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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 256 Documents
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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.
Klasifikasi Genre Musik Menggunakan Machine Learning Garda Zidane Dhamara; Sucipto
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2021

Abstract

This study examines the implementation of music genre classification using Machine Learning to develop an accurate and efficient music recommendation application. The main problem addressed is the automatic identification of music genres to improve recommendation personalization. The method used involves applying Machine Learning algorithms to a music dataset. The objective of this research is to build a system capable of automatically classifying music genres and serving as a foundation for a smarter recommendation system. Preliminary results indicate that Machine Learning is effective in music grouping, which will contribute to increased recommendation accuracy. This research is expected to make a significant contribution to the development of intelligent music applications.
Implementasi Naïve Bayes untuk Klasifikasi Peminatan Program Studi pada Penerimaan Mahasiswa Baru di Fakultas Ilmu Komputer Unika Munawirah, Munawirah; Arisha, Andriansyah Oktafiandi
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2142

Abstract

Fakultas Ilmu Komputer Universitas Tomakaka memiliki dua program studi, yaitu Sistem Informasi dan Teknik Informatika. Namun, dalam praktiknya, calon mahasiswa baru sering mengalami kebingungan dalam menentukan jurusan yang sesuai dengan kemampuan dan latar belakang akademiknya. Pemilihan program studi umumnya didasarkan pada tren jurusan favorit, dorongan eksternal, atau preferensi sosial tanpa mempertimbangkan jurusan asal di sekolah sebelumnya. Kondisi tersebut berpotensi menimbulkan ketidaksesuaian minat yang berdampak pada risiko penurunan motivasi belajar, pindah jurusan, berhenti kuliah, atau mengalami hambatan selama masa studi. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi program studi menggunakan metode klasifikasi Naïve Bayes guna memprediksi kecenderungan peminatan program studi berdasarkan atribut input seperti jenis kelamin, asal sekolah, dan jurusan asal sekolah. Dataset yang digunakan merupakan data historis penerimaan mahasiswa baru Fakultas Ilmu Komputer Universitas Tomakaka sejak tahun akademik 2015/2016 hingga 2024/2025, sebanyak 1.046 entri data. Proses analisis mencakup tahapan data mining, mulai dari seleksi dan pembersihan data, pembagian data latih dan data uji (80:20), hingga evaluasi performa menggunakan metode Confusion Matrix. Hasil evaluasi menunjukkan akurasi sebesar 87,14%, presisi 89,91%, recall 87,70%, dan F1-score 88,76%. Model ini diimplementasikan ke dalam aplikasi berbasis website menggunakan framework Flask, guna mempermudah pemberian rekomendasi jurusan secara real-time. Pendekatan ini memberikan kontribusi sistem rekomendasi berbasis data yang membantu institusi dalam memetakan minat mahasiswa, menyusun strategi promosi yang tepat sasaran, serta memberikan intervensi awal terhadap pilihan program studi mahasiswa baru yang kurang sesuai.
Face Recognition Motorcycle Rider Registration System for Rider Data Management Saputra S, Kana; Taufik, Insan; Ramadhani, Irham; Sasalia S, Putri; Syawali, Yusfi; Yusuf, Dede; Nadilla Putri, Rezkya; Latifah Hasibuan, Najwa; Hafiz Harahap, Fauzan
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2157

Abstract

This research aims to develop a motorcycle rider registration system using facial recognition technology that can improve the efficiency of rider data management. This system is designed to identify and authenticate riders with high accuracy, thereby simplifying the registration and monitoring process. The methods used in this research include collecting rider facial data through cameras, image processing for feature extraction, and implementing a facial recognition algorithm. Testing was conducted in several locations with varying lighting conditions and viewing angles to ensure the system's robustness. The results show that the developed system is capable of achieving facial recognition accuracy of up to 95%. In addition, this system provides an intuitive user interface to facilitate the registration and data management process. With the implementation of this system, it is expected to reduce the time and costs required in managing motorcycle rider data, as well as improve safety and comfort while riding.
Rancang Bangun Prototype Sistem Monitoring Dan Kontrol Tanaman Hidroponik Berbasis Internet of Things (IoT) Menggunakan Microcontroller ESP32 Ferdiansyah, Veri; Siska, Siska Atmawan Oktavia; Mulyanto, Yudi; Yunanri.W
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2198

Abstract

Hydroponic farming has emerged as an innovative solution to address land limitations and support sustainable food production. However, its success highly depends on consistent monitoring of light intensity and nutrient water availability. This study aims to design and develop a prototype monitoring and control system for hydroponic plants based on the Internet of Things (IoT) using the ESP32 microcontroller. The system employs an ultrasonic sensor to measure water level, a Light Dependent Resistor (LDR) sensor to detect light intensity, a 12V DC water pump, and LED grow lights as actuators. Environmental condition data is transmitted in real-time to the Blynk mobile application, which also provides automatic notifications when anomalies occur, such as low water levels or light intensity falling below the threshold. The development method used is Research and Development (R&D) with the ADDIE model, covering analysis, design, development, implementation, and evaluation stages. Testing results show that the system operates automatically and in real-time, achieving 100% detection accuracy for water level measurements and 98% for light intensity measurements. The implementation of this prototype is expected to improve the efficiency and effectiveness of small-scale hydroponic cultivation and serve as an affordable solution for farmers and the general public to adopt smart farming technology.

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