Judul: Model of sustainable sardine fisheries management in the Bali Strait
Abstrak: Fisheries management, particularly capture fisheries, is very complex and requiring the integration of biological, ecological, economical and social aspects. Adjusting a sustainable management of fisheries resources, it is needed attention to the aquatic ecosystem as the habitat for fish. Sardine (Sardinella lemuru Bleeker 1853) is the main source of livelihood for the fishermen on the coast of Bali Strait. Sardine is a small pelagic fish which caught for the public consumption and for canned fish industry. The main fishing gears used by the fishermen of the Bali Strait for catching sardine are purse seine (a major fishing gear). The other fishing gears which used are payang (boat seine net), gillnet, bagan (lift net), and beach seine. The exploitation of fishery resources in Bali strait waters were influence both in fish catch and revenue of the local government. Based on these facts, management of sardine fish resources in order to anticipate over fishing as well as to keep the fishers income sustainability is needed. Monitoring and control of sardine resource exploitation are needed to reach a sustainable and well-maintained management. To solves these problems the study has been conducted in Banyuwangi, East Jawa and Jembrana, Bali. The main purpose of this study is formulating a model of sustainable sardine fisheries management in the Bali Strait. Using sustainable approach, the detail aims of this study are to identify: (1) main factor effect of oceanography and climatology on the catch of sardine; (2) productivity and fishing effort of sardine (3) social and economic conditions of fishermen and the fishermen institutional role, (4) appropriate fishing gear of sardine fishes, and (5) developed model of dynamics system of sustainable sardine fisheries management. The results of this study showed that the chlorophyll-a and wind were influence the catch of sardine significantly. Sardines that identified as a plankton feeder have already utilized intensively at optimum level. Based on economic and technical aspects, purse seine has high opportunity to be developed in Bali Strait. Furthermore, using dynamics model this study suggested to operate 165 fishing effort (purse seine). This scenario was the better choices compared with existing condition and other scenario which operate 184 units fishing efforts.
Keyword: 

Judul: Model dinamis perikanan lemuru (Sardinela lemuru) di Selat Bali
Abstrak: Sardine fishery is single fishery in Bali Strait and has its dynamics that consist element of biology, fishing gear technology, human resources, and fish processing industries. The four subsystems are interrelated so that the fishery could lead to a dynamic system. Many studies on sardine stock estimation suggested that this fishery in Bali Strait has been over capacity. The number of purse seine fleet reached 423 units with 17,701 fishers (2009), and has 119 large industries and 370 small industries. The study aims to describe the dynamics of the sardine fishery in Bali Strait through simulation (using Powersim 2.5). The study was conducted in October and January-March 2011, Data mainly collected from secondary data of 2005 to 2010 from the Banyuwangi and Jembrana district. Analysis was using descriptive analysis method. Pressure on sardine fish resource is relatively high, so a decline in production was occurred due to over capacity. Since it is a renewable resource, recovery is still in place although cannot reach to maximum, where the MSY is 30,379.917 tons/year with EMSY is 4,600 trip/year. Scenario A of maximum production (72267.92 tons) is higher and faster (2016), but productivity per fleet is lower due to higher number of fishing fleet. Scenario B on maximum production reached 71912.15 tons, but produces one year slower production (2017), with better level of productivity due to lower number of fishing fleets. Scenario B is better from sardine resources perspective since pressure on resource is lower; hence there is a chance for natural recovery. Although, based on simulation both scenarios begin to experience the level of over capacity in 2009. Scenario A can support employment for 16,743 people, while scenario B for 13,385 people. The capacity of fish processing industry is too high compared to the existing resources; hence it is only able to produce approximately 6% of its maximum capacity. The sardine fishery is very dynamic and the fish resource could be threatened if not managed properly. Based on the simulation, we found gap between existing resources with production capacity of industry. This model has some limitation and still needs refinement to obtain better results
Keyword: 

Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
Keyword: