DIAGNOSIS GEJALA PENYAKIT TUBERKULOSIS MENGGUNAKAN FUZZY EXPERT SYSTEM BERBASIS WEB

Tuberkulosis (TB) adalah salah satu penyakit yang menyebabkan kematian tinggi pada manusia. Pencegahan penyakit ini telah dicari oleh para profesional medis dan peneliti. Sayangnya, penanganan TB masih dilakukan secara manual dan sangat tergantung pada ahli medis yang jumlahnya terbatas, sehingga dalam penelitian ini dilakukan pengembangan sistem informasi alternatif untuk mengatasi masalah tersebut. Sistem diagnosis gejala TB ini dikembangkan menggunakan metode sistem pakar fuzzy. Data masukan pada sistem ini adalah gejala yang diderita penderita, yang terdiri dari batuk, penurunan berat badan, sesak napas, kehilangan nafsu makan, demam, berkeringat di malam hari, dan malaise. Prosesnya dimulai dari memasukkan data gejala, kemudian diproses menggunakanfuzzy yang terdiri dari proses fuzifikasi, inferensi dan defuzifikasi.Aturan penyakit diberikan oleh para ahli yang ahli di bidangnya dan dari sumber jurnal. Keluaran dari sistem menampilkan antarmuka diagnosis penyakit di web. Hasil penelitian ini adalah sistem informasi yang dapat memberikan hasil diagnosis penyakit kepada pengguna. Perhitungan nilai akurasi juga dilakukan untuk mengetahui seberapa akurat fuzzy dalam sistem ini, dan dari hasil perhitungan ditemukan bahwa nilai akurasi yang didapat adalah sebesar 82% yang menunjukkan bahwa logika fuzzy baik untuk proses diagnosis. Kata kunci — TB, pakar, sistem pakar fuzzy, logika fuzzy, diagnosis Tuberculosis (TB) is one of the diseases that causes high mortality in humans. The prevention of this disease has been sought by medical professionals and researchers. Unfortunately, the handling of TB is still manual and very dependent on medical experts who are very limited in number. In this study we propose an alternative information technology to overcome this problem. To overcome this problem a TB diagnostic system is developed using a fuzzy expert system. Input data on this system are the symptoms suffered by the sufferer, which consists of cough, weight loss, breathless, loss of appetite, fever, sweat at night, and malaise. The input data is then processed using fuzzy logic which consists of a process of fuzification, inference and defuzification. The output of the system displays the disease diagnosis interface on the web. Disease rules are given by experts who are experts in their fields and from journal sources. The results of the study are information systems that can provide the results of disease diagnosis to the user. The calculation of the accuracy value is also done to find out how accurate the fuzzy logic is in this system, and from the results of these calculations it is found that the accuracy value is 82% which shows that fuzzy logic is good for the diagnostic process. Keywords—tuberculosis, expert, fuzzy expert system, fuzzy logic, diagnosi

Skin diseases diagnosis support system using fuzzy logic

There are many types of skin diseases and difficult to identify the categories of skin diseases. Skin diseases can easily get affected by all different ages either children or adults. There are many types of skin disease include lupus, acne, psoriasis, eczema and impetigo. However, this research only focuses on one type of skin disease only which is eczema. Based on the research that has been conducted, there are many previous researchers use image processing method to determine the skin diseases. The image processing requires more time to learn and need a large space of memory to install the software. Other than that, image processing also requires a high quality of camera or any devices to capture an image to get the accurate result. To buy the devices is costing and not all public users afford to buy it. Thus, this research has purposed a system to identify the type of eczema skin diseased based on factors such as skin irritation, skin condition, location of affection and family history The conceptual model also has been proposed as a logical diagram to show the system work. The conceptual model is based on diagnostic rules. The conceptual model and rules has been tested by using user knowledge improvement before and after using the proposed solution. The test that has been conducted not involved with rules verification because of attire constraint to meet with the dermatologist. The user knowledge tested show that the knowledge of user is increase about eczema compared to before they used the proposed solution. Thus, the proposed solution gave benefit to public user to understand their skin disease diseases and earlier treatment that possible they can applied

PENERAPAN LOGIKA FUZZY UNTUK MENENTUKAN KONDISI MESIN PADA MOTOR TRANSMISI MANUAL (STUDI KASUS HONDA CS-1)

Honda CS-1 merupakan salah satu jenis sepeda motor dengan transmisi manual dengan spesifikasi mesin SOHC (Single  Overhead Camshaft). Untuk  melakukan perawatan mesin tersebut dibutuhkan pengetahuan yang cukup. Beberapa mekanik memiliki tingkat pengetahuan yang berbeda sehingga dimungkinkan terdapat kesalahan dalam melakukan perawatan mesin.Untuk menghindari kesalahan tersebut, maka perlu dirancang sebuah aplikasi untuk membantu mekanik melakukan analisa kondisi mesin tersebut, sehingga mekanik dapat  menganalisa letak kerusakan mesin dan  memberikan saran penanganan yang tepat.Metode yang digunakan adalah metode mamdani. Dari hasil pengujian aplikasi ini didapatkan bahwa error rate untuk menentukan kondisi kerusakan mesin adalah 10%. Hal ini berarti bahwa aplikasi sudah dapat diterapkan untuk membantu mekanik untuk menentukan kondisi kerusakan mesin motor Honda CS-1

IQ Classification via Brainwave Features: Review on Artificial Intelligence Techniques

Intelligence study is one of keystone to distinguish individual differences in cognitive psychology. Conventional psychometric tests are limited in terms of assessment time, and existence of biasness issues. Apart from that, there is still lack in knowledge to classify IQ based on EEG signals and intelligent signal processing (ISP) technique. ISP purpose is to extract as much information as possible from signal and noise data using learning and/or other smart techniques. Therefore, as a first attempt in classifying IQ feature via scientific approach, it is important to identify a relevant technique with prominent paradigm that is suitable for this area of application. Thus, this article reviews several ISP approaches to provide consolidated source of information. This in particular focuses on prominent paradigm that suitable for pattern classification in biomedical area. The review leads to selection of ANN since it has been widely implemented for pattern classification in biomedical engineering

Decision support system for building information modeling (BIM) software selection: A case study in construction feasibility stage

The adoption of Building Information Modelling (BIM) software has proven to be beneficial to the construction industry to improve the design, analysis, construction, operation and data management. Due to the variety of BIM software on the market, choosing the right BIM software in construction projects is deemed to be a complicated decision making process. Previous studies revealed that software selection is mainly made based on popularity and recommendation from other companies. Consequently, inaccurate selection would lead to the underutilised features and negative effect the investment on the BIM software. Based on literature, there is a lack of systematic approach to select the right BIM software for specific project requirements. This highlights the needs for decision making tools to select the appropriate BIM software. This research aims to develop a Decision Support System (DSS) named topsis4BIM which integrates graphical user interfaces, BIM features database, Fuzzy TOPSIS and Web 2.0 tools. A real construction project was used as a case study for demonstrating and validating the DSS framework. The findings indicate that the use of topsis4BIM improves the BIM software selection process compared to the current practice. In addition, it also produce a new framework for the next generation DSS using Web 2.0 tools. The study introduces an innovative and economical decision making approach that can guide construction practitioners towards the betterment of BIM adoption

A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

This research provides a system that integrates the work of data mining and expert system for different tasks in the process of medical diagnosis, and provides detailed steps to the process of reaching a diagnosis based on the described symptoms and mapping them with existing diagnosis available on the web or on a cloud of medical knowledge based, aggregate these data in a fuzzy manner and produce a satisfactory diagnosis of the persisting problem. The mobile phone interface would make the system user-friendly and provides mobility and accessibility to the user, while posting updates and reading in details the steps that led to the decision or diagnosis that is reached by the K-mean and the fuzzy logic inference engine. The achieved results indicate a promising diagnosis performance of the system as it achieved 90% accuracy and 92.9% F-Score

Hierarchical Fuzzy Systems: Interpretability and Complexity

Hierarchical fuzzy systems (HFSs) have been regarded as a useful solution for overcoming the major issues in fuzzy logic systems (FLSs), i.e., rule explosion due to the increase in the number of input variables. In HFS, the standard FLS are reformed into a low-dimensional FLS subsystem network. Moreover, the rules in HFS usually have antecedents with fewer variables than the rules in standard FLS with equivalent functions, because the number of input variables in each subsystem is less. Consequently, HFSs manage to decrease rule explosion, which minimises complexity and improves model interpretability. Nevertheless, the issues related to the question of “Does the complexity reduction of HFSs that have multiple subsystems, layers and different topologies really improve their interpretability?” are not clear and persist. In this paper, a comparison focusing on interpretability and complexity is made between two HFS’ topologies: parallel and serial. A detailed measurement of the interpretability and complexity with different configurations for both topologies is provided. This comparative study aims to examine the correlation between interpretability and complexity in HFS

A Neuro-Fussy Based Model for Diagnosis of Monkeypox Diseases

The largest vertebrate viruses known, infecting humans, and other vertebrates are poxviruses including cowpox, vaccinia, variola (smallpox), and monkeypox viruses. Monkeypox was limited to the rain forests of central and western Africa until 2003. A smallpox-like viral infection caused by a virus of zoonotic origin, monkeypox belongs to the genus Orthopoxvirus, family Poxviridae, and sub-family Chordopoxvirinae. Monkeypox has a clinical presentation like ordinary forms of smallpox, including flulike symptoms, fever, malaise, back pain, headache, and characteristic rash. In view of the eradication of smallpox, such symptoms in a monkepox endemic region should be carefully diagnosed. The problem in diagnosing monkeypox lies in the fact that it is clinically indistinguishable from other pox-like illnesses making virus differentiation difficult. In this paper, we present a neuro-fuzzy based model for early diagnosis of monkeypox virus with a differentiation from other pox families