Judul: Perubahan Mutu Bakso Ikan Patin Yang Diiradasi Dengan Sinar Gamma (66Co) Selama Penyimpanan
Abstrak: Bakso ikan adalah salah satu alternatif usaha diversifikasi produk olahan ikan. Produk perikanan merupakan bahan pangan yailg mudah mengalami kerusakan (highly perishable), baik kerusakan fisik, kimia maupun mikrobiologi. Kerusakan produk perikanan antara lain disebabkan oleh mikroba. Salah satu teknik pengawetan makanan yang dapat digunakan untuk mengatasi masalah tersebut adalah teknik iradiasi. Tujuan penelitian ini adalah mengetahui kisaran dosis iradiasi yang mampu memperpanjang daya simpan bakso ikan patin pada suhu penyimpanan l0°C, mempelajari perubahan mutu bakso ikan patin setelah iradiasi, dan mengetahui umur simpan bakso ikan patin iradiasi.
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Judul: Pengaruh Iradiasi Sinar Gamma terhadap Sifat Gelatin Kulit Ikan Patin
Abstrak: Gelatin merupakan jenis protein yang diperoleh dari proses hidrolisis kolagen. Salah satu bahan baku gelatin adalah kulit ikan patin. Iradiasi sinar gamma Cobalt 60 mampu mengurangi jumlah populasi mikroba indegenus dengan cara merusak rantai DNA sehingga terjadi kematian sel, mutasi sel, atau transformasi sel. Penelitian ini bertujuan menentukan karakteristik fisik-kimia dan mikrobiologi gelatin kulit ikan patin dengan dosis iradiasi sinar gamma 0 kGy, 2 kGy, 4 kGy dan 8 kGy. Produksi gelatin dilakukan dengan pretreatment di dalam NaOH 0,2 M, asam asetat 0,05 M dan jeruk nipis 0,5%. Rancangan percobaan yang digunakan yaitu Rancangan Acak Lengkap (RAL) dan uji lanjut Duncan. Perbedaan dosis iradiasi sinar gamma tidak berpengaruh secara nyata terhadap kenampakan, kadar air, kadar abu, pH, dan setting point. Perlakuan dosis sinar gamma berpengaruh nyata terhadap kekuatan gel, viskositas dan jumlah mikroba. Gelatin kulit ikan patin dapat digunakan dalam produk pangan karena telah memenuhi standar GMIA 2019 dan SNI 8622-2018. Kata kunci : dosis, gelatin, mikroba, sinar gamma., Gelatin was a type of protein which has obtained from the hydrolisis of collagen process. One of the raw material for gelatin was Pangasius skin. Gamma irradation Cobalt-60 was able to reduce the number of indigenous microbial populations by damaging the DNA chain so that resulting cell death, cell mutation or cell transformation. This study aimed to determine the physico-chemical and microbiological characteristics of Pangasius skin gelatin at a dose of gamma ray irradiation 0 kGy, 2 kGy, 4 kGy and 8 kGy. Gelatin production was beginned by pretreatment in 0,2 M NaOH, 0,05 M asetic acid, and 0,5% lime. The experimental design used was Completely Randomized Design (CRD) and Duncan’s follow-up test. The difference of gamma irradiation dose had no effect on appearance, moisture content, ash content, pH, and setting point. The treatment of gamma irradiation had effect on gel strength, viscosity and number of microbial. The gelatin of Pangasius skin can be used in food product because it has met the standards of GMIA 2019 and SNI 8622-2018. Keyword : dose, gamma radiation, gelatin, microbial
Keyword: dose, gamma radiation, gelatin, microbial

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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