Apriori method to find the relationship of disease with drugs based on the value of support, trust, support value, trust is expected to be a reference for drug purchase recommendations so that there is no excess or emptiness of the drug.
VIDEO SURVEILLANCE SYSTEM WITH A DEEP LEARNING APPROACH Lestari, Puji; Manik, David Hamonangan D.; Br Sihotang, Nurseve Lina; Husein, Amir Mahmud
SinkrOn Vol 4 No 1 (2019): SinkrOn Volume 4 Number 1, October 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v4i1.10247

Abstract

Abstract? The application of in-depth learning methods has been successfully applied in computer vision task with the ability to learn the features of differences in real world images by directly from the original image by passing layer after layer to get the high dimensions image, in this study we applied the YOLO method approach with network adaptation features based on Darknet-53 on a video dataset recorded by the activities of University of Indonesia Prima (UNPRI) students with are conditions of video with different objects as a surveillance system, based on the results of research into object classification produces an overall accuracy of 93%, but for the classification of objects bikes, buses, and cars have the lowest accuracy of 30% for bikes, 54% of cars and buses by 40% so it is necessary to develop methods to improve accuracy.
Analisa Frekuensi Hasil Enkripsi Pada Algoritma Kriptografi Blowfish Terhadap Keamanan Informasi Riza, Ferdy; Sridewi, Nurmala; Husein, Amir Mahmud; Harahap, Muhammad Khoiruddin
Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP) Vol 1 No 1 (2018): JUTIkomp Volume 1 Nomor 1 April Periode 2018
Publisher : Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1139.424 KB)

Abstract

The ease of sending data with the development of internet technology technology is now a concern, especially the problem of data confidentiality, integrity and information security. Cryptography is one of the techniques used to maintain data confidentiality and information security, the application of cryptographic techniques for information security and data integrity is highly dependent on the formation of keys. In this study proposed a frequency analysis approach to measure the level of information security of blowfish encryption results to determine the distribution form of each character used in the text and find out the exact frequency of each character used in the test text data. The encryption algorithm and description of blowfish method against plaintext are proven to be accurate, but the longer the key character used will greatly affect the level of information security that came  from encryption process, this is based on the results of the frequency analysis conducted.
Analisis Performa Rasio Kompresi Pada Metode Differensiasi ASCII Dan Lempel Ziv Welch (LZW) Tommy, Tommy; Siregar, Rosyidah; Husein, Amir Mahmud; Harahap, Mawaddah; Riza, Ferdy
Jurnal Teknologi dan Ilmu Komputer Prima (JUTIKOMP) Vol 1 No 2 (2018): Jutikomp Volume 1 Nomor 2 Oktober 2018
Publisher : Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (636.874 KB)

Abstract

ASCII differentiation is a compression method that utilizes the difference value or the difference between the bytes contained in the input character. Technically, the ASCII differentiation method can be done using a coding dictionary or using windowing block instead of the coding dictionary. Previous research that has been carried out shows that the ASCII differentiation compression ratio is good enough but still needs to be analyzed on performance from the perspective of the compression ratio of the method compared to other methods that have been widely used today. In this study an analysis of the comparison of the ASCII Difference method with other compression methods such as LZW will be carried out. The selection of LZW itself is done by reason of the number of data compression applications that use the method so that it can be the right benchmark. Comparison of the compression ratio performed shows the results of ASCII differentiation have advantages compared to LZW, especially in small input characters. Whereas in large input characters, LZW can optimize the probability of pairs of characters that appear compared to ASCII differentiation which is glued to the difference values ​​in each block of input characters so that in large size characters LZW has a greater compression ratio compared to ASCII differentiation.