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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Compiler Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Imroatul Khuluqi Izzah
Universitas Ahmad Dahlan
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Social Sciences Other

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Evaluating IndoBERT for Fraudulent Tweet Detection on Social Media X Imroatul Khuluqi Izzah; Imam Riadi; Abdul Fadlil
Compiler Vol 15, No 1 (2026): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v15i1.3979

Abstract

The spread of fraudulent content on social media X has become an important issue because perpetrators often use persuasive, urgent, and misleading language to influence users to transfer money, share personal data, or access suspicious links. This research evaluates the performance of IndoBERT for binary classification of fraud and non-fraud Indonesian-language posts on social media X using a two-stage fine-tuning design. The dataset consists of 5,235 manually labeled posts, including 2,557 fraud and 2,678 non-fraud instances. In Stage 1, four IndoBERT variants, namely indobert-base-p1, indobert-base-p2, indobert-large-p1, and indobert-large-p2, were compared using a uniform training configuration to identify the best model. The results showed that indobert-large-p1 at epoch 5 achieved the best performance, with a validation F1-score for the fraud class of 0.8898 and a test accuracy of 0.8989. In Stage 2, the selected model was re-evaluated through a controlled grid search by varying epoch, learning rate, and batch size. Although the best Stage 2 configuration improved the validation F1-score to 0.8975, it did not surpass the best Stage 1 model on the test set. These findings indicate that IndoBERT is effective for fraud detection and that a two-stage evaluation design supports more systematic model selection.
Penguatan Kompetensi Teknologi Digital Siswa Melalui Workshop Keahlian Informatika SMK PUI Majalengka Tri Ferga Prasetyo; Dedy Sumarhadi; Imroatul Khuluqi Izzah; Dian Novianti; Abdul Fadlil; Imam Riadi
Darma Abdi Karya Vol. 5 No. 1 (2026): Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM POLITEKNIK LP3I

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/darmaabdikarya.v5i1.2991

Abstract

Kegiatan Program Pemberdayaan Umat (Prodamat) sebagai bentuk pengabdian kepada masyarakat ini bertujuan untuk memperkuat kompetensi teknologi digital siswa SMK PUI Majalengka melalui workshop keahlian informatika yang berorientasi pada literasi digital, berpikir komputasional, pengenalan pemrograman, pengelolaan data sederhana, keamanan digital, dan pemanfaatan perangkat lunak produktif. Kegiatan ini dilatarbelakangi oleh kebutuhan peserta didik SMK untuk memiliki keterampilan digital yang relevan dengan dunia kerja, pendidikan lanjut, dan tuntutan transformasi digital. Metode pelaksanaan menggunakan pendekatan pelatihan partisipatif yang terdiri atas analisis kebutuhan, penyusunan modul, pre-test, penyampaian materi, praktik terbimbing, simulasi penyelesaian masalah, post-test, refleksi, dan evaluasi keberlanjutan. Peserta kegiatan adalah siswa SMK PUI Majalengka yang mengikuti sesi workshop secara langsung di laboratorium komputer sekolah. Hasil kegiatan menunjukkan bahwa workshop mampu meningkatkan pemahaman siswa terhadap konsep dasar informatika, keterampilan menggunakan perangkat digital secara produktif, kesadaran etika dan keamanan digital, serta kemampuan menyusun solusi sederhana berbasis logika komputasional. Peningkatan terlihat dari partisipasi aktif siswa, penyelesaian tugas praktik, dan perbandingan hasil evaluasi awal dan akhir. Kegiatan ini menegaskan bahwa workshop informatika yang aplikatif, kontekstual, dan berbasis praktik dapat menjadi strategi penguatan kompetensi teknologi digital siswa SMK. Program lanjutan disarankan berupa klinik proyek digital, pendampingan portofolio, dan integrasi hasil workshop ke kegiatan ekstrakurikuler atau pembelajaran produktif sekolah.
ANALISIS KINERJA MACHINE LEARNING UNTUK DETEKSI KONTEN PENIPUAN BERBAHASA INDONESIA DI TWITTER Imroatul Khuluqi Izzah; Imam Riadi; Abdul Fadlil
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7124

Abstract

The development of information technology has changed the way people interact in the digital space, including through the Twitter platform, which is widely used to share information and opinions. However, this convenience has also led to the emergence of fraudulent content such as fake investments, fictitious sweepstakes, and fictitious donation requests. This study aims to analyze and compare the performance of five machine learning algorithms, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB), in detecting fraudulent Indonesian language content on Twitter. The dataset consists of 5.221 Indonesian language tweets that have been manually labeled into two classes, fraud and non-fraud. All tweets were processed through text preprocessing stages, including data cleaning, case folding, normalization, tokenization, filtering, and stemming, before being represented as numerical vectors using Word2Vec. Classification was performed using 10-fold cross-validation with evaluation metrics of accuracy, precision, recall, and F1-score. The results show that Random Forest achieved the best performance with accuracy of 85.6%, followed by SVM (84.0%), Logistic Regression (83.6%), Decision Tree (81.2%), and Naïve Bayes (78.4%). The main contribution of this study is to provide a systematic empirical comparative analysis of classification algorithms for detecting Indonesian fraudulent content on Twitter, which remains relatively underexplored. These findings show that the combination of Word2Vec and Random Forest can effectively capture the semantic context of short texts and can serve as a reference for developing automatic detection systems for fraudulent content on social media.
Co-Authors Abdul Fadlil Dedy Sumarhadi Dian Novianti Imam Riadi Tri Ferga Prasetyo