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: Perbandingan metode Wavelet Daubechies dan MFCC sebagai ekstraksi ciri pada pengenalan fonem dengan Probabilistic Neural Network (PNN) sebagai Classifier
Abstrak: Nowadays, the development of telecommunication research is rapidly increasing. One of the research is in sound area. Sound is a way for human to interact with computers, known as word recogniser. Word recogniser is a part of voice recogniser that make the computers possible to receive input from word that pronounced. Word that pronounced contain phonemes that arranged into sentence. Voice recognition technology can recognise and understand words that pronounce by digitalising it, and tuning the digital signal with certain pattern that has been saved in a hardware. The result from this word identification will be displayed into printed word. This research will compare between Wavelet Daubechies and MFCCas identity extraction on word recognition with (PNN) as pattern identifier. PNN is a pattern identifier that has high accuracy. The comparison between trained data and tested data in research is 75% : 25%. Tested data that has been used was vary, such as: testing with increase the noise (pure noise) and data with noise increasing from 30dB, 20dB and 10dB. The result from this research is that the identity extraction by using MFCC is much better than with Wavelet Daubechies. From the pure original data (without noise increasing) the accuracy is 92.3% and in data with noise increasing 30dB, noise 20dB and noise 10dB the accuracy is 50.96%, 26.92% and 19.23%.
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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