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All Journal Prosiding SNATIF International Journal of Artificial Intelligence Research Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal RESISTOR (Rekayasa Sistem Komputer) JURNAL SURYA ENERGY Building of Informatics, Technology and Science bit-Tech Jurnal E-Komtek Jurnal Digital Teknologi Informasi Informanika Aptekmas : Jurnal Pengabdian Kepada Masyarakat
Emilia Hesti
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Journal : Building of Informatics, Technology and Science

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Perancangan Helm Pintar dengan Fitur Keselamatan Deteksi Kantuk Berbasis NodeMCU dan Accelerometer Julianti, Amelia; Salamah, Irma; Hesti, Emilia
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5534

Abstract

Driving safety is a major focus given the high number of accidents caused by drowsy drivers. This article discusses the design of a smart helmet that detects drowsiness to improve rider safety. The smart helmet integrates technology with drowsiness detection to reduce the risk of accidents and provide a safer driving experience. The system uses NodeMCU and MPU6050 Accelerometer to monitor head movement, activating an alarm if the head moves more than 5 degrees, which indicates drowsiness or loss of focus. It is expected that the risk of accidents due to drowsiness can be significantly reduced with this approach. The test results show that the system is able to effectively detect unusual head movements and provide a quick alarm response, thus improving driving safety as expected. In the context of this measurement, the lower error values of 0.70% and 1.18% indicate that the MPU6050 sensor provides more accurate results in measuring the angle against a given reference angle. The angle measurement results between the reference and the MPU6050 sensor show that the value obtained from the sensor is not much different from the reference angle. Although there is a slight difference, the accuracy of the MPU6050 is still reliable for practical purposes, showing consistent performance and close to the actual value. This indicates that the MPU6050 sensor is capable of providing quite precise results, so it can be used as an effective angle measuring device in various applications. The integration of this sensor into smart helmets enables early detection of signs of drowsiness, which can then activate automatic alerts to improve driver safety. Test results also demonstrated the helmet's ability to monitor and send real-time data to ThingSpeak, providing easy-to-understand visualizations, historical data storage, and automatic notifications when signs of drowsiness are detected.
Deteksi URL Phishing Menggunakan Natural Language Processing Dan Support Vector Machine Berbasis Machine Learning Nabila, Nabila; Hesti, Emilia; Aryanti, Aryanti
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7443

Abstract

Phishing represents a significant danger in cybersecurity, using malicious URLs to mislead users into revealing critical information. This research seeks to create a phishing URL detection model using machine learning via the integration of structural URL feature extraction, Natural Language Processing (NLP) methodologies, and the Support Vector Machine (SVM) classification algorithm. Indicators of phishing trends are derived from features such as URL length, the quantity of dots, and slashes, while URL content is quantified as numerical vectors using Term Frequency-Inverse Document Frequency (TF-IDF). All characteristics are subsequently integrated as input into a support vector machine model with a linear kernel for classification. The evaluation results from the classification report indicate that the integration of TF-IDF and linear kernel SVM achieves optimal performance, with 90% accuracy, 92% precision, 89% recall, and 90% F1-score. Conversely, the confusion matrix reveals 90.29% accuracy, 91.66% precision, 88.62% recall, and 90.12% F1-score. This study primarily contributes by integrating NLP and SVM into a unified adaptive phishing detection model via the amalgamation of structural and textual aspects of URLs. This strategy facilitates enhanced phishing detection relative to techniques reliant only on manual characteristics. This model, unlike other research that concentrated on particular instances or excluded NLP, is engineered to identify many categories of phishing URLs broadly, hence enhancing its relevance in tackling the dynamic nature of assaults.
Co-Authors Ade Silvia Handayani Adewasti, Adewasti Afifah, Luthfia Agung, Muhammad Zakuan Ahmad Taqwa Ally Akbar Salim ana, Sarj Apriliza, Anggia Nur Apriliza, Anggia Nur Aryanti Aryanti Asriyadi Asriyadi Fatima, Siti Ibnu Ziad, Ibnu Irawan Hadi Irma Salamah Julianti, Amelia Kaila, Afifah Syifah Leni Novianti LINDAWATI M. Ardiansyah M.Zakuan Agung Nabila, Nabila Oktavia, Nadia Putri, Nurhaliza Aulia Rakhman, M Arief Rosita, Ella Sarjana Sarjana Sholihin Sholihin Simanjuntak, Luckyta Suroso Suroso Suruso, Suruso suzan zefi