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Yahya, Susilawati
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Computer Science & IT

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Analisis Sentimen Analisis Sentimen Publik Terhadap Pariwisata Aceh di Media Sosial X Menggunakan Algoritma Naive Bayes Classifier Yahya, Susilawati; Wahyuni , Sri
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1700

Abstract

Aceh has been synonymous with negative perceptions among people outside the province. This is due to the prolonged armed conflict and the devastating tsunami in 2004. Despite these challenges, Aceh possesses abundant potential for tourism, including natural attractions, historical sites, cultural arts, and religious tourism. However, negative perceptions continue to influence tourists' decisions to visit Aceh. Therefore, this study aims to analyze public sentiment or public opinion towards Aceh's tourism using the Naive Bayes algorithm on the X (Twitter) social media platform. Data for this study was collected from tweets on X (Twitter) using the keyword "Aceh tourism" and then underwent several data pre-processing stages to improve data quality, including text cleaning, case folding, word normalization, tokenization, stop word removal, and stemming. Afterward, the Naive Bayes algorithm was applied to classify tweet sentiment into positive and negative categories. Model evaluation was conducted using a confusion matrix, accuracy, and classification report. The results showed that Naive Bayes performed well in classifying public sentiment with an accuracy of 81%. This analysis indicates that public perception towards Aceh's tourism has begun to shift positively, presenting a promising opportunity for the future development of Aceh's tourism sector.
Analisis Sentimen Penerapan Deep Learning dan Analisis Sentimen terhadap Gap Kompetensi Lulusan Lembaga Pendidikan dan Pelatihan Vokasi terhadap Dunia Kerja dengan Metode Long Short-Term Memory (LSTM) Yahya, Susilawati; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2031

Abstract

The gap between vocational graduates’ competencies and labor market demands remains a pressing issue in Indonesia. This study aims to analyze alumni perceptions regarding the alignment between competencies acquired during their studies at LP3I Banda Aceh and real-world job requirements. A quantitative approach was adopted using a deep learning method based on Long Short-Term Memory (LSTM). Data were collected through an online survey containing open-ended responses from 934 alumni, followed by preprocessing, tokenization, lexicon-based sentiment labeling, and data splitting into training and testing sets. The models developed included pure LSTM, LSTM with class weights, and Bidirectional LSTM (BiLSTM). Results indicate that BiLSTM achieved the highest performance with 90% accuracy and a weighted F1-score of 0.91. Additionally, 44.5% of respondents expressed neutral or negative sentiments, highlighting a mismatch between acquired competencies and industry demands. These findings underscore the urgency of curriculum evaluation and stronger collaboration between vocational institutions and the labor market. This study demonstrates that deep learning offers an efficient and objective tool for competency mapping in vocational education.
Co-Authors Farta wijaya, Rian Muhammad Iqbal Nasution, Darmeli Sitorus, Zulham Wahyuni , Sri