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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL AKUNTANSI Journal of Computer Networks, Architecture and High Performance Computing
Asyaky, Muhammad Sidik
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Computer Science & IT Economics, Econometrics & Finance Education Social Sciences

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Analisis dan Perbandingan Bukti Digital Aplikasi Instant Messenger pada Android Asyaky, Muhammad Sidik; Widiyasono, Nur; Gunawan, Rohmat
Sinkron : jurnal dan penelitian teknik informatika Vol. 3 No. 1 (2018): SinkrOn Volume 3 Nomor 1, Periode Oktober 2018
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (737.152 KB)

Abstract

Perkembangan jumlah pengguna aplikasi Instant Messenger (IM) yang sangat pesat menyebabkan naiknya potensi tindakan kriminal dilakukan melalui aplikasi IM. Fitur keamanan data aplikasi IM yang ditujukan untuk melindungi privasi penggunanya, digunakan oleh pelaku kriminal untuk menyembunyikan bukti digital dari aktivitas kriminalnya. Penelitian ini membahas mengenai analisa dan perbandingan bukti digital dari aplikasi IM pada Android yang telah diunduh sebanyak 500 juta orang di Play Store, yaitu WhatsApp, Telegram, Line, dan IMO. Proses analisa dilakukan pada bukti digital dari penggunaan fitur yang ada di aplikasi IM, sehingga proses pengumpulan data dibantu dengan simulasi dari beberapa skenario yang berpotensi terjadi dalam tindakan kriminal. Teknik akuisisi data dilakukan dengan metode physical imaging untuk mendapatkan akses penuh pada memori smartphone. Hasil analisa disimpulkan dalam bentuk tabel perbandingan yang dapat dirujuk oleh investigator forensik ketika melakukan investigasi aplikasi IM yang diteliti. Hasil analisa menyatakan bahwa bukti digital dari aktivitas tukar menukar pesan, berkas media, dan kontak ditemukan. Hasil analisa juga memberikan penjelasan mengenai kemungkinan untuk menganbil bukti digital yang dihapus dan bagaimana cara memulihkannya dengan teknik data carving.
Sentiment Analysis on Short Social Media Texts Using DistilBERT Asyaky, Muhammad Sidik; Muhammad Al-Husaini; Hen Hen Lukmana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5836

Abstract

Sentiment analysis on short texts from social media, such as tweets, presents unique challenges due to their brevity and informal language. This study explores the effectiveness of transformer-based models, particularly DistilBERT, in performing sentiment analysis on short texts compared to traditional machine learning approaches including Support Vector Machine, Logistic Regression, and Naive Bayes. The objective is to assess whether DistilBERT not only enhances sentiment classification accuracy but also remains efficient enough for quick social media analysis. The models used in this study were trained and evaluated on stratified samples of 10,000, 30,000, and 50,000 tweets, drawn from the Sentiment140 dataset while preserving the original class distribution. The methodology involved data collection and sampling, data splitting, data cleaning, feature extraction, model training, and evaluation using accuracy and F1-score. Experimental results showed that DistilBERT consistently outperformed traditional models in both accuracy and F1-score, and demonstrated competitive results against BERT while requiring significantly less training time. Specifically, DistilBERT trained approximately 1.8 times faster than BERT on average, highlighting its computational efficiency. The best result was achieved by DistilBERT trained on the 50k subset, reaching an accuracy of 85% and an F1-score of 84%. These findings suggest that lightweight transformer models like DistilBERT are highly suitable for real-world sentiment analysis tasks where both speed and performance are critical.
Action Research in Information System and Internal Auditing Research Rustendi, Tedi -; Asyaky, Muhammad Sidik
JURNAL AKUNTANSI Volume 20, Nomor 1, Mei 2025
Publisher : Jurusan Akuntansi Fakultas Ekonomi Universitas Siliwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jak.v20i1.14423

Abstract

Action research is a research design that has the potential to be used in information systems and internal auditing research because of its similarities in the framework. Action research was initially qualitative, but as its methodology developed, quantitative components were added as an integral part of both the data collection and its analysis and interpretation phases. Mixed methods are considered effective in leveraging the strengths and reducing the weaknesses of both quantitative and qualitative approaches in either parallel or sequential designs. The author supports the idea that, methodologically, action research in information systems and internal auditing can adopt mixed methods in either parallel or sequential designs while considering scientific stages and ethical values.Keywords: action research, quantitative, qualitative, mixed methods.
Co-Authors Gunawan, Rohmat Hen Hen Lukmana Muhammad Al-Husaini Nur Widiyasono, Nur Rustendi, Tedi -