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All Journal Paradigma Jurnal Pengabdian Masyarakat
Lindung Parningotan Manik
Universitas Nusa Mandiri
Computer Science & IT

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Aspect-Based Sentiment Analysis on Indonesian Presidential Election Using Deep Learning Fadillah Said; Lindung Parningotan Manik
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 2 (2022): September 2022 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i2.1415

Abstract

The 2019 presidential election is a presidential election that has been a hot topic of discussion for some time, and people have even talked about this topic since 2018 on the internet. In predicting the winner of the presidential election, previous research has conducted research on the aspect-based sentiment analysis (ABSA) dataset of the 2019 presidential election using machine learning algorithms such as the Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN) and produces good accuracy. This study proposes a deep learning method using the BERT (Bidirectional Encoder Representation Form Transformers) and RoBERTa (A Robustly Optimized BERT Pretraining Approach) models. The results of this study indicate that the indobenchmark BERT and RoBERTa base-Indonesian single label classification models on target features with preprocessing produce the best accuracy of 98.02%. The indolem BERT model and the indobenchmark single label classification on the target feature without preprocessing produce the best accuracy of 98.02%. The BERT indobenchmark single label classification model on aspect features with preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on aspect features without preprocessing produces the best accuracy of 74.26%. The BERT indolem single label classification model on the sentiment feature with preprocessing produces the best accuracy of 93.07%. The BERT indolem single label classification model on the sentiment feature without preprocessing produces the best accuracy of 94.06%. The BERT indobenchmark multi label classification model with preprocessing produces the best accuracy of 98.66%. The BERT indobenchmark multi label classification model without preprocessing produces the best accuracy of 98.66%.
Implementasi Artificial Intelligence untuk Meningkatkan Administrasi Dakwah Digital pada Jaringan Pemuda Remaja Masjid Indonesia Laela Kurniawati; Lindung Parningotan Manik; Irwansyah Saputra; Zico Pratama Putra
Jurnal Pengabdian Masyarakat Vol 2 No 1 (2026): Jurnal Pengabdian Masyarakat Universitas Islam Cordoba Banyuwangi
Publisher : LPPM Universitas Islam Cordoba Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67008/1kzhnc25

Abstract

Perkembangan Artificial Intelligence (AI) memberikan peluang besar dalam meningkatkan efektivitas pengelolaan organisasi, termasuk organisasi dakwah. Namun, pemanfaatan teknologi digital pada organisasi pemuda dan remaja masjid masih menghadapi berbagai kendala, seperti pengelolaan data yang belum terpusat, dokumen administrasi yang belum terstandarisasi, serta proses penyusunan laporan dan publikasi yang masih dilakukan secara manual. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kemampuan pengurus dan anggota Jaringan Pemuda Remaja Masjid Indonesia (JPRMI) dalam memanfaatkan AI untuk mendukung administrasi dakwah digital. Metode yang digunakan meliputi tahap persiapan, pelatihan, pendampingan, dan evaluasi. Kegiatan dilaksanakan secara luring dengan melibatkan 10 peserta. Hasil kegiatan menunjukkan bahwa peserta mampu memanfaatkan AI untuk penyusunan surat, notulen, laporan kegiatan, pengelolaan data, dan pembuatan konten publikasi dakwah. Hasil evaluasi menunjukkan 80% peserta sangat setuju bahwa kegiatan memberikan manfaat, 70% peserta sangat berminat mengikuti kegiatan serupa, dan 60% peserta menyatakan sangat puas terhadap pelaksanaan kegiatan secara keseluruhan. Kegiatan ini berhasil meningkatkan literasi digital, keterampilan administrasi, serta efisiensi pengelolaan organisasi dakwah berbasis teknologi.
Co-Authors Fadillah Said Irwansyah Saputra Laela Kurniawati Zico Pratama Putra