Judul: Analisis Pengendalian Mutu Produksi Ikan Hias Air Tawar dengan Metode Six Sigma di PT DEF Indonesia
Abstrak: PT DEF Indonesia adalah salah satu perusahaan yang bergerak dibidang ekspor dan budidaya ikan hias air tawar. Tingginya persentase ikan mati memaksa PT DEF untuk terus melakukan pengendalian mutu. Tujuan dalam penelitian ini adalah mengidentifikasi penyebab cacat berupa ikan mati di karantina dan pengembalian ekspor menggunakan biaya mutu, menganalisis level sigma,mengidentifikasi faktor penyebab cacat. Data primer yang digunakan bersumber dari hasil observasi dan wawancara, sedangkan data sekunder merupakan data yang diberikan perusahaan. Penelitian menggunakan metode DMAIC (Define, Measure, Analyze, Improvement, Control).Tingginaya biaya kegagalan internal dan kegagalan eksternal yang mengakibatkan tingginya ikan mati karena rendahnya tindak pencegahan. Level sigma untuk ikan budidaya yaitu 2.63 dan ikan ekspor 3.56. Analisa penyebab banyaknya ikan mati dengan fishbone diagram yaitu tingginya nilai TDS air (proses), kualitas awal ikan hias (input), dan faktor eksternal konsumen (output).
Keyword: ikan hias, pengendalian mutu, six sigma

Judul: Analisis manajemen kualitas perspektif six sigma pada divisi produksi bagian fish fillet PT Dharma Samudera Fishing Industries Tbk Tanjung Priok, Jakarta Utara
Abstrak: Fish fillet merupakan komoditi unggulan PT Dharma Samudera Fishing Industries Tbk dan memiliki jumlah produksi serta permintaan yang tinggi. Hal tersebut memberikan peluang yang cukup besar terhadap tingkat kecacatan fish fillet akibat kesalahan yang terjadi pada proses produksi fish fillet.
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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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