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25 December 2012

New Form of Quantum Computation Promises Showdown With Ordinary Computers

New Form of Quantum Computation Promises Showdown With Ordinary Computers:
New Form of Quantum Computation Promises Showdown With Ordinary Computers
You've heard the hype a hundred times: Physicists hope to someday build a whiz-bang quantum computer that can solve problems that would overwhelm an ordinary computer. Now, four separate teams have taken a step toward achieving such "quantum speed-up" by ...

21 December 2012

El Niño-Southern Oscillation Myth 3: ENSO Has No Trend and Cannot Contribute to Long-Term Warming

El Niño-Southern Oscillation Myth 3: ENSO Has No Trend and Cannot Contribute to Long-Term Warming: Guest post by Bob Tisdale This is the 3rd part of a series of posts that present myths and misunderstandings about the tropical Pacific processes that herald themselves during El Niño and La Niña events. In the posts, I’m simply … Continue reading →

14 December 2012

IPCC AR5 draft leaked, contains game-changing admission of enhanced solar forcing

IPCC AR5 draft leaked, contains game-changing admission of enhanced solar forcing: UPDATE1: Andrew Revkin at the NYT weighs in, an semi endorses the leak, see update below – Anthony UPDATE2: Alternate links have been sent to me, should go faster now.  – Anthony Full AR5 draft leaked here, contains game-changing admission … Continue reading →

The Truth About 2012: Killer Solar Flares Are a Physical Impossibility

The Truth About 2012: Killer Solar Flares Are a Physical Impossibility:

NASA is trying to make sure that no one is taking the 2012 doomsday nonsense seriously, and just put out this video today detailing how a gigantic “killer solar flare” just ain’t gonna happen. Dr. Alex Young from the Goddard Space Flight Center explains how the Sun’s regular 11-year solar cycle is expected to peak in 2013 and 2014, not on December 21 of this year. Plus, this current solar cycle has been kind of a dud as far as wild activity goes, and scientists are not expecting the peak of this cycle to even be as strong as the previous one, which was rather mild.
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Read the rest of The Truth About 2012: Killer Solar Flares Are a Physical Impossibility (88 words)

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09 August 2012

Pendidikan Sains

Pendidikan Sains

Pengaruh Flare dan CME yang Kuat pada Sistem Komunikasi dan Navigasi

Pengaruh Flare dan CME yang Kuat
The Houw Liong

            Pada puncak aktivitas matahari Smax(sunspot maximum) kemungkinannya lebih besar untuk terjadinya flare dan CME (coronal mass ejection) yang kuat. Jika semburan plasma ini mengarah ke Bumi maka berkas partikel bermuatannya bisa merusak sistem instrumentasi yang ada pada satelit komunikasi dan navigasi (GPS) dan untuk daerah jauh dari khatulistiwa , arus induksi yang ditimbulkannya dapat merusak jaringan listrik/ transformator tegangan tinggi seperti yang terjadi di Kanada pada tahun 1859 yang disebut effek Carrington.
            Kehidupan modern sangat bergantung pada sistem navigasi dan komunikasi yang menggunakan satelit, sehingga jika semburan plasma yang kuat mengarah ke Bumi maka bencana yang ditimbulkan akan sangat besar.



Solar Activity | sainsfilteknologi:

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19 July 2012

INTELEJENSIA BUATAN PADA PERMAINAN BRIDGE DENGAN METODE RULE BASED

Tugas Akhir , ITHB, 2012 .

INTELEJENSIA BUATAN PADA PERMAINAN BRIDGE DENGAN METODE RULE BASED.

Rein Martha 1109014

Pembimbing 1 : Prof. Dr. The Houw Liong

Pembimbing 2 : Ken Ratri Retno W, S.Kom, M.T.

ABSTRAKSI

Permainan bridge adalah permainan kartu dengan menggunakan 52 kartu, dan diperlukan 4 pemain untuk memainkannya. Walaupun sudah banyak pertandingan bridge yang diselenggarakan di manca negara, masih sedikit aplikasi yang permainan bridge, apalagi aplikasi bridge yang memakai intelejensia buatan di dalamnya. Dengan dibuatnya aplikasi permainan bridge ini diharapkan dapat memperlihatkan tingkat kesulitan intelejensia buatan yang akan dibuat. Intelejensia buatan pada permainan bridge tidak pernah dapat dibuat sangat pintar, ini disebabkan karena tidak seperti permainan catur atau checker permainan kartu terutama bridge salah seorang pemain hanya bisa mengetahui informasi kartu yang ada di tangannya saja. Karena itu kemungkinan yang didapat dari kartu yang tidak terlihat sangatlah banyak dan tidak dapat diproses semuanya. Untuk aplikasi ini, intelejensia buatan yang dibuat menggunakan metode rule based dengan menggunakan strategi dari para pakar yang sering bermain bridge. Pada tahap bidding, peraturan yang dipakai menggunakan sistem High Card Point dan Distribution Point, lalu digunakan referensi dari William’s Bridge Club untuk proses bidding sendiri. Pada tahap ini, intelejensia buatan akan melakukan opening bid, respond bid, dan final bid. Tiap bid yang dilakukan akan menghitung HCP dan DP juga akan mencek bid partner untuk respond bid dan final bid. Pada tahap play, strategi yang dilakukan adalah finesse dan drop, finesse akan berusaha memenangkan trik dengan kartu yang rendah, dan drop akan memenangkan trik terus menerus dengan kombinasi kartu-kartu tertinggi. Selain itu juga strategi standar seperti trump, mengalah pada partner akan dilakukan seperti layaknya pemain pakar. Kata kunci : bridge, intelejensia buatan, metode rule based.

ABSTRACT :

Bridge is a game using a standard playing card game, it need 4 player to play the game. Even there is a lot of bridge tournament in the world, the application for the game itself still rarely seen, moreove an bridge application with an artificial intelligence in it. With this bridge game application, it will be expect to show the ingenuity of the artificial intelligence. In bridge the artificial intelligence never developed to be very smart, the problem is not like chess or checker all card games, especially bridge the player can only know information about the cards in their hand, therefore the possibility of the outcome is so many and it almost impossible to process all of it with a low amount of time and an average computer. At this game, the artificial intelligence developed using a rule based method utilized strategy from the expert in the bridge world. At bidding phase, the rule used a High Card Point and Distribution Point system, with a reference from William’s Bridge Club for the bidding process itself. At this phase the artificial intelligence will make an opening bid, respond bid, and final bid. Every bid will be made from HCP and DP calculation and for the respond and final bid, it will check the partner bid too. At the play phase strategy that will be use is finesse and drop, finesse will be use to win a trick with low point card, and drop will be use to win trick consecutively with the highest point card combination. Beside it, a standard strategy like trump, give partner to win trick were implemented like an expert play. Keywords: Bridge, Artificial Intelligence, Rule Based Method.

What else did the ’97% of scientists’ say? | Watts Up With That?

What else did the ’97% of scientists’ say? | Watts Up With That?:

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12 July 2012

Doubt Is Good for Science, But Bad for PR | Wired Science | Wired.com

Doubt Is Good for Science, But Bad for PR | Wired Science | Wired.com:

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THE PREDICTION OF DENGUE HAEMORRAGIC FEVER (DHF) IN CIMAHI USING HYBRID GENETIC ALGORITHM AND FUZZY LOGIC

International Conference on Informatics and Computational Intelligence, Bandung, 2011

THE PREDICTION OF DENGUE HAEMORRAGIC FEVER (DHF) IN CIMAHI USING HYBRID GENETIC ALGORITHM AND FUZZY LOGIC

Fhira Nhita, ST1, Prof.Thee Houw Liong2, Shaufiah, MT3

1,3 Informatics Program Study in Telkom Institute of Technology 2Bandung Institute of Technology 1fhiranhita@yahoo.com, 2houwthee@yahoo.co.id, 3shaufiah@gmail.com

Abstrak.

Kejadian Demam Berdarah Dengue (DBD) merupakan masalah nasional di bidang kesehatan. Setiap tahun angka kesakitan DBD masih tinggi. Khususnya di Cimahi, salah satu kota di provinsi Jawa Barat dimana angka kesakitan (Incidence Rate) tahun 2005 hingga 2010 di atas standar yang ditentukan Departemen Kesehatan RI.

Banyak faktor yang mempengaruhi kejadian DBD, antara lain iklim dan perilaku hidup bersih dan sehat (PHBS). Oleh karena itu, dalam penelitian ini, dibangun Sistem Prediksi demam berdarah yang dikaitkan dengan iklim, yang diharapkan bisa membantu memberikan informasi bagi Departemen Kesehatan tentang prediksi resiko DBD di tahun yang akan datang, sehingga Departemen Kesehatan dapat mengambil langkah preventif untuk mengurangi angka kesakitan DBD.

Sistem Prediksi yang dibangun dengan hybrid algorithm yaitu Algoritma Genetika dan Fuzzy Logic mampu menghasilkan akurasi testing 100% dalam memprediksi kondisi DBD di 6 bulan pertama pada tahun 2009 dan 2010 di kecamatan Cimahi Utara dan Cimahi Tengah. Sedangkan pada Cimahi Selatan diperoleh hasil prediksi 6 bulan pertama di tahun 2009 sebesar 100% tetapi pada tahun 2010 terjadi penurunan akurasi.

Kata kunci: demam berdarah, cimahi, algoritma genetika, logika fuzzy, system prediksi

Abstract

The incidence of Dengue Haemorrhagic Fever (DHF) is a national health problem in Indonesia. Every year dengue morbidity is still high. Particularly in Cimahi, one of the city in West Java province where the morbidity rate (Incidence Rate) 2005 to 2010 in the above national standard. Many factors can affect the incidence of dengue, among others, climate and living behavioral. Therefore, the development of DHF Prediction System which is associated with a climate that is expected to help provide information for the Department of Health about dengue risk prediction in the coming year, so the Health Ministry can take preventive action to reduce morbidity of DHF. The Prediction System that was built with a hybrid algorithm which Genetic Algorithms and Fuzzy Logic is able to obtain 100% testing accuracy in predicting the condition of dengue in the first 6 months in 2009 and 2010 in North Cimahi and Central Cimahi. While in South Cimahi, the prediction results obtained in the first 6 months of 2009 amounted to 100% but in 2010 a decline in accuracy.

Defying Gravity: When Strange Liquids Act Like Solids | Wired Science | Wired.com

Defying Gravity: When Strange Liquids Act Like Solids | Wired Science | Wired.com:

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Global Warming and Climate Change skepticism examined

Global Warming and Climate Change skepticism examined:

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19 May 2012

Fuzzy Hidden Markov Models for Indonesian Speech Classification

Fuzzy Hidden Markov Models for Indonesian Speech Classification:

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Fuzzy Hidden Markov Models for Indonesian Speech Classification


Intan Nurma Yulita*,** Houw Liong The**, and Adiwijaya**


*Faculty of Informatics, Telkom Institute of Technology 
**Graduate Faculty, Telkom Institute of Technology, Jalan Telekomunikasi No.1, DayeuhKolot, Jawa Barat 40257, Indonesia


Received: September 15, 2011

Accepted: November 15, 2011


Keywords: fuzzy logic, hidden Markov models, speech, classification, clustering

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.16, No.3 pp. 381-387, 2012

Abstract



Indonesia has many tribes, so that there are many dialects. Speech classification is difficult if the database uses speech signals from various people who have different characteristics because of gender and dialect. The different characteristics will influence frequency, intonation, amplitude, and period of the speech. It makes the system must be trained for the various templates reference of speech signal. Therefore, this study has been developed for Indonesian speech classification. The solution is a new combination of fuzzy on hidden Markov models. The result shows a new version of fuzzy hiddenMarkovmodels is better than hidden Markov model. 

Reference

[1] S. D. Shenouda, F. W. Zaki, and A. Goneid, “Hybrid Fuzzy HMm System for Arabic Connectionist Speech Recognition,” Proc. of the 5th WSEAS Int. Conf. on Signal Processing, robotics and Automation, pp. 64-69, 2006.
[2] L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. of the IEEE, Vol.77, No.2, 1989.
[3] P.Melin, J. Urias, D. Solano, et al., “Voice Recognition with Neural Networks, Type-2 Fuzzy Logic and Genetic Algorithms,” Engineering Letters, Vol.13, No.2, 2006.
[4] L. Chen, S. Gunduz, and M. T. Ozsu, “Mixed Type Audio Classification with Support Vector Machine,” Proc. of the IEEE Int. Conf. on Multimedia and Expo, 2006.
[5] R. Halavati, S. B. Shouraki, M. Eshraghi, and M. Alemzadeh, “A Novel Fuzzy Approach to Speech Processing,” 5th Hybrid Intelligent Systems Conf., 2004.
[6] S. E. Levinson, L. R. Rabiner, A. E. Rosenberg, and J. G. Wilpon, “Interactive Clustering Techniques for Selecting Speaker-Independent Reference Templates For Isolated Word Recognition,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.Assp-27, 1979.
[7] B. H. Juang and L. R. Rabiner, “Fundamentals of Speech Recognition,” Prentice-Hall, 1993.
[8] B. H. Juang and L. R. Rabiner, “Hidden Markov Models for Speech Recognition,” Technometrics, Vol.33, No.3, pp. 251-272, 1991.
[9] J. Zeng and Z.-Q. Liu, “Interval Type-2 Fuzzy Hidden Markov Models,” Proc. of Int. Conf. on Fuzzy Systems, Vol.2, pp. 1123-1128, 2004.
[10] J. Zeng and Z.-Q. Liu, “Type-2 Fuzzy Hidden Markov Models to Phoneme Recognition,” Proc. of the 17th Int. Conf. on Pattern Recognition, 2004.
[11] H. Riza and O. Riandi, “Toward Asian Speech Translation System: Developing Speech Recognition and Machine Translation for Indonesian Language,” Int. Joint Conf. on Natural Language Processing, 2008.
[12] D. P. Lestari, K. Iwano, and S. Furui, “A Larger Vocabulary Continuous Speech Recognition System for Indonesian Language,” 15th Indonesian Scientific Conf. in Japan Proceedings, 2006.
[13] H. Uguz, A. Ozturk, R. Saracoglu, and A. Arslan, “A Biomedical System Based on Fuzzy Discrete HiddenMarkov Model for The Diagnosis of The Brain Diseases,” Expert SystemsWith Applications, Vol.35, pp. 1104-1114, 2008.
[14] S. Kusumadewi, H. Purnomo, and A. Logika, “Fuzzy untuk Pendukung Keputusan,” Penerbit Graha Ilmu, pp. 84-85, 2004.
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15 February 2012

Predicting Students’ Academic Success Using Artificial Neural Network

PREDICTING STUDENTS’ ACADEMIC SUCCESS USING ARTIFICIAL NEURAL NETWORK

A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF TELKOM INSTITUTE OF TECHNOLOGY

BY ALDI RAMDHANI HERAWAN 213100001

Prof. The Houw Liong, Ph.D Supervisor

Shaufiah, M.T. Co-Supervisor

ABSTRACT

Predicting Students’ Academic Success Using Artificial Neural Network

Aldi Ramdhani Herawan

Supervisor : Prof. The Houw Liong, Ph.D

Co-Supervisor : Syaufiah, M.T

In an effort to improve the quality and competitiveness of scholars, universities must have specific strategies to achieve its objectives. Implementation of these strategies would require preparation and adjustments to the problem at hand. For that matter, need early identification what factors affect the success of a student's study. The success of a student's study can be viewed with a grade point average (GPA) student. This study seeks to identify what factors are affecting the success of a student's study indicated by the GPA. These factors are then used as input in the GPA prediction model using Artificial Neural Network (ANN). This study was also conducted on the reduction of data dimension using Principal Component Analysis (PCA). Finally, this study compares the results of predicted GPA, with the input data that has not been reduced and data have been reduced. For variables JUMLAH MATA KULIAH (number of courses) , JUMLAH SKS (sum of credits taken), JUMLAH SKS LULUS (sum of credits passed), and JUMLAH MUTU (average grades in the first year), component coefficients which measure correlations with GPA(average grades in the second year) are extracted. Component coefficient values for each variable are greater than 0.50, and the highest value is 0.958 for JUMLAH SKS LULUS. This shows that component extracted is evenly affected by these variables.

Keywords: Students’ Success, GPA, Prediction, Artificial Neural Network (ANN), Principal Component Analysis (PCA)

Prediction System of Economic Crisis in Indonesia using Time Series Analysis and System Dynamic Optimized by Genetic Algorithm

Master Technology in Informatics, IT Telkom, Bandung , 2012

Prediction System of Economic Crisis in Indonesia using Time Series Analysis and System Dynamic Optimized by Genetic Algorithm

Siti Sa’adah. Supervisor: Prof. The Houw Liong, Co-Supervisor: Adiwijaya, MSi

Abstract

Economic crisis that had happened at 1997-1998 in Indonesia stimulate the researchers to study further because economic that came from words ‘ecos’ and ‘nomos’ means value of life can be used as economic indicators. The economic indicators are GDP (Gross Domestic Product), inflation, population, and oil import per year from 1980-2011, will be tested using time series analysis and system dynamic optimized by algorithm genetic. The results are 93% - 99% accuracy in training and up to 90% accuracy for testing. These results proved that, the prediction system able to fit data in finding historical optimal and small error. Error that had been gotten in this system was caused by the time series data that been used is too short and economic is a chaotic complex system, so the error cannot be avoided.

7 Fakta Mengejutkan Dari Gempa Bumi ~ SERUPEDIA ZONE

7 Fakta Mengejutkan Dari Gempa Bumi ~ SERUPEDIA ZONE:

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13 February 2012

INDONESIAN SPEECH RECOGNITION SYSTEM USING DISCRIMINANT FEATURE EXTRACTION – NEURAL PREDICTIVE CODING (DFE-NPC) AND PROBABILISTIC NEURAL NETWORK

FEBRUARY 2012 INDONESIAN SPEECH RECOGNITION SYSTEM USING DISCRIMINANT FEATURE EXTRACTION – NEURAL PREDICTIVE CODING (DFE-NPC) AND PROBABILISTIC NEURAL NETWORK
A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF TELKOM INSTITUTE OF TECHNOLOGY
BY UNTARI NOVIA WISESTY 213100004
Prof. Dr. The Houw Liong Supervisor
Adiwijaya, SSi., MSi Co-Supervisor
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF INFORMATICS IN THE INFORMATICS STUDY PROGRAM
ABSTRACT
Along with advances in information technology, it has been developed the technology to facilitate human life, one of which is speech recognition. Speech recognition is widely applied to speech to text, speech to emotion, in order to make gadget and computer easier to use, or to help people with hearing disability. However, the development of speech recognition to produce the text from the input voice has not well developed because of time processing. This is certainly going to make the animators and engineers need more time using speech recognition. Therefore, it needs a method to solve the time processing problem and with a good accuracy. In this study proposes a speech recognition system using Discriminant Feature Extraction – Neural Predictive Coding (DFE-NPC) as feature extraction and Probabilistic Neural Network as recognition method. This system can accelerate time processing because it only uses one iteration in training process. Time processing of proposed method is decrease significantly until 1:95 compared to Fuzzy Hidden Markov Model. The best accuracy of the system is 100% when number of class is 2 and 3, and the worst one is 56% when number of class is 10. Keywords: Speech Recognition System, DFE-NPC, PNN, time processing.