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All Journal Jurnal Simantec Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer
Kamil, Fajrul Ihsan
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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PENERAPAN NAÏVE BAYES DAN LATENT DIRICHLET ALLOCATION (LDA) UNTUK ANALISIS SENTIMEN DAN PEMODELAN TOPIK PADA PROYEK KERETA CEPAT JAKARTA-BANDUNG Fatah, Doni Abdul; Kamil, Fajrul Ihsan; Soesilo, Budi; Mulaab, Mulaab Mulaab
Jurnal Simantec Vol 12, No 2 (2024): Jurnal Simantec Juni 2024
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v12i2.25440

Abstract

Proyek kereta cepat Jakarta – Bandung merupakan salah satu proyek besar yang saat ini sedang dibuat di Indonesia. Proyek kereta cepat Jakarta – Bandung menjadi ramai dibicarakan di media sosial Twitter. Karena dalam pembangunannya terdapat beberapa masalah, seperti banjir yang terjadi di Bekasi dan menyebabkan kemacetan dan mengganggu kelancaran logistik. Beberapa opini masyarakat dapat berupa sentimen positif dan negatif terhadap Pembangunan Kereta Cepat ini. Untuk mengetahui opini masyarakat tersebut maka perlu dilakukan analisis sentimen dan pemodelan topik menggunakan metode Naive Bayes dan Latent Dirichlet Allocation. Hasil pengujian menunjukkan bahwa pada analisis sentimen setelah dilakukan perhitungan menggunakan metode Naive Bayes diperoleh nilai akurasi sebesar 66%. Sedangkan pada pemodelan topik menggunakan metode Latent Dirichlet Allocation diuji menggunakan nilai koherensi terbaik diperoleh nilai sebesar 0,472 pada 9 topik.
Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes Suzanti, Ika Oktavia; Kamil, Fajrul Ihsan; Rochman, Eka Mala Sari; Azis, Huzain; Suni, Alfa Faridh; Rachman, Fika Hastarita; Solihin, Firdaus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1496

Abstract

Hidden paradise is a term that aptly describes the island of Madura, which offers diverse tourism potential. Through the Google Maps application, tourists can access sentiment-based information about various attractions in Madura, serving both as a reference before visiting and as evaluation material for the local government. The Multinomial Naïve Bayes method is used for text classification due to its simplicity and effectiveness in handling text mining tasks. The sentiment classification is divided into three categories: positive, negative, and mixed. Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. Additionally, the Adaptive Boosting ensemble method was used to enhance the accuracy of the Multinomial Naïve Bayes model. The dataset was split into training and testing sets using ratios of 60:40, 70:30, and 80:20 to evaluate the model’s stability and reliability. The results showed that the highest F1-score, 84.1%, was achieved using the Multinomial Naïve Bayes method with Adaptive Boosting, which outperformed the model without boosting, which had an accuracy of 76%.
Co-Authors Alfa Faridh Suni Fatah, Doni Abdul Fika Hastarita Rachman, Fika Hastarita Firdaus Solihin Huzain Azis Ika Oktavia Suzanti Mulaab Mulaab Rochman, Eka Mala Sari Soesilo, Budi