Judul: Analisis Gender dalam Rumah Tangga Nelayan Pengolah Hasil Perikanan Tangkap (Kasus Dukuh Garapan, Desa Tanjung Pasir, Kecamatan Teluknaga, Kabupaten Tangerang, Provinsi Banten).
Abstrak: Istri nelayan bertanggung jawab penuh dalam pekerjaan domestik, namun masih terbebani pekerjaan produktif seperti mengolah ikan. Sementara laki-laki belum dibebani oleh pekerjaan domestik. Pendekatan kuantitatif menggunakan kuesioner kepada tiga puluh rumah tangga nelayan serta data kualitatif melalui wawancara mendalam kepada staf desa, ketua kelompok nelayan, kepala dukuh serta pembeli produk olehan ikan dilakukan untuk mengkaji kesenjangan gender pada rumah tangga nelayan pengolah ikan. Pemilihan rumah tangga responden dilakukan secara sengaja (purposive) terhadap rumah tangga nelayan yang melakukan pengolahan ikan. Hasil penelitian memperlihatkan bahwa mayoritas suami dan istri responden berada pada kelompok umur produktif pertengahan dengan tingkat pendidikan yang rendah. Mayoritas responden tinggal di dalam rumah milik pribadi berukuran dibawah 73 m2 yang terdiri dari tiga hingga lima orang dalam satu rumah. Aset usaha perikanan yang dimiliki berupa satu buah perahu dan alat tangkap dengan status kepemilikan pribadi. Rata-rata sumbangan pendapatan istri sudah mencapai lebih dari dua puluh persen terhadap pendapatan rumah tangga nelayan. Tingkat sensitivitas gender baik istri maupun suami masih rendah sehingga berdampak pada pola pembagian kerja didalam rumah tangga. Pola pembangian kerja mempengaruhi posisi perempuan dalam memegang kontrol terhadap pendapatan baik berasal dari pengolahan ikan maupun penangkapan ikan. Pengintegrasian pendidikan gender ke dalam kurikulum diperlukan untuk memperbaiki kondisi kesenjangan gender generasi berikutnya.
Keyword: analisis gender, pengolahan hasil perikanan, rumah tangga nelayan

Judul: Peranan Gender dalam Rumah Tangga Perikanan di Desa Tanjung Pasir, Kecamatan Teluknaga, Kabupaten Tangerang
Abstrak: Tanjung Pasir is a coastal area in Tangerang District where majority of the inhabitans depends their livelihood to the fisheries resources. There are three fishery businesses in Tanjung Pasir, include fishing, fish-processing, and fish breeding carried out by women and men. Various programs have been implemented, but gender role and relation in the household still unresolved issue. The research objective is to study the role of gender and decision-making in the fisheries’s households. Survey method using questionnaire was used to collect data and in-depth interviews with key informants were also-conducted. Some factors in characteristic of household and socio-economic condition show corelation with the gender roles and decision-making in the fisheries’s household.
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Judul: Application of Mel Frequency Cepstrum Coefficients (MFCC) as Feature Extraction on Phoneme Recognition with Probabilistic Neural Network (PNN) as Classifier
Abstrak: Voice recognition (speech recognition) is one field of study in voice processing. This technology can convert voice signals into a form of written information (text). With this technology, people can interact with a computer. MFCC feature extraction computes the cepstral coefficients by considering the human hearing. This research is a phoneme recognition using feature extraction MFCC and PNN as feature matching model. This study compares the coefficient, overlap, and the test data without noise and with noise. MFCC is used with 13, 20, 26 coefficients and 0%, 25%, 50% overlap. Noise is added by 30 dB, 20 dB and 10 dB. From the comparison of the three overlaps, produced the best accuracy at 50% overlap with an accuracy of 94,71%. From comparing the three coefficients, better accuracy resulting in coefficients of 20 and 26 with an accuracy of 97,12% at 50% overlap. After comparing between the coefficients of 20 and 26 with overlap of 25% then the coefficient 26 is obtained that better accuracy of 94,23%. This shows that in this study the coefficient 26 is the best. In this research, there are three noise variables. The variables are 10 dB, 20 dB, and 30 dB. The best accuracy reached when the noise variable is 30 dB rather than 10 dB or 20 dB because the accuracy has the closest accuracy compared by the accuracy when noise was not added into the data. When the noise variable is 30 dB, the percentage of accuracy is 85,3%.
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