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All Journal JTEV (Jurnal Teknik Elektro dan Vokasional Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Prosiding Semnastek JUITA : Jurnal Informatika Annual Research Seminar SMATIKA JURNAL NASIONAL TEKNIK ELEKTRO Proceeding of the Electrical Engineering Computer Science and Informatics PROtek : Jurnal Ilmiah Teknik Elektro JETT (Jurnal Elektro dan Telekomunikasi Terapan) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Teknik Komputer AMIK BSI AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA Martabe : Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Sains dan Teknologi Terapan Jurnal Kreativitas PKM Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal RANDANG TANA - Jurnal Pengabdian Masyarakat bit-Tech Jurnal Teknologi Elekterika Journal of Research in Business, Economics, and Education Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Digit : Digital of Information Technology Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Joong-Ki : Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Logic : Jurnal Rancang Bangun Dan Teknologi Joong-Ki Jurnal Teknik Elektro dan Komputer semanTIK Joong-Ki Smatika Jurnal : STIKI Informatika Jurnal ITEJ (Information Technology Engineering Journals)
Irma Salamah
Politeknik Negeri Sriwijaya
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IoT-Based Alcohol Presence Detection in Soy Sauce Using MQ-3 and ESP32 Narita Tria Almirah; Ahmad Taqwa; Irma Salamah
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2811

Abstract

Ethanol is a compound that naturally forms during the fermentation process of products such as soy sauce, raising concerns about their halal status for Muslim consumers. This research develops an alcohol detection system based on the Internet of Things (IoT) that integrates an MQ-3 sensor, ESP32 microcontroller, and the Blynk mobile application. The MQ-3 sensor detects ethanol vapor, and the ESP32 processes the sensor data and transmits it via Wi-Fi, enabling real-time monitoring through both a 16x2 LCD display and the Blynk app. The system’s calibration process involves standard ethanol solutions with concentrations ranging from 0% to 10%. The sensor output is converted from analog-to-digital (ADC) values to voltage, parts per million (ppm), and percentage estimates. A regression analysis of the sensor data yielded the equation y = 684.59x + 3198.9, with an R2 value of 0.7288, indicating a moderate correlation between ethanol concentration and sensor readings. Using solutions with ethanol concentrations of 1% and 3%, a detection threshold of 5300 ppm was established. Testing on commercial soy sauce samples (0% and 3.08% ethanol) confirmed the system's ability to distinguish between products with and without detectable ethanol, validating its effectiveness. While not designed for precise quantitative analysis, this system offers a practical, economical, and portable solution for initial screening of alcohol in fermented food products, making it a valuable tool for halal product monitoring.
IoT-Based Cooking Oil Quality Monitoring System Using Thresholding Method on Android Application Nida Dhia Ulhaq; Irma Salamah; Suroso Suroso
ITEJ (Information Technology Engineering Journals) Vol. 10 No. 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.233

Abstract

Repeated use of cooking oil can reduce its quality and have a negative impact on health, but these changes are difficult to recognize with the naked eye. This research aims to develop an Internet of Things (IoT)-based cooking oil quality monitoring system with a thresholding method connected to an Android application. The system is equipped with a TCS3200 sensor to detect color, an LDR sensor to measure clarity, and a pH sensor to monitor oil acidity. The readings from the three sensors are used to classify the oil quality into three categories : good, medium, and unfit. The final classification is determined using unified decision logic based on the majority values from the sensors. Tests were conducted on six oil samples with a reading frequency of 60 times per sample. Data was sent in real-time to firebase and displayed through an android app. In addition, the system sent automatic notifications via telegram for remote monitoring. The results show that one-time use oil is classified as good, 2 to 4 times use is moderate, and 5 times use is categorized as unfit for consumption. The system offers a practical and efficient solution for digital and real-time monitoring of oil quality.
Implementasi Sistem Parkir Cerdas Berbasis IoT dan QR Code dengan OCR untuk Segmentasi Plat Nomor Kendaraan Muhammad Dio Alfajri; Suroso; Irma Salamah
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.202

Abstract

Fokus penelitian ini adalah membuat sistem parkir cerdas otomatis (IoT) yang menggunakan teknologi QR Code dan kecerdasan buatan (AI) untuk mengidentifikasi kendaraan dengan lebih cepat dan akurat. Meningkatnya jumlah kendaraan di kawasan perkotaan menyebabkan permasalahan parkir yang semakin kompleks, terutama pada sistem konvensional yang masih bergantung pada pencatatan manual. Kondisi ini menimbulkan antrean panjang, kesalahan pendataan, dan rendahnya efisiensi pengelolaan. Oleh karena itu, dibutuhkan solusi parkir modern yang cerdas, cepat, dan akurat. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem parkir cerdas otomatis berbasis Internet of Things (IoT) dengan integrasi QR Code sebagai identifikasi kendaraan dan Optical Character Recognition (OCR) berbasis kecerdasan buatan untuk pengenalan plat nomor. Metode penelitian menggunakan pendekatan eksperimental, mencakup perancangan perangkat keras dan perangkat lunak, integrasi sensor barcode GM66, kamera untuk akuisisi citra, mikrokontroler ESP32 sebagai pusat kendali, serta motor servo TD8120MG sebagai aktuator portal parkir. Sistem juga dilengkapi dengan algoritma segmentasi citra untuk mengekstraksi karakter plat nomor dan integrasi Telegram API untuk notifikasi real-time kepada pengguna. Hasil implementasi menunjukkan bahwa sistem mampu meningkatkan efisiensi waktu tunggu kendaraan, dengan akurasi identifikasi rata-rata sebesar 90%. Integrasi teknologi IoT dan AI menjadikan sistem ini sebagai solusi inovatif yang layak diterapkan dalam pengelolaan parkir modern di berbagai fasilitas publik seperti kampus, perkantoran, dan pusat layanan umum.  The focus of this research is to create an automatic smart parking system (IoT) that uses QR Code technology and artificial intelligence (AI) to identify vehicles more quickly and accurately. The increasing number of vehicles in urban areas has led to increasingly complex parking problems, especially in conventional systems that still rely on manual recording. This situation causes long queues, data entry errors, and low management efficiency. Therefore, a modern parking solution that is smart, fast, and accurate is needed. This study aims to design and implement an Internet of Things (IoT)-based smart automatic parking system with QR Code integration for vehicle identification and artificial intelligence-based Optical Character Recognition (OCR) for license plate recognition. The research method uses an experimental approach, including hardware and software design, integration of GM66 barcode sensors, cameras for image acquisition, ESP32 microcontrollers as control centers, and TD8120MG servo motors as parking portal actuators The implementation results show that the system is capable of reducing vehicle waiting times, with an average identification accuracy of 90%. The integration of IoT and AI technologies makes this system an innovative solution that is suitable for use in modern parking management in various public facilities such as campuses, offices, and public service centers
Image Identification System for Beef and Pork Using a Convolutional Neural Network Nadiyah Salsabila Fauzi; Irma Salamah; Irawan Hadi
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the modern era, assurance of the halalness of meat products has become a fundamental need for Indonesian Muslims, as awareness and sensitivity towards the consumption of halal products increases. This has led to the development of innovative solutions to ensure the authenticity of beef and distinguish it from pork. This research presents an Android-based meat image identification tool that relies on the Convolutional Neural Network (CNN) algorithm to process and analyze images. The research includes hardware design, deep learning model with CNN algorithm, and Android application for real-time integration of detection results. This tool is equipped with an LCD screen and speaker to display identification results. The results show the accuracy of the CNN model reaches 99% in distinguishing beef and pork on the test dataset. In real-time testing of the tool using fresh beef and pork samples, the system achieved 92% accuracy, demonstrating good performance under practical conditions. The system provides a reliable and practical solution for consumers to verify the type of meat, while contributing to efforts to ensure the halalness of food products in society.
Co-Authors A Rahman Abdul Rakhman Agnes Manik Ahmad Taqwa Ahmad Taqwa Ali Nurdin Anisah Meidella Aryanti Aryanti Asriyadi Asriyadi City Ardhela Alisya Desi Riama Manalu Dita Azzahra Dita Azzahra EKA SUSANTI Elmerillia Elmerillia Endri, Jon Fadhila Desti Rahmaniar Fahreza Fina Triana Handava Wardana Hermika Yuliana Sitompul Hj Lindawati Hj. Lindawati Indah Putri Lestari Irawan Hadi kurnia syafitri Lindawati Lindawati Lindawati M Permadi Yosa Nugraha M. Aris Ganiardi M. Redho Ali Said M.Munaza Fathsyah Meli Gustina Michelle Valerie Mohammad Fadhli Muhammad Aris Ganiardi Muhammad Dio Alfajri Muhammad Fadhli Muhammad Munaza Fathsyah Nadia Oktavia Nadiyah Salsabila Fauzi Narita Tria Almirah Nasron Nasron Nida Dhia Ulhaq R.D.Kusumanto Raden Kusumanto RD Kusumanto RD Kusumanto Rizky Tapera Rudy Mustaqim Shinta Sulistiya Ningrum Shohibulloh Bayu Anistiawan Sholihin Sholihin Suroso Suroso Suroso Suzanzefi Suzanzefi Tiara Putri Amanda Tri Rizkiah Wahyudi Rahman