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All Journal Infotech: Journal of Technology Information DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Nunik Pratiwi
Teknik Informatika, Universitas Muhammadiyah Prof. Dr. Hamka, Jakarta
Computer Science & IT

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ANALISIS KINERJA ALGORITMA MACHINE LEARNING DALAM MENDETEKSI ANOMALI KETINGGIAN AIR LAUT: STUDI PERBANDINGAN ONE-CLASS SVM DAN ISOLATION FOREST Dhafa Alifandra; Nunik Pratiwi
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.405

Abstract

This study aims to compare the performance of two machine learning algorithms for anomaly detection One-Class SVM and Isolation Forest in identifying anomalies in sea level data in Indonesia, a region with high tsunami risk. The data were obtained from an official Indonesian government source over a one-year period and underwent preprocessing, including data cleaning and standardization. The models were evaluated using statistical analysis (Mann-Whitney U test), clustering metrics (Davies-Bouldin Index and Silhouette Score), and visual inspection. The results indicate that Isolation Forest outperformed the other algorithm with a Davies-Bouldin Index of 0.8124, while One-Class SVM achieved the highest Silhouette Score at 0.4381, although its Davies-Bouldin Index was higher at 0.9163. This study contributes to the selection of effective algorithms for ocean monitoring systems as part of disaster mitigation strategies in Indonesia.
Perbandingan Kinerja Algoritma K-Nearest Neighbor dan Decision Tree dalam Analisis Sentimen Ulasan Aplikasi DANA pada Google Play Store Khofifah Dwi Fany; Irwansyah; Moh Shidqon; Nunik Pratiwi
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 2 (2026): April
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i2.11

Abstract

The rapid growth of digital wallet applications such as DANA has raised concerns regarding the quality of services provided to users. One effective approach to evaluate service quality is through sentiment analysis of user reviews on the Google Play Store platform. However, the large volume of available review data makes manual analysis inefficient. This study aims to identify the most optimal classification algorithm for sentiment analysis of DANA application reviews by comparing the performance of the K-Nearest Neighbor (K-NN) and Decision Tree algorithms. The dataset consists of 723 reviews obtained from Kaggle, divided into 578 training data and 145 testing data. The reviews are classified into three sentiment categories: positive, negative, and neutral. The research process includes data collection, filtering, preprocessing (case folding, tokenizing, stopword removal, and token length filtering), TF-IDF weighting, implementation of classification algorithms, and evaluation using a Confusion Matrix. The results show that the K-NN algorithm achieves an accuracy of 53.10%, precision of 90.32%, and recall of 41.79%, while the Decision Tree algorithm yields a higher recall but lower accuracy and precision. Based on the comparison of these evaluation metrics, the K-NN algorithm is recommended as the more optimal method, as it provides a better balance between prediction accuracy and error rate compared to the Decision Tree.
Co-Authors Dhafa Alifandra Irwansyah Khofifah Dwi Fany Moh Shidqon