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All Journal DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Irwansyah
Universitas Muhammadiyah Prof. Dr. Hamka Jakarta, Indonesia

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Klasifikasi Metode Naïve Bayes pada Ulasan Pengguna Aplikasi Dazzcam untuk Pengeditan Foto Vintage di App Store Salsa Dwi Agistina; Irwansyah; Agus Budiyantara
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.v1i2.9

Abstract

The rapid growth of mobile applications has increased the importance of user-generated reviews as a source of information for evaluating application quality and user satisfaction. Dazzcam, a photo editing application known for its vintage-style filters, has gained significant popularity among iOS users. This study aims to classify user reviews from the App Store into positive and negative sentiment categories using the Naïve Bayes algorithm and to evaluate the performance of the model. A total of 911 reviews were collected and divided into training and testing datasets with a ratio of 80:20. The research methodology includes data preprocessing, feature extraction using TF-IDF, and classification using Naïve Bayes, followed by evaluation with a confusion matrix. The results show that 712 reviews were classified as positive and 199 as negative, with an accuracy of 79.78%, precision of 79.89%, recall of 79.78%, and F1-score of 79.53%. These findings indicate that the Naïve Bayes algorithm demonstrates good performance and can be effectively utilized for sentiment analysis of application reviews.
Penerapan Algoritma XGBoost dalam Klasifikasi Jumlah Korban Kecelakaan Kereta Api di Indonesia Selphia Nur Azzahra; Irwansyah; Tupan Tri M
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.v1i2.10

Abstract

This study aims to classify the number of vehicle accident casualties caused by railway accidents in Indonesia into low, medium, and high-risk categories using the XGBoost algorithm, as well as to evaluate the model performance based on accuracy, precision, and recall metrics. The employed methodology is CRISP-DM, consisting of stages such as business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The dataset was obtained from official reports of the National Transportation Safety Committee (KNKT) and online news articles from 1991 to early 2025, resulting in 112 valid records after preprocessing, including data labeling, transformation of nominal attributes, and conversion of date data into numerical form. The classification process was carried out using RapidMiner. The results show that the XGBoost model achieved an accuracy of 88.39%, with the highest precision and recall values in the low-risk class (0.91 and 0.94) and high-risk class (0.88 and 0.87), while the performance for the medium-risk class remains relatively low (precision 0.75 and recall 0.68), indicating potential data imbalance or insufficient discriminative features. Based on these findings, it can be concluded that the XGBoost algorithm is effective in classifying railway accident risk levels; however, improvements in data quality and feature selection are still needed to achieve more optimal performance.
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
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 Agus Budiyantara Khofifah Dwi Fany Moh Shidqon Salsa Dwi Agistina Selphia Nur Azzahra Tupan Tri M