Judul: Pengenalan kata berbasiskan fonem dengan pemodelan resilient backpropagation
Abstrak: The aim of this research is to know the performance of Neural Network as a model for word recognition. The research uses Resilient Backpropagation for modeling and Mel-Frequency Cepstral Coefficient (MFCC) for feature extraction. The voice data used comes from one speaker. Total words used are 70 words that consist of 50 words as a dictionary of words and 20 words, that each word repeated 10 times. Dictionary words consist of 50 words from a combination of phonemes used in the research. Phonemes used in this research are 10 phonemes consist of 4 vowels and 6 consonants phonemes. From the 20 words, each word is repeated 10 times, 7 times used as training data and 3 times are used as test data. The output from testing process are word transcription. The convertion process from the word transcription into word is done manually by 5 person. This research produces two models. Average accuracy obtained from model with 100 hidden neuron is 75% for test data and 61% for the dictionary of words. The best average accuracy obtained is 93% for test data and 62 % for the dictionary of words generated by testing the model with 1000 hidden neurons. Overfitting occurs in the second model with 1000 hidden neurons. It causes the model can only produce good output for data that has been trained.
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Judul: Model Fonem dengan Pendekatan Distribusi Normal untuk Pengenalan Kata Menggunakan MFCC sebagai Ekstraksi Ciri
Abstrak: Voice recognition is a field study in voice processing. Research on voice signal has several methods of processing, one of them is the Mel Frequency Cepstrum Coefficients (MFCC). The purpose of MFCC is to extract voice features. The goal of this research is to apply MFCC as a feature extraction and the normal distribution as a method for word recognition. The first step of the research is data reprocessing, and then data extraction using MFCC. Afterwards, the normal distribution method is used to process the data. From the result, it can be concluded that the normal distribution method can be used for word recognition.The results obtained from the word recognition using normal distribution and MFCC as feature extraction have the highest accuracy of 100% for the phonemes /g/, /n/, /p/, /v/, /x/, /y/ and the lowest accuracy of 67% for the phonemes /a/ and /k
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Judul: Kandungan Logam Berat Pb, Hg, Cd, dan Cu pada Daging Ikan Rejung (Sillago sihama) di Estuari Sungai Donan, Segara Anakan Timur, Cilacap, Jawa Tengah.
Abstrak: Salah satu limbah hasil dari aktivitas industri di wilayah Segara Anakan adalah logam berat. Logam berat merupakan salah satu senyawa yang berbahaya, karena dapat terlarut di dalam air, terendap di dalam sedimen, dan terakumulasi di dalam tubuh ikan. Pengambilan contoh dilaksanakan setiap bulan dari Agustus 2015 sampai Januari 2016 di perairan estuari Sungai Donan. Tujuan penelitian ini adalah menganalisis kandungan logam berat Pb, Hg, Cd, dan Cu di daging ikan rejung, serta menentukan batas toleransi untuk mengkonsumsi ikan yang mengandung logam berat. Kandungan logam berat rata-rata pada daging ikan rejung berdasarkan pembacaan Atomic Absorption Spectrophotometry (AAS) pada setiap bulan telah melebihi nilai ambang batas. Nilai tersebut adalah 1,237-9,194 ppm (Pb), 304,499-4535,221 ppb (Hg), 0,107-0,564 ppm (Cd), dan 0,360-1,388 ppm (Cu). Batas maksimum berat daging ikan rejung yang boleh dikonsumsi untuk orang dewasa (50 kg bb) dan anak-anak (15 kg bb), masing-masing adalah 17,64 gram daging per minggu dan 5,29 gram daging per minggu.
Keyword: ikan rejung, logam berat Pb, Hg, Cd, Cu, Segara Anakan