Article Per Year (5 Year)
p-Index From 2021 - 2026
10.953
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jupiter Coding: Jurnal Komputer dan Aplikasi Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Informatika Upgris Jurnal Khatulistiwa Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI CYBERNETICS JURIKOM (Jurnal Riset Komputer) Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informasi dan Komunikasi Jurnal Teknologi dan Sistem Tertanam Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) CONSEN: Indonesian Journal of Community Services and Engagement Jurnal Informatika Polinema (JIP) The Indonesian Journal of Computer Science Jurnal Media Informasi Teknologi Mekongga: Jurnal Pengabdian Masyarakat Edu Komputika Journal Journal of Renewable Energy and Smart Device
Rahmi Hidayati
Universitas Tanjungpura
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 100 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 100 Documents
Search

 6   7   8   9   10 
Sistem Kendali Dan Pemantauan Gas Amoniak Pada Rumah Walet Berbasis Internet Of Things Putra, Bima; Hidayati, Rahmi; Nirmala, Irma
Coding: Jurnal Komputer dan Aplikasi Vol. 14 No. 1 (2026): Edisi April 2026
Publisher : Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/coding.v13i3.98053

Abstract

Tantangan yang dihadapi dalam budidaya burung walet terletak pada pemantauan kondisi lingkungan di dalam rumah walet. Kondisi yang ideal bagi burung walet dapat mempengaruhi kualitas sarang burung walet tersebut. Pemantauan diperlukan terutama pada kelembaban dan kadar gas amoniak yang berada di dalam rumah walet. Para pembudidaya biasanya melakukan pemantauan secara manual untuk mengetahui nilai kelembaban dan kadar zat amoniak di dalam rumah walet serta tidak memungkinkan dilakukan sepanjang waktu. Maka dari itu, permasalahan tersebut dapat diatasi dengan memadukan konsep Internet of Things yang dapat memantau dan melakukan kontrol agar kelembaban dan kadar zat amoniak berada dalam kondisi ideal bagi burung walet. Pemantauan tersebut dapat diketahui melalui aplikasi mobile berbasis android. Aplikasi ini membantu untuk mengetahui nilai suhu, kelembaban, dan gas amonik yang dikirim oleh setiap sensor secara realtime. Adapun pengontrolan terhadap kelembaban dan gas amoniak dilakukan dengan mengaktifkan exhaust fan dan mist maker. Adanya sistem ini memiliki pengaruh terhadap tercapainya kondisi ideal yang diperlukan rumah walet dengan rata-rata nilai kelembaban sebesar 80,03% sedangkan rata-rata kadar gas amoniak sebesar 15,36 ppm. Adapun pengujian pada pembacaan nilai pada masing-masing sensor memiliki akurasi sebesar 98,767% untuk suhu udara, 96,97% untuk akurasi pembacaan kelembaban udara, dan 82,91% untuk akurasi pembacaan gas amoniak. Kata kunci : Internet of Things, Walet, Android, Gas Amoniak.
Analisis Kontribusi Sensor IoT pada Deteksi Kebakaran Lahan Gambut Menggunakan Random Forest dan SHAP Hirzen Hasfani; Kartika Sari; Rahmi Hidayati
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.10516

Abstract

Peatland fires are disasters that impact the environment, health, and socio economic activities. Internet of Things (IoT) based early detection enables real-time monitoring of environmental conditions through various sensors. However, the specific contribution of each sensor to the detection process remains unclear. This study aims to analyze the contribution of multiple sensors within an IoT-based peatland fire detection system using the Random Forest (RF) algorithm. The dataset comprises 2,000 primary data points obtained from AMG8833, MAX6677, MQ-2, DHT22, and Water Float sensors. The model was trained on the primary data and tested against data representing transitional (overlapping) conditions between normal states and fire events. Model performance was evaluated using accuracy, precision, recall, F1-score, and a confusion matrix, while sensor contributions were analyzed via Feature Importance and validated using SHapley Additive exPlanations (SHAP). The results indicate that the RF model achieved an accuracy of 87.50%, precision of 100.00%, recall of 75.00%, and an F1-score of 85.71%. Feature Importance and SHAP analyses revealed that the DHT22 sensor (measuring humidity and temperature) made the most significant contribution, followed by the MAX6677, MQ-2, AMG8833, and Water Float sensors. These findings demonstrate that temperature and humidity serve as key indicators for peatland fire detection and provide a foundation for developing IoT-based detection systems.
Deteksi Anomali Data Sensor Kelembaban Tanah Menggunakan Kalman Filter dan Aturan 3-Sigma Yunita Erniajan; Irma Nirmala; Rahmi Hidayati
Journal of Renewable Energy and Smart Device Vol. 3 No. 2 April 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v3i2.426

Abstract

Monitoring kelembaban tanah berbasis Internet of Things (IoT) merupakan komponen penting dalam sistem pertanian presisi. Namun, sensor kelembaban tanah resistif seperti YL-69 kerap menghasilkan pembacaan yang tidak stabil akibat gangguan noise dan fluktuasi lingkungan, sehingga berpotensi menyebabkan kesalahan pada sistem pengambilan keputusan otomatis. Penelitian ini mengusulkan sistem deteksi anomali data sensor kelembaban tanah dengan mengintegrasikan algoritma Kalman Filter adaptif dan metode statistik aturan 3-sigma. Kalman Filter dengan nilai kovariansi noise proses (Q) yang bersifat dinamis diterapkan untuk mereduksi noise dan meningkatkan stabilitas pembacaan sensor, sementara deteksi anomali dilakukan berdasarkan rentang normal yang dihitung menggunakan standar deviasi kumulatif. Implementasi sistem menggunakan mikrokontroler NodeMCU ESP32 yang terhubung ke basis data MySQL dengan antarmuka berbasis website sebagai media visualisasi dan notifikasi. Pengujian dilakukan pada tiga kondisi kelembaban tanah, yaitu kering, lembab, dan basah, menggunakan total 101 sampel data yang di antaranya mencakup 23 injeksi data spike sebagai simulasi kegagalan sensor. Hasil pengujian menunjukkan bahwa Kalman Filter berhasil menurunkan koefisien variasi secara signifikan: dari 26,01% menjadi 6,00% pada kondisi kering, dari 36,31% menjadi 2,37% pada kondisi lembab, dan dari 57,82% menjadi 4,00% pada kondisi basah. Sistem juga berhasil mendeteksi seluruh data anomali yang diinjeksikan dengan akurasi 100%, disertai notifikasi pop-up secara real-time pada antarmuka website.
Combining IoT and Time Series Model for Minute-Level Outlier Detection in Wind Speed Forecasting Nur'ainul Miftahul Huda; Nurfitri Imro'ah; Rahmi Hidayati; Kartika Sari
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.35768

Abstract

Renewable energy optimisation and early warning systems require accurate short-term wind speed forecast. Anomalies in environmental data impair forecasting model reliability. This paper presents an integrated approach using IoT-based remote sensing and time series modelling to address the issue. IoT-based anemometer sensors collected wind speed data at one-minute intervals from December 24, 2024, to January 10, 2025. Aggregating the raw data into 5-minute intervals prepared it for the ARIMA model. This model determined temporal patterns and predicted short-term wind speeds. Analyzing residuals between observed and predicted results helped identify wind outliers. This approach is novel because it uses IoT-based continuous sensing and time series modeling for real-time environmental monitoring. Studies showed that a 65-minute frame with 5-minute intervals was best for replicating wind speed dynamics. Six cycles of outlier detection found 87 outliers. The ARIMA model improved predictions by include these outliers as exogenous variables. This emphasizes the importance of fixing time series model anomalies to improve prediction. The augmented ARIMA model with outlier corrections provides minute-level forecasts and reliable anomaly identification for renewable energy optimization and early warning systems. This study shows that new statistical methods and the Internet of Things (IoT) can improve real-time environmental and energy decisions.
Performance Evaluation of Machine Learning Methods for Real-Time Rainfall Classification Rahmi Hidayati; Kartika Sari
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.29322

Abstract

Reliable real-time rainfall intensity classification is essential for supporting early warning systems and disaster mitigation, particularly in regions vulnerable to hydrometeorological hazards. This study evaluates three machine learning algorithms SVM, Neural Network, and AdaBoost for multiclass rainfall intensity classification using real-time data collected from Internet of Things (IoT)-based sensors. Rainfall intensity is categorized into four classes: no rain, light rain, moderate rain, and heavy rain, based on threshold values defined by BMKG standards. The dataset is imbalanced and dominated by the no rain class, therefore, model performance is evaluated using imbalance aware metrics, including per-class precision and recall, macro F1-score, balanced accuracy, and overall accuracy. Experimental results show that SVM and Neural Network achieve very high overall accuracy of up to 99.46%, however, this performance is mainly influenced by accurate classification of the majority class, leading to low recall for minority rainfall classes. In contrast, AdaBoost provides a more balanced baseline performance, achieving an accuracy of 92.4% and a macro F1-score of 0.714 on the original dataset. To enhance minority class detection, the SMOTE is applied to the training data using an 80:20 train test split. After data balancing, AdaBoost demonstrates improved recall and macro F1-score for light and moderate rain classes, although overall accuracy decreases to 77.1%. These results are acceptable for early warning applications, where sensitivity to rainfall onset is prioritized over majority class dominance. Consequently, balanced AdaBoost, evaluated using time-based data partitioning and imbalance aware metrics, is considered an effective approach for real-time IoT-based rainfall classification.
Introduction and Implementation of the Internet of Things for Students Vocational High School 1 Punggur Besar Hirzen Hasfani; Uray Ristian; Hafiz Muhardi; Kasliono; Cucu Suhey; Tedy Rismawan; Ikhwan Ruslianto; Rahmi Hidayati; Syamsul Bahri; Dwi Marisa Midyanti; Irma Nirmala; Suhardi; Kartika Sari
MEKONGGA: Jurnal Pengabdian Masyarakat Vol. 3 No. 1 (2026): April 2026
Publisher : Digital Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69616/mekongga.v3i1.265

Abstract

The training program “Introduction and Implementation of IoT” at Vocational High School(VHS) 1 Punggur Besar aims to enhance students’ understanding and practical skills in developing IoT-based systems. The training introduces key IoT concepts, components such as sensors, actuators, and microcontrollers, and how devices communicate via the internet. Through hands-on sessions, students create simple projects like temperature and humidity monitoring systems, smart lighting, and sensor-based notifications. This program helps students build technical competence in hardware assembly and IoT programming while fostering creativity and problem-solving abilities. As a result, students gain better readiness to face industrial demands that rely on digital technologies and are encouraged to innovate in applying IoT to real-world challenges.
Local Weather Monitoring using WSN and IoT as an Early Warning for Extreme Weather RAHMI HIDAYATI; KARTIKA SARI; IRMA NIRMALA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 14, No 2: Published April 2026
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v14i2.154

Abstract

This research develops a local weather-monitoring system based on the Internet of Things (IoT) and a Wireless Sensor Network (WSN), employing four nodes to collect real-time data on temperature, humidity, air pressure, wind speed, and rainfall. Each node transmits its data to Firebase, where it is displayed on a web dashboard and used to trigger early-warning notifications via Telegram. Testing results show that the anemometer recorded an average deviation of 0.33 km/h, while the BME280 demonstrated high accuracy across three parameters: a 0.21°C (0.74%) deviation for temperature, 0.83% (1.31%) for humidity, and 0.28 hPa (0.02%) for air pressure. The system also exhibited stable data synchronization and rapid alert response times. The testing results demonstrate the potential of a multi-node approach to capture local microclimate variability and indicate its suitability for further development in machine learning–based predictive models.
Sistem Penentuan Kelayakan Minyak Jelantah Menggunakan Metode Naïve Bayes Irmina Rika; Suhardi Suhardi; Rahmi Hidayati
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 18 No 1 (2026): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18168112

Abstract

Used cooking oil is cooking oil that has been used repeatedly, causing its quality to deteriorate and potentially harm human health. Many people continue to use used cooking oil due to cost-saving considerations and the difficulty of directly assessing its quality. Therefore, a system capable of determining the suitability of used cooking oil is needed. This research aims to classify the suitability of used cooking oil based on clarity, viscosity, and color parameters using the Naïve Bayes method. The classification system utilizes an LDR sensor to detect clarity levels, a YF-S401 water flow sensor to measure viscosity, and a TCS3200 color sensor to read RGB values, with a NodeMCU ESP32 microcontroller as the processing unit. The test results show that the LDR sensor successfully detects clarity levels, the YF-S401 sensor achieves an accuracy of 97.73%, and the TCS3200 color sensor reads RGB values with accuracies of 99.85% for red, 97% for green, and 83% for blue. The dataset used in this study consists of 120 training samples and 30 test samples. The classification process using the Naïve Bayes method produced an accuracy of 96.67%, a precision of 94.74%, and a recall of 100%.
PEMANTAUAN CUACA LOKAL DENGAN SISTEM PENDETEKSI CURAH HUJAN BERBASIS IoT rahmi hidayati
Djtechno: Jurnal Teknologi Informasi Vol 5, No 3 (2024): Desember
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v5i3.4957

Abstract

Local weather is a weather condition such as rain, temperature, and humidity that occurs in a certain area. The presence of local weather that occurs unevenly can affect daily activities. There are many weather information providers, but the data presented usually covers a large area so that there can be differences in weather readings in local areas. To address this problem, a system was developed to monitor local weather with an IoT-based rainfall detection system. The purpose of this study is that users can see weather conditions through the website. The website developed can display weather data from each location. The results of the rain sensor test can work well, the rainfall sensor can measure rain intensity and the BME280 sensor for air temperature shows an average error difference of 0.21℃ and an average relative error of 0.74%. Meanwhile, the results of the air humidity test show an average error difference of 0.83°C and an average relative error of 1.31%.
SISTEM PEMANTAUAN KUALITAS UDARA SECARA REAL-TIME MENGGUNAKAN ESP32 DAN TEKNOLOGI IOT Rahmi Hidayati
Djtechno: Jurnal Teknologi Informasi Vol 5, No 2 (2024): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v5i2.4619

Abstract

Air quality is the level of cleanliness or air pollution in a certain area. Poor air quality hurts human health and the environment. One of the negative impacts of air quality is caused by forest burning, which causes haze. To overcome this problem, we can use Internet of Things (IoT) technology by building an air quality monitoring system. This research aims to provide air quality information to users by accessing a website that will display real-time data. This system can be utilized by the community to find out the air quality around their residence. The research method uses a waterfall model, which includes requirements analysis, system design, implementation, and testing. The data used is air quality data in Pontianak City, including temperature, humidity, CO, CO2, O2 and dust. The test results for 18 days showed normal values for temperature, humidity, and CO2, while the dust and CO2 sensors showed an increase in value with different category levels, with the lowest value of the dust sensor being 21 µg/m3 and the highest value being 497 µg/m3, while the CO2 value ranged from 433 ppm to 5441 ppm.
Co-Authors Achyar, Athif Tafrihan Adam, Chairul Agus Harjoko Aji, Maulana Setia Alfikri, Nadya Syifa Andreni, Gracella Aqila Zulfahmi Putri, Nessa Ariansyah Sudarsono Arif Rahmawan Arif Rahmawan Aris Fajrianto Basuki Ilham, Maulana Cucu Suhery Cucu Suhey Darmawansyah Darmawansyah Dea Rizki Febrinamas Dedi Triyanto Deni Deni Destia Arini Hairunnisa Dwi Marisa Midyanti Dwi Marisa Midyanti Ellif Ellif Fajar Mu'alim Firdaus, Nurul Fajri 'Aini Fransiskus Julian Adresman Frasila Frasila Gloria, Bela Priska Gunawan Gunawan Hafiz Muhardi Herawati, Yunita Hirzen Hasfani Ika Nurul Hidayah Ikhsan, Afif Muhammad Ikhwan Ruslianto Ilhamsyah Irma Nirmala Irmina Rika Irvando Aldo Renaldy Irwan Guntoro Irwan Guntoro Jaka, Jaka Junianti, Suci Kaffi, Muhammad Syahrul Kartika Sari Kartika Sari Kasliono Kasliono Kresna Satya Nugroho Krisna Madani Lestari, Ayu Lathalia Lestarry, Indah Advia Lindini Afira Marettania, Felisitas Marisa Midyanti, Dwi Michael Michael Muhamad Reksy Mulia Muhammad Luthfi Nur Fitriana Putri Nurfitri Imro'ah Nurfitri Imro'ah Nurul Mutiah NUR’AINUL MIFTAHUL HUDA PERMADANI, CHANDRA MONICA Pratama, Rakha Daffa Primus Mario Andaka Ginting Putra, Bima Rahmanita Widiyanti Rahmawan, Arif Riska Wulandari, Riska Rizky Risan R Sampe Hotlan Sitorus Saputra, Dimas Bagus Saputra, Yoga Sari, Ita Permata Subri, Hafiz Adlan Afrigi Suci Junianti Suci Pania, Tika Suhardi Suhardi Suhardi Suhardi Suwanty, Erina Sy Kamal Baraqbah Syamsul Bahri Syamsul Bahri Sylviana Kusuma Tedy Rismawan Teodora Fenny Aliansih Uray Ristian Utami, Retnaning Tyas Vidyana, Irma Yunita Erniajan