scholarly journals Perbandingan Kinerja Model Klasifikasi Decission Tree , Bayesian Classifier, Instance Base, Linear Function Base, Rule Base pada 4 Dataset Berbeda

2019 ◽  
Vol 5 (1) ◽  
pp. 70-78
Author(s):  
T Yudi Hadiwandra
Keyword(s):  
Linear Function ◽  
Bayesian Classifier ◽  
Rule Base ◽  
Missing Value ◽  
Model Instance ◽  
Function Base

Ada banyak metode klasifikasi yang telah dikembangkan namun kinerjanya selalu berbeda dari satu masalah ke masalah lain. Perlu dilakukan suatu upaya untuk memilih model klasifikasi yang relatif optimal, salah satunya dengan membandingkan kinerja beberapa algoritma dari beberapa kelas model berbeda menggunakan beberapa dataset dengan karakteristik yang berbeda pula sehingga dapat memberikan gambaran tentang kelas model mana yang relatif optimal untuk dipilih sebagai tahap awal dalam memilih algoritma yang akan diterapkan. Pada makalah ini dibandingkan kinerja metode klasifikasi dari model Decission Tree, Bayesian Classifier, Instance Base, Linear Function Base dan Rule Base menggunakan 4 dataset yang berbeda karakteristiknya.  Dari hasil pengujian diperoleh kesimpulan bahwa model Instance Base dan Decission Tree lebih robust dibanding kelas model lainnya bila diberi dataset yang mengandung noise dan missing value. Accuracy model Bayesian lebih stabil dibanding kelas model lainnya apabila diberi dataset dengan jumlah atribut yang lebih banyak. Semua kelas model mempunyai scalability yang baik dan mampu meningkatkan accuracy bila diberi jumlah record yang lebih besar. Kelas model Linear Function dan Decission Tree membutuhkan waktu training yang relatif lebih lama dibanding model lainnya, namun membutuhkan waktu testing yang relatif lebih cepat dibanding model lainnya. Secara umum kelas model Decission Tree lebih unggul dibanding kelas model lainnya

BayesFuzzy: using a Bayesian Classifier to Induce a Fuzzy Rule Base

2007 ◽  
Author(s):  
Estevam R. Hruschka ◽  
Heloisa de A. Camargo ◽  
Marcos E. Cintra ◽  
M. do Carmo Nicoletti
Keyword(s):  
Fuzzy Rule ◽  
Bayesian Classifier ◽  
Rule Base ◽  
Fuzzy Rule Base

Decision Support for Psychiatric Diagnosis Based on a Simple Questionnaire

1997 ◽  
Vol 36 (04/05) ◽  
pp. 349-351
Author(s):  
H. Mizuta ◽  
K. Kawachi ◽  
H. Yoshida ◽  
K. Iida ◽  
Y. Okubo ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Support ◽  
Psychiatric Diagnosis ◽  
Psychiatric Patients ◽  
Bayesian Classifier ◽  
Neural Network Classifier ◽  
Correct Decision ◽  
Neurotic Disorders ◽  
The Neural Network

Abstract:This paper compares two classifiers: Pseudo Bayesian and Neural Network for assisting in making diagnoses of psychiatric patients based on a simple yes/no questionnaire which is provided at the outpatient’s first visit to the hospital. The classifiers categorize patients into three most commonly seen ICD classes, i.e. schizophrenic, emotional and neurotic disorders. One hundred completed questionnaires were utilized for constructing and evaluating the classifiers. Average correct decision rates were 73.3% for the Pseudo Bayesian Classifier and 77.3% for the Neural Network classifier. These rates were higher than the rate which an experienced psychiatrist achieved based on the same restricted data as the classifiers utilized. These classifiers may be effectively utilized for assisting psychiatrists in making their final diagnoses.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

CLASS OF BOOLEAN FUNCTIONS CONSTRUCTED USING SIGNIFICANT BITS OF LINEAR RECURRENCES OVER THE RING ℤ2n

2019 ◽  
Vol 6 (2) ◽  
pp. 90-94
Author(s):  
Hernandez Piloto Daniel Humberto
Keyword(s):  
Linear Function ◽  
Boolean Functions ◽  
Hamming Distance ◽  
Linear Recurrences ◽  
Maximum Period ◽  
Non Linear ◽  
The Given ◽  
Class Of Functions

In this work a class of functions is studied, which are built with the help of significant bits sequences on the ring ℤ2n. This class is built with use of a function ψ: ℤ2n → ℤ2. In public literature there are works in which ψ is a linear function. Here we will use a non-linear ψ function for this set. It is known that the period of a polynomial F in the ring ℤ2n is equal to T(mod 2)2α, where α∈ , n01- . The polynomials for which it is true that T(F) = T(F mod 2), in other words α = 0, are called marked polynomials. For our class we are going to use a polynomial with a maximum period as the characteristic polyomial. In the present work we show the bounds of the given class: non-linearity, the weight of the functions, the Hamming distance between functions. The Hamming distance between these functions and functions of other known classes is also given.

PEMETAAN TEKNOLOGI INDUSTRI KELAPA SAWIT NASIONAL DAN KEBIJAKAN PENGEMBANGANNYA

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Subiyanto Subiyanto
Keyword(s):  
Palm Oil ◽  
Government Policy ◽  
Value Chain ◽  
Raw Materials ◽  
Value Added ◽  
Oil Industry ◽  
Missing Value ◽  
Technology Capability ◽  
The Government

Palm oil industry in Indonesia has been growing rapidly. But, unfortunately the growth is only effective on upstream industry with low value products, such that potential downstream value added are not explored proportionally. The government is therefore in the process of developing an appropriate policy to strengthen the national palm oil downstream industry. This paper proposes that an approriate policy for developing palm oil downstream industry could be derived from the maps of value chain and existing technology capability of the industry. The result recommends that government policy should emphasize on the supply of raw materials, infrastructure and utilities, as well as developing the missing value chain industry, especially ethoxylation and sulfonation.

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