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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Buletin Poltanesa Bulletin of Information Technology (BIT) Journal of Artificial Intelligence and Engineering Applications (JAIEA) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Muhammad Ibnu Sa'ad
STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering

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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.
Nonlinear Modeling of Agricultural and Environmental SDG Indicators in ASEAN Using Extreme Learning Machine Algorithms Ita Arfyanti; Muhammad Ibnu Sa'ad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7041

Abstract

This study presents a data-driven approach for modeling Sustainable Development Goal (SDG) indicators in ASEAN countries using the Extreme Learning Machine (ELM) algorithm. Focusing on SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), and SDG 15 (Life on Land), we utilized FAOSTAT datasets from 2020 to 2024 to forecast key indicators such as undernourishment, water use efficiency, and forest area. ELM, known for its rapid learning speed and capability to model nonlinear relationships, outperformed baseline models Linear Regression and Support Vector Machine (SVM) in terms of R² score, RMSE, and MAE. Specifically, ELM achieved R² values exceeding 0.93, with up to 54% RMSE reduction compared to linear models. The model successfully captured national development trends, including deforestation in Indonesia and Cambodia, water stability in Brunei, and varied progress in sustainable agriculture across the region. This study underscores the effectiveness of the Extreme Learning Machine (ELM) in forecasting Sustainable Development Goal (SDG) indicators and provides actionable insights to support evidence based policy planning, particularly in resource-constrained settings. The findings demonstrate that ELM’s combination of interpretability, computational efficiency, and scalability positions it as a highly valuable tool for real-time monitoring of sustainable development across Southeast Asia.
Sistem Pakar Berbasis Web untuk Diagnosis Penanganan Pasca Panen Kelapa Sawit Menggunakan Metode Naive Bayes Heny Pratiwi; Muhammad Ibnu Sa'ad; Muhammad Alamsyah Zakaria
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2.pp259-267

Abstract

This study aims to design a web-based Expert System that is able to diagnose post-harvest handling of oil palm using the Naive Bayes method. In addition, this study also aims to explore optimal harvesting and post-harvest handling management in order to produce high-quality oil yields. This study was conducted at PT Sawit Sukses Sejahtera, the location where the experts work. Data collection was carried out through interviews with experts related to post-harvest handling of oil palm fruit, as well as literature studies to obtain data relevant to the research topic. The Naive Bayes method is used based on the probability found in the post-harvest handling process of oil palm, while system development follows the ESDLC (Expert System Development Life Cycle) methodology, which is the basis for designing and developing expert systems.
Development of Web and Android Based Employee Attendance Monitoring Application Heny Pratiwi; Nur Fitriani; Eko Junirianto; Muhammad Ibnu Sa'ad
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.738

Abstract

This research was conducted to develop an Android-based employee attendance monitoring system that can assist the Department of Manpower and Transmigration of East Kalimantan Province in monitoring employee attendance, recapitulating employee attendance, and timely submission of attendance reports. The objective of this research is to simplify employee attendance monitoring and expedite the recapitulation of employee attendance lists at the Department of Manpower and Transmigration of East Kalimantan Province. The system development method used is the prototype model. This method consists of five stages: Communication, Quick Plan, Modeling Quick Design, Construction of Prototype, and Deployment Delivery & Feedback. The result of this research is a web-based information system for Administrators and Direct Supervisors to process data and monitor employee attendance, and an Android-based system for employees to record their check-in and check-out times. In the Android-based system, employees can also input attendance with various remarks such as early leave, absence, sick leave, personal leave, business trips, and external duties. The blackbox testing in this research shows that the system functions as expected, and the betabox testing results in a score of 89.60%.
Co-Authors Ahmad Fahrijal Pukeng Alysa Anggelia Y Eko Junirianto Fernanda Kalvin Fitra Abdurrachman Bachtiar Helmelya Putri Jelita Heny Pratiwi Ita Arfyanti Lailil Muflikhah Muhammad Alamsyah Zakaria Nur Fitriani Wahyuni - Wahyuni