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All Journal Jurnal Buana Informatika JURNAL ELEKTRO Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pemberdayaan Masyarakat Madani (JPMM) INOVTEK Polbeng - Seri Informatika MITRA: Jurnal Pemberdayaan Masyarakat Indonesian Journal of Computing and Modeling Jurnal Mantik Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Journal of Information Systems and Informatics JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Mnemonic Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pengabdian Masyarakat Asia Jurnal Restikom : Riset Teknik Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) International Journal of Information Technology and Business INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Christine Dewi
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Importance of Feature Selection for Multiple Disease Classification Andika, Rio Arya; Dewi, Christine
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

The performance of machine learning in disease classification heavily depends on effective feature selection. This study explores feature selection methods—Boruta and Recursive Feature Elimination (RFE)—with ensemble models like Random Forest, Decision Tree, Gradient Boosting, LightGBM, and XGBoost using Electronic Health Records (EHR) data. Results show that combining Boruta with LightGBM achieves the highest accuracy of 99%. Feature selection enhances precision by focusing on relevant variables and removing unnecessary ones. Further analysis reveals that features such as Red Blood Cells, Insulin, Heart Rate, and Cholesterol significantly influence the classification of specific diseases. These findings highlight the importance of feature selection in multi-disease classification and medical data analysis, improving the efficiency of machine learning systems. Future research should develop more flexible feature selection methods and test models on diverse disease datasets.
APRS and SSTV Technology for Audiovisual Data Transmission in Internet Blank Spot Areas to Increase the Effectiveness of SAR Activities Christanto, Febrian Wahyu; Handayani, Sri; Handayani, Titis; Dewi, Christine
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 1 (2025): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i1.3205

Abstract

Volcanic eruptions can be detected through several warning signs. The Indonesian National Disaster Management Agency (BNPB) reported that between 2010 and 2021, Indonesia experienced 156 volcanic eruptions. The most recent occurred in 2021 when Mount Semeru erupted, forcing 10,395 people to evacuate, injuring 104, and causing 51 fatalities. The BNPB often experiences problems in carrying out mitigation, evacuation, rehabilitation, and reconstruction in disaster areas. On average, the search and evacuation process for victims takes about 3-7 days, so the probability of finding disaster victims is only about 50%. The proposed solution is a combination of radio transmission with Auto Packet Reporting System (APRS) technology as a medium for determining evacuation locations and Slow-Scan Television (SSTV) as a medium for transmitting audio and images of disaster sites, called Radio All-in-One (RAIONE). Using the Prototype method, this research has been tested for about 7 months with continuous improvements. The results show that the maximum distance covered is approximately 20 km with a minimum central antenna height of 7-10 meters, which increases the time effectiveness of SAR operations. The probability of finding survivors in a disaster increases to 75%, and SAR operations speed up to 1-2 days because of acceleration in the determination of search and evacuation locations in the Blank Spot Areas, reaching 91.30%.
Seminar and Workshop on Object Recognition using Deep Learning at Sam Ratulangi University Manado Dewi, Christine
Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2024): Jurnal Pengabdian Masyarakat
Publisher : Institut Teknologi dan Bisnis Asia Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jpm.v5i1.1379

Abstract

Purpose: This seminar and workshop aim to address the lack of understanding among students regarding object recognition with deep learning. By exploring the concepts and applications of deep learning in object detection and recognition, participants will gain insights into this crucial aspect of computer vision. Method: The event will feature lectures, practical demonstrations, and hands-on workshops conducted by experts in the field. Participants will engage in interactive sessions to deepen their understanding of convolutional neural networks and other deep learning techniques for object recognition. Practical Applications: The knowledge gained from this seminar and workshop will have practical implications across various industries, including autonomous vehicles, healthcare, security systems, and robotics. Participants will learn how to apply deep learning algorithms to solve real-world problems related to object detection and recognition. Conclusion: By the end of the seminar and workshop, participants are expected to have acquired a deeper understanding of object recognition with deep learning and its practical applications. This will contribute to bridging the gap between theoretical knowledge and real-world implementation in the field of computer vision.
Integrating Real-Time Weather Forecasts Data Using OpenWeatherMap and Twitter Dewi, Christine; Chen, Rung-Ching
International Journal of Information Technology and Business Vol. 1 No. 2 (2019): April: International Journal of Information Techonology and Business
Publisher : Universitas Kristen Satya Wacana

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Abstract

Weather forecasts are made by collecting as much data as possible about the current state of the atmosphere (particularly the temperature, humidity, and wind) and using an understanding of atmospheric processes (through meteorology) to determine how the atmosphere evolves in the future. There are several reasons why weather forecasts are important. It forewarns the people about future weather conditions so that people can plan their activities accordingly. It warns people about the impending severe weather conditions and other weather hazards such as thunderstorms, hurricanes, and heavy rainfalls. Thus far, accurate weather predictions have been able to save the lives of many. At its core, Twitter is a real-time public broadcast channel. These characteristics make Twitter a natural platform for public safety communication and early-warning systems. Furthermore, Twitter became an essential source for up-to-date meteorological data and agency announcements. OpenWeatherMap processes all data in a way that it attempts to provide accurate online weather forecast data and weather maps, such as those for clouds and preciptations Besides, we will use Phyton programming language to get real-time weather data from OpenWeatherMap and post the information to our social media Twitter. Finally, OAuth and Tweepy are a very powerful library that enables the Python code to communicate with Twitter. Tweets about the weather could prove useful to anybody wanting to use it.
A Systematic Review of Deep Learning for Intelligent Transportation Systems with Analysis and Perspectives Hendrawan, Aria; Gernowo, Rahmat; Nurhayati, Oky Dwi; Dewi, Christine
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1085

Abstract

This study presents a systematic review of deep learning for intelligent transportation systems. Statistics are used to find the most cited articles, and the number of articles and quotes are used to find the most productive and influential authors, institutions, and countries or regions. Key topics and patterns of change are discovered using the authors’ keywords, and the most common issues and themes are revealed using flow maps and showing the corresponding trends. A co-occurrence keyword network is also developed to present the research landscape and hotspots in the field. The results explain how publications have changed over the past seven years. Researchers can use this study to have a deeper understanding of the current state and future trends in the role of deep learning in intelligent transportation systems.
YOLOv8 Analysis for Vehicle Classification Under Various Image Conditions Panja, Eben; Hendry, Hendry; Dewi, Christine
Scientific Journal of Informatics Vol 11, No 1 (2024): February 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i1.49038

Abstract

Purpose: The purpose of this research is to detect vehicle types in various image conditions using YOLOv8n, YOLOv8s, and YOLOv8m with augmentation.Methods: This research utilizes the YOLOv8 method on the DAWN dataset. The method involves using pre-trained Convolutional Neural Networks (CNN) to process the images and output the bounding boxes and classes of the detected objects. Additionally, data augmentation applied to improve the model's ability to recognize vehicles from different directions and viewpoints.Result: The mAP values for the test results are as follows: Without data augmentation, YOLOv8n achieved approximately 58%, YOLOv8s scored around 68.5%, and YOLOv8m achieved roughly 68.9%. However, after applying horizontal flip data augmentation, YOLOv8n's mAP increased to about 60.9%, YOLOv8s improved to about 62%, and YOLOv8m excelled with a mAP of about 71.2%. Using horizontal flip data augmentation improves the performance of all three YOLOv8 models. The YOLOv8m model achieves the highest mAP value of 71.2%, indicating its high effectiveness in detecting objects after applying horizontal flip augmentation. Novelty: This research introduces novelty by employing the latest version of YOLO, YOLOv8, and comparing its performance with YOLOv8n, YOLOv8s, and YOLOv8m. The use of data augmentation techniques, such as horizontal flip, to increase data variation is also novel in expanding the dataset and improving the model's ability to recognize objects.
Co-Authors Aditya, Michael Rio Andika, Rio Arya Angkur, Lusiana V.G Anjar Widhyo Sasongko Aria Hendrawan, Aria Bitra, Marcelino Bleskadit, Adri Agustinus Charmelita, Pauelina Chen, Rung-Ching Christanto, Henoch Juli Christo Sidupa, Bertnaldy Dagha, Willyam Chrisna Umbu Denny Jean Cross Sihombing Dienda Rizkya Hayuningtyas Roosaputri Emanuel Pabianan Faisal Rahutomo Febrian Wahyu Christanto Frans Robert Bethony Gerald Edgard Laukon Glorya Maya Marcia Sapan Bethony Handayani, Sri Hendry Henoch Juli Christanto Henoch Juli Christanto Hiuredhy, Davin Kurnia Jhosefhin, Nicola Van Robert Juli Christanto, Henoch Julius Victor Manuel Bata Kroons, Aquenov Alexandro Kumala Nindya Pramono Lim, Ricardo Jonathan Mavish, Steven Nindya Pramono, Kumala Nindya Pramono Oktaviani, Gracelya Oky Dwi Nurhayati Pabianan, Emanuel Panja, Eben Patandung, Gabriel Pratama, Yoga Candra Adi Rahmat Gernowo Ria Cantika Larasati sangga, harmanto Stephen Aprius Sutresno, Stephen Aprius Tanujaya, Matthew Tappi, Liputra Pronimus Titis Handayani Valentina, Vierena Yeremia Yulianto Yerik Afrianto Singgalen Yulianto, Yeremia