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All Journal International Journal of Advances in Intelligent Informatics Info Kripto
Indarjani, Santi
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mathematics

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Comparative study of predictive models for hoax and disinformation detection in indonesian news Adiati, Nadia Paramita Retno; Priambodo, Dimas Febriyan; Girinoto, Girinoto; Indarjani, Santi; Rizal, Akhmad; Prayoga, Arga; Beatrix, Yehezikha
International Journal of Advances in Intelligent Informatics Vol 10, No 3 (2024): August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i3.878

Abstract

Along with the times, false information easily spreads, including in Indonesia.  In Press Release No.485/HM/KOMINFO/12/2021 the Ministry of Communication and Information has cut off access to 565,449 negative content and published 1,773 clarifications on hoax and disinformation content. Research has been carried out regarding this matter, but it is necessary to classify fake news into disinformation and hoaxes. This study presents a comparison between our proposed model, which is an ensemble of shallow learning predictive models, namely Random Forest, Passive Aggressive Classifier, and Cosine Similarity, and the deep learning model that uses BERT-Indo for classification. Both models are trained using equivalent datasets, which contain 8757 news, consisting of 3000 valid news, 3000 hoax news, and 2757 disinformation news. These news were obtained from websites such as CNN, Kompas, Detik, Kominfo, Temanggung Mediacenter, Hoaxdb Aceh, Turnback Hoax, and Antara, which were then cleaned from all unnecessary substances, such as punctuation marks, numbers, Unicode, stopwords, and suffixes using the Sastrawi library. At the benchmarking stage, the shallow learning model is evaluated to increase accuracy by applying ensemble learning combined using hard voting.  This results in higher values, with an accuracy of 98.125%, precision of 98.2%, F-1 score of 98.1%, and recall of 98.1%, compared to the BERT-Indo model which only achieved 96.918% accuracy, 96.069% precision, 96.937% F-1 score, and 96.882% recall. Based on the accuracy value, shallow learning model is superior to deep learning model.  This machine learning model is expected to be used to combat the spread of hoaxes and disinformation in Indonesian news. Additionally, with this research, false news can be classified in more detail, both as hoaxes and disinformation
Pemulihan Kunci pada Simplified Data Encryption Standard (S-DES) Melalui Serangan Aljabar: Studi Kasus Paradise, Fadila; Indarjani, Santi
Info Kripto Vol 19 No 1 (2025)
Publisher : Politeknik Siber dan Sandi Negara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56706/ik.v19i1.114

Abstract

Salah satu metode kriptanalisi adalah serangan aljabar. Makalah ini bertujuan untuk menunjukkan bagaimana serangan aljabar dapat diterapkan pada S-DES sebagai media pembelajaran. Serangan dieksekusi dengan pendekatan sistem persamaan linier. Langkah awal menentukan persamaan polynomial yang merupakan representasi aljabar dari algoritma S-DES, meliputi: penentuan persamaan kunci putaran 1 dan 2, penentuan persamaan polinomial dari s-box S0 dan S1, serta pencarian persamaan polinomial dari setiap bit teks sandi. Proses pemulihan kunci dilakukan menggunakan algoritma Extended Linearization (XL) sebagai metode untuk mencari solusi dari sistem persamaan polinomial yang diperoleh. Dari hasil eksperimen dapat dibuktikan kunci input rahasia berhasil dipulihkan hanya dengan 2 percobaan berdasarkan persamaan polinomial yang diperoleh, dibandingkan 2^10 percobaan jika dilakukan total brute force attack. Penelitian ini bisa menjadi acuan proyeksi keamanan algoritma AES atau yang sejenis dan dapat menjadi referensi penerapan serangan aljabar pada algoritma sejenis.
Co-Authors Adiati, Nadia Paramita Retno Akhmad Rizal, Akhmad Beatrix, Yehezikha Dimas Febriyan Priambodo Girinoto, Girinoto Paradise, Fadila Prayoga, Arga