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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Information Systems and Informatics Journal of Business and Audit Information System (JBASE)
Sarah Rosdiana Tambunan
Institut Teknologi Del
Computer Science & IT Control & Systems Engineering Engineering

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 1 
C Source code Obfuscation using Hash Function and Encryption Algorithm Sarah Rosdiana Tambunan; Nur Rokhman
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 17, No 3 (2023): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.86118

Abstract

Obfuscation is a technique for transforming program code into a different form that is more difficult to understand. Several obfuscation methods are used to obfuscate source code, including dead code insertion, code transposition, and string encryption. In this research, the development of an obfuscator that can work on C language source code uses the code transposition method, namely randomizing the arrangement of lines of code with a hash function and then using the DES encryption algorithm to hide the parameters of the hash function so that it is increasingly difficult to find the original format. This obfuscator is specifically used to maintain the security of source code in C language from plagiarism and piracy. In order to evaluate this obfuscator, nine respondents who understand the C programming language were asked to deobfuscate the obfuscated source code manually. Then the percentage of correctness and the average time needed to perform the manual deobfuscation are observed. The evaluation results show that the obfuscator effectively maintains security and complicates the source code analysis.
Enhancing Credit Risk Classification Using LightGBM with Deep Feature Synthesis Tambunan, Sarah Rosdiana; Amalia, Junita; Sitorus, Kristina Margaret; Sibuea, Yehezchiel Abed Rafles; Hutabarat, Lucas Ronaldi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.902

Abstract

In the digital financial services era, Peer-to-Peer (P2P) lending has emerged as a significant innovation in fintech. However, credit risk remains a major concern due to the potential for payment failures, which can cause losses for platforms and investors. This research explores the impact of Deep Feature Synthesis (DFS) on credit risk classification and evaluates the performance of the Light Gradient Boosting Machine (LightGBM) algorithm with and without DFS. The data used in this study was sourced from Kaggle, a peer-to-peer lending company based in San Francisco, California, United States. The dataset contains 74 attributes, with a total of 887,379 rows. DFS automatically generates new attributes, while LightGBM is used for selecting the most important features, aiming to optimize credit risk predictions and simplify the model's complexity. The results of credit risk classification models using DFS and without it. Findings reveal that DFS enhances the accuracy of the credit risk classification, achieving a 0.99 accuracy rate compared to 0.97 without DFS, achieving a recall and F1-score of 0.94 and 0.96 with DFS and 0.68 and 0.81 without DFS. These results suggest that DFS is an effective feature engineering technique for boosting credit risk model performance. This research contributes significantly to the P2P lending industry by demonstrating that combining DFS with LightGBM can improve credit risk management, making it a valuable approach for financial platforms.
Enhancing Hate Speech Detection: Leveraging Emoji Preprocessing with BI-LSTM Model Amalia, Junita; Tambunan, Sarah Rosdiana; Purba, Susi Eva Maria; Simanjuntak, Walker Valentinus
Journal of Information System and Informatics Vol 7 No 2 (2025): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i2.1147

Abstract

Microblogging platforms like Twitter enable users to rapidly share opinions, information, and viewpoints. However, the vast volume of daily user-generated content poses challenges in ensuring the platform remains safe and inclusive. One key concern is the prevalence of hate speech, which must be addressed to foster a respectful and open environment. This study explores the effectiveness of the Emoji Description Method (EMJ DESC), which enhances tweet classification by converting emojis into descriptive text or sentences. These descriptions are then encoded into numerical vector matrices that capture the meaning and emotional tone of each emoji. Integrated into a basic text classification model, these vectors help improve detection performance. The research examines how different emoji preprocessing strategies affect the performance of a BI-LSTM model for hate speech classification. Results show that removing emojis significantly reduces accuracy (68%) and weakens the model’s ability to distinguish between hate and non-hate speech, due to the loss of valuable semantic context. In contrast, retaining emoji semantics either through textual descriptions or embeddings boosts classification accuracy to 93% and 94%, respectively. The highest performance is achieved through emoji embedding, highlighting its ability to capture subtle non-verbal cues critically for accurate hate speech detection. Overall, the findings emphasize the importance of incorporating emoji-aware preprocessing techniques to enhance the effectiveness of social media content classification.
Indonesian automated short-answer grading using transformers-based semantic similarity Situmeang, Samuel; Tambunan, Sarah Rosdiana; Ginting, Lidia; Simamora, Wahyu Krisdangolyanti; ButarButar, Winda Sari
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 3: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i3.pp1034-1043

Abstract

Automatic short answer grading (ASAG) systems offer a promising solution for improving the efficiency of reading literacy assessments. While promising, current Indonesian artificial intelligence (AI) grading systems still have room for improvement, especially when dealing with different domains. This study explores the effectiveness of large language models, specifically bidirectional encoder representations from transformers (BERT) variants, in conjunction with traditional hand-engineered features, to improve ASAG accuracy. We conducted experiments using various BERT models, hand-engineered features, text pre-processing techniques, and dimensionality reduction. Our findings show that BERT models consistently outperform traditional methods like term frequency-inverse document frequency (TF-IDF). IndoBERTLite-Base-P2 achieved the highest quadratic weighted kappa (QWK) score among the BERT variants. Integrating handengineered features with BERT resulted in a substantial enhancement of the QWK score. Utilizing comprehensive text pre-processing is a critical factor in achieving optimal performance. In addition, dimensionality reduction should be carefully used because it potentially removes semantic information.
Implementing Internal Audit for WebTrust CA/NS in Digital Signature Companies Tambunan, Sarah Rosdiana; Sihombing, Indah Elisa; Sibarani, Tiarani
JBASE - Journal of Business and Audit Information Systems Vol 8, No 2 (2025): JBASE - Journal of Business and Audit Information Systems
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/jbase.v8i2.8870

Abstract

Internal audit plays a crucial role in ensuring the readiness and compliance of Certificate Authorities (CAs) with internationally recognized information security standards, particularly the WebTrust for Certification Authorities and Network Security. This topic has become increasingly relevant due to the growing demand for reliable and regulation compliant digital signature services. This study aims to examine the implementation of internal audit based on the WebTrust standard within a digital signature service provider in Indonesia. The research adopts a qualitative descriptive approach, utilizing document analysis and in-depth interviews with personnel performing trusted roles. The internal audit process consists of five main stages: defining the audit scope, mapping relevant controls to the standard, collecting evidence from operational processes and systems, evaluating the implementation’s conformity, and compiling the final report along with improvement recommendations. The audit findings reveal that most issues are concentrated in CA Environmental Controls, which includes asset management, physical security, and the documentation of policies and procedures. These findings indicate the need for strengthening controls in these areas to enhance the organization’s security posture. The internal audit has proven effective in identifying weaknesses early, providing relevant improvement recommendations, and serving as a strategic step in preparing for the external WebTrust audit. Thus, internal audit can be a critical instrument in improving system reliability, strengthening information security governance, and maintaining trust in digital signature services.
Transformer and text augmentation for tourism aspect-based sentiment analysis Situmeang, Samuel; Tambunan, Sarah Rosdiana; Jevania, Jevania; Simanjuntak, Mastawila Febryanti; Sinaga, Sandro
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4614-4622

Abstract

The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Co-Authors ButarButar, Winda Sari Ginting, Lidia Hutabarat, Lucas Ronaldi Jevania, Jevania JUNITA AMALIA Nur Rokhman Purba, Susi Eva Maria Samuel Indra Gunawan Situmeang Sibarani, Tiarani Sibuea, Yehezchiel Abed Rafles Sihombing, Indah Elisa Simamora, Wahyu Krisdangolyanti Simanjuntak, Mastawila Febryanti Simanjuntak, Walker Valentinus Sinaga, Sandro Sitorus, Kristina Margaret Situmeang, Samuel