Judul: Shrimp And Tuna Industry In Indonesia Strategy To Increase Indonesian Fisheries Competitiveness For Shrimp And Tuna
Abstrak: The APEC Economic Leaders Meeting (AELM) on November 25, 1997, in Vancouver has decided to speed up trade and liberalization of fisheries products as one of seven recommended sectors. Several tough questions arouse on Indonesian fisheries industry such as how government provide a conducive business climate for fisheries entrepreneurs, and how fisheries producers set out clear strategies towards free trade eга. Those questions are very important because fisheries industry competitiveness is rely on the support of government and the role of fisheries entrepreneurs. In Indonesian fisheries industry, two major commodities that play an important role are shrimp and tuna. Both of them contributed around 70% of Indonesian fisheries export value and more than 30% of export volume in 1996. However, most of Indonesian exports are in fresh and semi-processed condition. In other word, endowment factors such as natural resources and low labor cost are dominantly determine the export. Meanwhile, detention from importing countries, still become a constrain for Indonesian exports. Limited products quality is the reason to embargo Indonesian products. The objectives of this study were to identify the general structure and performance of shrimp and tuna industries and to conduct business environmental assessment, including field research to evaluate strengths, weaknesses, opportunities, and threats of Indonesian shrimp and tuna industries. The result was then used to examine the strategies and business implications for the Indonesia shrimp and tuna industries in order to develop the industry, to improve competitiveness, and to maintain its sustainability. The study was conducted through the analysis of primary data and supported by secondary data. Primary data were obtained by the survey conducted at Indonesian Fisheries Federation (GAPPINDO), Indonesian Tuna Association (ASTUIN), PT. Usaha Mina (Persero), PT. Samudera Besar (Persero), PT. Harini Asribahari, PT. Sinar Mangkudijaya, PT. Triasta Citarate, and PT. Indramas Sempurna. For completing reliable data, actual information was also collected from interviews with experts, magazines, journals, and news papers. Analytical Hierarchy Process (AHP) technique was also applied to simplify the expert judgement before the SWOT matrix analysis was used to conduct the environmental assessment. The global supply of shrimp from 1991 to 1996 decreased by 5% affected by the declining volume of sea shrimp caught, while shrimp pond production fluctuated with the tendency to increase. The future shrimp trade is estimated to be more dependent on the shrimp pond production due to the decreasing sea shrimp carrying capacity in nearly all waters in the world. On the other hand, the global supply of tuna
Keyword: supply of shrimp, fisheries products, shrimp, tuna

Judul: Simulation model for tuna and shrimp production in Indonesia
Abstrak: The General objective of the research area : I. To recommendation a strategy of Indonesia to release commerce issue of marine fish product in facing up the international forum, 2. To estimation of Indonesian position in related to free trade issue in the sector of marine fisheries. The input data are collected from various source among other 1. the Ministry of Marine affairs and Fisheries of Indonesia 2. Statistics Indonesia of The Republic of Indonesia (BPS), 3. Center for Oceanography Research of The Indonesian Institute of Science ( P20 LIPI) and others
Keyword: 

Judul: Sistem Pengklasifikasian Entitas pada Pesan Twitter Menggunakan Ekspresi Regular dan Naïve Bayes
Abstrak: The widespread use of social networks like Twitter encourages some institutions to find out the entities that are discussed and the sentiment toward the entities. This research aims to build a system that can extract entities in tweets and classify the sentiment from the tweets. Entity extraction process is done using regular expressions. Writing regular expressions can capture the diverse entities. Sentiment classification is performed using naïve Bayes classification with Multinomial and Bernoulli models. Sentiment in this research consists of three classes, namely positive, negative, and neutral. Before entering the classification, tweets are normalized by replacing the non-standard words with the standard ones. The test results show that regular expression is quite effective for the entity extraction process. As for the results of the classification, naïve Bayes method produces high accuracy (96.75% for Multinomial and 96.33% for Bernoulli). However, the accuracy obtained depends on the neutral class which amounts to 97.88% for Multinomial and 99.95% for Bernoulli, while the positive class are 85.66% and 33.13% and negative class are 33.67% and 0%.
Keyword: