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All Journal JISICOM (Journal of Information System, Infomatics and Computing) Bulletin of Information Technology (BIT)
Lidya Nurmala Eva
Universitas Teknologi Yogyakarta
Computer Science & IT

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ANALISIS KLASIFIKASI KEPUASAN PELANGGAN TERHADAP PELAYANAN CUSTOMER SERVICE UNTUK PENINGKATAN LAYANAN MENGGUNAKAN DATA MINING DENGAN DECISION TREE Lidya Nurmala Eva; Arief Hermawan; Donny Avianto
Journal of Information System, Informatics and Computing Vol 10 No 1 (2026): JISICOM (June 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v10i1.2404

Abstract

This study analyzes customer satisfaction with customer service using data mining techniques and the Decision Tree algorithm. The data was obtained from customer questionnaires completed after transactions and were processed through pre-processing, attribute labeling, and missing value handling. The dataset was split into 80% training data and 20% testing data to build and evaluate a classification model with two target categories: satisfied and dissatisfied. The modeling results show that the Consideration Label is the most dominant factor in determining customer satisfaction, while the Suggestion Label serves as a supporting attribute. Model evaluation produced an accuracy of 58%, with precision, recall, and F1-score for the dissatisfied class of 0.62, 0.66, and 0.64, respectively, and for the satisfied class of 0.53, 0.49, and 0.51, respectively. Based on these results, the Decision Tree method can be used to classify customer satisfaction, although further improvement in model performance is still needed to obtain more optimal predictions.
Klasifikasi Kinerja Konten YouTube Mobile Legends Menggunakan LightGBM dengan Pelabelan Berbasis K-Means dan Interpretasi SHAP Wayan Praka; Ella Kristiantini Susan; Lidya Nurmala Eva
Bulletin of Information Technology (BIT) Vol 7 No 3: September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i3.3138

Abstract

YouTube is a video-sharing platform widely used by content creators, including those in the gaming category such as Mobile Legends. The intense competition among Mobile Legends content creators has made content performance analysis essential for developing data-driven content strategies. However, most previous studies have relied on manual data labeling, which is prone to subjectivity and lacks efficiency. Furthermore, high-accuracy classification models are often treated as black-box models, making it difficult to explain the factors influencing prediction outcomes. This study aims to develop a YouTube Mobile Legends content performance classification model by integrating K-Means, LightGBM, and Shapley Additive exPlanations (SHAP). The dataset consists of 3,052 videos collected using the YouTube Data API v3. The K-Means algorithm was employed to generate automatic labels, while LightGBM was used as the classification model and SHAP was applied to interpret the model's predictions. The results indicate that the optimal number of clusters was K=2, producing two performance classes: High Performance (72.44%) and Low Performance (27.56%). The dataset was subsequently divided into 80% for training and 20% for testing. On the testing dataset, the LightGBM model achieved an accuracy of 93.78%, precision of 93.87%, recall of 93.78%, and an F1-score of 93.81%. Furthermore, 10-fold cross-validation yielded a mean accuracy of 92.75% with a standard deviation of 0.0148, demonstrating the model's robustness and stability. SHAP analysis revealed that Subscriber Count was the most influential feature, followed by Duration, Video Age, and Title Length. These findings demonstrate that the integration of K-Means, LightGBM, and SHAP provides an effective approach for objectively, accurately, and transparently classifying the performance of Mobile Legends content on YouTube, while offering valuable insights to support content creators in developing data-driven content strategies.
Co-Authors Donny Avianto Ella Kristiantini Susan Wayan Praka