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All Journal JURNAL INTEGRASI Jurnal Elementer (Elektro dan Mesin Terapan) Proceeding Applied Business and Engineering Conference
Fahruzi, Iman
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Automatic Door Lock Based on Knock Pattern and Face Detection Zulhaq, Danang Fariz; Fahruzi, Iman
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Residential burglaries are prevalent offenses, frequently occurring during the absence of homeowners. These offensesgenerally entail breaching doors or windows. An innovative home security system is important to resolve this issue. This researchseeks to create an automated door locking mechanism utilizing knock patterns and facial recognition to improve residential security.The system incorporates a piezoelectric sensor for detecting knock patterns and an ESP32-Cam for facial recognition. The studymethodology entails the development and evaluation of a system that integrates two primary components, guaranteeing that thedoor unlocks solely upon the recognition of both an accurate knock pattern and a registered facial image. The system's accuracywas assessed under varying lighting situations and distances to determine its efficacy. The results indicate that the face detectionsystem operates effectively under optimal lighting settings, and the knock pattern system activates the door lock mechanism whenthe knock intervals correspond to the pre-registered pattern. Nonetheless, the system encounters difficulties in recognizing faces inlow-light conditions. The door lock is secure, as it will only unlock when both the appropriate facial recognition and knock patterncriteria are satisfied, thus improving security relative to conventional locks. This dual-layer security strategy mitigates the dangersinherent in traditional systems, such as keys or PINs, which are susceptible to theft or circumvention. The proposed technologysignificantly enhances home security, presenting a more secure and user-friendly alternative to current options. Futureenhancements may concentrate on augmenting the precision of facial detection in low-light environments and refining the systemfor wider practical applications.
Smart Access Control System Based On Uncontrolled Environment Human Face Recognition Using Convolutional Neural Network Akbar, Muhammad Ikram Andrianur; Rinaldi, Anggi; Fahruzi, Iman
ABEC Indonesia Vol. 12 (2024): 12th Applied Business and Engineering Conference
Publisher : Politeknik Negeri Bengkalis

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Abstract

Neural networks or other artificial intelligence methods have developed rapidly over the past four decades. Itsuse in various fields makes many people compete to develop it further. One of the applications of artificial intelligence isan automatic door opening and closing system. This development can provide many advantages for users, one of which isthat there is no need to make direct contact with the door handle. Armed with a capable PC and an esp32 microcontroller,the system works by detecting images of user facial expressions approaching the object using a webcam. If the requiredinput matches the system rules, the motor will move to open the door. By using convolutional neural network technique,the system can classify the image quickly. Several expressions such as angry, disgusted, scared, happy, sad, surprised, andnormal can be the door-opening key of the system. The user can select one to use as the input key to drive the motor toopen the door. The study outcomes for several predetermined facial expressions yielded an accuracy rate of 60% and adetection time of under 4 seconds. The detectable distance extends to ± 2 meters. Further study could enable thedevelopment of this autonomous door with an IoT-based system for enhanced efficiency. Hopefully, this research caninfluence the development of intelligent building systems and other fields of artificial intelligence technology.
Pengujian Komunikasi Perangkat Lora untuk Pengiriman Data Detak Jantung Menggunakan Topologi Point to Point Berbasis LoraWAN Fahruzi, Iman; Timanta, Febrian Harlim; Panjaitan, Junaedi Satrio; Ferdinan, Wisnu; Marpaung, John Purba; Silalahi, Laurent; Ricardo, Riki; Oktani, Dessy; Wikanta, Prasaja
JURNAL INTEGRASI Vol. 15 No. 2 (2023): Jurnal Integrasi - Oktober 2023
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v15i2.6296

Abstract

Penelitian ini merupakan implementasi protokol LoraWan untuk mengirimkan data sensor berupa rekaman detak jantung secara point to point. Memanfaatkan keunggulan jaringan LoraWAN, data yang dikirim memiliki jangkauan yang jauh dan berdaya rendah sehingga bisa digunakan untuk sistem telemedis pada daerah yang secara geografis sulit dijangkau untuk akses fasilitas kesehatan. Data rekaman detak jantung yang didapat dari setiap sensor yang terkoneksi dan terintegrasi dengan sistem telemedis akan melakukan pengiriman secara periodik. Selanjutnya data yang diterima diteruskan kepada server melalui LoraWAN Gateway. Sistem telemedis ini terdiri dari sensor EKG, arduino dengan jaringan lora, satu LoraWAN Gateway dan server yang terintegrasi dengan pusat data berbasis web dan aplikasi android. Hasil Pengujian menunjukkan konektivitas antara tiga titik sensor dari beberapa lokasi mampu mengirimkan data sensor dengan baik dengan jarak terbatas kurang dari satu kilometer.
Project Based Learning: Sistem Otentifikasi melalui Deteksi Wajah untuk Akses Pintu Otomatis Berbasis Raspberry Pi Alifiansyah, Irfan; Akmal, Muhamad Raihan; Febrianto, Wahyu; Dwijotomo, Abdurahman; Fahruzi, Iman
JURNAL INTEGRASI Vol. 16 No. 2 (2024): Jurnal Integrasi - Oktober 2024
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v16i2.7646

Abstract

Security concerns are of utmost importance in our daily lives. Conventional door locking systems that rely on physical keys possess vulnerabilities in terms of security. Physical keys are susceptible to tampering, theft, and effortless replication. Hence, it is imperative to devise a novel approach that may effectively mitigate this issue. An example of technological use for alternative locks involves utilizing face recognition techniques to grant or deny access to doors depending on the data associated with the individual seeking entry. The primary objective of this study is to create a facial identification approach by employing machine learning techniques, namely the histogram of oriented gradients (HOG) method in conjunction with a linear Support Vector Machine (SVM). This technique is designed to be easily implemented on a Raspberry Pi 4-based Single Board Computer (SBC) that features a video sensor for machine learning input and a doorlock solenoid output. Initially, it is important to train the machine learning algorithm to accurately identify and distinguish the individual who is granted access to the door. The facial data is obtained through the capture of photographs that encompass variations in facial expression, positioning, and lighting conditions. The facial data photos are further analyzed using machine learning techniques to generate a dataset algorithm model capable of accurately identifying faces. When the system is operational and identifies a face that closely matches the trained model, the Raspberry Pi will activate the doorlock solenoid to unlock the door, and conversely, to lock the door. This approach offers security benefits as it restricts access to only those individuals whose facial features are registered in the dataset, hence allowing them to unlock the door. The developed face detection system has an accuracy rate of 83% and is compatible with computing devices possessing constrained computational capabilities, such as the SBC Raspberry Pi 4.
Pengukuran Sinyal dari Sensor Accelerometer dan Gyroscope untuk Menentukan Gerak Jatuh pada Lansia Menggunakan Metode Ambang Batas (Threshold) Kristian Hutagalung, Yogi; Fahruzi, Iman
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 2 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i2.6695

Abstract

Penelitian ini bertujuan untuk mengembangkan perangkat wearable pendeteksi jatuh bagi lansia menggunakan metode ambang batas (threshold). Risiko jatuh pada lansia merupakan masalah serius karena dapat menyebabkan cedera berat hingga kematian, terutama jika bantuan tidak segera diberikan. Sebagai kelompok rentan, lansia memerlukan solusi teknologi yang mampu mendeteksi kejadian jatuh secara cepat dan akurat untuk meningkatkan keselamatan mereka. Sistem ini memanfaatkan sensor accelerometer, gyroscope, dan microphone berbasis Arduino untuk mendeteksi gerakan jatuh melalui pengukuran percepatan dan tekanan. Perangkat ini dirancang dengan pendekatan ergonomis dan konsumsi daya rendah serta dilengkapi modul GSM yang secara otomatis mengirimkan notifikasi ke ponsel keluarga ketika kejadian jatuh terdeteksi. Pengujian dilakukan pada lansia dengan berbagai skenario gerakan, mencakup aktivitas jatuh dan normal. Hasil pengujian menunjukkan tingkat akurasi sebesar 92,85%, sensitivitas 91%, dan spesifisitas 97,5%, yang menegaskan kemampuan perangkat dalam membedakan gerakan jatuh dari aktivitas biasa. Dengan desain yang sederhana, efisien, dan andal, perangkat ini diharapkan menjadi solusi inovatif untuk meningkatkan keselamatan lansia dan memudahkan keluarga dalam memantau risiko jatuh secara real-time.
Co-Authors Akbar, Muhammad Ikram Andrianur Akmal, Muhamad Raihan Alifiansyah, Irfan Dwijotomo, Abdurahman Febrianto, Wahyu Ferdinan, Wisnu Kristian Hutagalung, Yogi Marpaung, John Purba Oktani, Dessy Panjaitan, Junaedi Satrio Ricardo, Riki Rinaldi, Anggi Silalahi, Laurent Timanta, Febrian Harlim Wikanta, Prasaja Zulhaq, Danang Fariz