Garuda - Garba Rujukan Digital

PENINGKATAN KETERAMPILAN GURU MATEMATIKA SMP DALAM PENGELOLAAN DISTANCE LEARNING Abdulloh Jaelani; Damayanti, Auli; Alfiniyah, Cicik
Jurnal Abadimas Adi Buana Vol 5 No 02 (2022): Jurnal Abadimas Adi Buana
Publisher : LPPM Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/abadimas.v5.i02.a3976

Pandemik Covid-19 yang terjadi pada tahun 2020 telah berdampak secara signifikan pada semua bidang, salah satunya bidang pendidikan. Guru tidak lagi bisa melakukan proses pembelajaran secara tatap muka di kelas. Oleh karena itu guru dituntut untuk dapat melakukan proses belajar mengajar secara daring. Pada kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan guru dalam mengelola distance learning menggunakan Moodle. Rangkaian kegiatan diawali dengan proses instalasi moodle, proses perancangan pembelajaran, pembuatan konten, pembuatan forum interaksi, serta manajemen pengelolaan (desain, fitur, dan lain-lain) serta evaluasi. Peserta pengabdian kepada masyarakat ini adalah guru Matematika SMP yang tergabung dalam MGMP Matematika wilayah barat Kabupaten Jember sebanyak 30 orang dan semuanya dapat mengikuti pelatihan dari awal sampai akhir. Selain itu, peserta mampu membuat dan mengembangkan e-learning beserta kontennya menggunakan Moodle. Lebih lanjut diperolah peningkatan ketrampilan dan pengetahuan guru dalam mengelola distance learning menggunakan Moodle sebesar 33,09 %.

Hybrid Artificial Neural Network with Extreme Learning Machine Method and Cuckoo Search Algorithm to Predict Stock Prices Piping Prabawati; Auli Damayanti; Herry Suprajitno
Contemporary Mathematics and Applications (ConMathA) Vol. 1 No. 2 (2019)
Publisher : Universitas Airlangga

This thesis aims to predict the stock prices, using artificial neural network with extreme learning machine (ELM) method and cuckoo search algorithm (CSA). Stock is one type of investment that is in great demand in Indonesia. The portion ownership of stock is determined by how much investment is invested in the company. In this case, stock is an aggressive type of investment instrument, because stock prices can change over time. In this case, ELM is used to determine forecasting values, while CSA is applied to compile and optimize the values of weights and biases to be used in the forecasting process. After obtaining the best weights and biases, the validation test process is then carried out to determine the level of success of the training process. The data used is the daily data of the stock price of PT. Bank Mandiri (Persero) Tbk. the total is 291 data. Furthermore, the data is divided into 70% for the training process is as many as 199 data and 30% for the validation test as many as 87 data. Then compiled pattern of training and validation test patterns is 198 patterns and 82 patterns. Based on the implementation of the program, with several parameter obtained the result of MSE training is 0.001304353, with an MSE of validation test is 0.0031517704. Because the MSE value obtained is relatively small, this indicates that the ELM-CSA network is able to recognize data patterns and is able to predict well.

Sistem Pakar Diagnosa Hipertiroid Menggunakan Certainty Factor dan Logika Fuzzy Rizkita Apriliana; Auli Damayanti; Asri Bekti Pratiwi
Contemporary Mathematics and Applications (ConMathA) Vol. 2 No. 1 (2020)
Publisher : Universitas Airlangga

Hyperthyroidism is a condition when the function of thyroid gland becomes excessive. The excess function of thyroid gland increases thyroid hormone production which affect body metabolism and physiological activity. This study aims to make an expert system diagnose hyperthyroidism with certainty factor and fuzzy logic. The stages of the process of diagnosing hyperthyroidism including problem identification, needs analysis of symptoms and types of hyperthyroidism, determination of rules, system design, case examples implementation, system testing, and evaluation. Variables used were systolic blood pressure, triiodothyronine (T3) levels, thyroxine (T4) levels, thyroid stimulating hormones (TSH) levels, goiter, tremors, and excessive sweating. All variables are processed using fuzzy logic with fuzzyfication stages, rule determination, min implications, max rule composition, and defuzzyfication which then proceed with certainty factor with sequential CF and CF stages. The system output is diagnosis the condition of hyperthyroidism such as hyperthyroidism, subclinical hyperthyroidism, and normal accompanied by a certainty factor. Based on the evaluation result, the accuracy of the expert system according to expert diagnostics is 86.7%

Encryption and Decryption Application on Images with Hybrid Algorithm Vigenere and RSA Radifan Darari; Edi Winarko; Auli Damayanti
Contemporary Mathematics and Applications (ConMathA) Vol. 2 No. 2 (2020)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v2i2.23855

Digital image is digital pictures on a two-dimensional plane which consists of pixels, where every pixels has Red, Green, Blue (RGB) with varying intensity depending on the image. In this thesis digital image is encrypted using hybrid algorithm Vigenere and RSA. Vigenere algorithm is a symmetric key algorithm which is a variety from Caesar algorithm where the similarity is in both of them are based on shifting the index of alphabet letters. RSA algorithm are based on the difficulty of factorizing large numbers that have 2 and only 2 factors (Prime numbers). The encryption process starts with getting the RGB intensity of each pixels from the image, then the RGB values are encrypted using Vigenere algorithm, after that RSA Algorithm encrypt those values, the values of RSA Algorithm encryption are limited so the value can be within the intervals of RGB values and the after limitation the values after being limited become the RGB values in the encrypted image. The decryption process is the inverse of encryption process, which enables the encrypted image to become the initial image before encryption. The program for encrypting and decrypting image are made using Java programming language with Netbeans IDE 8.2 software. The result of this implementation on image file donbass.jpg with the length of Vigenere key of 5 those are k1=144, k2=166 , k3=38 , k4=204 , k5=98, and RSA Algorithm keys are n=2201, e=1139, d=59, the results from the encrypted image is a visually very different image from the initial image. While in the decryption process, the encrypted image is able to be decrypted back to the initial image.

Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing Sofiah Ishlakhul Abda; Auli Damayanti; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 3 No. 1 (2021)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v3i1.26941

Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.

Hybrid Jaringan Saraf Tiruan Backpropagation dengan Firefly Algorithm dan Simulated Annealing untuk Peramalan Curah Hujan di Surabaya Dicky Zulfikar Zurkarnain; Auli Damayanti; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 3 No. 1 (2021)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v3i1.26942

Indonesia mempunyai berbagai jenis iklim. Salah satu parameter iklim adalah curah hujan. Curah hujan yang dapat menjadi sumber bencana adalah curah hujan ekstrem, yaitu kondisi curah hujan yang cukup tinggi/rendah dari rata-rata kondisi normalnya. Informasi tentang peramalan curah hujan sangat berguna khususnya bagi pemerintah kota Surabaya dalam mengantisipasi kemungkinan kejadian-kejadian atau bencana yang diakibatkan oleh curah hujan ekstrem seperti, kekeringan, banjir, pohon tumbang, rusaknya fasilitas umum, dll. Tujuan dari penulisan skripsi ini adalah untuk mendapatkan nilai peramalan curah hujan di Surabaya pada bulan yang akan datang menggunakan Hybrid Jaringan Saraf Tiruan Backpropagation dengan Firefly Algorithm dan Simulated Annealing. Proses diawali dengan input dan normalisasi data, kemudian dilanjutkan dengan proses pelatihan untuk mencari bobot dan bias yang optimal. Setelah diperoleh bobot dan bias yang optimal, kemudian melakukan uji validasi, dan dilanjutkan dengan proses peramalan. Pada proses peramalan curah hujan, data yang digunakan sebanyak 120 data curah hujan bulanan dari bulan Januari 2008 hingga bulan Desember 2017 dengan ketentuan 80% data untuk pelatihan dan 20% data untuk uji validasi. Data yang digunakan, selanjutnya dilatih kemudian dicari nilai Mean Square Error (MSE) dan bobot yang optimal. Bobot optimal yang diperoleh, selanjutnya diuji dengan uji validasi untuk mengetahui seberapa baik pola yang dikenali. Berdasarkan implementasi pada data curah hujan tersebut, diperoleh nilai MSE hasil pelatihan sebesar 0.0395384228 dan nilai selisih rata-rata sebesar 3,75382. Sedangkan hasil peramalan untuk 3 bulan berikutnya yaitu bulan Januari hingga Maret 2018 berturut-turut adalah 6.1451, 8.5459, dan 7.7391.

Hybrid Extreme Learning Machine dan Firefly Algorithm untuk Meramalkan Nilai Tukar Rupiah terhadap Dolar Ilham Ramadhani; Auli Damayanti; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 3 No. 2 (2021)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v3i2.29802

Every country has a currency as a medium of exchange and the movement of its exchange rate can affect the economy of the country. In Indonesia, since the freely floating exchange rates system has been applied in August 1997, the value of rupiah currency in the foreign exchange market can change at any time. Considering the massive impacts of exchange rate fluctuation on the economy, then forecasting the exchange rate of rupiah against the US dollar is important to help Indonesia’s economic growth. The aims of this thesis is to predict the estimated exchange rate of rupiah against the US dollar in the future by using hybrid artificial neural network extreme learning machine (ELM) method and firefly algorithm (FA). In the training process, ELM-FA hybrid has a role to obtain the best weight and bias. The weight and bias that obtained will be used for forecasting and to know the success rate of the training process, the validation test process is required. Based on the implementation of program and simulation for some parameter values on the exchange rate data from Jan 2015 until Jan 2018, with four input and hidden nodes, and one output node, obtained the smallest MSE of the training is 0.000480513 with MSE of the testing is 0.0000854107. The relatively small MSE value indicates that ELM-FA network is able to recognize the data pattern well and able to predict the test data well.

PENINGKATAN KETERAMPILAN GURU MATEMATIKA SMP DALAM PENGELOLAAN DISTANCE LEARNING SEBAGAI PERLUASAN RAGAM PEMBELAJARAN DI KABUPATEN JEMBER Abdulloh Jaelani; Auli Damayanti; Cicik Alfiniyah
Jurnal Abadimas Adi Buana Vol 5 No 02 (2022): Jurnal Abadimas Adi Buana
Publisher : LPPM Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/abadimas.v5.i02.a3976

Pandemik Covid-19 yang terjadi pada tahun 2020 telah berdampak secara signifikan pada semua bidang, salah satunya bidang pendidikan. Guru tidak lagi bisa melakukan proses pembelajaran secara tatap muka di kelas. Oleh karena itu guru dituntut untuk dapat melakukan proses belajar mengajar secara daring. Pada kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan keterampilan guru dalam mengelola distance learning menggunakan Moodle. Rangkaian kegiatan diawali dengan proses instalasi moodle, proses perancangan pembelajaran, pembuatan konten, pembuatan forum interaksi, serta manajemen pengelolaan (desain, fitur, dan lain-lain) serta evaluasi. Peserta pengabdian kepada masyarakat ini adalah guru Matematika SMP yang tergabung dalam MGMP Matematika wilayah barat Kabupaten Jember sebanyak 30 orang dan semuanya dapat mengikuti pelatihan dari awal sampai akhir. Selain itu, peserta mampu membuat dan mengembangkan e-learning beserta kontennya menggunakan Moodle. Lebih lanjut diperolah peningkatan ketrampilan dan pengetahuan guru dalam mengelola distance learning menggunakan Moodle sebesar 33,09 %.

Classification of Review Text using Hybrid Convolutional Neural Network and Gated Recurrent Unit Methods Fiqih Fathor Rachim; Auli Damayanti; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 4 No. 2 (2022)
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/conmatha.v4i2.38262

Consumer reviews are opinions from buyers to sellers based on service satisfaction or product quality. The more consumer reviews cause the process of analyzing manually will be difficult. Therefore, an automated sentiment analysis system is needed. Each review will be grouped into a sentiment class which is divided into positive and negative classes. This study aims to classify review texts using the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) methods. The research stages in this study include collecting data on Tokopedia review texts, extracting hidden information from review texts using CNN, conducting learning on review texts using GRU. A total of 1000 review texts were divided into 80% training data and 20% test data. The review text is converted into matrix using One Hot Encoding algorithm and then extracted using CNN. The CNN process includes the convolution calculation, the calculation of the Rectified Linear Unit (ReLU) activation function, and the pooling stage. The extraction results in the CNN process are continued in the GRU process. The GRU process includes initializing parameters, GRU feed forward, Cross-Entropy Error calculation, GRU feedback, and updating weights and biases. The optimal weight is obtained when the error value in the training is less than the expected minimum error or the training iteration has reached the specified maximum iteration. Optimal weight is used for validation test on test data. The implementation of review text classification using the hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) method was made using the python programming language. The accuracy of the validation test is 88.5%

Title

Found 9 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 9 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 9 Documents
Search