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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT)
Nabila, Qotrunnada
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Testing Data Security Using a Vigenere Cipher Based on the QR Code Rachmawanto, Eko Hari; Gumelar, Rizky Syah; Nabila, Qotrunnada; Sari, Christy Atika; Ali, Rabei Raad
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1734

Abstract

Data, especially personal data, is sensitive and if misused, it can become a source of threats and crimes for ourselves or for others. Therefore, data security is very important. Cryptography is a way to secure data that aims to safeguard the information that contained in data, so the information contained is not known by unauthorized parties. Vigenere Cipher is a cryptographic method used to hide data with steganography. In the process, the Vigenere cipher converts information called plain text into ciphertext or text that has been steganographed. In this research, process of encryption was carried out on the text based on the given key. The results of the text encryption were stored in the form of a QR-Code which can later be decrypted from the QR-Code using the key, so that the text contained in the QR-Code can be identified.
Implementasi LDA, TF-IDF, dan BERT dalam Sistem Rekomendasi Dosen Pembimbing untuk Mahasiswa Syabilla, Mutiara; Zeniarja, Junta; Nabila, Qotrunnada
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6499

Abstract

The selection of thesis supervisors is often done manually, which tends to be time-consuming in matching students' research topics with the expertise of faculty members. This study develops a thesis supervisor recommendation system based on the title and abstract of students' final projects, integrating Latent Dirichlet Allocation (LDA), Term Frequency-Inverse Document Frequency (TF-IDF), and Bidirectional Encoder Representations from Transformers (BERT). The research dataset includes 1,096 records from 71 faculty members in the Informatics Engineering Department at Universitas Dian Nuswantoro, collected through Google Scholar. The analysis process begins with text preprocessing such as case folding, tokenization, and stemming, followed by topic analysis using LDA, term-specific weighting through TF-IDF, and context-rich vector representation using BERT. The model matches students' research topics with faculty expertise using Cosine Similarity. Evaluation results show an accuracy of 80%, precision of 66%, and recall of 19%, indicating that the model can provide accurate recommendations, though some relevant items are still missed. This model proves effective in facilitating the selection of thesis supervisors. This research is expected to assist students in finding suitable supervisors and help faculty members identify students with relevant research interests.
Comparative Analysis of Vectorization Methods for Academic Supervisor Recommendations Nabila, Qotrunnada; Luthfiarta, Ardytha; Syabilla, Mutiara; Ahmad, Azizu; Riyanto, Rozaki
Jurnal Teknologi Informasi dan Terapan Vol 11 No 2 (2024): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v11i2.438

Abstract

Selecting final project supervisors often poses challenges for students due to limited lecturer quotas and difficulties in finding suitable expertise matches. This study proposes using the Cosine Similarity method with vectorization approaches such as Bidirectional Encoder Representations from Transformers (BERT), FastText, Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and Word2Vec to enhance the accuracy of recommendation systems. Data sourced from Google Scholar underwent scraping, preprocessing, and vectorization to evaluate the most effective method for understanding context and recommending relevant supervisors. The analysis revealed that BERT and Word2Vec based approaches achieved superior performance, delivering a perfect hit ratio (1.00) and overcoming the limitations of TF-IDF and BoW in capturing technical language. This recommendation system is expected to streamline the supervisor selection process, minimize mismatches, and effectively support academic advisory processes across educational institutions
Co-Authors Ahmad, Azizu Ali, Rabei Raad Ardytha Luthfiarta Christy Atika Sari Eko Hari Rachmawanto Gumelar, Rizky Syah Junta Zeniarja Riyanto, Rozaki Syabilla, Mutiara