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All Journal Zenit ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat SNTE
Audyati Gany, Audyati
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Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pengaruh Learning Approach terhadap Prestasi Belajar pada Mahasiswa Fakultas Teknik Perguruan Tinggi Swasta di Bandung Gany, Audyati
Zenit Vol 4, No 1 (2015)
Publisher : Zenit

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Abstract

This research was conducted to determine the effect on Learning Approach toward theLearning Achievement of Engineering‟s students at one of the private Universities in Bandung, whotook the course in Rangkaian Listrik I on February – June 2013. The population of students who tookcourses in Rangkaian Listrik I on February – June 2013 are 110 students, and the sample used are 98students.The design used in this study is a correlation research design. Measuring instruments used inthis study based on Learning Approach measuring instrument which is a translation of the RevisedTwo-Factor Study Process Questinnaire (R-SPQ-2F), which consists of 20 items. Validity of measuring instrument have ranges 0.304–0.557, reliability is 0.816. Measuring instruments used to measure learning achievement is the quality of the course grades Rangkaian Listrik I. The dataobtained were processed and tested by the method of Pearson product moment correlation coefficient using SPSS 17.0.The conclusion is a weak influence on Learning Approach of the Learning Achievement.Suggestions for further research, testing can be performed on other factors of learning achievements, and can be done in a course of study in the field of Social or Science.Keywords: Learning Achievement, Learning Approach
IMPLEMENTASI SENSOR IMU UNTUK MENGETAHUI SUDUT ELEVASI KENDARAAN MENGGUNAKAN METODE LEAST SQUARE SARTIKA, ERWANI MERRY; GANY, AUDYATI; YUVENS, VINCENSIUS
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 2 (2020): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektro
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i2.301

Abstract

ABSTRAKKemiringan jalan menyebabkan pengendara sepeda motor lebih berhati-hati dalam mengendarai kendaraannya. Selain untuk keamanan, sudut elevasi jalan dapat mempengaruhi dalam pengendalian kendaraan sehingga dapat lebih menghemat energi. Pada paper ini sensor Inertial Measurement Unit (IMU) digunakan untuk mengetahui kemiringan kendaraan sepeda motor (naik/turun dan condong kiri/kanan). Dalam perancangannya beberapa data akselerasi dari sensor accelerometer IMU diolah dengan regresi sehingga diperoleh persamaan regresi yang kemudian digunakan untuk memperbanyak data sehingga data tersebut dapat digunakan untuk prediksi model antara 3 input nilai akselerasi dan 2 output nilai kemiringan sudut kendaraan. Prediksi model berhasil dengan indentifikasi menggunakan metode Least Square. Dari data pengamatan diperoleh bahwa rata-rata kesalahan absolut untuk kemiringan naik/turun dan condong kiri/kanan antara 5 o s/d 7 o, namun belum berhasil untuk sudut yang besar (70 o s/d 90 o).Kata kunci: IMU, accelerometer, sudut elevasi, Arduino, Least Square ABSTRACTThe slope of the road leads to awareness of motorcyclists ini riding their motorcycle addition to safety, the elevation angle of the road can affect vehicle control so that it can save more energy. In this paper the IMU sensor is used to determine the slope of a motorcycle (up / down and leaning left / right). In the design of some acceleration data from the IMU accelerometer sensor is processed so that the regression equation is obtained. The regression equation is used to generate the data to predict the model 3 input acceleration value and 2 output slope value of the vehicle. Model prediction was successful by identification using the Least Square method. Obtained from observational data that the average absolute error for the slope up / down and leaning left / right between 5 o to 7 o, but has not been successful for wide angles (70 o to 90 o).Keywords: IMU, accelerometer, elevation angle, Arduino, Least Square
Haur Galur Youth Organization Training in Understanding of Basic and Supporting Components of IoT Technology Sartika, Erwani Merry; Setiadikarunia, Daniel; Darmawan, Aan; Gany, Audyati; BR. Pasaribu, Novie Theresia; Nugroho, Vincensius
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 1, No 1 (2020): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v1i1.1-9

Abstract

Internet of Things (IoT) is a conceptual technology that aims to complement the benefits of internet connectivity that connected continuously. IoT is a paradigm that states that each object can be used as a device that can identify, sense, as long as it is connected to the telecommunications network and provides communication with other equipment that connected to the internet. PAR (Participatory Action Research) is a method that involves interested parties in assessing the actions being carried out to make changes for the better. Youth Organization is a place for the development of the young generation that grows from awareness and responsibility, and officially is supported by the government to develop the potential that exists in the area. Electrical Engineering Study Program of Universitas Kristen Maranatha supports Karang Taruna Haur Galur Sukagalih Village, Bandung City, by providing knowledge, competencies, and skills, especially in the Internet of Things technology. Through this training obtained the results that indicate an increase of 30%, specifically about the knowledge of the nature of capacitors, and knowledge of WiFi. While for some knowledge questions such as LDR, pull-up, and some symbols, a quite good correct answer percentage of around 40% (from all participants) was obtained. The motivation of participants to progress and develop is seen from 100% of participants felt training needed to be continued again.
Simulator Pelatihan Caesiopulmonary Resuscitation (CPR) menggunakan MMA dan FSR SARTIKA, ERWANI MERRY; BR. PASARIBU, NOVIE THERESIA; GANY, AUDYATI; JEREMY, DIMITRI; LIN, CHE-WEI; SETIAWAN, FEBRIAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 3: Published July 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i3.624

Abstract

ABSTRAKCardiac arrest merupakan permasalahan kesehatan yang signifikan. CPR dapat mengurangi resiko, namun tidak semua orang dapat melakukan CPR dengan benar. Berdasarkan permasalahan tersebut maka dibuat simulator pelatihan CPR yang dapat mengamati kedalaman dan frekuensi penekanan saat melakukan CPR. Simulator pelatihan CPR dibuat dengan sistem akuisisi data berupa estimasi kedalaman dan panduan kompresi. Sensor yang digunakan adalah accelerometer MMA 7361 dan force sensor FSR 406. Kedalaman dari akselerasi dapat diperoleh dengan mengintegralkan data accelerometer sebanyak dua kali. Integral dilakukan persiklus kompresi untuk mengurangi akumulasi error. Sistem panduan kompresi terdiri atas metronom sebagai acuan kecepatan kompresi, dan LED indikator frekuensi kompresi. Simulator pelatihan CPR ini memberikan estimasi kedalaman yang baik pada spons dan bantal menggunakan sampling rate sebesar 1 kHz dan integral per siklus kompresi.Kata kunci: Pelatihan CPR, Akuisisi Kedalaman, FSR 406, MMA7361 ABSTRACTCardiac arrest is a significant health problem. CPR can reduce risk, but not everyone can perform CPR correctly. Based on these problems, a CPR training simulator was created that can observe the depth and frequency of compressions when performing CPR. The CPR training simulator is built with a data acquisition system in the form of depth estimation and compression guidance. The sensors used are the MMA 7361 accelerometer and the FSR 406 force sensor. The depth of acceleration can be obtained by integrating the accelerometer data 2 times. The integral is performed per cycle of compression to reduce the accumulation of errors. The compression guidance system consists of a metronome as a reference for compression speed, and a compression frequency indicator LED. This CPR training simulator provides good depth estimation in sponges and pillows using a sampling rate of 1 kHz and integrals per compression cycle.Keywords: CPR Training, Depth Acquisition, FSR 406, MMA7361
Implementasi Sensor IMU untuk mengetahui Sudut Elevasi Kendaraan menggunakan Metode Least Square SARTIKA, ERWANI MERRY; GANY, AUDYATI; YUVENS, VINCENSIUS
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 8, No 2: Published May 2020
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v8i2.301

Abstract

ABSTRAKKemiringan jalan menyebabkan pengendara sepeda motor lebih berhati-hati dalam mengendarai kendaraannya. Selain untuk keamanan, sudut elevasi jalan dapat mempengaruhi dalam pengendalian kendaraan sehingga dapat lebih menghemat energi. Pada paper ini sensor Inertial Measurement Unit (IMU) digunakan untuk mengetahui kemiringan kendaraan sepeda motor (naik/turun dan condong kiri/kanan). Dalam perancangannya beberapa data akselerasi dari sensor accelerometer IMU diolah dengan regresi sehingga diperoleh persamaan regresi yang kemudian digunakan untuk memperbanyak data sehingga data tersebut dapat digunakan untuk prediksi model antara 3 input nilai akselerasi dan 2 output nilai kemiringan sudut kendaraan. Prediksi model berhasil dengan indentifikasi menggunakan metode Least Square. Dari data pengamatan diperoleh bahwa rata-rata kesalahan absolut untuk kemiringan naik/turun dan condong kiri/kanan antara 5 o s/d 7 o, namun belum berhasil untuk sudut yang besar (70 o s/d 90 o).Kata kunci: IMU, accelerometer, sudut elevasi, Arduino, Least Square ABSTRACTThe slope of the road leads to awareness of motorcyclists ini riding their motorcycle addition to safety, the elevation angle of the road can affect vehicle control so that it can save more energy. In this paper the IMU sensor is used to determine the slope of a motorcycle (up / down and leaning left / right). In the design of some acceleration data from the IMU accelerometer sensor is processed so that the regression equation is obtained. The regression equation is used to generate the data to predict the model 3 input acceleration value and 2 output slope value of the vehicle. Model prediction was successful by identification using the Least Square method. Obtained from observational data that the average absolute error for the slope up / down and leaning left / right between 5 o to 7 o, but has not been successful for wide angles (70 o to 90 o).Keywords: IMU, accelerometer, elevation angle, Arduino, Least Square
Segmentasi dan Klasifikasi Sel pada Citra Histologi dengan Menggunakan Jaringan Konvolusional Encoder-Decoder Olii, Laura Husain; Pasaribu, Novie Theresia Br; Hasugian, Meilan Jimmy; Gany, Audyati
Seminar Nasional Teknik Elektro Vol. 3 No. 1 (2023): SNTE II
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia Pusat

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Abstract

Analisis citra histologi merupakan hal yang esensial dalam penelitian medis untuk membantu diagnosis penyakit. Sel pada citra histologi dapat memiliki variasi bentuk dan ukuran yang beragam. Segmentasi dan klasifikasi sel pada citra histologi adalah langkah awal yang penting untuk dilakukan dalam analisis citra histologi untuk mengetahui kondisi dari sel pada jaringan agar dapat dilakukan analisis lebih lanjut. Dataset yang digunakan pada penelitian ini yaitu dataset Lizard yang disediakan secara publik pada kompetisi Conic 2022. Model jaringan konvolusional encoder-decoder arsitektur LinkNet dengan modifikasi pada bagian encoder dibuat untuk melakukan segmentasi dan klasifikasi sel pada citra histologi menjadi enam kelas yaitu sel neutrofil, sel epitel, sel limfosit, sel plasma, sel eosinofil, dan jaringan ikat. Model pretrained Convolutional Neural Network yang digunakan untuk menjadi encoder LinkNet adalah VGG16, DenseNet121, dan EfficientNet-B2. Berdasarkan hasil pengujian dengan menggunakan metric Mean Intersection over Union (mIoU) pada arsitektur LinkNet dengan masing-masing arsitektur encoder didapatkan hasil performansi terbaik dengan menggunakan arsitektur LinkNet dengan encoder DenseNet121 sebesar 0.5791 pada kelas sel epitel, 0.3935 pada kelas sel limfosit, 0.0444 pada kelas sel plasma, dan 0.4342 pada kelas jaringan ikat.
Co-Authors Daniel Setiadikarunia Erwani Merry Sartika HASUGIAN, MEILAN JIMMY Jeremy, Dimitri LIN, CHE-WEI Novie Theresia Br. Pasaribu Nugroho, Vincensius Olii, Laura Husain Pasaribu, Novie Theresia Br SETIAWAN, FEBRIAN Siti Zubaidah YUVENS, VINCENSIUS