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All Journal Jurnal technoscientia Prosiding SNATIF Journal of Engineering and Technological Sciences SMATIKA Sistemasi: Jurnal Sistem Informasi Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Sistem Informasi dan Aplikasi Patria Artha Technological Journal Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Jurnal Teknik Elektro dan Komputasi (ELKOM) JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) International Journal of Research in Vocational Studies (IJRVOCAS) Makara Journal of Technology Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Environment and Sustainability Education
Ahmad Taqwa
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Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Social Sciences Other

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Multisensor monitoring system for detecting changes in weather conditions and air quality in agricultural environments Ramadhani, Dwi; Taqwa, Ahmad; Handayani, Ade Silvia; Caesarendra, Wahyu; Husni, Nyayu Latifah; Sitompul, Carlos R
Journal of Environment and Sustainability Education Vol. 3 No. 2 (2025)
Publisher : Education and Development Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62672/joease.v3i2.103

Abstract

The increasing impact of climate change and the need for precision agriculture demand reliable environmental monitoring solutions.This study aims to develop a real-time, multisensor-based environmental monitoring system that displays data via an I2C LCD and a user-friendly web interface. The system utilizes an ESP32 microcontroller connected to a range of sensors, including the DHT22 (for temperature and humidity), MQ-7 and MQ-135 (for CO and CO₂), LDR (for light intensity), a rain sensor, and an anemometer (for wind speed). Testing was conducted over eight hours under various environmental conditions, both indoors and outdoors. Validation was performed by comparing the sensor readings with those from standard measuring instruments. The results showed that the DHT22 sensor had a low error rate of 0.62% for temperature and 0.38% for humidity. Other sensors demonstrated low standard deviation values, indicating stable and consistent measurements. The system also exhibited responsive and accurate performance in detecting changes in environmental parameters. Therefore, this system is effective as an environmental monitoring tool for agricultural applications and can support early decision-making based on environmental condition changes.
Wearable IoT Device for Real-Time Heart Rate and Body Temperature Monitoring Rafiif, Muhammad; Taqwa, Ahmad; Salamah, Irma
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 7, No 2 (2025): August
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v7i2.3076

Abstract

Heart disease remains one of the primary causes of death worldwide, largely due to sedentary lifestyles and the lack of continuous health monitoring. Many existing wearable health systems fail to provide real-time alerts or offer seamless integration between hardware, cloud platforms, and user interfaces. This study proposes a fully integrated Internet of Things (IoT)-based wearable device for real-time monitoring of heart rate and body temperature. The system utilizes an ESP32 microcontroller combined with MAX30102 and DS18B20 sensors and transmits physiological data via Wi-Fi to the Adafruit IO cloud platform using the MQTT protocol. A custom Android application developed using a low-code environment provides real-time visualization and alert notifications when user-defined thresholds are exceeded. Comparative testing against standard medical devices showed an average error of 1.99% for heart rate and 2.32% for body temperature, demonstrating reliable performance for non-clinical, preventive health monitoring. Unlike previous works, this system offers end-to-end integration, enabling real-time feedback, continuous data access, and user-friendly interaction. Future developments will focus on improving sensor calibration, enhancing ergonomic design, and incorporating advanced features such as historical data tracking and AI-based health alerts.
Implementation of Convolutional Neural Network in Mobile Applications for Solar Panel Crack and Efficiency Prediction Sodiq, Wisnu Kurniawan; Taqwa, Ahmad; Kusumanto
International Journal of Research in Vocational Studies (IJRVOCAS) Vol. 5 No. 2 (2025): IJRVOCAS - August
Publisher : Yayasan Ghalih Pelopor Pendidikan (Ghalih Foundation)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53893/ijrvocas.v5i2.430

Abstract

Solar panels, as a renewable energy source, are susceptible to efficiency degradation due to cracks in solar cells. Manual crack detection has many limitations, while the use of specialized tools like electroluminescence imaging is not economical for small-scale users. Therefore, this research aims to develop an image-based automatic detection system using the Convolutional Neural Network (CNN) method, specifically the YOLOv8 architecture, integrated into a web-based mobile application using the Flask framework. Solar panel image datasets were collected and annotated using Roboflow, then trained in Google Colab with the help of a GPU. The trained model is integrated into a web-based mobile application, allowing users to upload panel images, detect cracked areas, and estimate panel efficiency based on linear regression of the crack area. Testing results show that the system can function in real-time, although the accuracy of efficiency estimation can still be improved due to limitations in data quantity and variation. This research is expected to be an economical and practical solution for solar panel monitoring.
Integrasi Kamera dan YOLOv5 pada Sistem Keamanan Safety Box Berbasis IoT Rizky Tarmizi, Kgs.M.Dian Akbar; Taqwa, Ahmad; Rakhman, Abdul
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol 7, No 2 (2025): ELKOM
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v7i2.22636167

Abstract

Peningkatan keamanan terhadap akses fisik menjadi isu penting dalam pengembangan sistem berbasis Internet of Things (IoT), khususnya pada aplikasi autentikasi biometrik. Penelitian ini bertujuan untuk mengembangkan sistem keamanan safety box berbasis IoT yang mengintegrasikan kamera digital, algoritma YOLOv5 untuk autentikasi wajah real-time, dan mikrokontroler ESP32 sebagai pengendali aktuator pengunci elektronik. Sistem ini dirancang untuk mengenali wajah secara lokal tanpa ketergantungan pada layanan cloud guna meningkatkan efisiensi, privasi, dan kecepatan respons. Evaluasi dilakukan melalui pengujian fungsional dan analisis metrik performa, termasuk precision, recall, confusion matrix, dan mean Average Precision (mAP). Hasil pengujian menunjukkan bahwa sistem mampu mengidentifikasi wajah terdaftar dan menolak wajah yang tidak terdaftar secara akurat, dengan nilai mAP sebesar 66,3% pada threshold IoU 0,5. Sistem juga menunjukkan ketahanan terhadap variasi pencahayaan, sudut pandang, dan ekspresi wajah. Temuan ini menunjukkan bahwa kombinasi YOLOv5 dan ESP32 dapat diterapkan secara efektif dalam sistem autentikasi wajah real-time untuk aplikasi keamanan berbasis IoT berskala kecil hingga menengah.
Comparative Analysis of LSTM and GRU for River Water Level Prediction Faris, Fakhri Al; Taqwa, Ahmad; Handayani, Ade Silvia; Husni, Nyayu Latifah; Caesarendra, Wahyu; Asriyadi, Asriyadi; Novianti, Leni; Rahman, M. Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5054

Abstract

Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management.
Implementation of Fuzzy Logic Method to Get Estimation of Fluid Depletion on Smart Infusion Permata Sari, Mira; Taqwa, Ahmad; Silvia Handayani, Ade
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25589

Abstract

Technology plays an important role in improving healthcare, especially in the field of medical care, particularly in infusion. Infusions are essential in hospitals, requiring constant monitoring by healthcare professionals to ensure patient safety.  The system tracks the remaining infusion fluid and displays this data on the nurse's mobile device, enabling remote control of infusion levels in each patient room. The solution incorporates a load cell sensor to measure infusion weight and an optocoupler sensor to measure infusion drip speed. In addition, the solution uses a fuzzy logic control system to make decisions based on drip speed and infusion weight, estimating when the infusion will run out.Applying this automatic infusion drip monitoring device significantly improves the accuracy and reliability of infusion management, leading to substantial improvements in patient care and safety.In this test, the results can be seen that there is a difference between the weight weighed manually and the weight on the device. with the largest weight difference of 2.49%.
Perancangan Sistem Monitoring Infus Menggunakan Mikrokontroler Arduino Uno Secara Real - Time Handayani, Ade Silvia; Mardiani, Mega; Taqwa, Ahmad
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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

Abstract

Terapi intravena adalah prosedur medis yang menggunakan jarum untuk menggantikan cairan atau menyuntikkan obat ke dalam pembuluh darah. Namun kenyataan di lapangan, umumnya pemantauan infus dilakukan secara manual, yang seringkali menimbulkan masalah seperti infus habis tanpa sepengetahuan perawat. Dalam penelitian ini, dengan memanfaatkan teknologi Internet of Things (IoT) untuk pemantauan infus dari jarak jauh dan sejumlah komponen, termasuk sensor load cell, Optocoupler, Webcam, dan Arduino Uno sebagai pengendali utama, serta modul NodeMCU ESP32 yang digunakan untuk menghubungkan sistem ke internet, sehingga data pemantauan dapat diakses melalui server dengan komunikasi serial dan protokol MQTT secara real-time. Hasil penelitian menunjukkan bahwa sensor berkinerja baik, dengan tingkat akurasi sekitar 80% dalam pengujian. Data pemantauan infus dan kontrol pasien dapat diakses melalui aplikasi Android, memungkinkan pemantauan yang lebih efisien. Dengan teknologi ini, perawat dapat memantau infus dari jarak jauh, sehingga dapat meningkatkan kualitas perawatan dan keselamatan pasien secara keseluruhan.
Design of a 3600 Virtual Reality Website for the Department of Electrical Engineering Sriwijaya State Polytechnic Iqbal, Muhammad; Taqwa, Ahmad; Lindawati, Lindawati
Sistemasi: Jurnal Sistem Informasi Vol 12, No 3 (2023): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v12i3.3158

Abstract

Information technology plays a crucial role in meeting the aspirations of people seeking knowledge in higher education institutions. One effective strategy to showcase the unique features of each university is by employing a Virtual Reality Website 3600. Through this Virtual Reality Website, prospective students, visitors, and the general public can experience a more immersive understanding of the facilities and lecture rooms in the Department of Electrical Engineering at Sriwijaya State Polytechnic (POLSRI). Users can feel as if they are physically present and interact with the college environment virtually using the 3600 camera technology. In this research, to ensure the security of user data and prevent potential security threats, a One-Time Password (OTP) security mechanism is implemented on the website. The OTP system provides temporary access with unique codes generated each time a user accesses the website. This measure is taken to safeguard user data from unauthorized access and potential information leaks. The outcomes of this study will contribute to introducing users to the Virtual Reality Website 3600 technology and aid the Department of Electrical Engineering at POLSRI in virtually showcasing their facilities and lecture rooms to prospective students in an engaging manner.
Design and Development of Mango Ripeness Classification Tool using CNN Android-based Platform Mursalin, Zaldy Gumilang; Taqwa, Ahmad; Salamah, Irma
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4379

Abstract

Artificial ripening methods use calcium carbide (carbide) which often leaves harmful residues on the mango fruit. This research designs a classification tool for carbite and non-carbite mango fruit using the Android-based InceptionV3 Convolutional Neural Network method. The mango fruit image dataset consists of 1622 images (881 images of carbite mangoes and 811 images of non-carbite mangoes) used to train and test the model. The testing process is done by implementing the model on a Raspberry Pi B+ connected to a camera pi to take pictures of mangoes at a distance of 30 cm. The results showed that the CNN model developed achieved an average accuracy of 94.4% in classifying carbitan and non-carbitan mangoes. This result shows that the classification tool designed can provide significant benefits for farmers, traders, and consumers in ensuring marketed quality.
Sistem Deteksi Gas Pintar Berbasis IoT dan Terintegrasi Fuzzy-Logic untuk Keamanan Distribusi Gas secara Realtime Azizah, Putri Nur; Taqwa, Ahmad; Salamah, Irma
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.v7i1.7331

Abstract

Abstract−LPG gas is a widely used fuel for daily needs in households, industry, and commercial sectors. Although easy to use and affordable, LPG contains highly flammable compounds that can cause fires and explosions, especially if leaks go undetected. Field surveys show that most gas agents or depots still use manual methods relying on the sense of smell to detect gas leaks. This approach does not provide optimal or accurate results, making it ineffective and potentially harmful to health when excessive gas is inhaled. Therefore, this research aims to design a gas leak detection system based on the Internet of Things (IoT) using the Fuzzy Tsukamoto algorithm integrated with the Blynk application. The method involves the design of hardware and software using three sensors as input parameters: MQ-6 (gas), DHT22 (temperature), and Flame Sensor (fire), which are processed by the ESP32 microcontroller through fuzzy logic rules. The system outputs include a visual LED indicator, buzzer activation, status display on the LCD, notifications via Blynk, and automatic fan response to neutralize the gas. Based on results simulation and testing under three environmental condition scenarios, the system is able to detect gas leaks with average error of 0.315% and accuracy of 90.55%. This study demonstrates a reliable, effective, and responsive gas leak detection system. It is expected that the system can minimize the potential dangers of gas leaks and enhance gas storage safety.
Co-Authors -, sholihin Abdul Rakhman Ade Silvia Handayani Adi Tri Wibowo Amperawan, Amperawan Anisah, Masayu Asriyadi Asriyadi Chairunnisa, Dita Ayu Ciksadan, Ciksadan DWI RAMADHANI Emilia Hesti Endri, Jon Evelina, Evelina Fadhli, Mohammad Faradila, Lusi Risky Faris, Fakhri Al Farozi, Ahmad Fatima, Siti Fibriliyanti, Yanita Ihsan, Muhammad Nabil Irma Salamah Irma Salamah Ismail, Muhammad Fakhri Jaksen M.A Jullia Fatriana Putri Kusumanto Kusumanto, R.D Lanang, Jabbar Kala Leni Novianti Lindawati Lindawati Mardiani, Mega Mohammad Fadhli Muhammad Iqbal Mursalin, Zaldy Gumilang Nursaid, Muhammad Ibrahim Nyayu Latifah Husni, Nyayu Latifah Permata Sari, Mira Putri Nur Azizah, Putri Nur Qolbu, Mutiara R. Rusdianasari Rafiif, Muhammad Rahman, M. Arief Rahmat Darmawan, Rahmat Rasyad, Sabilal RD Kusumanto Rizky Tarmizi, Kgs.M.Dian Akbar Robby Setiadi RS, Carlos Rusdianasari Rusdianasari Shendy Pratama Sitompul, Carlos R Sodiq, Wisnu Kurniawan Soegijardjo Soegijoko Soegijardjo Soegijoko Sudarso, Hairad Sugihartono Sugihartono Sugihartono Sugihartono Suhartono Tjondronegoro Suhartono Tjondronegoro Suseno T, Moch Adjie Syakdani, Adi Wahyu Caesarendra Yohandri Bow