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All Journal Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Euis Widanegsih
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Industrial & Manufacturing Engineering

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Perbandingan Kinerja Algoritma Decision Tree dan Random Forest dalam Memprediksi Kepuasan Penumpang Maskapai Rahma Ayu Silvana; Nadila Anggiani; Athallah Labib; Risca Lusiana Pratiwi; Euis Widanegsih
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1759

Abstract

This study aims to conduct a comparative analysis of the performance of two classification algorithms, namely Decision Tree and Random Forest, in predicting the level of airline passenger satisfaction. The data used in this research were obtained from the Airline Passenger Satisfaction dataset available on Kaggle, which contains various variables related to passengers’ flight experiences. The research employed a quantitative experimental method using the CRISP-DM (Cross Industry Standard Process for Data Mining) approach, consisting of several stages including data understanding, data preparation, modeling, evaluation, and deployment. The modeling process was carried out using RapidMiner Studio, with the dataset divided into 70% for training and 30% for testing. The experimental results indicate that the Decision Tree algorithm achieved an accuracy rate of 91.77%, while the Random Forest algorithm achieved a higher accuracy of 93.37%. This difference demonstrates that Random Forest possesses better generalization capabilities and more stable performance in handling complex and varied data. Therefore, it can be concluded that the Random Forest algorithm performs more effectively in predicting airline passenger satisfaction levels. Moreover, this study highlights the importance of selecting an appropriate algorithm in data analysis processes to support data-driven decision-making within the aviation industry.
Klasifikasi Tingkat Kemiskinan di Indonesia Menggunakan Naive Bayes dengan RapidMiner Ahmad Rizki Sya’bani; Wahyu Nur Hidayat; Mikhael Valliano Benjamin; Risca Lusiana Pratiwi; Euis Widanegsih
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v5i3.1770

Abstract

Poverty is a multidimensional issue that has a significant impact on social and economic development in Indonesia. Accurate analysis of poverty levels is essential to support government policies in distributing aid and planning targeted development programs. This study aims to classify poverty levels in Indonesia using the Naive Bayes algorithm based on machine learning, assisted by the RapidMiner Studio software. The dataset consists of 155 entries with 12 key attributes reflecting social and economic indicators, such as household expenditure, education level, unemployment rate, and the Human Development Index (HDI). The research follows the CRISP-DM methodology, which includes the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The modeling results show that the Naive Bayes algorithm achieves an accuracy of 94.19%, with high precision and recall values, indicating consistent model performance in classifying poor and non-poor categories. These findings suggest that the Naive Bayes-based machine learning approach can serve as an effective analytical tool to understand poverty patterns in Indonesia. The implementation of this model is expected to assist the government in making data-driven decisions to improve the effectiveness of poverty alleviation programs.
Co-Authors Ahmad Rizki Sya’bani Athallah Labib Mikhael Valliano Benjamin Nadila Anggiani Nur Hidayat, Wahyu Rahma Ayu Silvana Risca Lusiana Pratiwi