Article Per Year (5 Year)
p-Index From 2021 - 2026
1.383
P-Index
This Author published in this journals
All Journal Creative Information Technology Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Voice Of Informatics JSAI (Journal Scientific and Applied Informatics) E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi JIKA (Jurnal Informatika) Journal of Computer Networks, Architecture and High Performance Computing Prosiding SNAST Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) The Indonesian Journal of Computer Science Sasambo: Jurnal Abdimas (Journal of Community Service)
Anisa Yulandari
Unknown Affiliation
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

Published : 12 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 12 Documents
Search

 1   2 
Pengenalan Pola pada Batik Lontara berbasis Kecerdasan Buatan Mohammad Yazdi Pusadan; Fuad Mahfud; Anisa Yulandari; Sabarudin Saputra
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Batik is an Indonesian cultural heritage in which almost every region has its own distinctive batik with diverse motifs. UNESCO designated batik as a world cultural heritage created by the Indonesian people in 2009. In South Sulawesi, there is also batik called Batik Lontara. Batik Lontara itself is a type of Bugis-Makassar batik unique to South Sulawesi that features motifs of the Lontara script. The purpose of this research is to implement the extraction of woven Batik Lontara and stamped Batik Lontara using the GLCM (Gray Level Co-occurrence Matrix) method and the KNN (K-Nearest Neighbor) algorithm to recognize the types of Batik Lontara. The Gray Level Co-occurrence Matrix (GLCM) is a feature extraction method that uses second-order texture calculations, considering pairs of two pixels from the original image. This research employs the K-Nearest Neighbor (KNN) algorithm, which is a method for classifying objects based on training data with the closest distance to the test data. The research material used is images of Batik Lontara with various motifs, namely woven Batik Lontara and non-woven Batik Lontara. Based on the Batik Lontara images, a process of converting the images from RGB to Grayscale will be carried out. The expected output of this research is a reputable international journal publication.
Penerapan Artificial Intelligence Untuk Chatbot Informasi Mata Pelajaran SMK Informatika Komputer Ampana Kota Anisa Yulandari; Sri Khaerawati Nur; Sukardi; Anwar Panyili
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7652

Abstract

Ampana City Computer Informatics Vocational School students have problems with enthusiasm for learning at school due to the lack of motivation provided. As a result, many students are lazy to listen or follow class subjects. From the description of the existing problem, the researcher tried to create information media to provide motivation to students which is related to the benefits and goals of learning, using Artificial Intelligence which is formed in a subject information chatbot.  The Naïve Bayes Classifier method is used to build chatbots. Assuming independence between features, Naive Bayes is able to work well on text-based classification such as processing questions and answers in chatbots. So you can answer students' questions accurately. The research results show that the use of chatbots contributes significantly to increasing learning motivation, as shown by the Paired Sample T-test of 0.05, indicating that there is a significant increase in student interest in learning. In addition, system testing shows an accuracy rate of 100% in providing responses to student questions. Artificial intelligence-based chatbots can be an effective innovation in supporting learning and increasing students' interest in learning in technology-based educational institutions.
Co-Authors Anita Ahmad Kasim Annas, Muh Yusril Asrizal Anwar Panyili Asro Nasiri Ayu Hernita Deni Luvi Jayanto Dony Ariyus Fizar Syafaat Fuad Mahfud Herha, Sri Khaerawati Nur Karim, Khairul Insan Lamasitudju, Chairunnisa Mohammad Yazdi Pusadan Risaldi, Moh Ryfial Azhar Sabarudin Saputra Siti Fatonah Siti Fatonah Sri Khaerawati Nur Sukardi Sukardi Sukardi Sukardi Suryani Risqika