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All Journal Jurnal INKOM Jurnal Elektronika dan Telekomunikasi
Pardede, Hilman F.
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Nonlinear Spectral Subtraction Berbasis Tsallis Statistics untuk Peningkatan Kualitas Sinyal Ucapan Pardede, Hilman F.
INKOM Journal Vol 7, No 1 (2013)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1310.222 KB) | DOI: 10.14203/j.inkom.228

Abstract

Adanya derau (noise) mengurangi kualitas dan inteligibilitas dari sinyal ucapan dan ini berakibat menurunnya performa dari aplikasi berbasis sinyal ucapan. Pengurangan spektral (spectral subtraction) adalah salah satu metode yang populer untuk menghilangkan derau tersebut. Akan tetapi, pengurangan spektral memiliki kelemahan, yaitu memperkenalkan musical noise. Telah banyak turunan dari pengurangan spektral yang diusulkan untuk mengurangi musical noise. Salah satunya adalah menggunakan oversubtraction dalam formulasi pengurangan spektral. Pendekatan ini disebut nonlinear pengurangan spektral. Akan tetapi, penentuan faktor ini secara heuristik. Dengan menggunakan Tsallis statistics, nonlinear subtraksi dapat diturunkan secara matematis. Varian baru spectral subtraction yang disebut q-spectral subtraction telah diturunkan. Metode ini telah terbukti efektif untuk meningkatkan performa sistem pengenalan ucapan terhadap noise. Akan tetapi, evaluasi metode ini untuk meningkatkan kualitas sinyal ucapan pada speech enhancement belum diinvestigasi. Pada paper ini, performa q-SS untuk speech enhancement akan diivestigasi. Dari hasil percobaan, ditemukan bahwa q-SS lebih baik dalam meningkatkan kualitas sinyal ucapan dibandingkan metode  pengurangan spektral lain. Kata kunci: speech enhancement, spectral subtraction, nonlinear spectral subtraction, derau musikal
DEEP CNNBASED DETECTION FOR TEA CLONE IDENTIFICATION Ramdan, Ade; Suryawati, Endang; Kusumo, R. Budiarianto Suryo; Pardede, Hilman F.; Mahendra, Oka; Dahlan, Rico; Fauziah, Fani; Syahrian, Heri
Jurnal Elektronika dan Telekomunikasi Vol 19, No 2 (2019)
Publisher : Indonesian Institute of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v19.45-50

Abstract

One factor affecting the quality of tea is the selection of plant material that would be planted on the field. Clonal selection is a common way to produce tea with better quality. However, as a natural cross pollination species, tea often consists of various clones or progenies of cross-pollinated process. This commonly occurs on plantations owned by smallholder farmers. To produce a consistent quality tea, the clones or progenies need to be identified. Usually, human experts distinguish the plants from leaves by visual inspection on the physical attributes of the leaves, such as the textures, the bone structures, and the colors. It is very difficult for non-experts or common farmers to do such identifications. In this, we propose a deep learning-based identification of tea clones. We apply deep convolutional neural network (CNN) to identify 3 types of tea clones of Gambung series, a series of tea clones developed at Research Institute of Tea and Cinchona. Our study indicates that the performance of the CNN systems are affected by the depth of the convolutional layers. VGGNet, a popular CNN architectures with 16 layers, achieves better accuracy compared to AlexNet, a CNN with 6 layers.
Co-Authors Ade Ramdan Dahlan, Rico Fani Fauziah, Fani Heri Syahrian, Heri Kusumo, R. Budiarianto Suryo Oka Mahendra Suryawati, Endang