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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer Prosiding Semnastek Jurnal Teknologi dan Sistem Komputer Jurnal Teknik Komputer AMIK BSI CogITo Smart Journal JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Journal of Information Technology and Computer Engineering Jurnal Hilirisasi IPTEKS (JHI) Journal of Appropriate Technology for Community Services Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking)
Ratna Aisuwarya
Universitas Andalas
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Veterinary Other

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Sistem Monitoring Debit dan Tingkat Kekeruhan Air Pada Kolam KOI Berbasis Mikrokontroler Budiman, Wahyu; Aisuwarya, Ratna
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.6.02.56-63.2022

Abstract

This system aims to make it easier for owners to monitor the condition of the koi pond, and minimize the death of koi fish, and can maintain water quality and maximize koi growth and development. Therefore, a system was created that can monitor the discharge and the level of turbidity of water in the koi pond. This system consists of Arduino Uno, NodeMCu, Waterflow Sensor, Turbidity Sensor and buzzer. This system can determine the condition of the pump, whether the pump is running maximally, the signal is not optimal or the pump off based on the water flow produced by the pump, which is then read by the waterflow sensor. When the water discharge > 40 L/hour then the pump turns on maximum, when 40 L/hour > discharge > 0, then the pump flame is not maximal, and when there is no water discharge produced by the pump then the pump is off, if the water turbidity value > 400 NTU, then the water is in a cloudy state and the buzzer as an alarm so that the owner checks the filter and the pool drain will sound, and later the data read by the sensor will be saved to the firebase and will be displayed on the mobile application, and tegram will send a notification in the form of a short message , when the pump is not running optimally, the pump is off, and when the water is cloudy.
Smart Plant Pot Robot with IoT Integration for Indoor Ornamental Plants Care Halawa, Hestin Nalurita; Aisuwarya, Ratna
JITCE (Journal of Information Technology and Computer Engineering) Vol. 7 No. 2 (2023)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.2.97-106.2023

Abstract

Caring for indoor ornamental plants is a challenge for the owner. They require special attention and treatment from the owner to grow well and beautifully. The most important and common thing that must be considered is choosing the type of plant that can adjust to the atmosphere and conditions in the room. Some indicators that determine the typical environmental conditions of potted ornamental plants are air temperature and humidity, light, and soil moisture. The smart plant pot robot is designed to provide smart care for indoor ornamental plants by incorporating a line follower mechanism. Equipped with functionalities like plant watering and environmental monitoring (including light, temperature, and humidity levels), this robot offers comprehensive plant care. Users can conveniently monitor these parameters via a smartphone using the Blynk application. In its design, the robot integrates various components such as temperature and humidity sensors, light intensity sensors, photodiode sensors, ultrasonic sensors, voltage sensors, Arduino Uno, relays, water pumps, RTC, ESP8266, DC motors, and motor drivers. The primary goal is to create a system capable of monitoring plant conditions and providing watering assistance, utilizing line tracking for transporting potted plants to sunlight exposure spots. This system aims to offer practical benefits to the community, especially in optimizing indoor ornamental plant care. The test results of this system, utilizing Arduino Uno as the microcontroller and ESP8266 for WiFi communication with the Blynk application, along with various sensors as input devices, demonstrate a remarkable 100% success rate. Notably, this accuracy is based on functional testing, encompassing everything from the robot's path detection to its connectivity with the Blynk application.
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%%.
Sistem Pemberian Pakan Hamster Otomatis berbasis Mikrokontroler Nurul Aulia, Farah; Aisuwarya, Ratna
CHIPSET Vol. 5 No. 01 (2024): Journal on Computer Hardware, Signal Processing, Embedded System and Networkin
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/chipset.5.01.35-39.2024

Abstract

This research aims to create an automatic hamster feeder system at night following the nocturnal nature of hamsters. The system will be given a command in the form of a message via the Telegram messaging application to fill the hamster's weight which will be calculated as a calculation of the hamster's daily feed portion. The nature of hamsters which are generally nocturnal, in contrast to humans who usually rest at night, makes it difficult for humans to feed their hamsters at night regularly. The daily portion of hamster food is around 0.11 times the hamster's body weight and this provision must be appropriate not to cause obesity in the hamster. Based on these circumstances, this research is proposed as a solution to this problem. After filling the hamster's weight information, the system will calculate the hamster's daily food weight and when the sensor catches that the hamster approaching the system, the system will immediately open the servo motor to pour the hamster food.
Co-Authors - Hendrick . Darwison Adi Arga Arifnur Afriyanti Dwi Kartika Ahmad Fauzi Akbar, Ricky Anton Hidayat Arrya Anandika Badidi, Julianto Rebbi Budi Rahmadya Budiman, Wahyu Damayanti, Elok Derisma Derisma Derisma Derisma Dini Ramadhani Salsabila Dodon Yendri Dody Ichwana Putra Farlie Angriawan Fatimah Fatimah Fauzi Ahmad Muda Ferdian, Rian Fitra Marta Yonas Fitri Ardiani Halawa, Hestin Nalurita Helmi, Monika Rianti Hendrick Hendrick Herry Wahyudi Hersyah, Mohammad Hafiz Ibrahim Saputra Ida Ayu Putu Sri Widnyani Indah Purnama Julianto Rebbi Badidi Kiki Amelia Lathifah Arief, Lathifah M. Hafiz Hersyah M. Hafiz Hersyah M. Ridho Heranof Maulana Maulana Maulana Maulana, Maulana Meilisa Prilisia Melia Amalia Mohammad Hafiz Hersyah Nefy Puteri Novani Nefy Puteri Novani Nefy Puteri Novani Nelia Afriyeni Novani, Nefy Puteri Novira Desry Anggy Nurleli Hidayati Nurul Aulia, Farah Nurul Fatimah Nurul Fatimah Purwoko, Agus Putri Utari Rahmi Eka Putri Rahmon Sandika Rahmat Raihan Annafi Resti Rahayu Rifki Suwandi Rifki Suwandi Rizka Hadelina Salsabila, Dini Ramadhani Sinta Silvia Sinta Silvia Styviandra Ardopa Tati Erlina Tati Erlina Teguh Dwiky Putra Tia Priska Sari Wahyu Budiman Wahyu Budiman Werman Kasoep Werman Kasoep Werman Kasoep Wiwit Fitria Yolanda, Desta Yuni Anggraini Yuni Marliza Zikri Rahman