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All Journal Journal of Economic, Business and Engineering (JEBE) Clean Energy and Smart Technology
Hidayatus Sibyan
Universitas Sains Al-Qur'an
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mechanical Engineering

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Journal : Clean Energy and Smart Technology

 1 
Automation Of Trash Can Collection Using Line Follower Based On Wireless Control Hidayatus Sibyan; M. Fuat Asnawi; Dwitya Ilham Kurniadi
Clean Energy and Smart Technology Vol. 1 No. 1 (2022): October
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v1i1.4

Abstract

Every school must have a cleaning staff whose job is to care for and maintain the cleanliness of the school building. At SMKN 2 Bawang, the building is spacious and separated between buildings, so collecting waste requires a lot of time and effort. An ineffective process occurs when the cleaning staff collects waste in a circular path. Automating garbage collection using a line follower robot with PID control and the blynk application to start and stop as well as a predetermined path at a particular gathering point, can save energy and time for cleaning staff. The tuning value for each PID Control constant P=0.2, I=0.3, and D=0.06 can smooth the robot's movement with the L298N motor driver. It is decided by the Arduino UNO microcontroller and wireless connection using NodeMCU (ESP8266) for direction taking. The reading sent by the infrared sensor with analogRead mode when dimmed is in the range of 175~270, and the maximum reading value of the sensor is 940~950. The decision to turn follows a predetermined line when the sensor is below 175, according to which the sensor hits the line. The robot will stop running when the ultrasonic sensor finds an object or obstacle less than 15 cm in front of it.
CLASSIFICATION OF NUTRITIONAL STATUS USING K-NEAREST NEIGHBOR (KNN) METHOD Lina Fauziah; Hidayatus Sibyan; Nur Hasanah; Iman Ahmad Ihsannudin; Nulngafan
Clean Energy and Smart Technology Vol. 1 No. 2 (2023): April
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v1i2.38

Abstract

According to a statement from the chairman of the Ngadimulyo village cadres, the prevalence of stunting in five-year-old babies (toddlers) in Ngadimulyo village in 2022 is 20%, which means there are 32 stunted toddlers out of 160 toddlers in Ngadimulyo village. The first 1000 days are the golden age for babies, but many toddlers aged 0-59 months still experience nutritional problems. In interviews with several mothers in Ngadimulyo village who have toddlers, many mothers still do not understand the calculation of nutritional status; they only rely on calculations from posyandu cadres recorded in the MCH book (Maternal and Child Health). Data recording is still conventional and produces physical data in the form of books, so data storage also has many risks, such as damage and loss. To reduce the risk of stunting, a system is needed that facilitates the calculation of under-five nutrition so that parents can independently calculate the nutritional status of their under-fives. Currently, many parents are still assisted in determining stunting criteria. Currently, parents only get stunting knowledge from counseling, so parents who have toddlers do not understand to analyze stunting symptoms independently. They are unaware if stunting occurs; therefore, with a system that can assist parents in calculating stunting rates, it is hoped that parents can provide maximum nutritional intake so that stunting cases decrease. Parents can also prevent stunting from occurring. One of the systems used to calculate the nutritional status classification of toddlers is the K-Nearest Neighbor Method (KNN). The reason for choosing the KNN method is because this method can meet other variables in determining the nutritional status of toddlers and is one of the most basic and simple grouping techniques.
EXPERT SYSTEM FOR CAT DISEASE DIAGNOSIS USING NAÏVE BAYES METHOD Muhammad Brilian Kalam; Dian Asmarajati; Hidayatus Sibyan
Clean Energy and Smart Technology Vol. 2 No. 1 (2023): October
Publisher : Nacreva Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/cest.v2i1.48

Abstract

Many people choose cats as pets. Cats can benefit humans, but when cats are sick, they can suffer damage, just as cat disease does in humans. Lack of knowledge about the management of cats is one of the leading causes of the high incidence of diseases in cats. The wonosobo area, the cat keeper and the abundance of wild cats, is no exception to the knowledge and insight needed in caring for good cats, and even the number of experts and animal clinics in particular is small. Therefore it requires a system that supports the role of a veterinarian in treating cat diseases, where it can quickly and accurately diagnose diseases in cats based on symptoms of diseases in cats based on previous medical summarizations. Systems built on websites for accessibility and usage. The system's accuracy rate reached 90%, with a 95% f1 score to give the overall size of a model.
Co-Authors Dian Asmarajati Dwitya Ilham Kurniadi Eka Sefia Lestari Iman Ahmad Ihsannudin Julia Sarisma Khafiska Khoerunnisak Khotib Surahman Lina Fauziah M. Fuat Asnawi Muhammad Brilian Kalam Nulngafan Nur Hasanah Rizki Indah Lestari Saifu Rohman