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All Journal TEKNIK Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Capture : Jurnal Seni Media Rekam Jambura Journal of Informatics Building of Informatics, Technology and Science Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika (JUTIF) Business Economic, Communication, and Social Sciences Journal (BECOSS) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Eduvest - Journal of Universal Studies Finger : Jurnal Ilmiah Teknologi Pendidikan Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Predict stock prices using the Generative Adversarial Networks Mohammad, Saiful Azhari; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i2.11405

Abstract

Predicting the price of a stock is very difficult. Due to very volatile prices. Many traders incorrectly predict stock prices, forex or trading commodities. It takes an analysis of each price movement. The purpose of the analysis is to predict price movements. One of them is the use of indicators that seek to help predict prices. Currently the development of Artificial Intelligence (AI) has grown very rapidly. Machine learning which is part of AI is also used to predict prices. Stocks are data that are related to time. Just like the weather. If the stock is analyzed then the suitable method is the time series method. The method used is Deep Learning, namely Recurrent Neural Network (RNN). A recurrent Neural Network is the same as Artificial Neural Network (ANN). ANN performs the processing of sequential data. RNN does not discard past problem data information, but will also enter past information as input. This is what distinguishes RNN and ANN. In the Recurrent Neural Network, there is a Long Short Term Memory Algorithm, Gated Recurrent Unit (GRU). One of the algorithms that can be used to predict stock prices is the Generative Adversarial Network. This algorithm was modified before being used. In the GAN algorithm, there are Generators and Discriminators. Because stock is a process that is carried out in the presence of time or time series, the Generative is modified with Long Short Term Memory and Discriminator uses Long Short Term Memory
Review Star Hotels Using Convolutional Neural Network Sze, Edward; Santoso, Handri; Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11836

Abstract

Currently the Deep Learning algorithm is developing very rapidly, where its application has helped a lot in individuals and businesses. One of its uses in conducting any review can be used this method. The review used in this case is to review five-star hotels. The hotel image is used as input for a review. So from the image of the hotel, it can be immediately known the level of the hotel. Usually the review is done using good sentences with compliments that tend to be positive sentiments. Meanwhile, sentences in the form of complaints tend to have negative sentiments. This study does not use sentences in conducting a review and uses a simple method in conducting the review process. The use of images as input is classified into five classes, namely one-star hotel class, two-star hotel class, three-star hotel class, four-star hotel class and five-star hotel class. The purpose of this research is to conduct a review on five-star hotels with image as input and hotel review as the output of the Deep Learning algorithm process. Deep Learning algorithm process using Convolutional Neural Network (CNN). The datasets used are public datasets and private datasets. The use of these datasets is a way to get better training model results. So that the accuracy in reviewing the image becomes better. The results of this study resulted in an accuracy reaching 98.48%, while for Loss it reached 0.0554.
Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201 Saputra, Adi Dwifana; Hindarto, Djarot; 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.v8i1.11906

Abstract

Rice is a plant that can grow in the tropics. This plant can produce food that can meet the needs of the people of a country. This plant can grow well if it is cared for properly. If the planting has used good care, such as providing adequate water, adding good fertilizer, it can be ascertained that it will produce a lot of rice fruit after harvesting. This often causes concern if rice growers have given good care but often produce less rice fruit because rice plants are attacked by various diseases. This is what makes the problem, that rice plants are attacked by diseases. Before spraying diseases or pests, farmers should have an understanding of diseases in rice. This makes farmers not wrong in choosing drugs for farmers' rice. It is very vulnerable if farmers do not know about the rice disease. Therefore, it is necessary to observe what types of rice diseases attack rice plants. Observations are not enough just to take pictures with a camera. But it is necessary to carry out further analysis of rice diseases. The presence of information technology is now able to recognize any type. One of the machine learning technologies is able to detect rice diseases. One of these branches of machine learning is deep learning. By using a dataset that focuses on rice disease, the model generated from deep learning training is able to detect rice disease. The purpose of this research is to predict disease in rice leaves using deep learning, namely DenseNet. Training using DenseNet, namely DenseNet121, DenseNet169 and DenseNet201. Accuracy using DenseNet121 reached 91.67%, DenseNet169 reached 90%, and DenseNet201 reached 88.33%. The model training time takes 24 seconds.
Design and Development of Coffee Machine Control System Using Fuzzy Logic Hadianto, Eko; Amanda, Djaja; 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.v8i1.11917

Abstract

The food and beverage industry is currently rife in urban and outside cities. Many locations are used as places to sell drinks, especially coffee which is a native plant of Indonesia. Nowadays, coffee compounding requires good technology. There are many coffee processing machines on the market. The coffee machine is capable of making expresso coffee, latte coffee and others. This coffee machine also combines coffee ingredients, sugar and milk as a carrier for a delicious aroma. In addition, the water pressure from the coffee machine heating boiler, the strong pressure of the coffee machine piston also affects the results of making a cup of coffee, the stronger the pressure, the thicker the coffee produced and the slower the flow of water in the coffee machine. glass of water because basically the stronger the pressure applied to it. the coffee grounds, the tighter and tighter the gaps that the water itself will pass, as well as the thickness of the resulting coffee water will be more concentrated. With Fuzzy Inference, it is possible to determine the optimal pressure to be exerted by the coffee machine piston based on the weight of the coffee grounds (grams) on the coffee machine piston and the specifications of the type of coffee machine used. Determining the optimal pressure on the coffee grounds will affect the taste of the coffee water produced and the speed of making a cup of coffee. This study uses the optimal pressure on the piston using the fuzzy inference method. The purpose of this research is to create a simulation for evaluating the performance of a coffee machine using fuzzy logic to solve the problem of damage to the piston. The fuzzy approach in this research uses the fuzzy Takagi Sugeno Kang method.
Enterprise Architecture for Payment System Industry in Industrial Era 4.0 Prawira, Khairul Thamrin; 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.v8i1.11933

Abstract

Today online payments are in demand by most people. Because of the practicality of making payments digitally. This phenomenon has encouraged many industries in the field of finance to make breakthroughs in technology to develop marketing through platforms that have been built. Of course, the platform that is built is a platform that is following the business process requirements of the company. Business processes in the finance industry have become a digital payment platform, making companies in the finance sector carry out digital transformations. Processes that have been carried out manually have been replaced with digital and online processes that bring many opportunities to the finance industry. At the same time creating market opportunities for the company. Thus creating a very broad market for companies carrying out digital transformation. Communities benefit from the changes made by the company. The benefit is that people do not need to make payments or transfer funds. No need to queue up payments at the bank or payment counters. By simply clicking on the smartphone, the payment can go directly to the intended account. This revolution in digital finance transformation has both positive and negative impacts. Positive impacts such as the ease of transferring funds, and negative impacts if the company does not carry out protection or cyber security will have an impact on the company and the user community. Therefore, finance companies must protect or secure data while using the payment platform. A lot of education is done for the people who use the payment platform, where the platform must always update the version. For this reason, payment platform provider companies must be able to make continuous changes. Continuous improvement and continuous development make it a must for payment system service operator companies. Related to the description above, payment system companies must implement Enterprise Architecture for the benefit of the company. The result of Enterprise Architecture is a blueprint of all company production components or an Information Technology Blueprint. This is the goal of research on payment systems for companies carrying out digital transformation
Implementation of Governance in the Treated Water Industry using the Enterprise Architecture Framework Amanda, Djaja; 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.v8i1.11937

Abstract

Today, the development of information technology is very sophisticated in supporting the company's operations. It is undeniable that all companies take advantage of this technology, starting from service to customers to the company's internal operations. Water utility companies basically process raw water into clean water that is ready to be used for household purposes, industrial purposes, offices, and many other uses. Therefore, the processing industry is very much needed by the community. Moreover, it is state tax revenue in the non-oil and gas sector. Therefore, information technology is very necessary for controlling and operating at this water treatment company. Before implementing an information system or an ongoing information system, a guide is needed for implementing the system. All aspects of information technology that support the company's business serve as input in building an integrated system. Business process trimming is needed in order to build better performance. In terms of implementing enterprise architecture, the chosen framework is The Open Group Architecture Framework (TOGAF). while the method chosen is the Architecture Development Method (ADM). The purpose of this research is to apply an enterprise architecture to a water treatment company to produce a blueprint and an IT roadmap within five years to provide the best service for customers. The focus of the discussion on Governance Implementation in this company's system uses Waterfall-based Project Management. By combining the TOGAF ADM Framework in the domains of Architecture Change Management, Implementation Governance, and Migration Planning with the Project Management Body of Knowledge Method, it produces new methods that complement each other.
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.
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 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.
Co-Authors Agus Anwar Akbar, Achmad Fathurrazi Akbariani, Nurhaliza Vania Amanda, Djaja Andrew Andrew, Andrew Andriani, Febri Anggraini, Astria Febrian Aprilia, Novia Pramesti Dazki, Erick Devanda, Faustin Eko Hadianto, Eko Firmansah, R Arif Fransisca, Viviana Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Ito Wasito Karjadi, Daniel Avian Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mayatopani, Hendra Mohammad, Saiful Azhari Muhammad Alfathan Harriz Nana Sutarna Panna, Ariya Perdana, Nanda Praharto, Suwandhy Prawira, Khairul Thamrin Prijantono, Sonny Priswanto, Budi Purwadi Purwadi Purwadi Putri, Gladys Rahman, Ben Ramadhoni, Moehammad Richardus Eko Indrajit Rochadiani, Theresia Herlina Ruddin, Isra Sakti, Irwin Sandjaya, Julio Santoso, Billy Cahyo Saputra, Adi Dwifana Sari, Muh Masri Sembiring, Agustinus Sentosa, Steve Sucipto, Nadya Rudie Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Tauhid, Tauhid Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman Wijaya, Oktavianus Hendry