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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik ITS IPTEK The Journal for Technology and Science Proceedings of KNASTIK JUTI: Jurnal Ilmiah Teknologi Informasi PROCEEDING IC-ITECHS 2014 JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Conference SENATIK STT Adisutjipto Yogyakarta SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati
Ahmad Saikhu
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : JUTI: Jurnal Ilmiah Teknologi Informasi

 1 
IMPLEMENTASI PENGEMBANGAN METODE DIFFERENTIAL EVOLUTIONUNTUK CLUSTERING PIXEL Saikhu, Ahmad; Fahmi, Hisyam
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 9, No 2, Juli 2011
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (277.541 KB) | DOI: 10.12962/j24068535.v9i2.a32

Abstract

Perkembangan metode komputasi telah mengalami percepatan yang luar biasa. Berbagai teknik komputasi untuk mendapatkan solusi dengan kinerja optimal terus berkembang. Sejumlah algoritma termasuk dalam rumpun Evolutionary Computation, diantaranya adalah Differential Evolution (DE) yang berhasil menyelesaikan masalah optimasi dalam berbagai bidang diantaranya masalah clustering. Keunggulan DE adalah karena implementasinya yangmudah dan kecepatan konvergensinya. Dalam clustering, DE menghadapi kendala penentuan jumlah cluster. Pada penelitian ini diimplementasikan sebuah algoritma Evolutionary Clustering (EC) yang merupakan pengembangan dari DE. EC diterapkan untuk melakukan pengelompokan pixel-pixel dari citra gray-scale atas beberapa area homogen yang berbeda satu dengan lainnya. EC tidak membutuhkan informasi awal tentang jumlah cluster yang akan terbentuk. EC menjadi salah satu solusi untuk menentukan jumlah cluster optimal dengan nilai validitas yang lebih baik. Kinerja dari EC akan dibandingkan dengan algoritma Fuzzy C-Means (FCM). Hasil dari EC dibanding FCM relatif sama dari segi nilai cluster validity index namun EC membutuhkan waktu relatif lebih singkat.
RANCANG BANGUN OPTIMASI KEBUTUHAN BAHAN BAKU MENGGUNAKAN ALGORITMA WAGNER-WHITIN Saikhu, Ahmad; Sarwosri, Sarwosri; Laila, Nur
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 4, Juli 2009
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (239.01 KB) | DOI: 10.12962/j24068535.v7i4.a87

Abstract

Lotting or purchasing raw materials is one step in Material Requirement Planning. Lotting technique that already known is the Wagner-Within algorithm. This algorithm is widely used because it provides optimal solutions for problem sizedeterministic dynamic reservation at a particular time period in which the needs of the entire period must be completed. It takes a application system of optimization planning raw material requirements using the Wagner-Whitin algorithm. The development of this process begins with building a power module of demand data using Arima method (1,1,1), then followed by forecasting modules of consumer demand for end product by using the multiplicative decomposition forecasting methods, and ends with the development of Materials Requirement Planning module (MRP I) using the Wagner-Whitin algorithm. The results of the test system with test data is the generation of data will form the same pattern that is likely up from week to week. Forecasting results have high accuracy registration of 99.48%, 99.64% and 99.68%. Wagner-Whitin algorithm always produces the combination of weeks. Result of the combination in the first week will produces the minimum cost for the entire week of production.
FACE RECOGNITION USING DEEP NEURAL NETWORKS WITH THE COMBINATION OF DISCRETE WAVELET TRANSFORM, STATIONARY WAVELET TRANSFORM, AND DISCRETE COSINE TRANSFORM METHODS Akbar, Afrizal Laksita; Fatichah, Chastine; Saikhu, Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 18, No. 2, July 2020
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v18i2.a1000

Abstract

Personal identification can be done by using face, fingerprint, palm prints, eye’s retina, or voice recognition which commonly called as biometric methods. Face recognition is the most popular and widely used among those biometric methods. However, there are some issues in the implementation of this method: lighting factor, facial expression, and attributes (chin, mustache, or wearing some accessories). In this study, we propose a combination method of Discrete Wavelet Transform and Stationary Wavelet Transform that able to improve the image quality, especially in the small-sized image. Moreover, we also use Histogram Equalization in order to correct noises such as over or under exposure, Discrete Cosine Transform in order to transform the image into frequency domain, and Deep Neural Networks in order to perform the feature extraction and classify the image. A 10-fold cross-validation method was used in this study. As the result, the proposed method showed the highest accuracy up to 92.73% compared to Histogram Equalization up to 80.73%, Discrete Wavelet Transform up to 85.85%, Stationary Wavelet Transform up to 64.27%, Discrete Cosine Transform up to 89.50%, the combination of Histogram Equalization, Discrete Wavelet Transform, and Stationary Wavelet Transform up to 69.77%, and the combination of Stationary Wavelet Transform, Discrete Wavelet Transform, and Histogram Equalization up to 77.39%.
PREDICTION OF MULTIVARIATE TIME SERIES DATA USING ECHO STATE NETWORK AND HARMONY SEARCH Al Haromainy, Muhammad Muharrom; Fatichah, Chastine; Saikhu, Ahmad
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 2, Juli 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i2.a1051

Abstract

Multivariate time series data prediction is widely applied in various fields such as industry, health, and economics. Several methods can form prediction models, such as Artificial Neural Network (ANN) and Recurrent Neural Network (RNN). However, this method has an error value more significant than the development method of RNN, namely the Echo State Network (ESN). The ESN method has several global parameters, such as the number of reservoirs and the leaking rate. The determination of parameter values dramatically affects the performance of the resulting prediction model. The Harmony Search (HS) optimization method is proposed to provide a solution for determining the parameters of the ESN method. The HS method was chosen because it is easier to implement, and based on other research, the HS method gets the optimum value better than other meta-heuristic methods. The methods compared in this study are RNN, ESN, and ESN-HS. Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are used to measure the error rate of forecasting results. ESN got a smaller error value than RNN, and ESN-HS produced a minor error value among the other trials, namely 0.782e-5 for RMSE and 0.28% for MAPE. The HS optimization method has successfully obtained the appropriate global parameters for the ESN prediction model.
Co-Authors Adi Setyo Nugroho Agung Teguh Setyadi Akbar, Afrizal Laksita Anak Agung Istri Sri Wiadnyani Anggraini, Ratih Nur Esti Arya Yudhi Wijaya Arya Yudhi Wijaya Arya Yudi Wijaya Arya Yudi Wijaya Baskoro, Fajar Bilqis Amaliah Bryan Alfadhori Buliali, Joko Lianto Chastine Fatichah Daniel Oranova Siahaan Dedy D. Prastyo Deneng Eka Putra Djoko Kuswanto F.X. Arunanto Fajar Baskoro Fajar Baskoro FX Arunanto Ginardi, R.V. Hari Gracius Cagar Gunawan Hari Ginardi Imam Hambali Ivanda Zevi Amalia Joko Lianto Joko Lianto Laili Rochmah Muhammad Muharrom Al Haromainy Muhammaf Sjahid Akbar Neni Alya Firdausanti Noor Fitria Azzahra Noor Fitria Azzahra NUR LAILA Nurotuz Zakiya Qolby, Lailly Syifa'ul Rakhmat Arianto Rully Soelaiman Rully Soelaiman Santi Puteri Rahayu Sarwosri Sarwosri Setiawan Setiawan Suwadi Suwadi Victor Hariadi Yudhi Purwananto Zakiya, Nurotuz