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All Journal Jurnal Ilmiah Teknik (JUIT)
Mariza Wijayanti
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Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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SISTEM KENDALI PINTU DENGAN SENSOR SENTUH, INFRARED, DAN KIPAS MENGGUNAKAN VOICE RECOGNITION BERBASIS NODEMCU Adisty Kamila; Mariza Wijayanti; Yuli Fitriyani
Jurnal Ilmiah Teknik Vol. 3 No. 1 (2024): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v3i1.1161

Abstract

Technology is developing rapidly and home security is very important for all householders. This door control system is designed so that house residents feel safe and comfortable when entering and leaving the house. Residents can use door magnets and touch sensors to lock doors and receive information via WhatsApp to monitor when creatures enter their homes.The design of this tool system uses a Nodemcu ESP8266 microcontroller to process data collected through touch, infrared and sound sensors. This touch sensor is used to open the door or move the servo to open the door, and the door solenoid valve is used to unlock the door. The infrared sensor will then detect when someone enters your home, a buzzer will sound, and a notification will be sent via WhatsApp. The sound sensor is used to turn the fan on and off.
PROTOTYPE SISTEM KETERSEDIAAN DAN KAPASITAS TEMPAT PARKIR MOBIL BERBASIS ARDUINO UNO Arya Dwitama; Mariza Wijayanti; Meta Meysawati; Fauziah Fauziah; Yuli Fitriyani; Dina Agusten
Jurnal Ilmiah Teknik Vol. 4 No. 1 (2025): Januari : Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v4i1.1841

Abstract

The current parking system still uses a manual parking system, vehicle owners still have difficulty finding an empty parking space because they have to go around the parking area so it is less efficient and takes a long time. This study aims to help car drivers save time in finding parking locations and knowing the availability of parking spaces. In this study, there are two types of input used: infrared sensors, which function to measure the capacity of the parking space, and RFID sensors, which are used to detect cards. The data processing process is carried out using Arduino Uno, while the LCD is used to display the number of available parking slots and the location of the slot. In addition, the servo motor functions to open the barrier. This system operates in a way, when a car enters and attaches a registered card, the servo motor will open, and the number of parking slots will decrease. The driver then selects a parking slot, which will be automatically marked as "full" on the LCD screen. When the infrared sensor detects a car that wants to exit, the servo motor will open again, and the number of parking slots on the LCD will increase.
Rupiah Banknote Classification Using MobileNetV2 Based on Image Data Aditya Rizky Fajriansyah; Dina Agusten; Sri Rahayu Puspita Sari; Fauziah; Yuli Fitriyani; Mariza Wijayanti
Jurnal Ilmiah Teknik Vol. 5 No. 1 (2026): Januari: Jurnal Ilmiah Teknik
Publisher : Asosiasi Dosen Muda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56127/juit.v5i1.2461

Abstract

Visually impaired individuals often struggle to independently identify Indonesian rupiah denominations because banknotes share similar colors, patterns, and layouts, increasing the risk of errors during cash transactions. Purpose: This study aims to develop an offline, image-based banknote denomination recognition system on Android that can provide accessible assistance without relying on internet connectivity. Methodology: A quantitative experimental design was applied using a dataset of 4,340 banknote images covering 14 classes (seven denominations with front and back sides). The classifier was built with MobileNetV2 using transfer learning, supported by data augmentation and hyperparameter optimization, and evaluated using validation accuracy and F1-score. The trained model was converted to TensorFlow Lite and integrated into a Flutter-based Android application with text-to-speech output for user assistance. Findings: The proposed model achieved 96.20% validation accuracy with an average F1-score of 0.95, indicating strong performance for lightweight on-device inference. Implications: The system can be deployed in real time on smartphones to support inclusive and safer cash handling for visually impaired users, and it demonstrates the feasibility of offline deep learning for accessible financial technology. Originality: This study provides an end-to-end offline solution for Indonesian rupiah recognition that combines a lightweight deep learning model, on-device deployment, and text-to-speech feedback while distinguishing both sides of multiple denominations, offering practical value beyond approaches that depend on cloud inference or limited class coverage.  
Co-Authors Adisty Kamila Aditya Rizky Fajriansyah Arya Dwitama Dina Agusten Fauziah Fauziah Fauziah Jayawarsa, A.A. Ketut Meta Meysawati Sri Rahayu Puspita Sari Yuli Fitriyani