cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Yeni Kustiyahningsih
Contact Email
ykustiyahningsih@trunojoyo.ac.id
Phone
+6282139239387
Journal Mail Official
kursor@trunojoyo.ac.id
Editorial Address
Informatics Department, Engineering Faculty University of Trunojoyo Madura Jl. Raya Telang - Kamal, Bangkalan 69162, Indonesia Tel: 031-3012391, Fax: 031-3012391
Location
Kab. bangkalan,
Jawa timur
INDONESIA
Jurnal Ilmiah Kursor
Published by Universitas Trunojoyo Madura
ISSN : 02160544     EISSN : 23016914     DOI : https://doi.org/10.21107/kursor
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Jurnal Ilmiah Kursor is published in January 2005 and has been accreditated by the Directorate General of Higher Education in 2010, 2014, 2019, and until now. Jurnal Ilmiah Kursor seeks to publish original scholarly articles related (but are not limited) to: Computer Science. Computational Intelligence. Information Science. Knowledge Management. Software Engineering. Publisher: Informatics Department, Engineering Faculty, University of Trunojoyo Madura
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 2 Documents
 1 
Search results for , issue "Vol. 13 No. 2 (2025)" : 2 Documents clear
A Multi-label book genre classification: Comparison of machine learning techniques and problem transformation methods Subroto, Eka Mira Novita; Faisal, Muhammad
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.389

Abstract

Books play an essential role in life as a source of knowledge and information. The increasing number of books published makes classification more complex, especially in a multi-label context where a book may belong to more than one genre. Furthermore, automatic classification of book genres is required due to the transition of books to e-book and audiobook formats. This research analyzes the application of machine learning techniques using Support Vector Machine (SVM), Logistic Regression (LR), and Multinomial Naive Bayes (MNB) for multi-label book genre classification by comparing their performance through stemming and unstemming process in text preprocessing with TF-IDF and K-Fold cross-validation (k = 10). In addition, two problem transformation methods, Binary Relevance (BR) and Label Powerset (LP), are evaluated. The results show that SVM combined with stemming outperforms other models across all metrics of accuracy, precision, recall, and F1-score. SVM is effective in handling complex and imbalanced data distributions, resulting in more accurate and consistent predictions. The stemming process positively contributes by reducing word variation and allowing the model to focus on word meanings. Among problem transformation methods, LP yields better results because it can capture relationships between labels more effectively than BR.
Comparative study of unsupervised anomaly detection methods on imbalanced time series data Hanifa, Riza Aulia; Thobirin, Aris; Surono, Sugiyarto
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.431

Abstract

Anomaly detection in time series data is essential, especially when dealing with imbalanced datasets such as air quality records. This study addresses the challenge of identifying point anomalies rare and extreme pollution levels within a highly imbalanced dataset. Failing to detect such anomalies may lead to delayed environmental interventions and poor public health responses. To solve this, we propose a comparative analysis of three unsupervised learning methods: K-means clustering, Isolation Forest (IForest), and Autoencoder (AE), including its LSTM variant. These algorithms are applied to monthly air quality data collected in 2023 from 2,110 cities across Asia. The models are evaluated using Area Under the Curve (AUC), Precision, Recall, and F1-score to assess their effectiveness in detecting anomalies. Results indicate that the Autoencoder and Autoencoder LSTM outperform the others with an AUC of 98.23%, followed by K-means (97.78%) and IForest (96.01%). The Autoencoder’s reconstruction capability makes it highly effective for capturing complex temporal patterns. K-means and IForest also show strong results, offering efficient and interpretable solutions for structured data. This research highlights the potential of unsupervised anomaly detection techniques for environmental monitoring and provides practical insights into handling imbalanced time series data.

Page 1 of 1 | Total Record : 2

 1 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol. 13 No. 2 (2025) Vol. 13 No. 1 (2025) Vol. 12 No. 4 (2024) Vol. 12 No. 3 (2024) Vol. 12 No. 2 (2023) Vol 12 No 1 (2023) Vol 11 No 4 (2022) Vol 11 No 3 (2022) Vol 11 No 2 (2021) Vol 11 No 1 (2021) Vol 10 No 4 (2020) Vol 10 No 3 (2020) Vol 10 No 2 (2019) Vol 10 No 1 (2019) Vol 9 No 4 (2018) Vol 9 No 3 (2018) Vol 9 No 2 (2017) Vol 9 No 1 (2017) Vol 8 No 4 (2016) Vol 8 No 3 (2016) Vol 8 No 2 (2015) Vol 8 No 1 (2015) Vol 7 No 4 (2014) Vol 7 No 3 (2014) Vol 7 No 2 (2013) Vol 7 No 1 (2013) Vol 6 No 4 (2012) Vol 6 No 3 (2012) Vol 6 No 2 (2011) Vol 6 No 1 (2011) More Issue