Judul: Management strategies of blue dolosi (caesio caerulaurea) at guraici islands South Halmahera of North Moluccas Province
Abstrak: Exploitation of blue dolosi (Caesio caerulaurea) at Guraici Islands are longstanding. The catches of fishermen decreased, while efforts to catch (trip) is increasing. This condition is feared to exceed the optimum sustainable. therefore it is necessary to evaluate the presence of blue dolosi fish stocks as a basis for sustainable management strategies. The data was collected directly by following the catching once a month throughout the year and filling the questionnaire. The results of the study, the size of the blue dolosi caught during the study ranged 165-327 mm. The length of the first fish caught by moroami is 247.83 mm. The size of fish that mature gonads is 241.30 mm. Spawning occurs throughout the year and the peak occurred in April and June. Catching occurred all year round and peak in August. The rate of exploitation (E) = 0.58 / year indicated that over fishing has occurred but did not achieve the maximum sustainable production. Maximum production is reached when the value of exploitation rate of 0.80 / year. Dolosi blue production at Guarici sea on May 2010 till April 2011 amounted to 49 361 kg / year. Maximum sustainable yield (MSY) amounted to 52 974 kg / year. To prevent overfishing, dolosi blue fish that first caught (Lc) maintained on the size 247.83 mm equivalent to the size of the mesh used in the bag is 0.75 inches and 2 inches on the wing, by maintaining the rate of exploitation of 0.58 / year, equivalent to the effort (trip) catching as many as 1956/year.
Keyword: 

Judul: Improving the Understanding of Small-scale Grouper and Snapper Fisheries in Saleh Bay-West Nusa Tenggara, Indonesia to Enhance Their Management
Abstrak: Groupers (Serranidae: Epinephelinae) are a crucial component of fisheries in coastal tropical and sub-tropical regions, providing essential protein and supporting livelihoods. While global landings of grouper have been steadily increasing, with China and Indonesia contributing over 60% of the total catch, challenges persist in managing their stocks, particularly in small-scale fisheries in developing nations. This study aimed to propose an effective management strategy for key grouper species in Saleh Bay, West Nusa Tenggara, Indonesia by addressing knowledge gaps and evaluating different management options. Specifically, this involved understanding the dynamics of these fisheries, including fishing methods and the composition of their landings, examining the life history and stock condition of a key but previously data-poor grouper species, i.e., brown-marbled grouper Epinephelus fuscoguttatus, and critically evaluating various management procedures using the Method Evaluation and Risk Assessment (MERA) tool. Findings reveal that 57.7% of the total catches of small-scale reef and demersal fisheries in Saleh Bay consisted of grouper and snapper (Lutjanidae). The fishery is complex, with eight fishing methods employed to catch 75 species. Four species, namely the leopard coral grouper (Plectropomus leopardus), orange-spotted grouper (Epinephelus coioides), spotted coral grouper (Plectropomus maculatus), and malabar blood snapper (Lutjanus malabaricus) were primarily targeted, using three main fishing methods, i.e., bottom longline, speargun, and handline, together accounting >90% of the total catch. The composition of catches using these methods changed significantly with fishing depth, season, and/or year. Fishing activities follow a temporal pattern influenced by monsoonal seasons, and fishers utilized specialized tactics to target high-value species for maximum profit. Preliminary biological studies and data from the fish landing monitoring program were used to re-evaluate the spawning potential ratio (SPR) of E. fuscoguttatus. This is one of 12 species targeted for management in Saleh Bay, and of concern as the SPR was previously estimated to range from only 0.05 to 0.07, well below the reference SPR limit of 0.20 and target of 0.30. A new estimate of L50 (length at which 50% of the population is mature) was derived which is ~100 mm smaller than the previous estimate. This new L50 estimate, when combined with the length-based SPR, yielded higher SPR estimates (0.11 to 0.25), although still below the target SPR. Finally, the study employs the MERA approach to assess management procedures for P. leopardus in Saleh Bay. Results indicated that the current size limit and spatial closure regulations may be ineffective due to low compliance and the limited extent of no-take zones within marine protected areas in Saleh Bay, which are smaller than the home range of this species. Recommendations for enhancing the sustainability of grouper and snapper fisheries in Saleh Bay include exploring total allowable catch controls, implementing seasonal closures to limit fishing effort, strengthening existing regulations, and conducting further studies on life-history aspects and stock assessment. In summary, this study underscores the significance of thoroughly understanding the dynamics of small-scale fisheries and the necessity for adaptive management strategies that align with specific local contexts. Sustained collaboration among researchers, fisheries managers, and stakeholders is crucial for tackling management challenges and enforcing effective management to ensure the
Keyword: small scale fishery, fishing dynamic, data limited fishery, method evaluation and risk assessment, management strategy evaluation, brown-marbled grouper, leopard coral grouper

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: