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All Journal Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA Journal of Informatics and Technology JSM (Jurnal SIFO Mikroskil) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Pertanian Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURNAL EKONOMI MANAJEMEN AKUNTANSI Jurnal Teknik Informatika (JUTIF) Makara Journal of Technology
Sukmawati Nur Endah
Faculty Of Mathematics And Natural Science, Diponegoro University, Tembalang-Semarang 50239, Indonesia
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Journal : telkomnika telecommunication computing electronics and control

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Suitability analysis of rice varieties using learning vector quantization and remote sensing images Annisa Apriliani; Retno Kusumaningrum; Sukmawati Nur Endah; Yudo Prasetyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12234

Abstract

Rice (Oryza Sativa) is the main food for Indonesian people, thus maintaining the stability of rice production in Indonesia becomes an important issue for further study. A strategy to overcome the issue is to apply precision agriculture (PA) using remote sensing images as a reference due to its effectiveness. The initial stage of PA is suitability analysis of rice varieties, including INPARA, INPARI, and INPAGO. While the representative features that can be extracted from remote sensing images and related to agriculture field are NDVI, NDWI, NDSI, and BI. Therefore, the aim of this study is to identify the best model for analyzing the most suitable superior rice varieties using Learning Vector Quantization. The results show that the best LVQ model is obtained at learning rate value of 0.001, epsilon value of 0.1, and the features combination of NDWI and BI values (in standard deviation). The architecture generates accuracy value of 56%.
Solid waste classification using pyramid scene parsing network segmentation and combined features Khadijah Khadijah; Sukmawati Nur Endah; Retno Kusumaningrum; Rismiyati Rismiyati; Priyo Sidik Sasongko; Iffa Zainan Nisa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.18402

Abstract

Solid waste problem become a serious issue for the countries around the world since the amount of generated solid waste increase annually. As an effort to reduce and reuse of solid waste, a classification of solid waste image is needed  to support automatic waste sorting. In the image classification task, image segmentation and feature extraction play important roles. This research applies recent deep leaning-based segmentation, namely pyramid scene parsing network (PSPNet). We also use various combination of image feature extraction (color, texture, and shape) to search for the best combination of features. As a comparison, we also perform experiment without using segmentation to see the effect of PSPNet. Then, support vector machine (SVM) is applied in the end as classification algorithm. Based on the result of experiment, it can be concluded that generally applying segmentation provide better source for feature extraction, especially in color and shape feature, hence increase the accuracy of classifier. It is also observed that the most important feature in this problem is color feature. However, the accuracy of classifier increase if additional features are introduced. The highest accuracy of 76.49% is achieved when PSPNet segmentation is applied and all combination of features are used.
Indonesian continuous speech recognition optimization with convolution bidirectional long short-term memory architecture Sukmawati Nur Endah; Rismiyati Rismiyati; Priyo Sidik Sasongko; Anwar Petrus F Noiborhu
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.24994

Abstract

Speech recognition can be defined as converting voice signals into text or lines of words by using algorithms implemented in computer programs. There are several types of speech recognition, including recognition for isolated word speech, continuous speech, spontaneous speech, and conversational speech. Research on continuous speech recognition, especially in Indonesian, has been developed using both stochastic methods such as Hidden Markov model (HMM) and deep learning methods. Currently, deep learning approaches are more widely used in speech recognition applications. This research optimizes Indonesian speech recognition by adding convolution layers to the bidirectional long short-term memory (Bi-LSTM) architecture. The goal of this research is to find the best architecture so that better Indonesian continuous speech recognition results can be obtained. The dataset used in this research was created by the intelligent systems research group in the Department of Informatics at Universitas Diponegoro. All speakers who participated in this dataset came from five ethnic groups in Indonesia, representing the dialects of their respective ethnic groups. The research results show that by adding a convolution layer to the Bi-LSTM architecture, speech recognition performance increases significantly with an average word error rate (WER) reduction of 15.56% compared to using only the Bi-LSTM architecture.
Co-Authors Adi Wibowo Afry Rachmat Amalia Rachma Yanti Andika Putra Pratama Angga Pradana Saputra Annisa Apriliani Anwar Petrus F Noiborhu Arif Lutfiyanto Aris Sugiharto Beta Noranita Beta Noranita Dinar Mutiara KN eko adi sarwoko Eko Adi Sarwoko Eko Prasetiawan Erlangga Erlangga Fachry Abda El Rahman Fitriana Prasari Dewi Frediansah Frediansah Ganang Surya Atmaja Ganis Khufad Arridho Hanifah Awliya Harjanto, Dhony Syafe’i Helmie Arif Wibawa Helmie Arif Wibawa Iffa Zainan Nisa Indriyati Indriyati Irfan Pradipta Kabul Kurniawan Khadijah Khadijah Khadijah, Khadijah Kushartantya Kushartantya Muhamad, Nida Muhammad Muhammad Muhammad Rahmat Widyanto, Muhammad Rahmat Nisa, Iffa Zainan Nurdin Bahtiar Nurdin Bahtiar Panji Wisnu Wirawan Perawaty Panjaitan Priyo Sidik Sasongko Priyo Sidik Sasongko Ragil Saputra Raina Stefani Budi Retno Kusumaningrum Rismiyati Rismiyati RISMIYATI RISMIYATI Rizka Ella Setyani Roby Hanintyo Nursio Sakti Samsul Ma’arif Sanggam Andreas Harahap Satriyo Adhy Satriyo Adhy Siradjuddin, Indah Agustien Situmeang, Axelliano Rafael Suryo Hartanto Sutikno . Sutikno, Sutikno T. Basaruddin Yudo Prasetyo Zulkarnaen, Harits Farras Zulkarnaen, Harits Farras