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Novani, Nefy Puteri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisa Detak Jantung dengan Metode Heart Rate Variability (HRV) untuk Pengenalan Stres Mental Berbasis Photoplethysmograph (PPG) Novani, Nefy Puteri; Arief, Lathifah; Anjasmara, Rima
JITCE (Journal of Information Technology and Computer Engineering) Vol. 3 No. 02 (2019)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.3.02.90-95.2019

Abstract

Emotions influence individual behavior and there is no emotional experience that has a stronger influence than stress. Prolonged stress has a direct negative influence on physical and emotional conditions. For that reason, it is important to know a person's mental stress state, so that further action can be taken later, so as not to have a serious impact on physical and mental health. In this study, the photoplethysmograph (PPG) approach is used to recognize mental stress conditions based on Heart Rate Variability (HRV) frequency domain analysis. In this study stress was identified by SVM classifier using LF, HF and LF / HF Ratio from HRV frequency domain analysis. The LF results were increased in mild stress conditions, HF increased in conditions of mild stress and medium stress and the LF / HF Ratio slowly increased from mild stress to severe stress. The training data obtained 80 data with 95% mild stress accuracy from 19 data, medium stress accuracy 96% from 49 data and 99% severe stress accuracy with 12 data.
Rancang Bangun Sistem Deteksi Kecepatan Kendaraan di Wilayah Zona Selamat Sekolah (ZoSS) Berbasis Mini PC Desprijon, Desprijon; Putri, Rahmi Eka; Novani, Nefy Puteri
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 01 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.01.41-51.2021

Abstract

This study aims to create a system to detect vehicle speed in the School Safe Zone area using Mini PC based with Computer Vision technology and Image Processing techniques. This research, was hoped for the drivers will more discipline in driving, so can create safe and comfortable traffic in the School Safe Zone area. This system was made using a camera module to take video of the track, Raspberry Pi was used as the main device for detection and speed calculation. Every vehicle which crossed the zone would be detected and tracked to follow every vehicle movement, then was conducted a process of saving the center point of the vehicle object based on the initial detection line. Finally, calculated the vehicle speed based on the distance and time the vehicle moved on the frame which was set based on the detection line. The results of the vehicle speed would be displayed on the LCD and the output was in the form of a sound from the speaker as a warning for drivers whose vehicle speed exceeds 25 km / hour. Based on results of this research, the system was capable for work well in detecting and getting the speed results of passing vehicles. However, for direct implementation the devices in this system are inadequate for video processing, so that the response time and accuracy level was obtained by the system did not match for actual conditions.
Sistem Pendeteksi Gejala Awal Tantrum Pada Anak Autisme Melalui Ekspresi Wajah Dengan Convolutional Neural Network Novani, Nefy Puteri; Salsabila, Dini Ramadhani; Aisuwarya, Ratna; Arief, Lathifah; Afriyeni, Nelia
JITCE (Journal of Information Technology and Computer Engineering) Vol. 5 No. 02 (2021)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.5.02.93-106.2021

Abstract

Tantrums are outbursts of anger and they can occur at any age. An attitude tantrum or what is commonly referred to as a temper tantrum is a child's outburst of anger that often occurs when a child shows negative behavior. Emotional outbursts of tantrums that occur in children with autism are not only to seek the attention of adults, but also as an outlet for a child's feelings for parents and those around him on a whim or feeling he is feeling, but the child cannot convey it. For this reason, researchers propose a system for detecting early symptoms of tantrums in children with autism through facial expressions with CNN. The CNN method is one of the deep learning methods that can be used to recognize and classify an object in a digital image. Then the preprocessing process is carried out using labeling on the data. Then the CNN architecture is designed with input containing 48x48x1 neurons. The data was then trained using 357 epochs with an accuracy rate of 72.67%%. Then tested using test data for children with autism to get an average accuracy value of 72.67%%.
Rancang Bangun Sistem Pengering Putar untuk Rumput Laut Berbasis Mikrokontroler Novani, Nefy Puteri; Afif, Awal
JITCE (Journal of Information Technology and Computer Engineering) Vol. 6 No. 02 (2022)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.6.02.44-49.2022

Abstract

Eucheuma cottonii is a type of seaweed that is cultivated in the coastal area of Nagari Sungai Pinang, Koto XI Tarusan District, Pesisir Selatan District, West Sumatra Province. Before being sold, seaweed farmers must dry their seaweed first, because what can be sold is dried seaweed. Drying the seaweed takes two to three days depending on the weather conditions. In this research a seaweed drying system has been designed with a tool that rotates the seaweed in the drying chamber. In this rotary drying system, the soil moisture sensor is used which functions to detect the moisture content of the seaweed, if the moisture content of the seaweed is still read > 30% then the microcontroller turns on the relay which forwards instructions to the heater and the DC motor starts rotating the player container. The DS18B20 sensor is used to detect the temperature of the drying chamber, if the total moisture content of the seaweed is 30%, then the state of dry seaweed has been reached. Tests have been carried out to determine the difference in time required between the seaweed drying process using the system that has been built in this study and the seaweed drying process that utilizes direct sunlight. To achieve a moisture content of 30% in the seaweed drying process with the system designed in this study it takes an average of ±50 minutes, while using direct sunlight it takes ±9 hours to dry the seaweed.
Co-Authors Afif, Awal Anjasmara, Rima Desprijon, Desprijon Lathifah Arief, Lathifah Nelia Afriyeni Rahmi Eka Putri, Rahmi Eka Ratna Aisuwarya Salsabila, Dini Ramadhani