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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmiah Kursor JURNAL INTEGRASI Jurnal Elementer (Elektro dan Mesin Terapan) INOVTEK Polbeng - Seri Informatika Technologia: Jurnal Ilmiah Journal of Applied Electrical Engineering Jurnal Nasional Teknik Elektro dan Teknologi Informasi Proceeding Applied Business and Engineering Conference
Iman Fahruzi
Politeknik Negeri Batam
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Sistem Pengaman Loker Menggunakan Smart Card PN532 RFID/NFC Faizal Rozy; Iman Fahruzi
JURNAL INTEGRASI Vol 14 No 2 (2022): Jurnal Integrasi - Oktober 2022
Publisher : Pusat Penelitian dan Pengabdian Masyarakat Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/ji.v14i2.4503

Abstract

Loker adalah sebuah tempat yang digunakan untuk menyimpan barang keperluan pribadi atau barang berharga. Loker biasanya ditempat pada fasilitas umum yang membutuhkan ruang untuk menyimpan barang seperti pakaian, sepatu, helm dan benda lainnya dengan beragam ukuran. Penggunaan loker yang tinggi dan sangat dibutuhkan merupakan layanan jasa yang sangat menjanjikan terutama jika disediakan di area publik dengan intensitas pergerakan barang dan orang yang terbatas, seperti sekolah atau kampus, bandara, rumah sakit, pusat kebugaran, bioskop, tempat olahraga dan lain sebagainya. Saat ini, loker berbagai ukuran dan fitur sudah banyak disediakan, berbayar atau gratis, kunci manual atau kunci otomatis. Pada penelitian ini, rancangan pengaman loker memungkinkan pengguna menggunakan smart card berteknologi frekuensi radio untuk mengakses buka dan tutup pintu loker. Skema identifikasi meggunakan RFID terdiri dari smart card sebagai masukan bagi RFID reader, mikrokontroler Arduino sebagai unit pengolah data, dan LCD dan Seven Segment sebagai unit keluaran untuk menginformasi kepada pengguna seperti indikasi loker penuh atau tidak, nomor loker, ID dan informasi posisi pintu terbuka dan tertutup. Pengujian dilakukan menggunakan kartu mahasiswa atau KTM sebagai smart card untuk mengakses loker. Hasil pengujian menunjukkan, sistem pengaman loker dengan smart card mampu melakukan buka dan tutup pintu loker dengan baik.
Identifikasi Label Kode Pipa pada Sistem Konveyor untuk Pipe Handling Berbasis Template Matching Imam Mulyono; Iman Fahruzi
ABEC Indonesia Vol. 10 (2022): 10th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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Abstract

Nowadays, technological advancements are growing need for computer programs that might benefitindustrial purposes. Image recognition is crucial in technological advancements spanning from medical demandsto security and other industries. One of them is the necessity in the oil and gas industry for a computerapplication that can recognize pipe code labels, particularly in pipe handling. Because of the requirement forproduction assistance, to recognize the pipe code label image, an NI Vision control system was built. There arenumerous stages of label identification in this study, including taking photographs with a webcam camera(capture), cropping, grayscale, and saving data. The system can provide an accuracy of 88% based on test resultson 25 samples of ¾ inch PVC pipe code labels with a camera distance to the pipe code label field of 85 cm, 493color weights, and an optimal score level of 75%.
SLEEP DISORDER IDENTIFICATION FROM SINGLE LEAD ECG BY IMPROVING HYPERPARAMETERS OF 1D-CNN Iman Fahruzi
Jurnal Ilmiah Kursor Vol 11 No 4 (2022)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i4.302

Abstract

Disruption of the flow of breathing during sleep will result in significant heart problems if not treated seriously. An electrocardiogram (ECG) recording is one of the most used methods for detecting sleep disorders early on. An ECG is a representation of electrical activity in the heart while it is beating. The irregularities of the morphology and the complexity of the recordings have clinical significance that can be used as a tool for diagnosing sleep disorders. This study uses engineering to obtain features from ECG recordings that are carried out automatically using deep learning machine learning with a Convolutional Neural Network (CNN) model approach. The ECG recordings were processed to remove noise before being used in the CNN model. Tests are carried out on the most optimal model to get good accuracy by applying two scenarios. The test results of the two scenarios show that scenario one has an accuracy of 83.03% compared to scenario two with an accuracy of 76.88%. Meanwhile, the precision, sensitivity, cohens kappa and ROC UAC levels were 81.78%, 87.78%, 65.73% and 82.68% in scenario one testing on the CNN model with the most optimal parameter settings, respectively
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.
Alat Pemantau dan Penghitung Jumlah Pengunjung Berbasis Aplikasi Telegram Rindang Alamsyah; Ika Karlina Laila Nur Suciningtyas; Nanta Fakih Prebianto; Iman Fahruzi; Imam Sholihuddin; Abdurahman Dwijotomo; Lalu Kaisar Wisnu Kita; Indra Daulay
Journal of Applied Electrical Engineering Vol. 7 No. 1 (2023): JAEE, June 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaee.v7i1.5459

Abstract

A visitor monitoring and counting device is a tool designed to send information in the form of images and the number of visitors detected through a camera and sensors installed on the device. This device is based on the Telegram application using the ESP32-CAM module as the camera to capture images of each incoming visitor. The device is equipped with an infrared sensor used to detect incoming visitors. The device works by counting the number of visitors when they enter, and then sending a signal to the ESP32-CAM to capture a physical image as evidence. The data from the device's operation is sent through the Telegram application. The research results show that the device functions well at a distance of 60 cm, but it does not function properly at a distance of 90 cm. The average time required for the device to send an image to the application is 2.5 seconds. The fastest time required by the system to read data between one visitor and the next is 15,4 seconds.
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 Abdurahman Dwijotomo Agus Wahyudi Akbar, Muhammad Ikram Andrianur Akmal, Muhamad Raihan Alifiansyah, Irfan Anton Sutrisno Efrianto Efrianto Faizal Rozy Febrianto, Wahyu Ferdinan, Wisnu I Ketut Eddy Purnama Ika Karlina Laila Nur Suciningtyas Imam Mulyono Imam Sholihuddin Indra Daulay Kristian Hutagalung, Yogi Lalu Kaisar Wisnu Kita Marpaung, John Purba Mauridhi Hery P Mauridhi Hery Purnomo Nanta Fakih Prebianto Nicco Nicco Oktani, Dessy Panjaitan, Junaedi Satrio Parulian Sepriadi Ricardo, Riki Ridwan Ridwan Rinaldi, Anggi Rindang Alamsyah Silalahi, Laurent Siti Aisyah Stevanus Hardiristanto Timanta, Febrian Harlim Wikanta, Prasaja Zulhaq, Danang Fariz