Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System

Heart disease is any disease that affects the normal condition and functionality of heart. Coronary Artery Disease (CAD) is the most common. It is caused by the accumulation of plaques within the walls of the coronary arteries that supply blood to the heart muscles. It may lead to continued temporary oxygen deprivation that will result in the damage of heart muscles. CAD caused more than 7,000,000 deaths every year in the worldwide. It is the second cause of death in Malaysia and the major cause of death in the world. To diagnose CAD, cardiologists usually perform many diagnostic steps. Unfortunately, the results of the diagnostic tests are difficult to interpret which do not always provide defmite answer, but may lead to different opinion. To help cardiologists providing correct diagnosis of CAD in less expensive and non- invasive manner, many researchers had developed decision support system to diagnose CAD. A fuzzy decision support system for the diagnosis of coronary artery disease based on rough set theory is proposed in this thesis. The objective is to develop an evidence based fuzzy decision support system for the diagnosis of coronary artery disease. This proposed system is based on evidences or raw medical data sets, which are taken from University California Irvine (UCI) database. The proposed system is designed to be able to handle the uncertainty, incompleteness and heterogeneity of data sets. Artificial Neural Network with Rough Set Theory attribute reduction (ANNRST) is proposed is the imputation method to solve the incompleteness of data sets. Evaluations of ANNRST based on classifiers performance and rule filtering are proposed by comparing ANNRST and other methods using classifiers and during rule filtering process. RST rule inq'u ction is applied to ANNRST imputed data sets. Numerical values are discretized using Boolean reasoning method. Rule selection based on quality and importance is proposed. RST rule importance measure is used to select the most important high quality rules. The selected rules are used to build fuzzy decision support systems. Fuzzification based on discretization cuts and fuzzy rule weighing based on rule quality are proposed. Mamdani inference method is used to provide the decision with centroid defuziification to give numerical results, which represent the possibility of blocking in coronary, arteries. The results show that proposed ANNRST has similar performance to ANN and outperforms k-Nearest Neighbour (k-NN) and Concept Most Common attribute valueFilling (CMCF). ANNRST is simpler than ANN because it has fewer input attributes and more suitable to be applied for missing data imputation problem. ANNRST also provides strong relationship between original and imputed data sets. It is shown that ANNRST provide better RST rule based classifier than CMCF and k-NN during rule filtering process. Proposed RST based rule selection also performs better than other filtering methods. Developed Fuzzy Decision Support System (FOSS) provides better performance compared to multi layer perceptron ANN, k-NN, rule induction method called C4.5 and Repeated Incremental Pruning to Produce Error Reduction (RIPPER) applied on UCI CAD data sets and Ipoh Specialist Hospital's patients. FOSS has transparent knowledge representation, heterogeneous and incomplete input data handling capability. FOSS is able to give the approximate percentage of blocking of coronary artery based on 13 standard attributes based on historical, simple blood test and ECG data, etc, where coronary angiography or cardiologist can not give the percentage. The results of FOSS were evaluated by three local cardiologists and considered to be efficient and useful

Diagnosis Gangguan Permulaan Transformation Dengan JaringanSyaraf Learning Vector Quantization

The objective of this research is to find the optimum learning vector quantization (LVQ) neural network for power transformer incipient faults diagnosis based on dissolved gas in oil analysis (DGA). The research has been conducted by designing LVQ neural network topologies based on DGA. The topologies were compared each other in accuracy by varying input preprocesses. The optimum result was compared with conventional DGA methods to know the accuracy. Variables investigated are topologies, learning velocity, accuracy on training and testing data, and accuracy compared with conventional DGA methods. The research results show that LVQ neural network with topology of six nodes in competitive layer and fuzzy input preprocess has the best performance for the training and testing data compared with other topologies investigated in this research. LVQ neural network also has better performance compared with conventional DGA methods for the data investigated in this research. Thus LVQ neural network can be an alternative method in power transformer incipient faults diagnosis

Studi komputer tentang transien pembukaan pemutus beban pada saluran transmisi

ABSTRACT Opening of a circuit breaker results in an electrical transient. In many cases, it produces overvoltages on electrical components involved. Because the maximum transient over voltage on load that uses the circuit breaker can be predicted, thell apparatus damaged can be avoided. This research is to predict maximum circuit breaker opening transient voltage on apparatus using software that can simulate restriking transient processes. Simplification has been done in computation. Computer program was formulated usillg Runge-Kutta method differentiation to represent the parameter involved. Simulation results show that high dielectric strength and high interrupting capability of circuit breaker can reduce the risk of damaged of the apparatus Keywords: Transien Pemutus beban, saluran transmis

Studi Literatur : Inkoporasi Keuangan Komersial Dan Sosial Islam Untuk Meningkatkan Konsistensi Sistem Keuangan Islam

Keuangan Islam merupakan embrio kekuatan ekonomi di negara ini, di zamannya ia mampu menjadi sistem yang bisa mensejahterakan umatnya. Di masa krisis, ia mampu lolos dari kebangkrutan, sekalipun tidak mendapat bantuan dana BLBI. Konsep yang mengandung ke-Islaman ini harus menjadi kekuatan baru dalam membangkitkan kembali perekonomian negeri ini. Keuangan Islam ini berkembang pesat memainkan peranan penting dalam mengalokasikan sumber daya dan meningkatkan pembangunan ekonomi. Tujuan penelitian ini yang merupakan studi literatur dari beberapa pendapat para ahli Ekonomi Islam, penelitian-penelitian sistem keuangan syariah dan tulisan-tulisan ilmiah lainnya, untuk mengungkapkan bahwa materi yang dijadikan bahasan mengenai integrasi keuangan komersial dan sosial Islam sangatlah diperlukan,  sebagai upaya untuk meningkatkan konsistensi sistem keuangan dan pembangunan sosial ekonomi di Indonesia. Hasil dari studi literatur penelitian ini adalah, setidaknya ada lima langkah dalam mempercepat perkembangan sistem keuangan Islam, baik secaranasional maupun internasional.Pertama, perlunya memperkuat sistem pengaturan dan pengawasanlembaga keuangan Islam. Kedua, perlunya koordinasi dan kerjasama internasional. Ketiga, perlunya kolaborasi di tingkat pengawasan sistem keuangan Islam lintas negara. Keempat, perlunya model bisnis sistem keuangan Islam khususnya diperbankan syariah, dengan memberikan penekanan pada bisnis di sektor rillketimbang pasar keuangan. Kelima, perlunya penetapan acuan rate of return berdasarkan prinsip Islam yang sesungguhny

DIAGNOSIS GANGGUAN PERMULAAN TRANSFORMATOR DAYA DENGAN JARINGAN SYARAF TIRUAN

            Penelitian ini adalah studi tentang aplikasi jaringan syaraf tiruan untuk diagnosis gangguan permulaan pada transformator daya. Jaringan syaraf yang digunakan adalah jaringan syaraf multi-layer perceptron melalui variasi metode pembelajaran resilient backpropagation, scaled conjugate gradient, dan Levenberg-Marquardt serta pengolah awal data masukan penskalaan, pembagian dengan rerata, normalisasi rerata dan deviasi standard. Diagnosis gangguan permulaan berbasis dissolved gas in oil analysis.            Jaringan syaraf tiruan yang digunakan mempunyai enam masukan dengan tiga keluaran. Pembelajaran dilakukan dengan data gangguan permulaan transformator dari suatu penelitian. Penelitian dilakukan dengan membandingkan jaringan syaraf tiruan dalam  topologi, metode pembelajaran, pengolah awal data masukan divariasi untuk mendapat yang terbaik dari sisi kebenaran diagnosis, rerata kebenaran ,waktu yang dibutuhkan, kemampuan mencapai target untuk beberapa pembelajaran dengan inisialisasi Nguyen-Widrow yang bersifat acak.            Hasil penelitian menunjukkan bahwa jaringan syaraf tiruan topologi gabungan multi layer perceptron dengan pengolah awal data masukan dibagi rerata serta metode pembelajaran resilient backpropagation adalah pilihan terbaik. Hasil penelitian didapatkan dengan membandingkan dengan topologi lain yang diteliti dalam penelitian ini. Jaringan syaraf tiruan juga lebih baik dari metode konvensional gas kunci dan perbandingan gas untuk kasus transformator yang diteliti dalam penelitian ini sehingga metode jaringan syaraf tiruan ini diharapkan dapat menggantikan pakar diagnosis gangguan mula transformator

Eye Blink Classification for Assisting Disability to Communicate Using Bagging and Boosting

Disability is a physical or mental impairment. People with disability have more barriers to do certain activity than those without disability. Moreover, several conditions make them having difficulty to communicate with other people. Currently, researchers have helped people with disabilities by developing brain-computer interface (BCI) technology, which uses artifact on electroencephalograph (EEG) as a communication tool using blinks. Research on eye blinks has only focused on the threshold and peak amplitude, while the difference in how many blinks can be detected using peak amplitude has not been the focus yet. This study used primary data taken using a Muse headband on 15 subjects. This data was used as a dataset classified using bagging (random forest) and boosting (XGBoost) methods with python; 80% of the data was allocated for learning and 20% was for testing. The classified data was divided into ten times of testing, which were then averaged. The number of eye blinks’ classification results showed that the accuracy value using random forest was 77.55%, and the accuracy result with the XGBoost method was 90.39%. The result suggests that the experimental model is successful and can be used as a reference for making applications that help people to communicate by differentiating the number of eye blinks. This research focused on developing the number of eye blinks. However, in this study, only three blinking were used so that further research could increase these number

Valuasi ekonomi limbah

Tujuan dari penelitian ini adalah untuk mengetahui valuasi ekonomi limbah penjual es kelapa di Kecamatan Samarinda Utara. Data yang digunakan dalam penelitian ini adalah data primer dan sekunder. Penelitian ini menggunakan jenis penelitian kualitatif dan kuantitatif. Penelitian kuantitatif menggunakan perhitungan valuasi ekonomi dengan menggunakan metode harga non-pasar pendekatan nilai kekayaan. Hasil penelitian ini menunjukkan bahwa kuantitas limbah yang dihasilkan oleh penjual es kelapa berdasarkan kelapa perbulan minimum rata-rata sebanyak 63 karung dan maksimum rata-rata sebanyak 100 karung, valuasi ekonomi limbah penjual es kelapa yang diperoleh penjual dalam waktu satu bulan minimum rata-rata sebesar Rp. 3.200 dan maksimum rata-rata sebesar Rp. 14.009, sedangkan valuasi ekonomi yang diperoleh oleh pembeli limbah dalam satu bulan minimum rata-rata sebesar Rp. 202.563 dan maksimum rata-rata sebesar Rp. 607.688

Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

EEG signals are obtained from an EEG device after recording the user's brain signals. EEG signals can be generated by the user after performing motor movements or imagery tasks. Motor Imagery (MI) is the task of imagining motor movements that resemble the original motor movements. Brain Computer Interface (BCI) bridges interactions between users and applications in performing tasks. Brain Computer Interface (BCI) Competition IV 2a was used in this study. A fully automated correction method of EOG artifacts in EEG recordings was applied in order to remove artifacts and Common Spatial Pattern (CSP) to get features that can distinguish motor imagery tasks. In this study, a comparative studies between two deep learning methods was explored, namely Deep Belief Network (DBN) and Long Short Term Memory (LSTM). Usability of both deep learning methods was evaluated using the BCI Competition IV-2a dataset. The experimental results of these two deep learning methods show average accuracy of 50.35% for DBN and 49.65% for LSTM

Analisis Motivasi Hedonis Seseorang Dalam Menggunakan Media Sosial: Studi Kasus Instagram

Abstract.Hedonic motivation, which is often called as intrinsic motivation, plays a role in encouraging a person to use a system to meet their needs. Currently, the popular systems used in the fulfilment of one's needs are games and social media. It has been recorded that the users of Instagram, which has been ranked as the second most popular social media in America, has increased as many as 100 thousand people since the middle of 2016, with the total registered users of 600 million. This development raises a question of what drives a person to use social media. This study aims to identify factors that affect a person to use Instagram based on Hedonic Motivation System Adoption Model (HMSAM). The data were then analyzed using Partial Least Square (PLS). After the research was conducted on 245 respondents, the results prove that the motivating factors of a person to use Instagram are perceived ease of use, perceived enjoyment, and control.Keywords: hedonic motivation system adoption system (hmsam), structural equation model (sem), partial least square (pls), social media, instagram. Abstrak.Motivasi hedonis atau sering kali juga disebut dengan motivasi intrinsik berperan dalam mendorong seseorang untuk menggunakan suatu sistem demi memenuhi kebutuhannya. Saat ini sistem yang populer digunakan dalam pemenuhan kebutuhan seseorang tersebut adalah game dan social media. Instagram yang menduduki peringkat ke dua sebagai social media terpopuler di Amerika, tercatat mengalami pertumbuhan sebanyak 100 ribu orang sejak pertengahan 2016 dengan total pengguna yang tercatat sebanyak 600 juta orang. Melihat perkembangan tersebut memunculkan pertanyaan apa yang mendorong seseorang untuk menggunakan sosial media. Penelitian ini akan melihat faktor yang mempengaruhi seseorang menggunakan Instagram berdasarkan Hedonic Motivation System Adoption Model (HMSAM) yang kemudian dianalisis menggunakan metode Partial Least Square (PLS). Hasilnya setelah dilakukan penelitian pada 245 responden terbukti bahwa yang menjadi faktor pendorong seseorang menggunakan Instagram adalah percieve ease of use, percieved enjoyment, dan control.Kata Kunci: hedonic motivation system adoption system (hmsam), structural equation model (sem), partial least square (pls), social media, instagram

Narrow Window Feature Extraction for EEG-Motor Imagery Classification using k-NN and Voting Scheme

Achieving consistent accuracy still big challenge in EEG based Motor Imagery classification since the nature of EEG signal is non-stationary, intra-subject and inter-subject dependent. To address this problems, we propose the feature extraction scheme employing statistical measurements in narrow window with channel instantiation approach. In this study, k-Nearest Neighbor is used and a voting scheme as final decision where the most detection in certain class will be a winner. In this channel instantiation scheme, where EEG channel become instance or record, seventeen EEG channels with motor related activity is used to reduce from 118 channels. We investigate five narrow windows combination in the proposed methods, i.e.: one, two, three, four and five windows. BCI competition III Dataset IVa is used to evaluate our proposed methods. Experimental results show that one window with all channel and a combination of five windows with reduced channel outperform all prior research with highest accuracy and lowest standard deviation. This results indicate that our proposed methods achieve consistent accuracy and promising for reliable BCI systems