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All Journal Dinamik Bulletin of Electrical Engineering and Informatics Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Computer Networks, Architecture and High Performance Computing Data Sciences Indonesia (DSI)
Amir Mahmud Husein, Amir Mahmud
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Computer Science & IT Education Electrical & Electronics Engineering Physics

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Sensor lampu lalu lintas jalur kereta api untuk mengantisipasi kemacetan di persimpangan jalan Husein, Amir Mahmud; Willim, Alfredy; Nainggolan, Yandi Tumbur; Simanggungsong, Antonius Moses; Banjarnahor, Prayoga
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 2B (2021): Article Research October 2021
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v6i1.11074

Abstract

Traffic congestion is a problem that has long occurred in Indonesia, especially in big cities. Traffic congestion that occurs can cause various losses, one of which is time loss because it can only run at a very low speed. Then it will create a waste of energy, because going at low speed will require more fuel. Congestion is also able to increase the saturation of other road users, not only that traffic jams also have a bad impact on nature which causes air pollution. And there are many more impacts of traffic jams that can make traveling very uncomfortable. One of the locations of traffic jams often occurs on roads located around railroad crossings. Therefore, In this study, it is proposed to make a traffic light sensor adjacent to the train track to anticipate long traffic jams based on atmega8 and infrared sensors, with the stages of collecting data, recording transportation activities at the location of the jam, then designing a sensor device. The system built is to read the volume of vehicles on the road and prioritize the road with the highest volume of vehicles to get the green traffic light condition. Based on the results of the manufacture of infrared sensors and atmega8 can be tested to reduce the level of congestion at crossroads adjacent to the railroad.
Video Surveillance System with a Deep Learning Approach Lestari, Puji; Manik, David Hamonangan D.; Br Sihotang, Nurseve Lina; Husein, Amir Mahmud
Sinkron : jurnal dan penelitian teknik informatika 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 | Full PDF (410.084 KB) | 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.
Combination Grouping Techniques and Association Rules For Marketing Analysis based Customer Segmentation Husein, Amir Mahmud; Dodi Setiawan; Andika Rahmad Kolose Sumangunsong; Andreas Simatupang; Shela Aura Yasmin
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11571

Abstract

Changes in people's transaction behavior using the internet resulted in the exponential growth of e-commerce. With the growth of digital shopping transactions, it is difficult to predict customer segments and patterns using traditional mathematical models. Timely identification of emerging trends from large volumes of data plays a major role in business processes and decision making. This is different from previous research works that apply the RFM model based on K-Means Clustering to find potential customers as an ingredient in determining marketing targets. In this study, a clustering technique approach is proposed to classify customer data which is evaluated using the Davies Bouldin, Calinski Harabasz and Silhouette methods to determine the optimal number of clusters, then the results are used in the Apriori algorithm to find patterns of goods that are often purchased together. Based on the test results on the K-Means Clustering, Spectral Clustering, and Gaussian Mixture Model techniques produced 5 clusters with 76% more accurate the K-Means Clustering method than the other two methods so that it was determined as a method in the RMF model, then the results of customer grouping were used on the Apriori algorithm to find patterns of concurrent product purchases by customers that are expected to be useful in future marketing management.
Sentiment Analysis Od Face To Face School Policy On Twitter Social Media With Support Vector Machine(SVM) Husein, Amir Mahmud; Sipahutar, Berninto; Dashuah, Ramonda; Hutauruk, Eben
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11950

Abstract

Twitter social media is one way to get fast information, especially related to face-to-face learning system where during covid-19 pandemic learning is held online. In this case government has informed related to the face-to-face learning system as well as the community or students gave an enthusiastic response to the policies provided by the government including giving a good response to these policies and some of them disagreeing with these policies. In this case, the researcher analyzes public opinion on government policies related to face-to-face learning on Twitter social media using the Support Vector Machine algorithm. By doing an analysis related to government policies regarding learning during the COVID-19 pandemic, the government can find out how the public responds and can make decisions. Based on a series of processes that have been carried out previously using the Support Vector Machine method by applying the TF-IDF weighting function, the results can reach 93%. To see the level of accuracy of the proposed method, the researchers made a comparison by applying several other methods. The accuracy results obtained from the support vector machine method are 93%, based on the accuracy obtained, it can be determined that the level of accuracy using the Support Vector Machine method is quite high in classifying sentiment data, but when compared to other methods, namely nave Bayes, which obtains an accuracy of 94%, Logistic Regression which obtained 93% accuracy, and K-NN which obtained 90% accuracy. Thus, the accuracy results of four methods are not too different.
Sentiment Analysis Of Hotel Reviews On Tripadvisor With LSTM And ELECTRA Husein, Amir Mahmud; Livando, Nicholas; Andika, Andika; Chandra, William; Phan, Gary
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12234

Abstract

This study examines the importance of hotel review data analysis and the use of Natural Language Processing (NLP) technology in predicting hotel review sentiment. In this study, deep learning models such as Long Short-Term Memory (LSTM) and Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA) are used to predict hotel review sentiment in Indonesian. Hotel review data was obtained through a data scraping process with webscraper.io from the Tripadvisor website and a total of 977 hotel review data were obtained from Grand Mercure Maha Cipta Medan Angkasa. Before the sentiment prediction process is carried out, hotel review data must go through the text preprocessing stage to remove punctuation marks, capital letters, stopwords, and a lemmatizer process is carried out to facilitate further data processing. In addition, sentiments that were previously unbalanced need to be balanced through the undersampling process. The data that has been cleaned and balanced is then labeled as negative (0), neutral (1) and positive (2) sentiments. The test results show that the ELECTRA model produces better performance than the LSTM with an accuracy of 47% by ELECTRA and 30% by LSTM.
Vehicle Detection and Identification Using Computer Vision Technology with the Utilization of the YOLOv8 Deep Learning Method Telaumbanua, Agustritus Pasrah Hati; Larosa, Tri Putra; Pratama, Panji Dika; Fauza, Ra'uf Harris; Husein, Amir Mahmud
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12787

Abstract

Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and data augmentation processes need to be considered in the future
Computer Vision-Based Intelligent Traffic Surveillance: Multi-Vehicle Tracking and Detection Husein, Amir Mahmud; Noflianhar Lubis, Kevi; Salim Sidabutar, Daniel; Yuanda, Yansan; Kevry; Waren, Ashwini
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13204

Abstract

The application of vehicle detection in real-time traffic surveillance systems is one of the challenging research fields with different objectives. One of the problems is the detection of many vehicles simultaneously in a video sequence sourced from CCTV cameras. In many works, the focus is only on detecting vehicle classes such as motorcycles, buses, trucks, and cars or special vehicles such as ambulances and others. In this research, we propose to apply 13 classes of vehicle types and implement YOLOv4 in the traffic surveillance task. More specifically, all classes are labeled, and then the YOLOv4 model is trained on 800 images and tested on 23 videos from three intersections in Medan City, namely Juanda Katamso Intersection, Gatot Subroto Intersection, and Uniland Intersection. Based on the test results, YOLOv4 proves successful in detecting many vehicles in frame-by-frame sequence with various types of vehicles. All vehicle detection data will be stored in the file.
SIAKAD Mobile With API Service To Improve Academic Services Husein, Amir Mahmud; Simanjuntak, Andre Juan; Sinaga, Candra Julius; Tampubolon, Mei Monica; Situmorang, Priskila Natalia C.
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13519

Abstract

Developing SIAKAD (commonly called SIAM, Student Academic Information System) Mobile using API Service to improve academic services for students is the goal of researchers doing so because it supports the implementation of education to create better information distribution services for everyone who wants access to it. And this also has an impact on academic performance, it is easier to organize lecturer attendance schedules, value recapitulation, and so on. Conventional SIAKAD (SIAM) which can be accessed via a computer or laptop has limited accessibility and practicality, which can hinder students from accessing academic information flexibly. Therefore, after researchers have examined and paid attention to several systems that can be implemented to assist users in accessing them, the development of SIAKAD in the form of a mobile application is a solution to increasing accessibility and ease of access to academic information. The API service is used as a communication bridge between the SIAKAD mobile application and the backend system. Through this, the Mobile Application can communicate (send requests and receive responses from the backend system) quickly and efficiently. But to shorten the application development time we use the SCRUM method and for the business process model, we use BPMN to create, design and design this application. The results of this study the authors see a compare of the time that can increase after using Mobile in access SIAKAD (SIAM).
Lung Cancer Classification Using Combination Of Efficientnet And Visual Geometry Group Algorithm Husein, Amir Mahmud; Astasachindra, Rishi; Sormin, Pedro Samuel; Lovely, Veryl; Gultom, Atap
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.13831

Abstract

Lung cancer is one of the leading causes of mortality All around the world. It is classified into three main types: Adenocarcinoma of the lung (ACA), Non-small cell lung cancer (N), and Squamous Cell Carcinoma of the lung (SCC). Lung Cancer Classification is crucial on development of effective treatments. This study aims to improve the accuracy of lung cancer classification through the integration of a hybrid model, which combines two Convolutional Neural Networks architectures, namely EfficientNet-B7 and VGG-16. A set of histopathology images was subjected to testing, with the data split into three categories: 60% for training, 30% for validation, and 10% for testing. Prior to use, each image underwent a preprocessing process, wherein it was resized to 256x256 pixels. The model test results achieved an accuracy, precision, recall, and F1-score of 98.73%, which is superior to the EfficientNet-B7 base model. The findings of this study demonstrate the potential of hybrid models to improve accuracy in lung cancer classification. The utilization of hybrid models has the potential to contribute significantly to the beginning diagnosis and appropriate Lung Cancer Therapies. Future research will focus on improving the model through the application of image segmentation techniques and expanding the scope of classification to other types of lung cancer. Optimization of the hybrid model architecture using novel techniques such as the attention mechanism or transfer learning will be conducted to improve the efficiency and accuracy of the model. Additionally, a system that can be integrated into clinical practice will be developed
Skin cancer classification using EfficientNet architecture Harahap, Mawaddah; Husein, Amir Mahmud; Kwok, Shane Christian; Wizley, Vincent; Leonardi, Jocelyn; Ong, Derrick Kenji; Ginting, Deskianta; Silitonga, Benny Art
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7159

Abstract

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.
Co-Authors Ambarwati, Lita Andika Andika Andika Rahmad Kolose Sumangunsong Andreas Simatupang Anugrah Putri, Gustie Vaniest Astasachindra, Rishi Banjarnahor, Prayoga Br Sihotang, Nurseve Lina Brandlee, Rio Christopher Christopher Damanik, Melky Eka Putra Dashuah, Ramonda Daulay, Tri Agustina Dodi Setiawan Fauza, Ra'uf Harris Feri Imanuel Fernandito, Peter Ginting, Deskianta Gracia, Andy Gulo, Befi Juniman Gulo, Steven Eduard Gultom, Atap Gunawan, Nico Hasibuan, Muhammad Haris Hendiko, Kennyzio HS, Christnatalis Hutauruk, Eben Kevin kevin Kevry Kosasi, Tommy Kwok, Shane Christian Larosa, Tri Putra Laurentius, Laurentius Leonardi, Jocelyn Linardy, Alvin Livando, Nicholas Lovely, Veryl Lubis, Fachrul Rozi Manik, David Hamonangan D. Mawaddah Harahap, Mawaddah Muhammad Arsyal, Muhammad Muhammad Khoiruddin Harahap Nainggolan, Yandi Tumbur Noflianhar Lubis, Kevi Ong, Derrick Kenji Phan, Gary Pratama, Panji Dika PUJI LESTARI Purba, Windania Purwanto, Eko Paskah Jeremia Salim Sidabutar, Daniel Shela Aura Yasmin Sihombing, Zein Adian Laban Silitonga, Benny Art Simanggungsong, Antonius Moses Simanjuntak, Andre Juan Simarmata, Allwin M Simarmata, Harry Binur Pratama Sinaga, Candra Julius Sinaga, Sutrisno Sinurat, Watas Sipahutar, Berninto Sirait, Agrifa Darwanto Siringo-Ringo, Dewi Sahputri Siti Aisyah Situmorang, Priskila Natalia C. Sormin, Pedro Samuel Syahputa, Hendra Tambun, Bella Siska Tambunan, Razana Baringin Daud Tampubolon, Hotman Parsaoran Tampubolon, Mei Monica Telaumbanua, Agustritus Pasrah Hati Tommy, Tommy Waren, Ashwini Waruwu, Seven Kriston William Chandra Willim, Alfredy Wizley, Vincent Yuanda, Yansan Yulizar, Dian Zagoto, Mariana Erfan Kristiani