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All Journal Jurnal Transformatika Engagement: Jurnal Pengabdian Kepada Masyarakat The Indonesian Journal of Computer Science
Theresia Herlina Rochadiani
Pradita University
Religion Arts Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Social Sciences

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Sentiment Analysis of YouTube Comments Toward Chat GPT Theresia Herlina Rochadiani
Jurnal Transformatika Vol 21, No 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.7033

Abstract

Sentiment analysis is used for analyzing the emotions and attitudes expressed in text data. In this study, sentiment analysis is used to understand people’s enthusiasm toward Chat GPT. The primary objective of this study is to investigate the acceptance of people of new artificial intelligence technology, Chat GPT, that may change the future. To get a deep understanding of it, a large dataset of user comments from YouTube is collected and then data pre-processing is done by removing stop words, punctuations, and irrelevant information. Using Text Blob and VADER approaches, comments are classified into positive, neutral, and negative categories. The result shows that most users have a positive sentiment to receive and use Chat GPT. The contribution of this study is to provide insights into the sentiment of people’s response to Chat GPT, which can inform user acceptance of the language model development and give guide its future applications.
Efforts to Improve the Welfare of Ornamental Fish Farmers in Kalipaten Village Through the Implementation of LoRaWAN-Based IoT Technology William Widjaja; Theresia Herlina Rochadiani; Handri Santoso; Ninuk Yasmarini; Sherensia Putri Angeliani; Gabriel Alexander
Engagement: Jurnal Pengabdian Kepada Masyarakat Vol 7 No 2 (2023): November 2023
Publisher : Asosiasi Dosen Pengembang Masyarajat (ADPEMAS) Forum Komunikasi Dosen Peneliti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29062/engagement.v7i2.1339

Abstract

The ornamental fish business is currently one of the popular businesses in the community. The relatively fast reproduction cycle, around 0.5 – 1.5 months, with a relatively high selling price, makes this ornamental fish business much in demand by the public. The maximum utilization of technology will help MSMEs in increasing their income. This service activity aims to build a LoRaWAN-based IoT system for ornamental fish farming along with a mobile-based ornamental fish monitoring application to help manage ornamental fish livestock, which ultimately has an impact on improving the quality of ornamental fish and the income of CV Home Aquafish partners. The method utilizes a service-learning approach through stages: Identify, design, and build a LoRaWAN-based IoT and mobile-based monitoring system, implementing, mentoring, and measuring the effectiveness of the LoRaWAN device in improving the quality of ornamental fish and the income of CV Home Aquafish partners. As a result, LoRaWAN can effectively help minimize mortality in ornamental fish seedlings so that the quality of the fish is maintained. The income of CV Home Aquafish's ornamental fish nursery partners in Kalipaten Village, Gading Serpong, Tangerang also increases.
Machine Health in a Click: A Website for Real-Time Machine Condition Monitoring Theresia Herlina Rochadiani; Handri Santoso; Novia Pramesti Aprilia; Justin Laurenso; Vartin Suhandi
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3592

Abstract

Globalization in the current digital era has made it easier to use information technology to obtain fast and accurate information. One source of information is a website that can be used to monitor machine conditions in the industry. A good machine maintenance strategy is needed to maintain and increase machine productivity. Therefore, this research aims to build a website to monitor machine conditions in real-time. The machine condition is monitored using sushi sensors to track parameters such as temperature, acceleration, and velocity. Deep learning analysis is then used to identify anomalies in the machine. Using the SCRUM method, this website was successfully built. From the results of tests carried out using unit testing and integrated testing, every feature on this website can run well and according to user needs.
Pendekatan Transfer Learning Untuk Klasifikasi Tangisan Bayi Dengan Imbalance Dataset Theresia Herlina Rochadiani
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3834

Abstract

Klasifikasi tangisan bayi dapat dimanfaatkan untuk mengidentifikasi masalah kesehatan bayi dan memenuhi kebutuhan bayi dengan cepat. Dalam studi ini, teknik transfer learning, dengan model terlatih YAMNet, diterapkan untuk klasifikasi bayi dengan dataset terbatas dan tidak seimbang. YAMNet, sebuah model Convolutional Neural Network khusus untuk analisis audio, mengatasi keterbatasan metode tradisional yang bergantung pada interpretasi manusia. Dengan mempelajari fitur-fitur audio secara otomatis, memungkinkan kinerja klasifikasi yang lebih akurat. Dalam studi ini, dilakukan eksplorasi dan analisis manfaat penggunaan YAMNet, melalui perbandingan dengan model baseline tanpa teknik transfer learning. Hasilnya menunjukkan bahwa model YAMNet tidak hanya nilai akurasinya yang tinggi 0.8106, namun juga nilai skor-F1nya tinggi yaitu mencapai 0.9831. Terbukti bahwa penggunaan transfer learning dapat meningkatkan kinerja dalam klasifikasi tangisan bayi, terutama dalam mengatasi ketidakseimbangan data dan meningkatkan prediksi untuk kelas minoritas.
Image Captioning untuk Gambar Rambu Lalu Lintas Indonesia Menggunakan Pretrained CNN dan Transformer Novia Pramesti Aprilia; Theresia Herlina Rochadiani
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.4012

Abstract

This research aims to address the lack of understanding of traffic signs in Indonesia through the development of an image captioning model using Inception V3 and Transformer. With this approach, a dataset of traffic sign images consisting of 9,594 images with 31 classes was collected and modified. Model evaluation was conducted using BLEU, ROUGE-L, METEOR, and CIDEr metrics. The research results show good performance with BLEU-1 score of 0.89, BLEU-2 = 0.82, BLEU-3 = 0.75, BLEU-4 = 0.68, CIDEr = 0.57, ROUGE-L = 0.25, and METEOR = 0.26. From these results, it can be indicated that this model can enhance understanding of Indonesian traffic signs. This approach can assist road users in better understanding traffic signs and has the potential to be applied in practical applications to improve traffic safety
Co-Authors Gabriel Alexander Handri Santoso Justin Laurenso Ninuk Yasmarini Novia Pramesti Aprilia Sherensia Putri Angeliani Vartin Suhandi William Widjaja