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All Journal JOURNAL OF INDUSTRIAL AND MANUFACTURE ENGINEERING JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Pelita Masyarakat Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Susilawati Susilawati
Universitas Medan Area
Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Physics Public Health Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Analisis Fast Fourier Tansform untuk Pengenalan Voice Register Wanita dalam Teknik Bernyanyi Muhathir Muhathir; Susilawati Susilawati; Rizki Muliono
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 2 (2019): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v2i2.2166

Abstract

Automatic speech recognition merupakan kemampuan untuk menerima dan mengidentifikasi kata-kata yang diucapkan dengan mengubah sinyal analog ke digital, dan mengekstraksi karakteristik vokal unik seperti pitch, frekuensi, nada dan irama untuk membentuk model speaker atau sampel suara. Sampel suara yang digunakan yaitu voice register, voice register adalah pembagian wilayah suara manusia berdasarkan sumber suara, sensasi ruang resonansi, bentuk, warna, timbre suara, dan tinggi rendahnya nada yang dihasilkan. Fast Fourier Transform digunakan sebagai transformasi untuk mengolah sample suara yang akan diklasifikasi. FFT dalam mentransormasikan sinyal voice register hanya mampu mengklasifikasikan dengan rata-rata true positive rate 65.4%. 
Motion Monitoring System Based on IoT Susilawati Susilawati; Zulfikar Sembiring; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i2.3326

Abstract

Every object is always moving, and movement of an object can occur at any time. The value of the motion which produced from an object can be very small to very large, and the impact of the movement can be at risk until very risky. For this reason, the movement of objects at risk must be observed whenever changes and observations for data retrieval can be done remotely. The purpose of this research to design an Internet of Things (IoT) devices that can observe and detect changes in the motion of an object.  The device is designed to be small, around 44 x 48 millimeters with very low power consumption. The design phase begins with recording motion data using the MPU6050 accelerometer sensor as a motion detector, arduino nano as a control device, WiFi ESP8266 as a communication medium for sending data from a receiver apllication motion data with UDP protocol. The test results show that this device is very sensitive to detect changes in the motion and angle of X, Y and Z of an object.
Perancangan Kunci Public RSA dan ElGamal pada Kriptografi untuk Kemananan Informasi susilawati susilawati
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 1, No 2 (2018): Edisi Januari
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v1i2.1429

Abstract

Penggunaan bilangan prima berukuran sangat besar pada kriptografi kunci public sangat diperlukan untuk menghindari proses dekripsi pesan secara tidak sah. Dalam implementasinya, pembangkitan bilangan prima berukuran sangat besar ini terkendala pada operasi perpangkatan dengan bilangan yang besar. Makalah ini membahas teknik pembangkitan bilangan berukuran sangat besar menggunakan algoritma Linear Congruential Generator (LCG) serta pengujian bilangan prima secara probabilistik menggunakan algoritma Miller-Rabin. Bilangan prima yang dihasilkan diuji dan dianalisa pada sistem kriptografi kunci public RSA dan ElGamal dengan tujuan untuk keamanan informasi.
Analisis Pengaruh Fungsi Aktivasi, Learning Rate Dan Momentum Dalam Menentukan Mean Square Error (MSE) Pada Jaringan Saraf Restricted Boltzmann Machines (RBM) Susilawati Susilawati; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 2, No 2 (2019): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v2i2.2162

Abstract

Restricted boltzmann machines (RBM) merupakan algoritma pembelajaran jaringan syaraf tanpa pengawaas (unsupervised learning) yang hanya terdiri dari dua lapisan yang visible layer dan hidden layer. Kinerja RBM sangat dipengaruhi oleh parameter-parameternya seperti fungsi aktivasi yang digunakan untuk mengaktifkan neuron pada jaringan dan learning rate serta momentum untuk mempercepat proses pembelajaran. Pemilihan fungsi aktivasi yang tepat sangat mempengaruhi kinerja dalam menentukan Mean Square Error (MSE) pada jaringan saraf RBM. Fungsi aktivasi yang digunakan pada jaringan RBM adalah fungsi aktivasi sigmoid. Beberapa varian dari fungsi aktivasi sigmoid seperti fungsi sigmoid biner dan sigmoid tangen hiperbolik (tanh). Dengan menggunakan dataset MNIST untuk pembelajaran dan pengujian, terlihat bahwa tingkat keberhasilan untuk klasifikasi pada fungsi aktivasi sigmoid biner, ditentukan oleh nilai MSE yang kecil. Berbeda dengan fungsi aktivasi tangen nilai MSE menaik seiring bertambahnya jumlah epoch. Fungsi aktivasi sigmoid biner dengan learning rate 0.05 dan momentum 0.7 memiliki tingkat pengenalan tulisan tangan yang tinggi sebesar 93.42%, diikuti dengan learning rate 0.01 momentum 0.9 yakni 91.92%, learning rate 0.05 momentum 0.5 yakni 91.31%, learning rate 0.01 momentum 0.7 sebesar 90.56% dan terakhir learning rate 0.01 momentum 0.5 sebesar 87.49%.
Analysis of The Multilayer Perceptron Algorithm on Twitter User’s Sentiment Towards The COVID-19 Vaccine Fordinand Halomoan Pasaribu; Nurul Khairina; Dian Noviandri; Susilawati Susilawati; Rahmad Syah
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 1 (2023): Issues July 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i1.9664

Abstract

The World Health Organization (WHO) declared COVID-19 a global pandemic due to its rapid spread and infection of people worldwide. The emergence of COVID-19 vaccines has garnered both support and rejection from the public. Some people support the vaccines, while others remain cautious, even though the government provides them for free. The procurement of coronavirus vaccines has generated diverse opinions in society. COVID-19 vaccines have become a trending topic on social media, particularly on Twitter. This research aims to explore public opinions on the COVID-19 vaccine. The methods used in this study include data collection, text preprocessing, TF-IDF, multilayer perceptron algorithm, and testing with confusion matrices. Out of a total of 228,208 positive, negative, and neutral opinions from Twitter users about the COVID-19 vaccine, with a training-to-testing ratio of 90% to 100%, the model will learn more by using a large amount of training data. The performance results of this research obtained the highest accuracy of 81.2%, precision of 83.8%, and recall of 71.2%. The results of sentiment analysis can be seen in the public opinions on the COVID-19 vaccine, which are divided into three categories: 35% positive opinions, 16.3% negative opinions, and 48.7% neutral opinions. The word cloud results show that positive opinions revolve around three topics: availability, cost, and dosage. Negative opinions from Twitter users about the COVID-19 vaccine focus on two main issues: vaccine side effects and deaths. Neutral opinions cover three topics, including dosage, availability, age, and expiration date
Co-Authors Bayu Jaka Valendio Dian Noviandri Dian Wahyudi Eky Ermal Muttaqin Fordinand Halomoan Pasaribu Jecksond Hutagalung Marali Banjarnahor Muhathir, Muhathir Ninny Asnidar Siregar Nurul Khairina Rahmad B.Y Syah Rizki Muliono Sirmas Munte Suswati Suswati Yudi Daeng Polewangi Yuni Syahputri Zulfikar Sembiring