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All Journal DIGINTEL-AI : DIGital INnovation and inTELligence – AI
Tupan Tri M
Sistem Informasi, Institut Sosial dan Teknologi (ISTEK) Widuri, Jakarta

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Implementasi Algoritma K-NN Pada Sosial Media X Untuk Analisis Sentimen Pengalaman Warganet Tinggal Di Luar Negeri Salsa Billa Permana Putri; Irwansyah; Tupan Tri M
DIGINTEL-AI : DIGital INnovation and inTELligence – AI Vol. 1 No. 1 (2025): October
Publisher : PT Ajira Karya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66217/digintel-ai.v1i1.4

Abstract

The development of information technology, especially through social media such as Twitter, has changed the way people search for information. With more than 6.43 million users in Indonesia, Twitter has become the main platform for sharing opinions. This study aims to analyze the sentiments of Indonesian citizens (WNI) living abroad, who often face challenges and opportunities in adapting to new environments. Given the increasing number of WNI, reaching over 9 million in 2020, understanding their sentiments is crucial. The K-Nearest Neighbor (KNN) method was used to classify sentiments as positive, negative, or neutral. This study involved data collection through the tweet-harvest technique, where 1,060 comments were successfully collected, and 600 of them met the relevance criteria for analysis. The analysis results showed that 60.4% of sentiments were neutral, 34.1% were positive, and 5.5% were negative, with the KNN model achieving an accuracy of 81.67%. Model evaluation revealed the highest precision in the neutral class and a recall of 1.00, although the positive and negative classes require further optimization. This study is expected to provide insights for the public and decision-makers regarding the experiences of Indonesian citizens abroad.
Penerapan Algoritma XGBoost dalam Klasifikasi Jumlah Korban Kecelakaan Kereta Api di Indonesia Selphia Nur Azzahra; Irwansyah; Tupan Tri M; Firman Noor Hasan
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.
Co-Authors Firman Noor Hasan Irwansyah Irwansyah Salsa Billa Permana Putri Selphia Nur Azzahra