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Implementasi dan Analisis Fitur Keamanan Protokol MQTT pada Telehealthcare Dian Rachmadini; Ira Puspasari; Jusak
Journal of Technology and Informatics (JoTI) Vol. 2 No. 1 (2020): Vol.2 No.1 (2020)
Publisher : Universitas Dinamika

Protokol MQTT merupakan salah satu protokol IoT dengan konsep publisher, subscriber, dan broker. Penerapan protokol MQTT dengan fitur keamanan dapat menggunakan Transport Layer Security (TLS). Pada makalah ini, fitur keamanan TLS pada MQTT akan diimplementasikan untuk mengirim data sinyal EKG. Sinyal EKG berbeda setiap orang dan merupakan privasi bagi pasien, karena dari sinyal EKG ini juga dapat digunakan untuk melihat penyakit pasien tersebut. Proses transmisi pada MQTT Security (MQTTS) akan menggunakan file kunci yang telah dibuat oleh broker. File kunci ini akan diberikan kepada client supaya bisa melakukan komunikasi, mengirim dan menerima data yang telah terenkripsi. Enkripsi data dilakukan karena adanya file kunci ini sehingga proses transmisi lebih aman. Hasil analisis perhitungan selisih besar paket sebelum dan setelah pengiriman pada QoS 0 adalah 152,6458 byte dan 139,4504 byte. Sedangkan QoS 1 sebesar 99,7932 byte dan 115,5321 byte. Kedua QoS menunjukkan selisih yang cukup besar, tetapi disisi lain pegiriman data menjadi lebih aman. Pada pengujian waktu yang diperlukan untuk proses enkripsi, QoS 0 menghasilkan waktu rata-rata 0,7 ms, QoS 1 menunjukkan hasil lebih lama yaitu 9,6 ms dikarenakan penambahan sinyal kontrol pada QoS 1. Hasil uji integritas data dengan cross-correlation , QoS 0 dan QoS 1 menunjukkan nilai 1 pada lag ke-0 yang artinya data yang dikirim dan diterima tidak terdapat perubahan (sama).

Rancang Bangun Automatic Liquid Filling Machine Berbasis IoT (Internet of Things) ahmad syarif; Harianto; Ira Puspasari
Journal of Technology and Informatics (JoTI) Vol. 2 No. 2 (2021): Vol.2 No.2 (2021)
Publisher : Universitas Dinamika

Perkembangan ilmu sains dan teknologi pada pengisi cairan secara otomatis kedalam botol memerlukan mekanik pada sistem untuk membuat sistem tersebut efisien dan berimbas baik pada dunia industri. Membutuhkan mesin dimana dapat melakukan monitoring dan keamanan data dari jauh. Untuk mengatasi masalah tersebut dengan membuat pengisian cairan kedalam botol secara otomatis menggunakan mikrokontroler sebagai sistem utama pada mesin. Membuat mesin dimana sensor dan aktuator untuk mengisi cairan kedalam botol. Untuk dapat membuat pengisian cairan kedalam botol otomatis dan berbasis pada IoT (Internet of Things) untuk monitoring dan keamanan data maka penulis membuat mesin pengisi dengan 100% tingkat keberhasilan dalam mengisi kedalam botol. Kecepatan untuk melakukan pengisian kedua botol 100ml selama 6,8 detik. Rata-rata selisih dari kedua botol 1.3 ml. Rata-rata produksi per menit 10 botol. Waktu untuk sekali proses produksi selama 12 detik. Menggunakan protokol MQTT untuk terkoneksi dengan IoT (Internet of Things) dengan persentase keberhasilan 100%. Waktu rata-rata kecepatan pengiriman data 2 detik. Keamanan data dengan menggunakan aplikasi MQTT Panel memiliki persentase keberhasilan sebesar 100%. Pengisi cairan kedalam botol secara otomatis dapat melakukan monitoring dimana kecepatan pengiriman data lebih cepat dibanding dengan kecepatan sistem produksi.

Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin Ira Puspasari; Jusak Jusak; Weny Indah Kusumawati; Ekasari Oktarina
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Process of identifying fetal heart sound signals is imperative in recognizing congenital heart function that caused by many factors, such as hereditary factors and food intake of pregnant mothers. This study developed a method for processing heart signals to separate normal fetal phonocardiogram signals from noise by utilizing the Complete Empirical Mode Decomposition (CEEMD) algorithm which is integrated with the Pearson Distance metric. Heart signals that have been separated from noise are then processed using the Shannon Energy equation in order to sharpen the intensity of the first heart signal (S1) and the second heart signal (S2), but at the same time suppress the intensity of the residual noise in the signal. Based on the experiment results from 75 normal fetal heart sound cycles, the model that has been developed is able to identify the S1 signal and S2 signal, the time duration of T11 (S1-S1), and the time duration of T12 (S1-S2). Average duration of T11 and T12 acquired in this research can possibly be used as a reference for measuring the normal duration of fetal heart sound signals.

Model Identifkasi Sinyal Jantung Pertama (S1) dan Sinyal Jantung Kedua (S2) pada Janin Ira Puspasari; Jusak Jusak; Weny Indah Kusumawati; Ekasari Oktarina
Jurnal Rekayasa Elektrika Vol 16, No 1 (2020)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v16i1.14991

Process of identifying fetal heart sound signals is imperative in recognizing congenital heart function that caused by many factors, such as hereditary factors and food intake of pregnant mothers. This study developed a method for processing heart signals to separate normal fetal phonocardiogram signals from noise by utilizing the Complete Empirical Mode Decomposition (CEEMD) algorithm which is integrated with the Pearson Distance metric. Heart signals that have been separated from noise are then processed using the Shannon Energy equation in order to sharpen the intensity of the first heart signal (S1) and the second heart signal (S2), but at the same time suppress the intensity of the residual noise in the signal. Based on the experiment results from 75 normal fetal heart sound cycles, the model that has been developed is able to identify the S1 signal and S2 signal, the time duration of T11 (S1-S1), and the time duration of T12 (S1-S2). Average duration of T11 and T12 acquired in this research can possibly be used as a reference for measuring the normal duration of fetal heart sound signals.

Performance Evaluation of IIR and FIR Filters Using Windowing Techniques for EEG Aurellio Indra Dewanta; Fillah Arhinzah; Muhammad Wildan Atho'Illah; Weny Indah Kusumawati; Ira Puspasari
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.15503

Electroencephalogram (EEG) signals are highly susceptible to noise artifacts such as muscle activity, eye blinks, and power-line interference, which degrade signal quality and analysis accuracy. Digital filtering is therefore essential to preserve the EEG frequency band of interest (0.5–40 Hz) while suppressing unwanted components. This study presents a systematic comparison of Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters for EEG signal denoising. FIR filters were implemented using Hamming, Hann, and Blackman window methods, whereas IIR filters included Butterworth, Chebyshev type I, Chebyshev type II, and Elliptic designs. All filters were applied to the same EEG signal under identical conditions. Performance was evaluated using visual inspection and Signal-to-Noise Ratio (SNR) analysis, followed by pole–zero stability assessment of the best-performing filters. The results show that FIR filtering achieved a higher SNR improvement (11.0 dB) than IIR filtering (8.61 dB), indicating superior noise suppression and stability for EEG signal processing.

An IoT-Based Weather Monitoring and Flood Early Warning System Sandi Setyansah; Brilliant Elyon Prasetyo; Moch Diva Firmansyah; Harianto .; Ira Puspasari
MDP Student Conference Vol 5 No 2 (2026): The 5th MDP Student Conference 2026
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v5i2.15507

This study presents the design and implementation of an integrated Internet of Things based weather monitoring and flood early warning system. The system simultaneously monitors temperature, humidity, rainfall intensity, and water level in real time using a NodeMCU ESP8266 microcontroller integrated with DHT11, rain, and ultrasonic sensors. Experimental results show that the system successfully classifies environmental conditions, where temperatures below 25 °C and humidity levels above 80% indicate heavy rainfall, while rainfall sensor values range between 2000–3000 ADC units. Water-level testing demonstrates that the early warning buzzer is activated when the water level reaches a minimum threshold of 5 cm and remains active below 10 cm, indicating potential flood conditions. Sensor data are transmitted reliably in real time via the MQTT protocol and visualized on a monitoring dashboard without significant delay. These results confirm that the proposed system provides accurate environmental monitoring and effective early flood warning, offering a low-cost and practical solution for flood disaster preparedness.

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