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Bulletin of Information Technology (BIT)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27220524     DOI : 10.47065/bit.v2i3.106
Core Subject : Science,
Computer Science & IT
Jurnal Bulletin of Information Technology (BIT) memuat tentang artikel hasil penelitian dan kajian konseptual bidang teknik informatika, ilmu komputer dan sistem informasi. Topik utama yang diterbitkan mencakup:berisi kajian ilmiah informatika tentang : Sistem Pendukung Keputusan Sistem Pakar Sistem Informasi, Kriptografi Pemodelan dan Simulasi Jaringan Komputer Komputasi Pengolahan Citra Dan lain-lain (topik lainnya yang berhubungan dengan teknologi informasi)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 17 Documents
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Search results for , issue "Vol 6 No 2: Juni 2025" : 17 Documents clear
Rekomendasi Sparepart Pada Bengkel Robbi Motor Berbasis Algoritma Apriori Suharni; Putri Husain, Nursuci; Atsari Hardiman, Ashriyanto
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.v6i2.2024

Abstract

The development of transportation, especially two-wheeled motorized vehicles, drives the increasing demand for maintenance services and the availability of spare parts. However, many workshops still face challenges in managing spare parts stock, which is handled manually. This study aims to design and develop a spare parts recommendation system at Robbi Motor Workshop using the Apriori Algorithm, as well as to test the performance of the developed system. The method used is data mining with association techniques, where the Apriori Algorithm is applied to discover spare parts purchasing patterns from transaction data. The system enables users to analyze transactions based on a selected time range without the need to manually input minimum support and confidence values. The results show that the system is capable of generating relevant association rules, such as: “If consumers buy Engine Oil, then consumers will also buy Axle Oil”, with a support value of 67% and a confidence value of 86%. In addition, the system’s accuracy was tested using the lift value against two recommendation rules: (1) Engine Oil → Axle Oil with a lift value of 0.9949, and (2) Inner Tire → Axle Oil with a lift value of 1.0714. A lift value > 1 indicates that the combination of items has a stronger association than random occurrence. The system is implemented as a web-based application using the Laravel framework, equipped with features for transaction data management, Apriori analysis, analysis history, and exporting analysis results to PDF format. Testing using the blackbox method shows that the system operates according to specifications and produces accurate outputs. With this recommendation system, it is expected that the workshop can improve the efficiency of spare parts stock management.
Implementasi Seed Phrase Dalam Keamanan Dompet Kripto Pada Metamask Kalvin, Fernanda; Sa'ad, Muhammad Ibnu; Pukeng, Ahmad Fahrijal
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.
Evaluation Of COCOMO Model Accuracy In Software Effort Estimation Jeklin, Umar; Ibnu Saad, Muhammad; ekawati, Hanifah
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.v6i2.2027

Abstract

Accurate effort estimation underpins on-time,on-budget software delivery. This study empirically assesses the baseline Constructive cost Model (COCOMO) by applying standard organic-mode parameters (a = 2.4, b = 1.05) to the COCOMONASA dataset, which contains 63 NASA projects ranging from 2 KLOC to 100 KLOC. Model ourputs are benchmarked against recorded person-month effort using Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), and Predcitions at 25 percent error (PRED 0.25). Results show MAE values 295-661 person-months and an MMRE near 1.0, indicating average relative error of ~100 percent. PRED (0.25) equals 0.0, meaning no project is estimated within the industry-accepted 25% band. Sensitivity tests on 5- and 20-project subsets reveal similar patterns, confiriming that the inaccuracy is systemic rather than dataset-specific. Using uncalibrated COCOMO in present-day projects poses a high risk of severe under- or over allocation of resources, potentially trigerring budget overruns and schedule slips. By quantitatively exposing where and how the baseline model fails, this work provides a benchmark for and a roadmap toward-targeted parameter calibration and hybrid approaches that incorporate additional cost drivers or machine-learning techniques. Future research should explore automatic parameter tuning and context-aware hybrid models to achieve dependable effort estimation in contemporary software engineering.
Penerapan Algoritma Naïve Bayes Dalam Analisis sentiment Masyarakat Terhadap STMIK Widya Cipta Dharma Putri Jelita, Helmelya; Ibnu Sa'ad, Muhammad; 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.
Analisis Sentimen Penerapan Deep Learning dan Analisis Sentimen terhadap Gap Kompetensi Lulusan Lembaga Pendidikan dan Pelatihan Vokasi terhadap Dunia Kerja dengan Metode Long Short-Term Memory (LSTM) Yahya, Susilawati; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
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.v6i2.2031

Abstract

The gap between vocational graduates’ competencies and labor market demands remains a pressing issue in Indonesia. This study aims to analyze alumni perceptions regarding the alignment between competencies acquired during their studies at LP3I Banda Aceh and real-world job requirements. A quantitative approach was adopted using a deep learning method based on Long Short-Term Memory (LSTM). Data were collected through an online survey containing open-ended responses from 934 alumni, followed by preprocessing, tokenization, lexicon-based sentiment labeling, and data splitting into training and testing sets. The models developed included pure LSTM, LSTM with class weights, and Bidirectional LSTM (BiLSTM). Results indicate that BiLSTM achieved the highest performance with 90% accuracy and a weighted F1-score of 0.91. Additionally, 44.5% of respondents expressed neutral or negative sentiments, highlighting a mismatch between acquired competencies and industry demands. These findings underscore the urgency of curriculum evaluation and stronger collaboration between vocational institutions and the labor market. This study demonstrates that deep learning offers an efficient and objective tool for competency mapping in vocational education.
Implementasi Sistem Pembuatan Soal Otomatisasi Pembelajaran Pendidikan Agama Islam Dengan Menggunakan Langchain Dan Llm Berbasis Gemini Fahrezy, Irgi; Harahap, Nazruddin Safaat; Wulandari, Fitri; Agustian, Surya
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.v6i2.2032

Abstract

This study examines the implementation of an automated question generation system in the context of Islamic Religious Education (PAI) using LangChain technology and a Gemini-based Large Language Model (LLM). The research methodology includes data collection, needs analysis, system architecture design, implementation, and validation through black-box testing and expert evaluation. The system is designed to automatically generate questions aligned with instructional content and the cognitive levels of Bloom's Taxonomy, ranging from factual knowledge to evaluation and creation. The testing process involved six expert evaluators with educational backgrounds and experience in PAI. Results indicate that the system successfully produced high-quality questions, with an average approval rate of 96.89%. Differentiated scores revealed the highest performance in theoretical interpretation questions (100%) and the lowest in critical analysis (30%), indicating varying system capabilities across the cognitive spectrum. This study demonstrates that integrating artificial intelligence into question generation is highly feasible and yields significant outcomes, although challenges remain in producing questions that stimulate higher-order and authentic thinking.
Sentiment Analysis Classification of E-commerce User Reviews Using Natural Language Processing (NLP) and Support Vector Machine (SVM) Methods Iqbal Wiranata Siregar, Jimmy; Putera Utama Siahaan, Andysah; Iqbal, Muhammad; Nasution, Darmeli; Farta Wijaya, Rian
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.v6i2.2018

Abstract

In the swiftly changing digital age, e-commerce has become a vital component of everyday living. Individuals actively share product reviews, whether favorable or unfavorable, which companies can utilize to grasp users' views on their services. An efficient approach for evaluating and categorizing user sentiments is required to aid in analyzing these reviews. In this scenario, the Support Vector Machine (SVM) and Natural Language Processing (NLP) methods offer the appropriate answer. This research intends to develop a classification model capable of sorting e-commerce user feedback into positive, negative, or neutral sentiments. Utilizing NLP methods to analyze the review text and SVM as the classification approach, this model aims to achieve high accuracy in identifying user sentiment. Words that do not affect sentiment analysis, like "and," "that," "for," are eliminated, and SVM is utilized once the review data is converted into vectors via the TF-IDF method. The labeled sentiment training data will be used to train the SVM model.

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