Judul: Pemanfaatan Gel Lidah Buaya (Aloe vera L.) untuk Memperpanjang Umur Simpan Buah Tomat Ceri (Lycopersicon lycopersicum).
Abstrak: Tomat ceri tergolong sebagai komoditas buah penting sebagai sumber vitamin dan mineral. Buah ini memiliki nilai ekonomi tinggi, tetapi mudah rusak dan umur simpannya relatif pendek pada penyimpanan suhu ruang. Salah satu upaya untuk mempertahankan mutu dan memperpanjang umur simpan dari tomat ceri adalah dengan penggunaan coating. Tujuan dari penelitian ini adalah menguji pengaruh konsentrasi lidah buaya sebagai bahan coating terhadap perubahan mutu tomat ceri selama penyimpanan. Pelapisan lidah buaya dilakukan pada buah tomat ceri dengan 2 konsentrasi, yaitu 25% dan 50% dibandingkan dengan 0% (kontrol) lalu disimpan pada suhu ruang. Hasil analisis menunjukan bahwa untuk mempertahankan mutu buah tomat ceri adalah dengan pelapisan lidah buaya konsentrasi 50%. Mutu buah tomat ceri dengan pelapisan lidah buaya pada suhu ruang dapat mempertahankan hingga 10 hari penyimpanan dibandingkan dengan kontrol yang bertahan 4 hari penyimpanan.
Keyword: coating, lidah buaya, mutu, penyimpanan, tomat ceri

Judul: Pemanfaatan Gel Lidah Buaya Sebagai Edible  Coating Untuk Memperpanjang Umur Simpan  Paprika (Capsicum Annum Varietas Sunny)
Abstrak: Edible coating is one of packaging technique to prolong shelf life of agricultural products, decrease quality degradation and weight loss. There are three items that can be used to make edible coating namely polysacharida, lipid and protein. One of agricultural product that can be used as an edible coating is aloe vera. Aloe vera gel consistss of polysacharida and another potential component that is capable to hamper fresh product from damage. Further more aloe vera gel is also able to take care of dampness by controlling water loss and exchange of dissolve components. One of agricultural products that was choosen for application is paprica. Paprica is one of horticulture product which is lately recognized by Indonesian. Indonesia was exporting paprica to several countries such as Taiwan and Singapore. In 2003, Indonesia exported paprica to Taiwan until 1.5 billion rupiahs with volume export as much 155.955 kilograms. Because of the level amount of export, we have to maintain the quality and paprica freshness so that it can be accepted by consumers. One of way to maintain the quality and paprica frehness is by coated paprica with aloe vera gel. The purpose of this study is to get aloe vera gel formula. This study consists of five steps which are physical and chemical analysis of Aloe vera, physical and chemical analysis of paprica, effect of CMC addition to Aloe vera gel viscosity during storage, effect of CMC addition to Aloe vera gel pH during storage and paprica coating by Aloe vera gel and quality degradation during srorage. This study was done by using three kinds of CMC concentration (0.5%, 1% and 1.5%) and immersed time (1 minute, 3 and 5 minutes). From the study is found that aloe vera gel is capable to prolong shelf life of paprica until 21 days. Increasing of weight loss, hardness and brightness level, soluble solid, acid total and vitamin C content occurred in paprica which was coated by aloe vera gel. The best condition of this study is paprica which was immersed during one minute in aloe vera gel with 1% CMC concentration. It had the smallest weight loss and hardness value compared with another conditions. Generally the result of organoleptic test in textur, colour and odor showed that paprica still can be accepted after 21 days storage.
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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%.
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