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All Journal International Journal of Electrical and Computer Engineering Jurnal Masyarakat Informatika MATEMATIKA Jurnal INKOM Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran Sinergi International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Journal of Information System IT JOURNAL RESEARCH AND DEVELOPMENT
Retno Kusumaningrum
Universitas Diponegoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : TELKOMNIKA (Telecommunication Computing Electronics and Control)

 1 
WCLOUDVIZ: Word Cloud Visualization of Indonesian News Articles Classification Based on Latent Dirichlet Allocation Retno Kusumaningrum; Satriyo Adhy; Suryono Suryono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8194

Abstract

Latent Dirichlet Allocation (LDA) is a widely implemented approach for extracting hidden topics in documents generated by soft clustering of a word based on document co-occurrence as a multinomial probability distribution over terms. Therefore, several visualizations have been developed, such as matrices design, text-based design, tree design, parallel coordinates, and force-directed graphs. Furthermore, based on a set of documents representing a class (category), we can implement classification task by comparing topic proportion for each class and topic proportion for the testing document by using Kullback-Leibler Divergence (KLD). Therefore, the purpose of this study is to develop a system for visualizing the output of LDA as a classification task. The visualization system consists of two parts: bar chart and dependent word cloud. The first visualization aims to show the trend of each category, while the second visualization aims to show the words that represent each selected category in a word cloud. This visualization is subsequently called WCloudViz. It provides clear, understandable and preferably shared the result.
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.
Continuous speech segmentation using local adaptive thresholding technique in the blocking block area method Roihan Auliya Ulfattah; Sukmawati Nur Endah; Retno Kusumaningrum; Satriyo Adhy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13958

Abstract

Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2.
Co-Authors Adi Wibowo Aniati Murni Arymurthy Annisa Apriliani Annisa Ulizulfa Apriliyanto, Andi Bambang Subeno Helmie Arif Wibawa Hisar Maruli Manurung Iffa Zainan Nisa Juwanda, Farikhin Khadijah, Khadijah Manurung, Hisar Maruli Prajanto Wahyu Adi Prastya, Dwiyan Panji Priyo Sidik Sasongko Rismiyati Rismiyati Rismiyati Rismiyati Rizka Putri Nawangsari Roihan Auliya Ulfattah Salman Dziyaul Azmi Satriyo Adhy Sri Mulyono Sukmawati Nur Endah Sukmawati Nur Endah Suryono Suryono Wahyul Amien Syafei Widiyanto Yeny Rochmawati, Yeny Yudo Prasetyo