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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing International Journal Software Engineering and Computer Science (IJSECS) Abdi Implementasi Pancasila: Jurnal Pengabdian kepada Masyarakat
Hindarto, Djarot
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Public Health Social Sciences

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Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection Suherman, Endang; Hindarto, Djarot; Makmur, Amelia; Santoso, Handri
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.v8i1.11944

Abstract

Rice is a staple food for people in tropical countries. Indonesia is a country that needs a lot of rice for its people in providing food. This country has implemented various ways to plant rice properly. Many agricultural fields have implemented harvests up to three times a year, due to the role of technology which has helped a lot in agriculture. Planting to harvest already uses advanced technology and tools. A good rice harvest can improve the welfare of the surrounding community. Meanwhile with lots of rice products because many rice plants produce with lots of rice. The type of rice from different regions of origin, the yield of rice is also different from other regions of origin. But with advances in technology, it is possible to plant rice whose types of plants come from other regions. The rice sold to the public varies, so that people who are unfamiliar with the types of rice find it difficult to detect the types of rice. Machine learning is present in detecting various kinds of rice. Machine learning, especially deep learning can make better detection, because one of the deep learning methods works similar to the human brain. In the human brain there are millions or even billions of neurons. This research uses neural networks in experiments using public datasets. Experiments using Artificial Neural Networks achieve an training accuracy of 98.2%, loss: 0.2351. It takes about 10 minutes of training. Testing accuracy reaches accuracy: 96%, loss: 0.6641. By conducting experiments using the Convolution Neural Network, it achieves an accuracy of 99.3% and the training time requires around 18 hours. The purpose of this research is to classify the rice image dataset and detect the rice image.
Comparison of Accuracy in Detecting Tomato Leaf Disease with GoogleNet VS EfficientNetB3 Saputra, Adi Dwifana; Hindarto, Djarot; Rahman, Ben; Santoso, Handri
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.v8i2.12218

Abstract

Tomato diseases vary greatly, one of which is tomato leaf disease. Some variants of leaf diseases include late blight, septoria leaf, yellow leaf curl virus, bacteria, mosaic virus, leaf fungus, two-spotted spider mite, and powdery mildew. By knowing the disease on tomato leaves, you can find medicine for the disease. So that it can increase the production of tomatoes with good quality and a lot of quantity. The problem that often occurs is that farmers cannot determine the disease in plants, they try to find suitable herbal medicines for their plants. After being given the drug, many plants actually died due to the pesticides given to the tomato plants. This is detrimental to tomato farmers. This problem is caused by incorrect disease detection. Therefore, this study aims to solve the problem of disease detection in tomato plants, in a more specific case, namely tomato leaves. Detection in this study uses a deep learning algorithm that uses a Convolutional Neural Network, specifically GoogleNet and EfficientNetB3. The dataset used comes from kaggle and google image. Both data sets have been pre-processed to match the data set class. Image preprocessing is performed to produce appropriate image datasets and improve performance accuracy. The dataset is trained to get the model. The training using GoogleNet resulted in an accuracy of 98.10%, loss of 0.0602 and using EfficientNetB3 resulted in an accuracy of 99.94%, loss: 0.1966.
Application of Enterprise Architecture in Digital Transformation of Insurance Companies Prawira, Khairul Thamrin; Hindarto, Djarot; Indrajit, Eko
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.v8i2.12302

Abstract

Implementation of enterprise architecture is a major requirement for companies that want to develop business processes according to their needs. Business architecture is the company's initial plan to support the company's operations. An insurance company is a company that provides insurance services. Insurance is a form of contract in which the guaranteed party pays a premium to the insurance company. Insurance companies provide payment guarantees in the event of certain risks that are guaranteed in the insurance contract. Therefore, this company must think about operational forms to provide the best service for its customers. Structuring business processes starting from the sales process, administrative processes, claims processes, to financial processes which are the most vital processes. Insurance companies are different from other financial services companies, such as banking companies, fintech companies and others. The insurance company's unique process is to provide guarantees to its customers in carrying out risk protection. Before starting to implement enterprise architecture, the company already has a business architecture blueprint which is the enterprise architecture of a company. The design begins with running architectural business processes, architectural applications, architectural databases and architectural technology. Enterprise architecture implementation certainly cannot be separated from project management. Because project management is a process that manages the project so that the project becomes more organized in its implementation
Smartphone Application for Support Library Operations in the Internet of Things Era Hadianto, Eko; Hindarto, Djarot; Santoso, Handri
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.v8i2.12306

Abstract

The library can be referred to as a storage place for books and other references. The reference can be in the form of digital storage media. Libraries if not managed properly will cause chaos in the library organization. Many books were lost due to the entry and exit of books that were out of control. Currently, the library is not only a place to store books but can be maximized by managing and adding other digital devices. The use of Radio Frequency Identification (RFID) in libraries adds sophistication to the management of books and library items. In addition, currently many libraries have taken advantage of Internet of Things Technology, by adding various sensors and integrating with cloud-based storage devices. It provides a service that makes it easy for library members to find and track the current whereabouts of books. This research does not only create a library by providing hardware in the form of sensors to be installed in the library. This paper also proposes the use of smartphones as an alternative in replacing sensor hardware. This study uses a QR Code sensor to match the book you are looking for and simulates dancing a book in blocks and bookcases. with augmented reality. The purpose of this research is to make a smart library prototype to make it easier for library members to find books or other references. The results of the experiment to find books and DVDs that have been carried out achieve an accuracy of 83.33%.
Implementation of Cyber-Security Enterprise Architecture Food Industry in Society 5.0 Era Sari, Ratih Titi Komala; Hindarto, Djarot
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.v8i2.12377

Abstract

The application of Enterprise Architecture is an important topic in the development of the food industry in the Society 5.0 Era. Enterprise Architecture is used to integrate and optimize corporate information systems so as to generate higher business value. This study aims to evaluate the effectiveness of Enterprise Architecture implementation in improving the performance of the food industry in Era Society 5.0 and implementing Cyber-Security as a defense against the system to be implemented. This study uses a case study method by collecting data from several companies in the food industry. The data collected includes information about the implementation of Enterprise Architecture, business performance, and factors that influence the successful implementation of Enterprise Architecture. The results of the study show that the implementation of Enterprise Architecture has helped companies improve their business performance, especially in terms of operational efficiency, better decision making, and the ability to adapt to changes in the business environment. Factors that influence the successful implementation of Enterprise Architecture include management support, involvement of business users, and availability of resources. In conclusion, the application of Enterprise Architecture can help the food industry in Era Society 5.0 improve its business performance. However, the implementation of Enterprise Architecture must be accompanied by strong management support, greater involvement of business users, availability of adequate resources and adequate Cyber-Security. The novelty of this research is implementing Cyber-Security as protection in implementing Enterprise Architecture.
Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects Suherman, Endang; Rahman, Ben; Hindarto, Djarot; Santoso, Handri
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.v8i2.12378

Abstract

Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that is well-known for its effectiveness in image recognition tasks, while DETR utilizes Transformers to study object representations directly from images. We tested our system performance on the COCO dataset and demonstrated that ResNet-50 + DETR achieves a better level of accuracy than DETR models that do not use ResNet-50. In addition, we also show that ResNet-50 + DETR can detect objects more quickly than similar traditional CNN models. The results of our research show that the use of ResNet-50 in the DETR system can improve object detection performance in images by about 90%. We also show that using ResNet-50 in DETR systems can improve object detection speed, which is a huge advantage in real-time applications. We hope that the results of this research can contribute to the development of object detection technology in images in the world of computer vision.
Drowsy Detection in the Eye Area using the Convolutional Neural Network Wedha, Alessandro Benito Putra Bayu; Rahman, Ben; Hindarto, Djarot; Wedha, Bayu Yasa
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.v8i2.12386

Abstract

Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.
Proposed Enterprise Architecture on System Fleet Management: PT. Integrasia Utama Wedha, Alessandro Benito Putra Bayu; Rahman, Ben; Hindarto, Djarot; Wedha, Bayu Yasa
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.v8i2.12387

Abstract

An information technology consulting firm that specializes in Global Positioning Systems provides fleet management services for many of its clients. The systems currently used by companies require more advanced modernization to ensure optimal service delivery. To overcome this challenge, a proposed enterprise architecture on system fleet management is presented in this paper. The proposed enterprise architecture is a comprehensive solution that includes the necessary hardware, software and operational processes to improve fleet management services. The proposed architecture is based on the Enterprise Architecture, which enables the integration of various systems and applications used by companies. The proposed architecture includes modules for vehicle tracking, fuel management, maintenance scheduling and driver performance monitoring. These modules work together to provide real-time data on fleet operations, enabling companies to make informed decisions regarding their fleet management services. The proposed architecture also incorporates an easy-to-use interface that simplifies the fleet management process and enhances customer satisfaction. The proposed system is scalable and easily adaptable to meet service requirements across multiple customers. In conclusion, the proposed enterprise architecture for system fleet management provides a comprehensive solution to the current challenges faced by companies as a corporate fleet service provider. The proposed architecture will improve service, reduce costs, and increase customer satisfaction.
Diagnostic on Car Internal Combustion Engine through Noise Sjah, William Surya; Rahman, Ben; Hindarto, Djarot; Wedha, Alessandro Benito Putra Bayu
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.v8i2.12392

Abstract

Internal Combustion Engines are known for their unique sound characteristics. Through these sound characteristics, an experienced car mechanic will be able to diagnose the type of engine damage just by listening to the sound. This reduces the need to disassemble components to pinpoint machine faults which also contributes to a significant reduction in overall repair time. The main aim of this paper is to build a process to identify faulty machines through engine noise analysis with visual data to determine machine faults at an early stage. By capturing various types of engine sounds, data visualization uses healthy engine sounds and broken engine sounds obtained from cars as well as various types of broken engine sounds that are usually found in vehicles. This audio data will be used in audio signal processing combined with a linear regression classification algorithm. Visualization data can distinguish various types of sounds that are commonly found in damaged or damaged engines such as clicks, ticks, knocks and other types of sounds to determine the types of damage that are usually found in internal combustion engines. The data used comes from Kaggle, which is public data which is widely used as general data for data science activities. Visually, data from vehicle engines can be seen from the data on which car brand is the best in terms of sound. The results using linear regression show the R-squared score (R^2) or also called the coefficient of determination reaching 91.95%.
Detects Damage Car Body using YOLO Deep Learning Algorithm Gustian, Yonathan Wijaya; Rahman, Ben; Hindarto, Djarot; Wedha, Alessandro Benito Putra Bayu
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.v8i2.12394

Abstract

This journal presents a technique for detecting scratches, cracks and other damage to car bodies using machine learning methods. This method is used to improve process efficiency and checking accuracy and can also reduce the cost and time required for manual inspection. The method includes collecting image datasets of cars in good and damaged condition, followed by preprocessing and segmentation to separate damaged or damaged car parts. not broken. Then, it is followed by a deep learning algorithm, namely You Only Look Once, or Faster Region-based Convolutional Neural Networks, which is used to build a detection model. The model is trained and tuned using the collected data, then evaluated using the test data to measure the accuracy and precision of the detection results. The experimental results show that the proposed method achieves high accuracy and efficiency in detecting scratches, cracks, and other defects on the car body, with precision of an average of more than 70%. This method provides a promising approach to improving the car body inspection process which can be used by taxi companies to help inspect and maintain vehicles more quickly and accurately, to help with insurance, avoid accidents and so on.
Co-Authors Afarah, Sabrina Fajrul Afarini, Nihayah Alim, Syariful Alwi, Muchamad Nurdin Amalia, Nadia Amanda, Djaja Andrianingsih Anna Marina Arie Gunawan Arko Djajadi Dazki, Erick Desmana, Satriawan Djunarto Eko Hadianto, Eko Endah Tri Esti Handayani Fauziah Fauziah Fauziah Gustian, Yonathan Wijaya Handayani, Endah Tri Esti Haryono Haryono Hendrata, Ferial Indrajit, Eko ISWAHYUDI ISWAHYUDI Judijanto, Loso Mochamad Hariadi Nurfaizi, Kelfin Pamungkasari, Panca Dewi Prawira, Khairul Thamrin Putra, Tri Dharma R. Eko Indrajit Rahman, Ben Ramadhan, Duta Pramudya Ratih Titi Komalasari Ririn Ikana Desanti Santoso, Handri Saputra, Adi Dwifana Saputra, Adi Dwifana Saputra Sari Ningsih Sentot Imam Wahjono Setiawan, Adrian Tri Sholihati, Ira Diana Sjah, William Surya Suherman, Endang Sze, Edward Usman, Sabrina Fajrul Ula Wahyuddin, Mohammad Iwan Wedha, Alessandro Benito Putra Bayu Wedha, bayu Yasa WIJANARKO, SIGIT Wijaya, Yunan Fauzi Wirayasa, I Ketut Adi Yudhakusuma, Dudi Yulianto, Lili Dwi