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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) Perfecting a Video Game with Game Metrics CommIT (Communication & Information Technology) Jurnal Infra International Journal of Industrial Research and Applied Engineering Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Teknik Elektro: Ilmu dan Teknologi
Indar Sugiarto
Petra Christian University
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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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)
Publisher : Jurnal Infra

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Abstract

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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Abstract

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%.
Skill Upgrading untuk Meningkatkan Kompetensi Siswa dan Guru Di SMK Kristen Petra Handy Wicaksono; Indar Sugiarto; Tience Debora Valentina
Society : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 2 No. 2 (2022): Vol.2 No.2, April 2022
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/society.v2i2.216

Abstract

Kompetensi siswa dan guru SMK Kristen Petra perlu ditingkatkan untuk dapat menghadapi tantangan di masa mendatang. Setelah melakukan persiapan, pelatihan soft skill (dengan tema: pentingnya tanggung jawab dan berani menghadapi tantangan) dan hard skill (terkait pemrograman Programmable Logic Controller – PLC) dilakukan secara online, diikuti dengan penyerahan training kit PLC serta pendampingan untuk peserta (guru) yang akan mengikuti sertifikasi kompetensi bidang PLC. Para peserta menilai pelatihan – pelatihan tadi bermanfaat dan tepat sasaran untuk meningkatkan tanggungjawab dan keberanian menerima tantangan (53.3 % sangat setuju, dan 46.7 % setuju) serta untuk meningkatkan pengetahuan dan skill dalam pemrograman PLC (46.7 % sangat setuju, 46.7 % setuju). Sebuah training kit PLC juga telah dihibahkan ke SMK Kristen Petra untuk media eksperimen di sekolah. Pelatihan dan pendampingan terbukti efektif karena dua orang guru yang telah mengikuti pelatihan berhasil mendapatkan sertifikat dari BNSP setelah melalui uji kompetensi.
From Adaptive Reasoning to Cognitive Factory: Bringing Cognitive Intelligence to Manufacturing Technology Indar Sugiarto; Cristian Axenie; Jörg Conradt
International Journal of Industrial Research and Applied Engineering Vol 1, No 1 (2016)
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1186.697 KB) | DOI: 10.9744/JIRAE.1.1.1-10

Abstract

There are two important aspects that will play important roles in future manufacturing systems: changeability and human-machine collaboration. The first aspect, changeability, concerns with the ability of production tools to reconfigure themselves to the new manufacturing settings, possibly with unknown prior information, while maintaining their reliability at lowest cost. The second aspect, human-machine collaboration, emphasizes the ability of production tools to put themselves on the position as humans’ co-workers. The interplay between these two aspects will not only determine the economical accomplishment of a manufacturing process, but it will also shape the future of the technology itself. To address this future challenge of manufacturing systems, the concept of Cognitive Factory was proposed. Along this line, machines and processes are equipped with cognitive capabilities in order to allow them to assess and increase their scope of operation autonomously. However, the technical implementation of such a concept is still widely open for research, since there are several stumbling blocks that limit practicality of the proposed methods. In this paper, we introduce our method to achieve the goal of the Cognitive Factory. Our method is inspired by the working mechanisms of a human’s brain; it works by harnessing the reasoning capabilities of cognitive architecture. By utilizing such an adaptive reasoning mechanism, we envision the future manufacturing systems with cognitive intelligence. We provide illustrative examples from our current research work to demonstrate that our proposed method is notable to address the primary issues of the Cognitive Factory: changeability and human-machine collaboration.
Omni-Directional Mobile Robot Control using Raspberry Pi and Jetson Nano Evert Oneil; Indar Sugiarto
International Journal of Industrial Research and Applied Engineering Vol 4, No 2: OCTOBER 2019
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jirae.4.2.57-62

Abstract

The use of robots continues to increase in various fields in society today. Current developments require robots that are more effective and efficient in its application, especially in terms of its movement. This research is intended to design robots that move omni-directionally, making it easier to move in all directions by using the omniwheels, and the robots can detect simple object around them. The robot using 3 DC motors with encoder as feedback. System movement is controlled using Raspberry Pi 4, to move the robot to destination postition from user’s input. For robot to be able to detect certain object, the robot is equipped with infrared sensor for measure the distance and a camera for image processing purpose with jetson nano as a controller. By using inverse kinematics and odometry calculations for robot movement, it has an error of 9.51% on the x-axis and 8.12% on the y-axis at the robot's final position. The robot can detect objects using infrared sensors with error rate 0.87% and measure object sizes using a camera and image processing with error rate of 30.02% for object’s width readings and 41.8% for object’s height readings.
Hand Symbol Classification for Human-Computer Interaction Using the Fifth Version of YOLO Object Detection Sugiarto Wibowo; Indar Sugiarto
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8520

Abstract

Human-Computer Interaction (HCI) nowadays mostly uses physical contact, such as people using the mouse to choose something in an application. However, there are certain problems that people face in using conventional HCI. The research tries to overcome some problems when people use conventional HCI using the computer vision method. The research focuses on creating and evaluating the object detection model for classifying hand symbols. The research applies the fifth version of YOLO with the architecture of YOLOv5m to classify hand symbols in real time. The methods are divided into three steps. Those steps are dataset creation consisting of 100 images in each class, training phase, and performance evaluation of the model. The hand gesture classes made in the research are ‘ok’, ‘cancel’, ‘previous’, ‘next’, and ‘confirm’, the dataset is made by the researchers custom. After the training phase, the validation results show 93% for accuracy, 99% for precision, 100% for recall, and 99% for F1 score. Meanwhile, in real-time detection, the performance of the model for classifying hand symbols is 80% for accuracy, 95% for precision, 84% for recall, and 89% for F1 score. Although there are differences, it still acceptable for the research and can be improved in future research.
RANCANG BANGUN SISTEM METER LISTRIK PRABAYAR DENGAN PEMBAYARAN MENGGUNAKAN QRIS DI RUMAH KOST Gregorio Diovani Wahanie; Resmana Lim; Indar Sugiarto
Jurnal Teknik Elektro Vol. 16 No. 1 (2023): Maret 2023
Publisher : Institute of Research and Community Outreach

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jte.16.1.5-10

Abstract

Pada penelitian ini dikembangkan suatu sistem yang dapat memantau konsumsi listrik dengan metode memasang meter listrik digital berbasis mikrokontroler. Tidak seperti kWh Meter Prabayar PLN yang Offline, namun meter prabayar ini terhubung dengan internet/Online sehingga tidak memerlukan motode token, dan dapat dimonitor dan top-up pulsa listrik dapat dilakukan langsung secara online. Sistem ini nantinya akan terintergasi secara online melalui aplikasi payment gateway untuk keperluan transaksi secara non-tunai. Sistem ini terdiri dari mikrokontroler ESP32, bagian output terdiri dari LCD 16x2, buzzer, SSR dan LED, dan bagian input terdapat sensor PZEM untuk mengukur energi listrik yang digunakan. Sistem ini juga dapat dimonitor secara internet berbasis web. Hasil Pengujian akurasi pengukuran energi PZEM dengan modul pembanding sejenis SDM120 secara pembacaan dan Analisa matematis menujukan parameter yang mendekati sama. Pengujian pengisian pulsa berhasil melakukan pengisian ulang pulsa listrik sebesar 1 kWh dengan tarif yang dipasang sebesar Rp 1500,- per kWh. Web dashboard yang dikembangkan telah menunjukan hasil fungsi monitoring dan report dari sistem meteran yang dibuat.
Co-Authors Agustinus Bimo Gumelar Alexander Setiawan Andreas Prasetyo Andy Susanto Astri Yogatama Cristian Axenie Derry Pramono Adi Djoni Haryadi Setiabudi Eric Tirtana Evert Oneil Felix Pasila Frismanda Frismanda Gregorio Diovani Wahanie Handy Wicaksono Jehezkiel Hardwin Tandijaya Jörg Conradt Justinus Andjarwirawan Kartika Gunadi Kartika Gunadi Kevin Koesoemo Lauw Lim Un Tung Liliana Liliana Michael Anggreawan Alexander Mohammad Ismail Rahman Mulia Rudy Petrus Santoso Resmana Lim Rudy Adipranata Samuel Febrian Tumewu Saravanakumar Natarajan Siti Halimah Baki Steve Furber Sugiarto Wibowo Suryadi Thiang Thiang Tience Debora Valentina Timothy Joy Siswanto Victor Christian Yohanes TDS Yoken Adinata