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All Journal International Journal of Electrical and Computer Engineering International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmu Komputer Agrikultura Jurnal Teknik ITS IPTEK Journal of Proceedings Series TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Buana Informatika Perfecting a Video Game with Game Metrics JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Ilmiah Kursor SCAN Proceeding International Conference on Information Technology and Business Seminar Nasional Informatika (SEMNASIF) Dental Journal (Majalah Kedokteran Gigi) Jurnal Ilmiah Pertanian JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Indonesian Journal of Electrical Engineering and Computer Science Makara Journal of Technology
Anny Yuniarti
Institut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Effect of exopolysaccharide-producing Azotobacter and cow manure on nutrient uptake and root-to-shoot ratio of sorghum Reginawanti Hindersah; Anny Yuniarti; Hidiyah Ayu Ratna Ma’rufah
Jurnal Ilmiah Pertanian Vol. 17 No. 2 (2021): Jurnal Ilmiah Pertanian
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/jip.v17i2.5205

Abstract

Nitrogen-fixing Azotobacter synthesizes exopolysaccharide, which is important among other to improve aggregate stability and hence nutrients uptake. A pot experiment has been conducted to determine the effect of exopolysaccharide-producing Azotobacter and organic matter on nitrogen, phosphor, and potassium uptake by the shoot of sorghum (Sorghum bicolor (L.) Moench), and plant growth. The pot experiment was setup in randomized block design which test eight combination treatments of Azotobacter isolates (AS5, AS6, and AS5 + AS6) and organic matter application (with and without 20 t ha-1 of cow manure). The result showed dual inoculation of Azotobacter AS5 and AS6 inoculation combined with cow manure application increased N and P uptake. The dual inoculation treatment did not affect root length; but increased the shoot height and dry weight when accompanied by the application of cow manure. The ratio of root and shoot dry weight was not influenced by single or dual Azotobacter inoculation with or without organic matter.
Pengenalan Wajah Menggunakan Two Dimensional Linear Discriminant Analysis Berbasis Optimasi Feature Fusion Strategy Sahmanbanta Sinulingga; Chastine Fatichah; Anny Yuniarti
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 3 No 1 (2016): JATISI SEPTEMBER 2016
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (619.761 KB) | DOI: 10.35957/jatisi.v3i1.59

Abstract

The era of technology today,, research on biometric image is not common to do. One well researched biometric image is a face recognition (face recognition). Problems on the human face recognition is a diversity of features or shape between one another face to face. Therefore, the need for facial feature extraction and classification using a particular method so that the classification can be recognized correctly.In this study proposed feature extraction method that can overcome the problems of non-linear automatic data contained in the face image, called the Two Dimensional Linear Discriminant Analysis based on Feature Fusion Strategy (TDLDA-FFS). Not stopping on feature extraction, classification methods proposed also faces that can overcome the problems of the adaptive matrix which aims to study the benefit of weight on each - each input with the method Relevanced Generalized Learning Vector quantization (GRLVQ).This research integrates methods TDLDA-FFS and GRLVQ for face recognition. With the combination of both methods are proven to provide optimal results with a level of recognition accuracy ranged between 77.78% to 82.22% with a pilot using a databaseof facial images from the Institute of Business and Information Stikom Surabaya. While the test uses a database derived from YaleB Database achieve accuracy levels ranging from 88.89% to 94.44%.
Ensemble Method for Indonesian Twitter Hate Speech Detection M. Ali Fauzi; Anny Yuniarti
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 1: July 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i1.pp294-299

Abstract

Due to the massive increase of user-generated web content, in particular on social media networks where anyone can give a statement freely without any limitations, the amount of hateful activities is also increasing. Social media and microblogging web services, such as Twitter, allowing to read and analyze user tweets in near real time. Twitter is a logical source of data for hate speech analysis since users of twitter are more likely to express their emotions of an event by posting some tweet. This analysis can help for early identification of hate speech so it can be prevented to be spread widely. The manual way of classifying out hateful contents in twitter is costly and not scalable. Therefore, the automatic way of hate speech detection is needed to be developed for tweets in Indonesian language. In this study, we used ensemble method for hate speech detection in Indonesian language. We employed five stand-alone classification algorithms, including Naïve Bayes, K-Nearest Neighbours, Maximum Entropy, Random Forest, and Support Vector Machines, and two ensemble methods, hard voting and soft voting, on Twitter hate speech dataset. The experiment results showed that using ensemble method can improve the classification performance. The best result is achieved when using soft voting with F1 measure 79.8% on unbalance dataset and 84.7% on balanced dataset. Although the improvement is not truly remarkable, using ensemble method can reduce the jeopardy of choosing a poor classifier to be used for detecting new tweets as hate speech or not.
Knowledge Dictionary for Information Extraction on the Arabic Text Data Saputra, Wahyu Syaifullah Jauharis; Arifin, Agus Zainal; Yuniarti, Anny
Makara Journal of Technology Vol. 16, No. 2
Publisher : UI Scholars Hub

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Information extraction is an early stage of a process of textual data analysis. Information extraction is required to get information from textual data that can be used for process analysis, such as classification and categorization. A textual data is strongly influenced by the language. Arabic is gaining a significant attention in many studies because Arabic language is very different from others, and in contrast to other languages, tools and research on the Arabic language is still lacking. The information extracted using the knowledge dictionary is a concept of expression. A knowledge dictionary is usually constructed manually by an expert and this would take a long time and is specific to a problem only. This paper proposed a method for automatically building a knowledge dictionary. Dictionary knowledge is formed by classifying sentences having the same concept, assuming that they will have a high similarity value. The concept that has been extracted can be used as features for subsequent computational process such as classification or categorization. Dataset used in this paper was the Arabic text dataset. Extraction result was tested by using a decision tree classification engine and the highest precision value obtained was 71.0% while the highest recall value was 75.0%.
Effect of Number of Face Images based on Illumination Variation in the Training Process on Face Recognition Results Budi Nugroho; Anny Yuniarti; Eva Yulia Puspaningrum
Prosiding International conference on Information Technology and Business (ICITB) 2019: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 5
Publisher : Proceeding International Conference on Information Technology and Business

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The research is related to face recognition which is influenced by illumination factor. The method used is the Robust Regression, which has a better performance than many other methods. The empirical experiment, which uses Yale Face Database B Cropped, is conducted to determine the effect of number of face images in the training process on face recognition perfomance. The hypothesis proposed in this research is the greater number of face images will result in higher facial recognition performance. The empirical experiment was conducted on this research to prove the hypothesis. Based on experiments that have been done, in general, the process of data training with many images will result in high performance of face recognition. But, this trend only occurs in images in the similar illumination condition. Illumination variation of face images also have significant impact on face recognition results. The process of training data with images of illumination variations (from several subsets of the face database) results in better face recognition performance than the process of training data with images of similar illumination conditions (from a subset of the face database). By using 19 images from subset 5 of the face database, face recognition accuracy is obtained at 95.11%. Whereas by only using 5 images from several subsets, obtained face recognition accuracy up to 96.10%. Even by using 7 images from several subsets, the accuracy obtained is up to 99.47%.Keywords: Face Recognition Performance, Robust Regression, Data Training
Multilevel Thresholding of Color Image Segmentation Using Memory-based Grey Wolf Optimizer With Otsu Method, Kapur, and M.Masi Entropy I Made Satria Bimantara; Anny Yuniarti
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 12 No. 2 (2023)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v12i2.62874

Abstract

Determining the optimal threshold value for image segmentation has become more attention in recent years because of its varied uses. Otsu-based thresholding methods, minimum cross entropy, and Kapur entropy are efficient for solving bi-level thresholding image segmentation problems (BL-ISP), but not with multi-level thresholding image segmentation problems (ML-ISP). The main problem is exponentially increasing computational complexity. This study uses the memory-based Gray Wolf Optimizer (mGWO) to determine the optimal threshold value for solving ML-ISP on RGB images. The mGWO method is a variant of the standard grey wolf optimizer (GWO) that utilizes the best track record of each individual grey wolf for the global exploration and local exploitation phases of the problem solution space. The solution candidates are represented by each grey wolf using the image intensity values and optimized according to mGWO characteristics. Three objective functions, namely the Otsu method, Kapur Entropy, and M.Masi Entropy are used to evaluate the solutions generated in the optimization process. The GridSearch method is used to determine the optimal parameter combination of each method based on 10 training images. Evaluation of the performance of the mGWO method was measured using several benchmark images and compared with five standard swarm intelligence (SI) methods as benchmarks. Analysis of the results was carried out qualitatively and quantitatively based on the average PSNR, RMSE, SSIM, UQI, fitness value, and CPU processing time from 30 tests. The results were analyzed further with the Wilcoxon signed-rank test. The experimental results show that the performance of the mGWO method outperforms the benchmark method in most experiments and metrics. The mGWO variant also proved to be superior to the standard GWO in resolving multi-level color image segmentation problems. The mGWO performance results are also compared with other state-of-the-art SI methods in solving ML-ISP on grayscale images and was able to outperform those methods in most experiments.
Co-Authors Agus Arifin Agus Arifin, Agus Agus Z. Arifin, Agus Z. Agus Zainal Arifin Agus Zainal Arifin Ahmad Mustofa Hadi Ahmad Mustofa Hadi Akira Asano Akira Taguchi Andi Baso Kaswar Andi Baso Kaswar Anindhita Sigit Nugroho Anindita Sigit Nugroho Arif Fathur Mahmuda Arifzan Razak Aris Fanani Aris Tjahyanto Arya Yudhi Wijaya Betty Natalie Fitriatin Bilqis Amaliah Budi Nugroho Chastine Fatichah Darlis Heru Mukti Darlis Herumurti Devira Wiena Pramintya Dhian Satria Yudha Kartika Dini Adni Navastara, Dini Adni Eva Yulia Puspaningrum Eva Yulia Puspaningrum Feni Siti Fauziah2 Fetty Tri A. Fiandra Fatharany Gulpi Qorik Oktagalu Pratamasunu Handayani Tjandrasa Hani Ramadhan Hidiyah Ayu Ratna Ma’rufah Hudan Studiawan I Made Satria Bimantara I Made Widiartha I Putu Gede Hendra Suputra Imam Kuswardayan Ishardan Ishardan Isye Arieshanti Lutfiani Ratna Dewi M. Ali Fauzi M. Ali Fauzi Maulana, Hendra Mohamad Dion Tiara Muhammad I. Rosadi, Muhammad I. Nadya Anisa Syafa Nanik Suciati Nur Nafi'iyah Oviyanti Mulyani Pasnur Pasnur Ratri Enggar Pawening Reginawanti Hindersah Ridho Rahman Hariadi Riduwan, Muhammad Sahmanbanta Sinulingga Saiful Bahri Musa Saprina Mamase Saputra, Wahyu Syaifullah Jauharis Takashi Nakamoto Wahyu Syaifullah Jauharis Saputra Wijayanti Nurul K Wijayanti Nurul Khotimah