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All Journal Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Ilmiah Matrik Informatik : Jurnal Ilmu Komputer SEINASI-KESI Jurnal Riset Informatika JSR : Jaringan Sistem Informasi Robotik INTEGRITAS : Jurnal Pengabdian SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat (Nadimas) Journal of Current Research in Blockchain
Ria Astriratma
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Public Health Social Sciences

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Classification of Indonesian Tale Categories using Support Vector Machine and FastText Feature Extraction Irmanda, Helena Nurramdhani; Astriratma, Ria; Zaidiah, Ati; Hadi, Muhammad Rahman; Putra, Nayandra Agastia
Telematika Vol 21 No 2 (2024): Edisi Juni 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i2.10867

Abstract

The purpose of this work is to develop a model to classify the various kinds of Indonesian folktales and to assess how well the support vector machine (SVM) approach and fastText feature extraction perform. The first phase of the study process is the gathering of data, namely the fairy tale dataset that has been annotated with categorizations for each genre of fairy tale. Following the collection of data, the pre-processing step is conducted. The purpose of the pre-processing step is to prepare the data for further processing in the subsequent stage. Following the completion of the preprocessing step, the training data and testing data are segregated. The subsequent step involves doing feature extraction using fastText. Moreover, the classification process is conducted using the Support Vector Machine (SVM) approach in order to get the ultimate outcome of the modeling process. The last phase involves assessing the performance of the constructed model. The categorization model for Indonesian fairy tales has a commendable accuracy rate of 85%, indicating its effectiveness. The aforementioned findings are substantiated by an accuracy metric of 85%, a recall metric of 85%, and an F1-score of 86%, indicating favorable outcomes.Previous researchs have not conducted any studies on the categorization of types of Indonesian fairy tales.
A Study of Known Vulnerabilities and Exploit Patterns in Blockchain Smart Contracts Astriratma, Ria
Journal of Current Research in Blockchain Vol. 2 No. 3 (2025): Regular Issue September 2025
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v2i3.40

Abstract

Blockchain smart contracts are pivotal to decentralized applications, yet their security remains a critical challenge. This study analyzes a dataset of 1,000 smart contracts to investigate known vulnerabilities, audit practices, and exploit patterns. The results reveal that audited contracts are significantly less prone to exploitation, with 75% exhibiting no exploit history compared to 55% of non-audited contracts. "Integer Overflow" and "Unchecked Call" were identified as the most prevalent vulnerabilities, contributing to 60% and 50% exploit rates, respectively. The study highlights the importance of transparent audit reporting, as contracts without available reports were exploited in 35% of cases. Additionally, hidden vulnerabilities in ostensibly secure contracts underscore the evolving sophistication of blockchain threats. This research emphasizes the need for robust security practices, including stricter coding standards, comprehensive audits, and advanced vulnerability detection techniques such as formal verification and machine learning. Future works aim to integrate security tools into development workflows and foster industry-wide collaboration to standardize auditing practices, thereby enhancing the security and trustworthiness of blockchain ecosystems.
Co-Authors Andhika Octa Indarso Fitria Adyati Mardha Fosu , Agyei Hadi, Muhammad Rahman Henki Bayu Seta Irmanda, Helena Nurramdhani Irmanda, Helena Nurramdhani Isnainiyah, Ika Nurlaili Jayanta Jayanta Mega Santoni, Mayanda Muslim, Muhammad Panji Niqotaini, Zatin Nurramdhani Irmanda, Helena Putra, Nayandra Agastia Santoni, Mayanda Mega Theresiawati Widi Pradnyana, I Wayan Widiastiwi, Yuni Zaidiah, Ati