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All Journal Buletin Poltanesa Bulletin of Information Technology (BIT)
Muhammad Ibnu Sa'ad
STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Computer Science & IT Education

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Implementasi Seed Phrase Dalam Keamanan Dompet Kripto Pada Metamask Fernanda Kalvin; Muhammad Ibnu Sa'ad; Ahmad Fahrijal Pukeng
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2026

Abstract

Seed phrases are a crucial element in the security system of non-custodial crypto wallets like MetaMask. These phrases allow users to recover their wallets and serve as the primary key to access digital assets. This research aims to analyze and implement seed phrase-based security in crypto wallets, using MetaMask as a case study. Through literature review and technical simulation, this study explains how seed phrases function, the potential risks if compromised, and possible mitigation strategies. The results show that while seed phrases are vital for maintaining user asset security and integrity, they can be a vulnerability if not properly protected.
Penerapan Algoritma Naïve Bayes Dalam Analisis sentiment Masyarakat Terhadap STMIK Widya Cipta Dharma Helmelya Putri Jelita; Muhammad Ibnu Sa'ad; Wahyuni
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2029

Abstract

This study applies the Naïve Bayes algorithm to analyze public sentiment toward STMIK Widya Cipta Dharma using Google Maps reviews as the primary data source. The research aims to classify community perceptions into three categories: positive, neutral, and negative. The methodology follows the CRISP-DM framework, incorporating stages such as data preprocessing (text cleaning, stopword removal, and stemming), TF-IDF for feature extraction, and SMOTE to address class imbalance. Sentiment labels were derived from a combination of review ratings (1–5 stars) and textual content. Results indicate that Naïve Bayes achieved 91% accuracy in classifying the majority (positive) class but struggled with minority classes (neutral and negative), yielding 0% precision and recall for these categories. After applying SMOTE, recall for the negative class improved to 100%, although overall accuracy dropped to 38%, reflecting a trade-off between balanced class recognition and model performance. The study highlights the algorithm's effectiveness in handling large-scale text data but underscores challenges in managing imbalanced datasets. These findings provide actionable insights for STMIK Widya Cipta Dharma to enhance service quality and institutional image by leveraging public feedback. Future research could explore hybrid algorithms or advanced preprocessing techniques to optimize sentiment analysis accuracy across all classes.
Perbandingan SVM dan IndoBERT untuk Analisis Sentimen Layanan Akademik Mahasiswa Muhammad Ibnu Sa'ad; Lailil Muflikhah; Fitra Abdurrachman Bachtiar
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2913

Abstract

Digital transformation in higher education has generated an increasing volume of textual data, including student comments, academic service evaluations, and feedback on academic information systems. These data contain valuable information for supporting decision-making; however, their unstructured and contextual nature makes manual analysis inefficient. This study aims to compare the performance of a TF-IDF-based Support Vector Machine (SVM) model and a Transformer-based IndoBERT model for sentiment analysis of academic services from student feedback. The dataset consists of 1,700 text entries, combining template-based synthetic data and real-world data collected from social media, which were classified into positive, negative, and neutral sentiment categories. The research process involved exploratory data analysis, text preprocessing, feature extraction, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that both models achieved very high performance on the dataset, with an accuracy of 100% on the test set. These findings indicate that both traditional machine learning and Transformer-based approaches are capable of identifying sentiment patterns within the dataset. Nevertheless, the results should be interpreted cautiously, as the relatively homogeneous nature of the dataset and the inclusion of synthetic data may affect the models’ generalizability. The main contribution of this study lies in the comparative evaluation of SVM and IndoBERT within the context of higher education academic services, as well as the development of a sentiment analysis framework that can support data-driven service quality monitoring. Future studies should employ larger, more diverse datasets derived entirely from real-world sources to further validate the findings.
Analysis of Visitor Sentiment Towards the Public Facilities at Teras Samarinda Using Naive Bayes Algorithm Alysa Anggelia Y; Muhammad Ibnu Sa'ad; Heny Pratiwi
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3712

Abstract

This study aims to analyze visitor sentiment toward public facilities at Teras Samarinda based on user-generated reviews collected from digital platforms. The increasing number of online reviews provides valuable insights into visitor satisfaction; however, manual analysis is inefficient due to the large volume of data. Therefore, this research applies a text mining approach to automatically classify sentiments into positive, negative, and neutral categories. The dataset consists of 165 comments obtained from YouTube, representing visitor experiences and opinions. The preprocessing stage includes case folding, cleaning, tokenization, stopword removal, and stemming to ensure data quality. Subsequently, Term Frequency–Inverse Document Frequency is used to transform textual data into numerical features. The classification process is performed using the Naive Bayes algorithm. The dataset is divided into training and testing data to evaluate model performance using accuracy, precision, recall, and F1-score metrics. The results show that the model achieves an accuracy of 75.75%, indicating a relatively good performance in classifying sentiments. However, the model demonstrates limitations in distinguishing negative and neutral sentiments due to imbalanced data distribution. The findings reveal that most visitors express positive sentiment toward public facilities at Teras Samarinda, suggesting overall satisfaction. This study contributes to providing insights for improving facility quality and highlights the importance of handling imbalanced datasets in sentiment analysis.
Co-Authors Ahmad Fahrijal Pukeng Alysa Anggelia Y Fernanda Kalvin Fitra Abdurrachman Bachtiar Helmelya Putri Jelita Heny Pratiwi Lailil Muflikhah Wahyuni - Wahyuni