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All Journal International Journal of Electrical and Computer Engineering TRANSIENT: Jurnal Ilmiah Teknik Elektro Jurnal Sains dan Teknologi Coding: Jurnal Komputer dan Aplikasi Jurnal Simetris CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Informatika dan Teknik Elektro Terapan CESS (Journal of Computer Engineering, System and Science) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Journal of Electrical Technology JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL REKAYASA TEKNOLOGI INFORMASI PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JurTI (JURNAL TEKNOLOGI INFORMASI) Building of Informatics, Technology and Science Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Teknologi dan Sistem Tertanam Jurnal Dialektika Informatika (Detika) Journal of Telecommunication, Electronics and Control Engineering (JTECE) JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) JIKTI : Jurnal Ilmiah Komputer Terapan dan Informasi SISFOTENIKA Jurnal Media Informasi Teknologi Mekongga: Jurnal Pengabdian Masyarakat Edu Komputika Journal
Kartika Sari
Universitas Tanjungpura
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Sistem deteksi dan klasifikasi polusi udara terhadap penderita asma menggunakan metode Naïve Bayes Lindini Afira; Rahmi Hidayati; Kartika Sari
JITEL (Jurnal Ilmiah Telekomunikasi, Elektronika, dan Listrik Tenaga) Vol. 6 No. 1: March 2026
Publisher : Jurusan Teknik Elektro, Politeknik Negeri Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35313/jitel.v6.i1.2026.61-70

Abstract

Polusi udara berdampak negatif terhadap kesehatan manusia, terutama pada sistem pernapasan. Salah satu penyakit yang dapat dipicu oleh polusi udara adalah asma. Serangan asma dapat terjadi akibat paparan polutan, asap rokok, dan cuaca dingin. Dalam lingkungan dalam ruangan, kualitas udara berperan penting sebagai faktor risiko lingkungan bagi penderita asma. Penelitian ini mengembangkan sistem untuk mendeteksi kadar beberapa polutan udara, termasuk Particulate Matter (PM2.5), Karbon Monoksida (CO), dan Nitrogen Dioksida (NO2), yang dapat memicu serangan asma. Sistem ini mengklasifikasikan kualitas udara menggunakan metode Naïve Bayes ke dalam tiga kategori: aman (ISPU 1-50), berisiko (ISPU 51-200), dan berbahaya (ISPU 201+). Dataset yang digunakan terdiri dari 120 data pelatihan dan 30 data uji. Hasil pengujian menunjukkan tingkat akurasi sebesar 97%. Selain itu, dalam eksperimen yang dilakukan di ruangan dengan konsentrasi polusi udara tinggi, sistem ini menentukan bahwa kualitas udara masih tergolong aman bagi penderita asma dengan nilai ISPU sebesar 30.
IMPLEMENTASI FUSI DATA SENSOR IMU DENGAN COMPLEMENTARY FILTER PADA SISTEM PELACAKAN KENDARAAN BERBASIS IOT Romi Ardiansyah; Kartika Sari; Irma Nirmala
JET (Journal of Electrical Technology) Vol 11, No 1 (2026): EDISI FEBRUARI
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/jet.v11i1.13281

Abstract

Sistem pelacakan kendaraan berbasis Internet of Things (IoT) umumnya memanfaatkan Global Positioning System (GPS) untuk pemantauan posisi kendaraan, namun akurasinya dapat menurun pada kondisi lingkungan tertentu. Penelitian ini bertujuan untuk merancang sistem pelacakan kendaraan berbasis IoT dengan mengintegrasikan sensor GPS Neo-6M dan sensor IMU MPU6050. Pada sistem ini, metode Complementary Filter diterapkan pada sensor IMU MPU6050 untuk menggabungkan data accelerometer dan gyroscope sehingga diperoleh estimasi arah pergerakan kendaraan yang lebih stabil. Sistem dibangun menggunakan mikrokontroler ESP32 dan dihubungkan dengan chatbot Telegram sebagai media penyampaian informasi posisi dan arah pergerakan kendaraan. Pengujian dilakukan pada berbagai kondisi lingkungan, meliputi area terbuka, antar gedung, di dalam gedung parkir, serta area menanjak dan menurun. Hasil pengujian menunjukkan bahwa penerapan Complementary Filter mampu mengurangi rata-rata selisih pengukuran sudut, dari 16,57° menjadi 3,18° di area terbuka, dari 19,36° menjadi 5,22° di area antar gedung, dari 9,19° menjadi 3,65° di gedung parkir, dan dari 21,72° menjadi 9,38° di area menanjak dan menurun. Sistem juga berhasil mengirimkan notifikasi otomatis maupun berdasarkan permintaan pengguna melalui chatbot Telegram. Hasil penelitian menunjukkan bahwa Complementary Filter efektif dalam meningkatkan akurasi arah pergerakan kendaraan dan mendukung pelacakan yang andal diberbagai kondisi lingkungan.
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.
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.
Implementasi Sistem Internet of Things (IoT) Berbasis Energi Terbarukan Untuk Deteksi Kebakaran Irvando Aldo Renaldy; Rahmi Hidayati; Kartika Sari
JST (Jurnal Sains dan Teknologi) Vol. 14 No. 3 (2025): October
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jst-undiksha.v14i3.102871

Abstract

Kebakaran merupakan permasalahan serius yang berdampak pada lingkungan, kesehatan, dan aktivitas masyarakat, khususnya di daerah yang tidak memiliki infrastruktur listrik maupun internet. Penelitian ini bertujuan untuk menganalisis sistem deteksi kebakaran berbasis IoT yang mampu beroperasi secara mandiri menggunakan energi panel surya tanpa ketergantungan pada jaringan internet. Metode penelitian menggunakan pendekatan Research and Development (R&D) dengan tahapan identifikasi masalah, studi literatur, perancangan, pengembangan, pengujian, dan evaluasi. Subjek penelitian berupa satu perangkat prototipe yang terdiri atas mikrokontroler ESP32, sensor AMG8833 untuk thermal sensing, DHT22 untuk memantau suhu dan kelembaban panel box, MAX6675 untuk mengukur suhu lingkungan, serta MQ2 untuk mendeteksi konsentrasi gas. Sistem diuji pada tiga skenario uji coba (tanpa api, api kecil, dan api sedang) guna mengamati sensitivitas sensor dan kestabilan komunikasi data. Data sensor dikumpulkan melalui komunikasi LoRa dari perangkat transmiter ke penerima, kemudian diolah dan ditampilkan pada web dashboard berbasis Laravel. Analisis data dilakukan secara deskriptif-komparatif dengan membandingkan perubahan nilai sensor pada kondisi normal dan kondisi kebakaran. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi adanya api melalui peningkatan suhu termal dan konsentrasi gas, serta dapat beroperasi secara kontinu dengan suplai energi panel surya rata-rata 12 V/10 Wp. Simpulan dari penelitian ini adalah sistem yang dikembangkan efektif untuk mendeteksi kebakaran pada kondisi terbatas infrastruktur, sekaligus dapat beroperasi secara mandiri tanpa listrik eksternal.
Co-Authors AA Sudharmawan, AA Abdul Malik Achyar, Athif Tafrihan Adlian, Adlian Aji, Maulana Setia Alfikri, Nadya Syifa Ananda, Muharam Rizqi Andy Wijaya Anggraini, Yessa Tria Arief Rahman Arif Rahmawan Astaman, Andi Ainur Ridha Azhar, Sahari Bahri, Muhammad Syamsul Baiti, Nurul Banyuriatiga Banyuriatiga Cucu Suhery Cucu Suhey Dwi Marisa Midyanti Elegant, Gustavo Dipo Fioni Puspa Sari Fitriandini, Laila Hafiz Muhardi Hirzen Hasfani Ikhwan Ruslianto Immanuel, Immanuel Indriana, Fitrialfi Irma Nirmala Irvando Aldo Renaldy Irvando Aldo Renaldy Kafriandeni, Okhari Kasliono Kasliono Lindini Afira Marettania, Felisitas Marisa Midyanti, Dwi Nanda Rizky Mahardika Niken Candraningrum Nurfitri Imro'ah NUR’AINUL MIFTAHUL HUDA Prayuda, Herry Rahmawan, Arif Rahmi Hidayati Rahmi Hidayati Randily, Dhea Ramadhanti Putri Romi Ardiansyah Salsabila, Bella Sari, Fioni Puspa Suhardi Suhardi Suhardi Suhardi Suwanty, Erina syaddam, syaddam Syamsul Bahri Syamsul Bahri Tedy Rismawan Teodora Fenny Aliansih Uray Ristian Wulandari, Siti Oktavia