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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Informatika Jurnal Teknik Elektro Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) CommIT (Communication & Information Technology) Jurnal Infra Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) International Journal of Industrial Research and Applied Engineering Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Teknik Elektro: Ilmu dan Teknologi
Indar Sugiarto
Petra Christian University
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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Journal : Jurnal Infra

 1 
Aplikasi Deteksi Jumlah Orang pada Area Indoor Untuk Mendukung Pelaksanaan PPKM dengan Metode YOLO Yoken Adinata; Kartika Gunadi; Indar Sugiarto
Jurnal Infra Vol 10, No 1 (2022)
Publisher : Universitas Kristen Petra

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Abstract

Lately, the world is being hit by the Covid-19 outbreak which has caused several activities and sectors to be hampered, one of which is the buying and selling sector, both inside shopping centers and outside. Finally a solution emerged where each store was given a limit on the number of visitors to minimize the spread of the Covid-19 virus. However, there is a problem, namely monitoring the number of visitors is done manually so it is less effective and efficient.Because of the need for an application that can monitor the number of visitors when it has reached a certain limit the application will send a notification to the user. The method that used in this application to detecting the number of people is You Only Look Once (YOLO) The application has a feature so that the user can configure the parameters that will be used.Overall, the detection system used still has ambiguity in detecting small objects so that sometimes they are not detected as people. The rest, the system runs without problems from large to medium sized objects. On the other hand, overall survey respondents are satisfied with this system, this can be seen in the results of a survey taken from 15 respondents regarding the assessment of how this application helps the implementation of PPKM, and getting an average score of 8.53 out of 10, while the assessment of the ease of use of the application and user interface is 8.6 out of 10 and 8.4 out of 10.
Klasifikasi Motif Batik menggunakan metode Deep Convolutional Neural Network dengan Data Augmentation Samuel Febrian Tumewu; Djoni Haryadi Setiabudi; Indar Sugiarto
Jurnal Infra Vol 8, No 2 (2020)
Publisher : Jurnal Infra

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Related researches before used Convolutional Neural Network (CNN) VGG to classify batik motif which limited only on geometrical pattern and implemented 2 augmentation consist of scale and rotation. Therefore, this research uses CNN Residual Network (Resnet) with 4 augmentation technique on both geometrical and non geometrical batik pattern.This research use (Resnet) as a main architecture of CNN to identify batik pattern. Batik motives for this research are from geometric category which is ceplok, kawung, lereng, nitik, and parang. And from nongeometri category are semen and lunglungan. Furthermore, the dataset will be applied scale, random erase, rotation, and flip augmentation to increase the quantity and variation of batik dataset.The results show that CNN Resnet with data augmentation on training dataset gives accuracy up to 84,52% on Resnet-18 and 81,90% on Resnet-50. furthermore, rotation augmentation adds 4,06%, random erase augmentation adds 9,38%, scale augmentation adds 6,52%, and flip augmentation adds 8,58% on accuracy
Sistem Presensi Mahasiswa Berbasis Animated QR Code Menggunakan Raspberry Pi Michael Anggreawan Alexander; Justinus Andjarwirawan; Indar Sugiarto
Jurnal Infra Vol 8, No 2 (2020)
Publisher : Jurnal Infra

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Now the use of technology in the field of information systems technology has expanded more widely in all fields, including in educational institutions. The current presence system that is currently running on the teaching and learning system in several educational institutions still uses conventional methods. This causes need more time in the presence process. Previous research has implemented a QR Code added to the Mobile System application. Presence features using the QR Code can be found on the Schedule and Absentee menus. The presence will automatically follow the number of meetings that have been conducted.So with this description will be done designing and creating a presence system workflow with optimizing QR Code that is equipped with Animated, so that it can run using the Android application and using the Raspberry Pi 4 system which is equipped with a camera to read the Animated QR Code, where the student data will be sent to the database using Internet connectivity.Based on the results of tests that have been done, students managed to get an Animated QR Code as presence data by logging in to the application that is incorporated in the Internet network and storing the presence data in the Database. Raspberry Pi Camera is able to read Animated QR Codes from the Android application by taking a few seconds using Raspberry Pi 4. Facilitates students in making presence using an Android-based application.
Penerapan Metode YOLO dan Tesseract-OCR untuk Pendataan Plat Nomor Kendaraan Bermotor Umum di Indonesia Menggunakan Raspberry Pi Eric Tirtana; Kartika Gunadi; Indar Sugiarto
Jurnal Infra Vol 9, No 2 (2021)
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Parking system is a common thing to find in public places. Parking system usually comes with a program that enables to detect and read license plates. With the advancement of technology, there are many systems / programs that are able to automatically detect and read license plates, but they come with a costly price. In this research, Raspberry Pi 4 will be used as the main platform. With the usage of Raspberry Pi, it is expected to reduce the cost needed to achieve the same output. However, by using Raspberry Pi, the hardware specifications are not as good as computer in general. In this research YOLO will be used to detect the license plate and Tesseract-OCR is used to read the characters on the license plate. From this research, it can be concluded that program can implement YOLO and Tesseract-OCR to detect and read public transportation license plates while being run on Raspberry Pi 4. To get the optimal results, the input image needs to be taken at daytime, using high quality camera, and implement only the necessary pre-processing methods.
Implementasi Sistem Pakar Deteksi Dini Resiko Penyakit Jantung Koroner Menggunakan Metode Backward Chaining dan Certainty Factor pada Android Andreas Prasetyo; Rudy Adipranata; Indar Sugiarto
Jurnal Infra Vol 9, No 2 (2021)
Publisher : Jurnal Infra

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Lifestyle in the modern era today can have a harmful impact on ourlives, one of which is coronary heart disease. Coronary heartdisease is the chronic and acute heart's inability to pumpoxygenated blood due to a lack of blood supply in the heart's musclecells.This research, using the backward chaining method, serves to tracethe facts and combine them with hypotheses that can strengthenthose facts. Certainty factor is the method used to measure thecertainty of the facts that have been made and provide results in theform of scoring to determine the level of accuracy of the facts thathave been given by experts.This research will produce an expert-system application that isuseful for early detection of coronary heart disease. Output will bein the form of grouped results, which is the results of a person'spotential risk of coronary heart disease, along with a percentagebased risk potential of developing said disease. It is hoped that thecreation of this system will make it easier for a person to detect thedisease based on daily habits, so it can later be used as the initialdiagnosis whether or not a person has said disease.
Chatbot untuk Website Utama UK Petra dengan Hidden Markov Model dan k-Nearest Neighbor untuk Generate Jawaban Kevin Koesoemo; Alexander Setiawan; Indar Sugiarto
Jurnal Infra Vol 9, No 2 (2021)
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Petra Christian University has various services for general information about university majors and student admissions, such as social media and WhatsApp. However, these services still limited by number and working time of operators as human. Therefore, with this chatbot, information about PCU can be found anytime. Chatbot Study by S. C. P & Afrianto needs method to match chatbot question with the dataset. This thesis uses two methods, namely kNN (k-Nearest Neighbor) and HMM (Hidden Markov Model) to solve these problem. In this chatbot, it will try to combine and compare these two methods, and see if it can produces answers that can be understood and in accordance with various difficulty questions given. The kNN is used as a classification for questions given to chatbot which approximately match with questions on the chatbot’s knowledge base. HMM is used to assemble answer words from the selected knowledge base. Chatbot’s answers will be tested in terms of validity of the answers by two respondents (Public Relation and Admission staff) also the length of time it takes to produce answers. The results of the chatbot with kNN has an accuracy of 64.44% (45 questions), with average system runtime of 0.08 seconds. While the results of chatbot with kNN-HMM produces random and irregular answers, with average system runtime of 0.12 seconds, cause by HMM which is a probability based method.
Klasifikasi dalam Pembuatan Portal Berita Online dengan Menggunakan Metode BERT Jehezkiel Hardwin Tandijaya; Liliana Liliana; Indar Sugiarto
Jurnal Infra Vol 9, No 2 (2021)
Publisher : Jurnal Infra

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Internet helps human by making various information from many online news platform accessible. But nowadays, there are a lot of news that can be accessed in different online news platform and needs to be categorized. The news that can be accessed in some of the sources don’t have high credibility about an event, because the publishers use false and misleading information to push their agendas. So in order to check the credibility of an event, it is needed to also read from other sources and not only from 1 source. However, this is not effective because the reader has to look for another news source with different URL address. In this research scraping will be done to retrieve the news that are available in a news platform. After the scraping process is done, the news will be classified to determine the category of the news. The method that will be used is Bidirectional Encoder Representations from Transformers. From the testing of this research, the news can be retrieved and classified. The testing with a pre-trained model indobenchmark /indobert-base-p1 get a very good result where the accuracy reaches 87.548%.
Analisis Sentimen Dari Keywords Yang Dimasukan Pengguna Di Twitter Indonesia Untuk Penunjang Pembelajaran Strategi Komunikasi Di Program Studi Ilmu Komunikasi Universitas Kristen Petra Dengan Metode Cnn-Bidirectional Lstm Andrianto Saputra Linardi Lie; Djoni Haryadi Setiabudi; Indar Sugiarto
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Abstract

To increase online media traffic, the first effort made by online media is to examine the trending phenomenon with the right marketing strategy. One of the methods that online media is used is a communication strategy that utilizes the sentiment analysis method. In reality, students of Communication Science Major at Petra Christian University are not optimally using sentiment analyst system because the sentiment analysis system for the Communication Studies Study Program (Netray) cannot be run by more than one student or is not used simultaneously and the price of the application is still not affordable if the students want to subscribe Netray. So a sentiment analysis system is needed to support the learning of the Communication Science Major at Petra Christian University. In previous related research, there was research that discussed the analysis of the #crowdfunding campaign on Twitter but there was not include sentiment analysis, there are only topological analysis, spatial analysis and others analysis. In addition, there are studies that use various deep learning methods of sentiment analysis, by researching CNN, DNN, RNN, Bi-Lstm, but none of them combine these methods. So it can be concluded that research will be made that analyzes sentiment analysis and combines deep learning methods. Sentiment analysis is the process of using text analytics to obtain various data sources from the internet and various social media platforms. Sentiment analysis can be utilized with artificial intelligence or with computing, because it is more efficient . Sentiment analysis can be complemented by methods from artificial intelligence systems, namely deep learning CNN-BILSTM. CNN-BILSTM is a combination of the two methods of CNN and bidirectional LSTM where CNN is the input layer and bidirectional LSTM is the layer that extracts features from the input. The dataset used in this application is retrieved from github by adopting the CC BY-NC (Common Creative Non Commercial) License. Data used in the deep learning model which contains a collection of Indonesian tweets containing neutral, positive, negative sentiments.From two testing this thesis using twitter as the online media. From the first test, 20 tweets were searched, the tweet contain "Shin tae yong” and yielded an accuracy of 30%. The second test was tested by 45 students of the Petra Christian University Communication Science Program at Petra Christian University Surabaya in the Q2.505 building where this application was tried and applied, after that the application was assessed with a satisfaction questionnaire which resulted in an average score of 4.01, so this application can meet the needs of the Petra Christian University Communication Science Program with the initial target of a satisfaction questionnaire of 3.75.
Aplikasi Sistem Pengontrolan Turtle Tub Untuk Pemeliharaan Kura-Kura Red Belly Nelsoni Dengan Arduino Kevin Pramana Pongmasak; Silvia Rostianingsih; Indar Sugiarto
Jurnal Infra Vol 10, No 2 (2022)
Publisher : Universitas Kristen Petra

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Having turtles, especially Red Belly Nelsoni turtle is very common, but usually the owner don’t really know how to properly taking care of their turtles, according to the parameters needed in turtle maintenance. The problem that the author wants to solve is by utilizing Blynk application and Internet of Things tools that has a function to control, monitor and maintain all parameters that needed in turtle maintenance, so that the owner of the turtle can more easily taking care of the turtle in the turtle tub. The test was carried out by giving 2 turtle tubs containing Red Belly Nelsoni turtles to 2 volunteers who carried out the turtle care and maintenance in different ways. From the result of the test carried out, the application has been able to help the volunteers in taking care of the turtles according to the parameter aspects that needed in turtle maintenance.
Co-Authors Agustinus Bimo Gumelar Alexander Setiawan Andreas Prasetyo Andrew Wijaya Andrianto Saputra Linardi Lie Andy Susanto Astri Yogatama Astri Yogatama Candra, Ranggastika Prahlada Cristian Axenie Derry Pramono Adi Djoni Haryadi Setiabudi Djoni Haryadi Setiabudi Edward Tanujaya Eric Tirtana Evert Oneil Felix Pasila Frismanda Frismanda Gondowardoyo, Sean Gregorio Diovani Wahanie Hafizah Kamarudin, Nazhatul Halim, Jason Handry Khoswanto Handy Wicaksono Handy Wicaksono Handy Wicaksono Iwan Halim Sahputra Jehezkiel Hardwin Tandijaya Joni Dewanto Jörg Conradt Justinus Andjarwirawan Kartika Gunadi Kartika Gunadi Kevin Koesoemo Kevin Pramana Pongmasak Lauw Lim Un Tung Liliana Liliana Michael Anggreawan Alexander Mohammad Ismail Rahman Mulia Rudy Petrus Santoso Petrus Santoso Pratomo, Hariyo Priambudi Setyo Resmana Lim Rudy Adipranata Samuel Febrian Tumewu Saravanakumar Natarajan Setyono Yudo Tyasmoro Silvia Rostianingsih Siti Halimah Baki Soesanto, Agnes Steve Furber Sugiarto Wibowo Suryadi Thiang Thiang Tience Debora Valentina Timothy Joy Siswanto Tumbelaka, Hanny Hosiana Victor Christian Yohanes TDS Yoken Adinata