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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS)
Derwin Suhartono
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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The Application of Chatbot for Customer Service in E-Commerce Billy Wibowo; Helen Clarissa; Derwin Suhartono
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 3 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i3.6531

Abstract

The use of intelligent machines such as chatbot have been increasing each day. The main idea that we wanted to address in our paper is that with the capabilities that intelligent machines have it will be able to replace humans in doing a certain task and be able to bring better advantages in the long run. The focus of this study is that we are able to manipulate the ability of chatbot to impersonate how humans interact with each other and will enable it to play a role as to handle the customer service in e-commerce websites or applications which will be an alternative to replace the traditional customer service
Utilization of QR Code for Tracking Digital Expenses Gerardo Axel Lwiantoro; Farhan Putra Salsabil; Gregorius A. N. Aditantyo; Derwin Suhartono
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 2 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i2.6533

Abstract

Today, a lot of us have a lot of digital wallet services that we use. Because of that tracking our spending has become significantly harder because most people use more than one digital wallet. This study aims to find out how can we utilize QR code in the payment receipt at out digital transaction, to keep track on our transaction on various digital wallet. To help keep track of the expenses from the various digital wallet, we use QR code that is generated from the receipt of the transaction that contain the encrypted data of the transaction that occurs. This data can be scanned so it can be added to a personal budget tracker app so the user can manage their digital expenses in one view.
Psychological Stress Detection Using Transformer-Based Models Derwin Suhartono; Irfan Fahmi Saputra; Andhika Rizki Pratama; Gabriel Nathaniel
ComTech: Computer, Mathematics and Engineering Applications Vol. 15 No. 1 (2024): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v15i1.11105

Abstract

Stress is a significant mental health problem that results in a lack of concentration. It has been more widely identified through social media since people who are under stress usually post about their physical pain and tiredness. However, stress assessment through social media by professionals can be expensive and time-consuming. The research aimed to produce a stress detection system trained using a Twitter dataset to predict stress using the user’s input sentence. The experiments that were done in the research used transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) and Robustly Optimized BERT (RoBERTa). The research involved data pre-processing, model training, and model evaluation to ensure high-quality train data. Since the data were imbalanced, data trimming was performed in pre-processing to select data randomly until the balance matched. This process ensured the model’s effectiveness in the training and evaluation stages. The features used in these experiments were features from each pre-trained model. In evaluating the model, accuracy, loss, and F1 score were used as metrics. In the result, for BERT, accuracy reaches 0.848 with an F1 score of 0.847. Meanwhile, RoBERTa has an accuracy of 0.837 and 0.834. The results prove that BERT and RoBERTa can be used to classify stress with accuracy and an F1 score above 0.8. The experiment result shows that the BERT deep learning model can detect stress using the Twitter datasets.
Co-Authors Andhika Rizki Pratama Antoni Wiguna Bagas Pradipabista Nayoga Billy Wibowo Chintya De Christian Eka Saputra Daniel D. Albesta Farhan Putra Salsabil Ferlan Gisman Putra Gabriel Nathaniel Gerardo Axel Lwiantoro Gregorius A. N. Aditantyo Hans Christian Heinrich Reagan Salim Helen Clarissa Irfan Fahmi Saputra Ivan Christanno Jody Johansyah Maulana Michael L. Jonathan Mikhael Pramodana Agus Muhammad Jawad Ngarap Imanuel Manik Nicholas Daniel Pratamaputra Oktovianus Hardiawan Priscilla Priscilla Rini Wongso Ryan Adipradana Ryan Suryadi Veronica Ong William Surya Permana Yunggih Nursalim