Judul: Pengaruh Hydrocooling dan Pengemasan terhadap Mutu Pak Choi (Brassica rapa var. Chinensis) selama Transportasi Darat
Abstrak: Pak choi (Brassica rapa var. Chinensis) adalah salah satu sayuran subtropik yang saat ini banyak ditanam di Indonesia khususnya di dataran tinggi. Pak choi mempunyai umur simpan yang pendek dan memiliki sifat yang sangat mudah rusak, mudah layu, menguning dan segera membusuk setelah panen bila tidak ditangani dengan baik. Penanganan pasca panen pak choi juga perlu memperhatikan aspek kondisi jalan dan jarak tempuh dari lahan ketempat penaganan selanjutnya. Praktek penanganan pasca panen dan cara pengemasan yang baik akan dapat mempertahankan mutu dan umur simpan pak choi.
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Judul: Kombinasi Suhu Air dan Lama Perendaman pada Hydrocooling untuk Mempertahankan Kesegaran Sawi Hijau (Brassica juncea)
Abstrak: Green cabbage is a vegetable which is susceptible to heat and easy to wilt. Hydrocooling treatment is an effort to preserve the freshness of green cabbage. The objective of hydrocooling treatment is to reduce field heat of material after harvesting. Based on that fact, this research was conducted to determine the combination of water temperature and immersion time in hydrocooling treatment which gives the best influence to preserving the quality of green cabbage. The method was to determine the optimal temperature of material based on the respiration rate and quality changes of green cabbage during storage. Then, the determination of the combination between water temperature and immersion time for hydrocooling conducted based on the optimal temperature and observes the hydrocooling effect in changes quality of green cabbage. The optimal temperature for green cabbage is 13oC. The result of water temperature and immersion time combination for reach the temperature of 13oC is: the hydrocooling on 5oC during 3.1 seconds and hydrocooling on 10°C during 3.9 seconds. Hydrocooling have a good influence on all quality parameters of green cabbage.
Keyword: storage, quality, hydrocooling, Green cabbage

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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