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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 Building of Informatics, Technology and Science TEPIAN 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 Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Performance Comparison of Supervised Learning Using Non-Neural Network and Neural Network Hindarto, Djarot; Santoso, Handri
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol 11, No 1 (2022)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v11i1.40768

Abstract

Currently, the development of mobile phones and mobile applications based on the Android operating system is increasing rapidly. Many new companies and startups are digitally transforming by using mobile apps to provide disruptive digital services to replace existing old-fashioned services. This transformation prompted attackers to create malicious software (malware) using sophisticated methods to target victims of Android phone users. The purpose of this study is to identify Android APK files by classifying them using Artificial Neural Network (ANN) and Non-Neural Network (NNN). ANN is a Multi-Layer Perceptron Classifier (MLPC), while NNN is a method of KNN, SVM, Decision Tree. This study aims to make a comparison between the performance of the Non-Neural Network and the Neural Network. Problems that occur when classifying using the Non-Neural Network algorithm have problems with decreasing performance, where performance is often decreased if done with a larger dataset. Answering the problem of decreasing model performance, the solution is used with the Artificial Neural Network algorithm. The Artificial Neural Network Algorithm selected is Multi_layer Perceptron Classifier (MLPC). Using the Non-Neural Network algorithm, K-Nearest Neighbor conducts training with the 600 APK dataset achieving 91.2% accuracy and training using the 14170 APK dataset decreases its accuracy to 88%. The use of the Support Vector Machine algorithm with the 600 APK dataset has 99.1% accuracy and the 14170 APK dataset has decreased accuracy to 90.5%. The use of the Decision Tree algorithm to conduct training with a dataset of 600 APKs has an accuracy of 99.2% and training with a dataset of 14170 APKs has decreased accuracy to 90.8%. Experiments using the Multi-Layer Perceptron Classifier have increased accuracy performance with the 600 APK dataset achieving 99% accuracy and training using the 14170 APK dataset increasing the accuracy reaching 100%.
Pelatihan Membuat Ruang Belajar Metaverse Bagi Guru-Guru Di Wilayah Tangerang Selatan Heni Jusuf; Magdalena, Maria; Istiyowati, Lucia Sri; Dazki, Erick; Santoso, Handri
Jurnal Pengabdian kepada Masyarakat UBJ Vol. 7 No. 1 (2024): January 2024
Publisher : Lembaga Penelitian Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/p86vz820

Abstract

After covid passed, many schools innovated in the learning process, so that the learning process could still run well. Students who have become accustomed to online learning need to be innovative again after COVID-19 has passed. The use of the metaverse has now been widely discussed for use in the learning process. Metaverse, a 3D digital space mixed with the real and virtual worlds, has been heralded as a future educational trend with great potential. However, as an emerging item, existing studies rarely discuss the metaverse from an educational perspective, so far, the metaverse is still used for games or product promotion. The community service that will be carried out is, so that teachers understand the concept of the metaverse, explain the role of the metaverse in the world of education and learning, design and build a simple metaverse ecosystem, operate metaverse applications, manage metaverse administration, choose pedagogic models that are suitable in the metaverse and represent the results of metaverse design. Thus, teachers are expected to make online learning fun. Training is held for ten (10) meetings, and each meeting is held for seven (7) hours. The results of the 100% training of participants consisting of 23 teachers ranging from kindergarten teachers to high school teachers, succeeded in creating a virtual learning space in the metaverse world.
Penerapan Gamma Correction Dalam Peningkatan Pendeteksian Objek Malam Pada Algoritma YOLOv5 Fransisca, Viviana; Santoso, Handri
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3553

Abstract

YOLOv5 (You Only Look Once) is a popular object detection method used in the field of computer vision. YOLOv5 is often used to detect objects in images and videos in real-time with high speed and accuracy. This method is easy to use because it is open-source, so it can be directly used to create a model that fits the object you want to detect. YOLOv5 can easily recognize objects detected during the day, but this method has difficulties when it is made to detect objects at night. With the improvisation of the YOLOv5 method which can accurately detect objects at night, other researchers who wish to conduct research related to object detection at night can use the exact technique to produce more accurate object detection. This study uses the Gamma Correction method by adding a Gamma of 2 so that the trained image dataset becomes bright and YOLOv5 can recognize objects at night more easily. As a result, an improvised technique using Gamma Correction can make YOLOv5 recognize objects and make detections at night more accurately, where the average accuracy obtained before improvisation is 0.846, while after improvisation the results obtained are 0.918. From these average results, it can be stated that the gamma correction method can improve the accuracy results in object detection on YOLOv5
Personal Training with Tai Chi: Classifying Movement using Mediapipe Pose Estimation and LSTM Suhandi, Vartin; Santoso, Handri
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5536

Abstract

This research aims to tackle challenges in the practice of Tai Chi Bafa Wubu (BWTC), where limited access to trained instructors and daily schedules hinder training consistency. The proposed approach combines Human Pose Estimation technology using Mediapipe with Long Short-Term Memory (LSTM) models to classify BWTC movements. This method utilizes video datasets collected from the internet and augmented to train LSTM models, focusing on An, Ji, and Zhou movements. Experimental results show that the model can predict movements with high accuracy in training and direct user trials. The development of these techniques facilitates more effective self-training in Tai Chi, leveraging advanced AI technology to improve movement supervision and user movement interpretation accuracy. This study not only offers a practical solution to enhance Tai Chi training efficiency and accessibility but also explores the potential application of pose estimation technology and machine learning in broader sports movement monitoring and evaluation. It is expected that this research will make a significant contribution to health and fitness by enabling individuals to independently practice Tai Chi with technological guidance, promoting better mental and physical health among the general public.
Digital Marketing: A Case Study of Social Media Marketing of Indonesia Real Estate Companies Widjaja, Herman; Santoso, Handri
Business Economic, Communication, and Social Sciences Journal (BECOSS) Vol. 6 No. 2 (2024): BECOSS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/becossjournal.v6i2.11794

Abstract

Real Estate industry, like other industries, is also heavily influenced by digital marketing especially the social media. Websites, Facebook, Instagram and YouTube become necessity in modern marketing of real estate. Indonesia’s real estate industry is a dynamic industry considering the country’s economy growth, population size and growth. Although several research has been conducted in this area, the topic focusing on Indonesia’s real estate Social Media Management System (SMMS) is still very limited. The qualitative comparative study is intended to explore and compare social media marketing strategy among top developers in Indonesia, and how they utilize the platforms to distribute marketing content and company’s other information. The data are collected from observation of the companies’ official websites and 138 accounts in YouTube, Facebook and Instagram. The study shows that each company has different strategy, depends on project locations, product / project size, project / product lifetime, target audience (prospects, affiliates, public in general, community etc), project’s ownership structure (fully owned, joint venture, franchise), sales / recurring / operational, management of social media team. Among the surveyed platforms, Instagram has been the most popular to distribute sales information for either recurring products, sales products group of products and even corporate or general public information.
Super Resolution Generative Adversarial Networks for Image Supervise Learning Lupitha, Mariska; 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.v7i2.11373

Abstract

The E-Tilang application system has been widely used to support modern traffic, whereas protocol roads in big cities in Indonesia are already widely used. In principle, the plate number detection tool uses image recognition for detection. Image number plates on vehicles cannot always be read clearly, this is what causes the detection method to be a problem if the image plate number is further processed. The method for processing the plate number image uses deep learning and computer vision methods. For the condition of the image plate number that is not clear, the process of improving the image resolution from low resolution to high resolution is carried out, by applying Generative Adversarial Networks. This method consists of two main parts, namely Generate and Discriminator. Generate serves to generate an image and the Discriminator here is to check the image, can the image plate number be read or not? So that if the image plate number cannot be read, then the process is carried out again to the Generator until it is received by the Discriminator to be read. The process does not end here, the results will be carried out in the next process using Convolutional Neural Networks. Where the process is to detect the plate number image according to the classification of the plate number according to the region. The point is that an unclear image becomes clear by increasing the resolution from low resolution to high resolution so that it is easily read by the Convolutional Neural Network (CNN) algorithm so that the image is easily recognized by the CNN Algorithm. This becomes important in the CNN algorithm process because it gets the processed dataset. To produce a good model, preprocessing of the dataset is carried out. So that the model can detect the image well in terms of model performance.
Style Transfer Generator for Dataset Testing Classification Wedha, Bayu Yasa; Karjadi, Daniel Avian; Wedha, Alessandro Enriqco Putra Bayu; 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.v7i2.11375

Abstract

The development of the Generative Adversarial Network is currently very fast. First introduced by Ian Goodfellow in 2014, its development has accelerated since 2018. Currently, the need for datasets is sometimes still lacking, while public datasets are sometimes still lacking in number. This study tries to add an image dataset for supervised learning purposes. However, the dataset that will be studied is a unique dataset, not a dataset from the camera. But the image dataset by doing the augmented process by generating from the existing image. By adding a few changes to the augmentation process. So that the image datasets become diverse, not only datasets from camera photos but datasets that are carried out with an augmented process. Camera photos added with painting images will become still images with a newer style. There are many studies on Style transfer to produce images in drawing art, but it is possible to generate images for the needs of image datasets. The resulting force transfer image data set was used as the test data set for the Convolutional Neural Network classification. Classification can also be used to detect specific objects or images. The image dataset resulting from the style transfer is used for the classification of goods transporting vehicles or trucks. Detection trucks are very useful in the transportation system, where currently many trucks are modified to avoid road fees
Compare VGG19, ResNet50, Inception-V3 for Review Food Rating Andrew, Andrew; 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.v7i2.11383

Abstract

The food industry is undergoing a phase of very good improvement, where business actors are experiencing very rapid growth. Creative ideas are many and creative on several social media. When an online business is growing rapidly, many managers in the food sector market their products through online media. So it is quite easy for customers to place orders via mobile. Especially during the COVID-19 pandemic, where a ban on gatherings has become a government recommendation for many food business actors to sell online. Since then, almost all food industry players have made their sales online. There are many advantages of doing business online. The food served is in the form of pictures that attract market visitors so that it can create its own charm. Food is just a click away to order, and the order comes. No need to queue and everything has been delivered to the ordered goods. After the ordered goods arrive, the customer reviews the food or drink. Because customer reviews are the result of customer ratings. The result of the review is one of the sentiment analyses, which in this study is in the form of a review of the images available on the display marketplace. The method used is Convolutional Neural Network. The dataset will be extracted features and classifications. The research will do a comparison using VGG19, ResNet50, and Inception-V3. Where the accuracy of VGG19 = 96.86; Resnet50 : 97.29; Inception_v3 : 97.57.
CycleGAN and SRGAN to Enrich the Dataset Priswanto, Budi; 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.v7i2.11384

Abstract

When developments in the field of computer science are growing rapidly. For example, the development of image or video predictions for various fields has been widely applied to assist further processes. The field of computer vision has created many ideas about processing using deep learning algorithms. Sometimes the problem with using deep learning or machine learning is in the availability of the dataset or the unavailability of the dataset. Various methods are used to add to or enrich the dataset. One way is to add an image dataset by creating a synthetic image. One of the well-known algorithms is Generative Adversarial Networks as an algorithm for generating synthetic images. Currently, there are many variations of the GAN to around 500 variants. This research is to utilize the Cycle GAN architecture in order to enrich the dataset. By doing GAN as a synthetic image generator. This is very important in procuring image datasets, for training and testing models of Deep Learning algorithms such as Convolutional Neural Networks. In addition, the use of synthetic images produces a deep learning model to avoid overfitting. One of the causes of the overfitting problem is the lack of datasets. There are many ways to add image datasets, by cropping, continuously rotating 90 degrees, 180 degrees. The reason for using Cycle Generative Adversarial Networks is because this method is not as complicated as other GANs, but also not as simple. Cycle GAN synthetic images are processed with Super Resolution GAN, which aims to clarify image quality. So that it produces a different image and good image quality.   
Gesture Recognition using Conditional Generative Adversarial Networks Putri, Gladys; 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.v7i2.11397

Abstract

Sign language is very useful for giving signs to communication partners, in this case, signs are not only for people with disabilities but can also be used by normal people. Even in children's or adult games, sign language is used as a language or a means of communication with one another. Recognition of sign language using a computer by doing several methods, because the computer does not recognize the image of a sign with a certain meaning. Therefore, it is necessary to train computers to recognize these signs. One of the fields that discusses gesture image recognition is the field of Computer Vision. Where the science of computer vision is able to process the image. In addition, in image processing, it is necessary to carry out deep learning processes such as Convolutional Neural Networks. In the Convolutional Neural Network algorithm, there are also many methods or architectures such as VGG16, VGG19, ResNet-50, DenseNet, Inception_V3, and many more. The use of architecture is used in accordance with existing needs. Therefore, the choice of architecture will determine the model is built or not to build from scratch, only transfer learning or pre-train. Pre-training is done by using the initial model and then using it only. Or do some training. The purpose of this study was to detect sign language using the Generative Adversarial Network (GAN). Actually, the Generative Adversarial Network method is widely used in making synthetic images, but this time the Generative Adversarial Network can also detect images from sign language.
Co-Authors Agus Anwar Agustiana, Puspa Riri 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 Han, Hanif Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Ito Wasito Karjadi, Daniel Avian Karjadi, Markus Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mohammad, Saiful Azhari 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 Sentosa, Steve Sucipto, Nadya Rudie Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Tauhid, Tauhid Teddy Mantoro Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman Wijaya, Oktavianus Hendry