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Fuzzy logic is a commonly used method to build a system whose reasoning resembles human intuition or reasoning. Tsukamoto fuzzy logic and certainty factor are two methods which have in common that contain uncertainty. Today, the well known process of childbirth is normal childbirth and cesarean where the choice of this is dubious. Inappropriate choice of delivery will endanger the safety of both mother and baby. This study was conducted to determine the type of cesarean or normal delivery with the development of Tsukamoto fuzzy logic with certainty factor to prevent the problem of doubt about the choice of delivery. This study uses age, blood pressure, pelvic size, fetal weight, breech presentation, and cesarean history as input. The research output is a type of normal or cesarean delivery. The test was conducted on 30 people who had given birth whose medical records were processed using a web application. The results obtained are the type of delivery that was tested using those 30 medical record data which had 100% similarity results with the development of Tsukamoto fuzzy.

Photovoltaic (PV) Farm is one of the effective way to produce high scaled electrical energy from sunlight. Many research has been done, is powered optimization method on PV Farm generator string cluster base. But, this method needed a bigger inverter power capacity about 240% from it was before. So this research offer a joint powered optimization method on PV Farm generator string cluster base also can reduce the needs of inverter power capacity. Reducing the needs on inverter capacity can be engineered through inverter position by using Smart Control Algorithm(SCA). This research used a PV Farm with 150 kWp capacity, 50 PV string, every string is consist of 12 module. When it got the shading effect then PV string grouped them into three cluster. The cluster criteria is base on voltage difference between string is more than 20%. Next those three output cluster connect to 3 inverter through switch matrix, where the power capacity comes to the three inverter continously are 75%, 20%, and 5% of power capacity in PV Farm. Inverter position controlled is determined SCA. its controller function to raise the ON/Off signal switch on switch matrix series. Process on raising the ON/Off by SCA base on the voltage input and string current PV Farm. The Voltage and electrical current value is captured through electrical sensor reading and voltage sensor on each cluster. With the controller, power of PV Farm could be channel by inverter with a minimize power capacity on the inverter. Base on simulation result , this method could pressure the need of inverter capacity on PV Farm generator cluster base about 140% (has the same power capacity as generator). So it's concluded that this method could work better on optimizing the power and reducing the inverter capacity in PV Farm generator cluster base on normal/partial shading radiation

Methadone Maintenance Therapy is a therapy of methadone drug substitution to manage the opioid dependence in drug addictions. Prescribing the optimal dose of methadone is crucial and complex. It involves avoiding the opioid withdrawal consequences, suppressing cravings, and preventing relapse to illicit opioids use. Hence, this study provides prediction for methadone optimal dose by applying the adaptive neuro-fuzzy inference system (ANFIS) technique. ANFIS has the advantage of knowledge learning capabilities from both clinical data and the expert knowledge (i.e. fuzzy rules), and it is well suited for managing complex problems. The ANFIS model was developed by using MATLAB (i.e. Fuzzy Logic Toolbox). The results obtained show close agreement between the predicted and actual optimal doses as the obtained coefficient value are close to unity in both in both training and testing datasets. This indicates the ANFIS model is able to deal with real clinical data and it is viable to be used for predicting the optimal methadone doses in MMT patients.

Power transmission lines are extremely important for the power system to deliver energy of electricity from the plant to the load. The short circuit of fault often occurs in the transmission line and may lead to disconnecting the power supply to the load. This study implements a hybrid technique that is Discrete Wavelet Transformation (DWT) and Support Vector Machine (SVM) for classification of fault in the transmission line. The DWT was created to extract the detailed signal of transient D8 and D9 (order of 4) at 50 kHz for sampling frequency. The value of Root Mean Square (RMS) will be determined by the coefficients D8 and D9 for training and test data using SVM technique. Furthermore, SVM is utilized to detect the fault for each phase and the ground is discovered in the type of fault. The SVM technique has been run using parameter C and kernel scale to achieve the great results of classification of the fault. Type of simulating fault has a variation of location of the fault, fault of resistance and initial angle. The training and test data run for the Test System of Riau, Indonesia. The test result for the classification of fault reaches the highest accuracy of 100%.

Wind energy is a promising alternative energy source that can be used to produce electricity. Wind energy is converted into electrical energy by the principle of converting the kinetic energy of the wind into electrical energy. This working system is often called the wind energy conversion system. Work equipment for this conversion process occurs at wind power plants. One part of the wind power generation system that is no less important is the monitoring section. Monitoring section functions to determine conditions and to control wind power plants. In this paper, a web-based monitoring system has been established. By using Arduino and a data logger system to store data on wind speed and voltage generated by the generator. Data obtained from the anemometer sensor and voltage sensor are processed by Arduino. Then the sensor data is stored on the SD card connected to the data logger shield. Furthermore, data can be viewed through the website based on data sent by GSM Shield sim900. So that users can monitor wind speed and voltage data through the website.

Solar Photovoltaic (SPV) generation system is one of the renewable energy sources that is currently experiencing increased penetration on the grid. Generally, SPV panels are installed at a certain tilted angle. This type of installation has its own drawback such as when the sun’s position is behind the SPV panel, the energy produced decreases. To overcome this condition, the SPV panel with the dual axis sun tracker system is designed and implemented. The designed dual axis sun tracker of this research has several main components, such as 4 LDR sensors for detecting the position of the sun and the Arduino microcontroller used to read the measurement from the LDR sensors, processing these measurement data and instructing the actuator to drive the SPV panels at a perpendicular incident angle. The actuator is made from 2 servo motors for each vertical and horizontal axis respectively. It can be seen from the results that the use of dual axis sun tracker is capable to increase the “energy gain” up to 60 %. It is also worth noting that the use of this dual axis sun tracker has a big potential to apply in the tropical region such as Pekanbaru City, Indonesia.

Solar energy system is one of the most promising renewable energy resources to meet the energy demand. This resource has been developed and utilized in many countries. Indonesia, as one of the tropical countries has been blessed with a huge amount of radiant energy from the sun during the whole year. However, the solar energy resource data is very limited. Therefore, the purpose of this research is to develop an automatic solar station to measure the potential of solar energy resource in the unique tropical region like Pekanbaru City. The availability of solar energy resource data are the foundation of information for designing, utilizing and deploying of solar energy generation and technologies. It is expected that the automatic solar station has capability to measure not only solar radiation data but also other related parameters such as air temperature and humidity. The measured solar energy resource data are also designed as foundation of solar energy resource data base of Pekanbaru City Indonesia.

Providing decision support for the medical prescribing process remains a challenge and complicated task. The implementation of Clinical Decision Support System (CDSS) in clinical practice today is not fully integrated and achieved. There are major issues involved in developing and implementing CDSS that challenge and hinders the success of CDSS applications in clinical practice. This paper review and discover the barriers, challenges and considerations of existing CDSS applications that need to be addressed. This study hopes to yield some useful findings in both the literature and the practice of CDSS as well as the contribution to usage of CDSS in improving health care services.

The hybrid Analytic Hierarchy Process-Fuzzy Data Envelopment Analysis is commonly used in solving Multiple Criteria Decision Making problems to cater for the uncertainty and subjectivity of human judgments. However, the use of Z-number in hybrid techniques is a new research area. Hence, the study on the impact and feasibility of Z-number extension has not been fully investigated. The main objective of this paper is to present an extension of Analytic Hierarchy Process-Fuzzy Data Envelopment Analysis method using Z-number. Two different multiplication relation operations were used to convert fuzzy numbers into Z-numbers by integrating the reliability component R into the restriction component ς. The next step is to integrate the criteria weights into Z- Data Envelopment Analysis process. A numerical example is given to illustrate the application of the proposed Analytic Hierarchy Process-Z-Number Data Envelopment Analysis methodology in ranking the risk priority of 20 bridge structures. The results showed that it is highly feasible to use the proposed hybrid Analytic Hierarchy Process-Fuzzy Data Envelopment Analysis in risk ranking. This information is very useful for improving characteristics of the alternatives understudied.

This paper aim to identify and understand the challenges that have to be faced by a small and medium offshore accounting outsourcing provider implementing knowledge management (KM). This study used interpretive in-depth single case study in order to understand the challenges and innovation in implementing the KM in company. Findings show that the middle manager plays an important role in managing challenge to implement KM by create innovations. This study will contribute to further understand the KM implementation in a small and medium company especially in offshore accounting outsourcing provider.

Analytic Hierarchy Process or better known as AHP is the method of structuring, measuring, and synthesizing numerous judgments in finding solutions to decision making problems. There are three common methods of determining the criteria weights in AHP; extent analysis, logarithmic least square method (LLSM), and Lambda-Max. This paper identifies the strengths and weaknesses of these three methods based on the weight accuracy and computational level involved in generating the criteria weights using the data of selecting the best catering company by Wang et al [1].

Degradable plastic is produced by combining oil palm products such as oil palm biomass (OPB); palm oil (PO); additives and polyethylene (PE). Several combinations of these input parameters are used in the formulation to produce degradable plastics of different physical properties. In this study, a Fuzzy Logic (FL) model has been developed to predict the physical properties of plastics based on the same three input components. The formulation of degradable plastics with the most bioactive components in it with desirable physical properties measured by its Melt Flow Index (MFI) and Density could be identified using the FL model developed. The model demonstrates high prediction accuracy reflected in small RMSE value of 0.000129 for the prediction of MFI and 0.009337 for the prediction of Density.