Judul: Process and Lean Manufacturing Analysis of Ready to Eat Curry Sauce Product with Value Stream Mapping Method in PT XYZ
Abstrak: Produktivitas dapat ditingkatkan dengan mengeliminiasi atau meminimalisir pemborosan yang terjadi pada lini produksi. Penelitian ini bertujuan untuk mengidentifikasi aliran dan tahapan proses, mengevaluasi kendala serta pemborosan yang terjadi serta merancang perbaikan pada produksi saus gulai. Value stream mapping adalah tools pada konsep lean manufacturing yang berguna untuk mengetahui aliran informasi dan material serta mengidentifikasi pemborosan yang terjadi pada lini produksi melalui pemetaan. Berdasarkan konsep Toyota Production System (TPS), memborosan teridentifikasi pada lini produksi adalah pergerakan, waktu menunggu dan proses produksi berlebih. Total lead time pada pemetaan sekarang adalah 25621,4 detik. Analisis pada pengembangan peta masa depan menunjukkan penggunaan sumberdaya yang masih kurang optimal sehingga menyebabkan berbagai pemborosan pada lini produksi. Usulan perbaikan pada peta masa depan seperti penerapan 5S, standarisasi tugas serta jumlah pekerja, sistem andon dan proses FiFo. Usulan perbaikan dapat mengurangi lead time menjadi 25018,2 detik., Productivity is described through good use of resources. Productivity can be increased by eliminating or minimizing wastes that occur on the production line. This research is aimed to identify the flow of information and stages of the process, evaluate wastes that occur and design improvement to the production of curry sauce. Value stream mapping is a tool in the lean manufacturing concept that is usefull for knowing the flow of information and materials as well as identifying wastes that occur in the production line through mapping. According to Toyota Production System (TPS) concept, this research has identified several wastes, motion, waiting and extra-processing. Total lead time on the current mapping was 25621,4 seconds. Analysis on the future map showed that used of resources was still less than optimal. The future map suggested implementation of 5s, standardization of tasks and number of workers, andon system and FiFo process.The suggested improvement was able to reduce the lead time to 25018,2 seconds.
Keyword: Eliminasi pemborosan, Lean manufacturing, Produktivitas, Value stream mapping

Judul: Analisis Lean Manufacturing pada Produk Teh Celup dengan Metode Value Stream Mapping
Abstrak: Produktivitas pada lini produksi dapat ditingkatkan dengan mengeliminasi segala pemborosan yang ada pada lantai produksi. Penelitian ini bertujuan untuk mengidentifikasi dan merancang perbaikan berkelanjutan untuk produk Sarimurni Teabag 25 yang diproduksi di PT Y. Value stream mapping merupakan sebuah tool pada lean manufacturing yang mengidentifikasi pemborosan berikut penyebab permasalahan melalui current dan future state map. Mengacu pada Toyota production system (TPS), teridentifikasi bahwa terdapat beberapa pemborosan yang terjadi di lini produksi seperti transportasi, inventori, gerakan, waktu menunggu, produksi berlebih dan produk cacat. Total lead time efektif adalah 1 325 detik dari total lead time keseluruhan 4 408 detik. Analisis produktivitas menunjukkan besarnya persentase aktivitas tidak bernilai tambah akibat kerusakan mesin pada proses akhir yang sering terjadi. Upaya dari perbaikan produktivitas diilustrasikan pada peta kondisi masa depan dengan penerapan beberapa lean tools: one-piece flow, 5S, poka yoke, dan andon. Pendekatan lean pada lini produksi dapat mengurangi 53% lead time per satu kardus produk menjadi 2 069 detik.
Keyword: perbaikan berkelanjutan, lean manufacturing, Toyota production system, value stream mapping, eliminasi waste

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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