Article Per Year (5 Year)
p-Index From 2020 - 2025
1.789
P-Index
This Author published in this journals
All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Register: Jurnal Ilmiah Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Teknik Informatika Academia Open Teknika Insyst : Journal of Intelligent System and Computation JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Inovasi Teknologi dan Edukasi Teknik
Gunawan
Institut Sains Dan Teknologi Terpadu Surabaya
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Physics Social Sciences Other

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Indonesian Language Term Extraction using Multi-Task Neural Network Joan Santoso; Esther Irawati Setiawan; Fransiskus Xaverius Ferdinandus; Gunawan Gunawan; Leonel Hernandez
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p160-167

Abstract

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two multilayer perceptron neural networks to perform Part-of-Speech (PoS) labeling and Noun Phrase Chunking. Our models were trained as a joint model to solve this problem. Our proposed method, with an f-score of 86.80%, can be considered a state-of-the-art algorithm for performing term extraction in the Indonesian Language using noun phrase chunking.
Pengenalan Varietas Ikan Koi Berdasarkan Foto Menggunakan Simple Linear Iterative Clustering Superpixel Segmentation dan Convolutional Neural Andy Hermawan; Ilham Zaeni; Aji Wibawa; Gunawan Gunawan; Yosi Kristian; Shandy Darmawan
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 1 No. 11 (2021)
Publisher : Universitas Ngeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (533.052 KB)

Abstract

Object segmentation and image recognition are two computer vision tasks which are still being developed until today. Simple Linear Iterative Clustering is an algorithm which is very popular to help with object segmentation tasks because it is the best in terms of result and speed. In image recognition, Convolutional Neural Networks are also one of the best approaches for any kind of recognition tasks because of their efficiency and the ability to recognize objects like animals do. Koi fish have become a very interesting object to be researched because they are difficult to segment and distinguished between their varieties. The dataset consists of 600 images of Koi fish from 10 different varieties. The Koi fish’s recognition process begins with generating super pixels for the input image. The next step is to merge all neighborhood super pixels by their color similarities. After this step, almost all the background pixels should be detected so that the actual object, the Koi fish, can be segmented. The segmented image is then given to a Convolutional Neural Networks, to learn any important features which distinguish every Koi fish variety from one another. A trained Convolutional Neural Networks can then give a Koi fish variety prediction for an input image. Based on a series of segmentation and model tests performed, it is proven that the segmentation technique, which uses Simple Linear Iterative Clustering in this project, performs exceptionally well across almost all the images in the dataset. The model produced from this project is also able to classify a wide range of Koi fish varieties accurately at 90 percent accuracy with segmentation and 87 percent without segmentation. Segmentasi dan pengenalan objek pada gambar masih merupakan dua buah masalah pada computer vision yang masih terus diteliti dan dikembangkan hingga saat ini. Simple Linear Iterative Clustering merupakan salah satu algoritma segmentasi superpixel yang cukup populer untuk membantu melakukan segmentasi objek karena memiliki hasil superpixel yang baik dan dapat berjalan dengan cepat. Untuk pengenalan objek, Convolutional Neural Networks masih merupakan salah satu yang terbaik untuk berbagai masalah karena efisien dan mampu mengenali objek pada gambar layaknya hewan mengenali objek yang dilihatnya. Ikan koi menjadi sebuah objek yang menarik untuk diteliti karena sulit untuk disegmentasi dan dikenali jenisnya bahkan oleh manusia. Dataset yang digunakan berisi 600 gambar yang terdiri dari 10 varietas ikan koi. Pengenalan ikan koi diawali dengan melakukan generate superpixel pada gambar input, kemudian menggabungkan superpixel-superpixel terdekat yang memiliki warna yang mirip. Dengan cara ini, maka hampir seluruh pixel background dapat dideteksi sehingga objek ikan koi dapat disegmentasi. Gambar hasil segmentasi kemudian dilatihkan ke Convolutional Neural Networks yang akan mempelajari fitur-fitur penting pada setiap jenis ikan koi yang diteliti. Convolutional Neural Networks yang telah dilatih kemudian dapat memberikan prediksi varietas ikan koi dari sebuah input gambar. Berdasarkan hasil uji coba segmentasi dan model yang digunakan, dibuktikan bahwa teknik segmentasi yang memanfaatkan Simple Linear Iterative Clustering yang dilakukan berhasil untuk hampir seluruh gambar pada dataset. Model yang dibuat mampu mengklasifikasikan varietas ikan koi dengan akurasi 90 persen dengan segmentasi dan 87 persen tanpa segmentasi.
Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts Gunawan Gunawan; Fitria Fitria; Esther Irawati Setiawan; Kimiya Fujisawa
Knowledge Engineering and Data Science Vol 6, No 1 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i12023p57-68

Abstract

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.
Utilization of the Particle Swam Optimization Algorithm in Game Dota 2 Hendrawan Armanto; Harits Ar Rosyid; Muladi Muladi; Gunawan Gunawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 2 (2024): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i2.3503

Abstract

Dota 2, a Multiplayer Online Battle Arena game, is widely popular among gamers, with many attempting to create efficient artificial intelligence that can play like a human. However, current AI technology still falls short in some areas, despite some AI models being able to play decently. To address this issue, researchers continue to explore ways to enhance AI performance in Dota 2. This study focuses on the process of developing artificial intelligence code in Dota 2 and integrating the particle swarm optimization algorithm into Dota 2 Team's Desire. Although particle swarm optimization is an old evolutionary algorithm, it is still considered effective in achieving optimal solutions. The study found that PSO significantly improved the AI Team's Desire and enabled it to win against Default AI of similar levels or players with low MMR. However, it was still unable to defeat opponents with higher AI levels. Furthermore, this study is expected to assist other researchers in developing artificial intelligence in Dota 2, as the complexity of the development process lies not only in AI but also in language, structure, and communication between files.
Co-Authors Adriel Ferdianto Aji Wibawa Andy Hermawan Anita Guterres Christian Aditya Santoso Edwin Pramana Esther Irawati Setiawan F.X. Ferdinandus Fitria Fitria Harits Ar Rosyid Hendrawan Armanto Joan Santoso Kevin Setiono Kimiya Fujisawa Leonel Hernandez Mandar, Gamaria Mauridhi Hery Purnomo Mikhael Setiawan Mochamad Alfan Rosid Muladi Rachmadany, Andry Ruslan Ruslan Shandy Darmawan Stefanus Nico Soenardjo Suhatati Tjandra Surya Sumpeno Yosi Kristian Yuliana Melita Pranoto Zaeni, Ilham Ari Elbaith