Articles
<![CDATA[Simulasi Sistem Penghapus Bising Kendaraan Dengan Least Mean Square Adaptif Menggunakan Program Matlab ]]>
<![CDATA[Dianta, indra Ava]]>;
<![CDATA[Prasetyowati, Sri Arttini Dwi]]>;
<![CDATA[Budi Susila, Eka Nuryanto]]>
<![CDATA[ ELKOM : JURNAL ELEKTRONIKA DAN KOMPUTER Vol 10, No 1 (2017) ]]>
Publisher : <![CDATA[Sekolah Tinggi Elektronika dan Komputer (STEKOM) ]]>
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<![CDATA[Pada masa sekarang ini banyak pengembangan dan penelitian yang dilakukan bertujuan agar semakin banyak aplikasi yang dapat dimanfaatkan lebih jauh terutama dalam perkembangan dibidang teknologi. Penggunaan peredam kadang kurang cocok untuk kondisi ruang yang relatif kecil, selain itu penggunaan peredam juga membutuhkan biaya yang tinggi. Keadaan ini menjadikan hambatan dalam pemakaian alat peredam bising.Bising merupakan permasalahan yang sering muncul dalam sistem komunikasi karena dapat mengakibatkan kesalahan dalam penyampaian informasi dari sumber informasi ke penerima informasi sehingga informasi yang diterima tidak sesuai dengan informasi yang dikirim bahkan dapat juga menghilangkan informasi yang dibawa.Untuk menghilangkan bising yang ditimbulkan tersebut dapat diselesaikan dengan menggunakan Algoritma Least Mean Square( LMS ). Algoritma Least Mean Square (LMS) merupakan algoritma yang paling sederhana diantara algoritma-algoritma dalam sistem adaptif. nilai optimal yang dapat diambil dalam simulink penghapus bising kendaraan jenis motor adalah ukuran langkah ( ? ) = 0,07 dan panjang filter ( L ) = 32 Kata Kunci : LMS Adaptif , Penghapus Bising, Simulasi sistem, Program Matlab]]>
<![CDATA[Desain dan Implementasi Akuisisi Data Suhu Murid Sekolah Berbasis Arduino Untuk Monitoring Kesehatan Komunal ]]>
<![CDATA[Ismail, Munaf]]>;
<![CDATA[Prasetyowati, Arttini Dwi]]>;
<![CDATA[Hapsari, Jenny Putri]]>
<![CDATA[ JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 2: July 2019 ]]>
Publisher : <![CDATA[Jurusan Teknik Elektro Universitas Andalas ]]>
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DOI: 10.25077/jnte.v8n2.640.2019
<![CDATA[Abstrak—Jumlah kasus demam berdarah dengue sebanyak 1.204 kasus terjadi di provinsi Jawa Tengah pada periode Januari 2019. Salah satu sekolah dasar di kecamatan Genuk kota Semarang terjadi endemi, dimana 2 siswa meninggal dan 5 siswa lainnya dirawat di rumah sakit karena difteri pada tahun 2018. Kesadaran untuk menjaga kesehatan dan memeriksa kesehatan secara teratur di rumah atau sekolah penting untuk menghindari generasi penerus bangsa dari penyakit berbahaya. Penelitian ini merancang dan mengimplementasikan sensor suhu tubuh yang terintegrasi dengan sensor sidik jari basis Arduino serial komputer. Akuisisi data suhu tubuh siswa sangat penting pada anak-anak yang tidak dapat memahami demam tubuh mereka. Inovasi sensor suhu MLX90614 dikombinasikan dengan sensor sidik jari berbasis Arduinoserial komputer. Aplikasi ini berfungsi sebagai perekam suhu tubuh siswa dan kehadiran siswa untuk disimpan di komputer. Hasil pengukuran menunjukkan hasil yang baik karena kesalahan rata-rata di bawah 5%, kesalahan terbesar adalah 3,41% dan terendah adalah 2,01%. Hasil pengukuran juga menunjukkan nilai presisi tertinggi 95,88% dan akurasi tertinggi 99,24%. Database suhu siswa akan berfungsi sebagai deteksi dini demam siswa serta pemeriksaan kesehatan berkala sebagai upaya bersama untuk menjaga kesehatan anak-anak sekolah dan memberikan informasi yang akurat ketika siswa sakit dan dibawa ke dokter.]]>
<![CDATA[Multiple Processes for Least Mean Square Adaptive Algorithm on Roadway Noise Cancelling ]]>
<![CDATA[Sri Arttini Dwi Prasetyowati]]>;
<![CDATA[Adhi Susanto]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 2: April 2015 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v5i2.pp355-360
<![CDATA[Noise is a problem often found in daily life. Noise also make people could not concentrate to do their work. Efforts to reduce noise have been proposed, but, due to variety of the noise’s characteristics, every noise problem requires different solution. This research aim to cancel the vehicle’s noise while maintaining the information heard. These conditions happened in the hospitals classrooms, or work room near the roadway. The vehicle’s noise change very fast, so the adaptive system is the good solution candidate for solving this problem. On the beginning, the simulation process had the trouble with the iterations. Matlab software only can execute the certain range of iteration. It could not cancel the noise, even the information becomes criptic. The problem is how to cancell the vehicle’s noise with the restriction software and still manage the important information. This research will modify the LMS adaptive algorithm so that the iteration could be done by the system and the main goal of the research could be reached. The modification of the algorithm is based on the filter length (L) used to adapt with the noise. Therefore, this research conducted simulation of the Adaptive Noise Cancelling with two process steps. The output of the first adaptive process have the.same characteristics with the noise that would be cancelled, thus the first adaptive process have the error near to zero. The second adaptive process changes the input by the output of the first process and mix the information into the noise. Error occured in the final process is the information heard as the dominant output.]]>
<![CDATA[Artificial Neural Network for Healthy Chicken Meat Identification ]]>
<![CDATA[Fajar Yumono]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>
<![CDATA[ IAES International Journal of Artificial Intelligence (IJ-AI) Vol 7, No 1: March 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijai.v7.i1.pp63-70
<![CDATA[Indonesia is the country with the largest number of Muslims in the world. Every Muslim is taught to consume thoyyiban halal meat or healthy chicken because it is slaughtered in the right way and stored in a good way too. But the reality in the market of many chicken meat on the market can not meet that criteria. Identification of healthy chicken meat can be done with laboratory experiments, but that is not simple and takes time. This experiment offers a cheaper, faster approach, with very high accuracy. The experimental approach is based on color and texture analysis on 5 types of meat quality based on healthy value. Color analysis was performed using artificail neural network (ANN) while texture analysis used Canny edge detection. Experimental results show that the color histogram approach with ANN is better than the texture approach, ie 94% versus 66%. It can be concluded that the freshness of a chicken does not have much effect on the texture of the meat but it has an effect on the color change in the meat.]]>
<![CDATA[Cosine Similarity Measurement for Indonesian Publication Recommender System ]]>
<![CDATA[Darso D]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Prasetyowati Sri Arttini Dwi]]>
<![CDATA[ Journal of Telematics and Informatics Vol 5, No 2: September 2017 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
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DOI: 10.12928/jti.v5i2.
<![CDATA[So many publications are increasing every year and it causes an overflow of data and it makes the information function on the site cannot be delivered entirely to the visitors. As a scientific publication site, the IPI garuda portal (Index of Indonesian publications) has more than 4000 Indonesian journals in the database. Therefore it will be designed a system that can provide information and knowledge for the user. The system can also provide recommendations related articles and relevant for users. Recommendations are made by calculating similarities between related documents. The similarity calculation method used in this research was cosine similarity. The steps taken were pre-processed, weight calculation, vector length and then they were calculated using cosine similarity so that the value would be achieved that ranged from 0 to 1. Performance testing of recommendation system used precision and recall. Performance system with average precision value about 0.664 meant that the document had good system accuracy, and then the average recall point with value about 0.962 meant that documents were successfully returned by the system.]]>
<![CDATA[Forward and Inverse Kinematic on Wheeled Soccer Robot ]]>
<![CDATA[La Ode Muhamad Idris]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>;
<![CDATA[Imam Much Ibnu Subroto]]>
<![CDATA[ Journal of Telematics and Informatics Vol 6, No 4: December 2018 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
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DOI: 10.12928/jti.v6i4.
<![CDATA[In this research rotary encoder sensor and gyroscope sensor are used as main sensor for moving wheeled soccer robot. Rotary encoder is used to measure the speed and distance of each robot wheel and the gyroscope is used to measure the orientation of the robot, then both of them expressed in cartesian diagrams (x, y, θ). A rotary encoder type magnetic incremental encoder is used in this research and it mounted on each wheel. Inverse kinematic and forward kinematic are used to control the movement of the robot when positioning in the field or making decision when kicking the ball towards the opponent’s side and the other decision. From the test results, robot can move to any direction from the starting position to the end position with a different robot orientation and robot can measure the coordinates of the robot with average error of x = 5.37%, y = 5.2%, and Theta = 0.26%. The robot is also able to move in the direction of the coordinates given with a traveling time of 3-4 seconds.]]>
<![CDATA[Analysis of Bone Fracture Detection Based on Harris Corner Detector Method ]]>
<![CDATA[Bahrun Niam]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>
<![CDATA[ Journal of Telematics and Informatics Vol 6, No 2: June 2018 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
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DOI: 10.12928/jti.v6i1.
<![CDATA[Bone is part of the human body to sustain other parts of the body. One of the bones is leg bone. Leg bones have often cracks or fractures caused by collision. Leg bone fractures can be identified by using x-ray manually. Eye train can cause less accurate in identifying the result of roentgen so that it needs a method to make radiologist easier in determining leg bone fractures. The recommended method in this research was Harris corner detector method. Before identified, the object was firstly in pre-processing such collecting data, grayscale and cropping image. The research result was Harris corner detector method could identify leg bone fractures with 70% accuracy. Bone is part of the human body to sustain other parts of the body. One of the bones is leg bone. Leg bones have often cracks or fractures caused by collision. Leg bone fractures can be identified by using x-ray manually. Eye train can cause less accurate in identifying the result of roentgen so that it needs a method to make radiologist easier in determining leg bone fractures. The recommended method in this research was Harris corner detector method. Before identified, the object was firstly in pre-processing such collecting data, grayscale and cropping image. The research result was Harris corner detector method could identify leg bone fractures with 70% accuracy.]]>
<![CDATA[EMG Signal Recognition of Gait Pattern Using Back Propagation Neural Network for Stroke Disease Rehabilitation ]]>
<![CDATA[Diah Arie WK]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>;
<![CDATA[Arief Marwanto]]>
<![CDATA[ Journal of Telematics and Informatics Vol 6, No 2: June 2018 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
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DOI: 10.12928/jti.v6i2.
<![CDATA[Electromyography (EMG) is the electrical activity obtained from muscles activity. Gait pattern of leg muscles will be measured and recognized by EMG signals. The EMG signal on the leg muscles is measured by six electrodes which are filtered with 0.33Hz high pass filter (HPF) and a low pass filter (LPF) for anti aliasing. Maximum frequency of EMG is 600 Hz, that sampled perfectly by Analog to Digital Converter (ADC) using 2 KHz. Artificial Neural Networks (ANN) algorithm is applied to obtain the accuracy and optimization of EMG signal. The performance of the results are investigates based on two type of human condition, first is healthy person and second is severe person. The combination of EMG measurements with ANN has gives better results compares than without an ANN model. The results showed that the measurement for healthy individuals during normal walking conditions was 4.56 volts with a frequency of 0,00582Hz; the measurement of stroke patients which walking at normal speed is 8.80 volts and the frequency is 1,231Hz. Therefore, proposed prototype that combined using ANN algorithm has increased capability of measurement of EMG signal for normal and severe humans models. Keywords : EMG, ANN, Gait]]>
<![CDATA[Optimizing Group Discussion Generation Using K-Means Clustering And Fair Distribution ]]>
<![CDATA[Alfano Endra Wardhana]]>;
<![CDATA[Imam Much Ibnu Subroto]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>
<![CDATA[ Journal of Telematics and Informatics Vol 6, No 2: June 2018 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
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DOI: 10.12928/jti.v6i2.
<![CDATA[The development of computer-based learning system today can provide a different learning process in a teaching and learning process, but the problems faced by a teacher is the difficulty in grouping discussion group that has a different value of knowledge and skills, because usually this selection of discussion groups in e-learning is done based on the wishes of each student or randomly regardless of the data of knowledge and skills. This research was conducted with the aim of grouping the discussion groups based on the indicators of knowledge and skill by using k-means clustering analysis at SMK Sore Tulungagung. The knowledge and skills scores of class X students in Pekerjaan Dasar Elektromekanik subjects, The Competence of Electricity Installation Engineering will be used as the basic scores. Then, the students of class X were divided into 2 groups, namely the k-means based group and the random based group for further research. The mean score of knowledge and skills are before the learning process and after the results of the evaluation of the discussion group on the k-means based and the random based group. The k-means based class score increases 4,083 from the average. Before the learning, it was 83.292 and it becomes 87.375 after the evaluation, while the random based class only experienced an increase 0,083 from the average. Before the learning it was 81,250 and it becomes 81,333 after the learning evaluation. Based on the result, grouping the discussion group in a fair way in e-learning on the indicators of knowledge and skills using k-means clustering method shows more visible improvement, so k-means clustering is a more optimal method.]]>
<![CDATA[THE PREDICTION OF NATIONAL EXAM SCORES OF JUNIOR HIGH SCHOOL STUDENTS USING K-NEAREST NEIGHBOR (k-NN) ALGORITHM ]]>
<![CDATA[Pranoto Wibowo]]>;
<![CDATA[Sri Arttini Dwi Prasetyowati]]>;
<![CDATA[Imam Much Ibnu Subroto]]>
<![CDATA[ Journal of Telematics and Informatics Vol 7, No 1: MARCH 2019 ]]>
Publisher : <![CDATA[Universitas Islam Sultan Agung ]]>
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DOI: 10.12928/jti.v7i1.
<![CDATA[The prediction of the acquisition of national exam scores for Junior High School (JHS) students is intended to know the results of the student’s national exam early when students take the national examination. Knowledge gained from the results of this prediction will be important information for the school to take appropriate steps so that the acquisition of student national exam scores can be improved even better. The acquisition of student national exam scores is low and there is no prediction model that is used to predict the achievement of student national exam scores is a problem that needs to be addressed. This paper propose a predicting student’s national exam scores for four national exam subjects (INDONESIAN, ENGLISH, MATHEMATICS and SCIENCE) using K-Nearest Neighbor (k-NN) as a prediction method and compare it with Decission Tree method. The results of the study showed that the prediction k-NN model had better performance than the prediction model of Decission Tree. Performance results obtained by evaluating using derivatives of the confussion matrix terminology to determine the value of accuracy, sensitivity (recall), and precission each subjects. To measure the performance of predictive methods used the value of accuracy in each method and each subject. The greater the accuracy value ( max 1 ), then the better performance of the prediction model used. Performance of k-NN in average accuracy=0.85, precision=0.87, recall=0.91 is better than Decission Tree method performance with accuracy=0.82, precision=0.85, and recall=0.89. ]]>
