Garuda - Garba Rujukan Digital

Penerapan Cuckoo Search Algorithm (CSA) untuk Menyelesaikan Uncapacitated Facility Location Problem (UFLP) Asri Bekti Pratiwi; Nur Faiza; Edi Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 1 No. 1 (2019)
Publisher : Universitas Airlangga

The aim of this research is to solve Uncapacitated Facility Location Problem (UFLP) using Cuckoo Search Algorithm (CSA). UFLP involves n locations and facilities to minimize the sum of the fixed setup costs and serving costs of m customers. In this problem, it is assumed that the built facilities have no limitations in serving customers, all request from each customers only require on facility, and one location only provides one facility. The purpose of the UFLP is to minimize the total cost of building facilities and customer service costs. CSA is an algorithm inspired by the parasitic nature of some cuckoo species that lay their eggs in other host birds nests. The Cuckoo Search Algorithm (CSA) application program for resolving Uncapacitated Facility Location Problems (UFLP) was made by using Borland C ++ programming language implemented in two sample cases namely small data and big data. Small data contains 10 locations and 15 customers, while big data consists 50 locations and 50 customers. From the computational results, it was found that higher number of nests and iterations lead to minimum total costs. Smaller value of pa brought to better solution of UFLP.

Flower Pollination Algorithm (FPA) to Solve Quadratic Assignment Problem (QAP) Derby Prayogo Samdean; Herry Suprajitno; Edi Winarko
Contemporary Mathematics and Applications (ConMathA) Vol. 1 No. 2 (2019)
Publisher : Universitas Airlangga

The purpose of this paper is to solve Quadratic Assignment Problem using Flower Pollination Algorithm. Quadratic Assignment Problem discuss about assignment of facilities to locations in order to minimize the total assignment costs where each facility assigns only to one location and each location is assigned by only one facility. Flower pollination Algorithm is an algorithm inspired by the process of flower pollination. There are two main steps in this algorithm, global pollination and local pollination controlled by switch probability. The program was created using Java programming language and implemented into three cases based on its size: small, medium and large. The computation process obtained the objective function value for each data using various values of parameter. According to the pattern of the computational result, it can be concluded that a high value of maximum iteration of the algorithm can help to gain better solution for this problem.

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.

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