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Sriwijaya Journal of Informatics and Applications
Published by Universitas Sriwijaya
ISSN : -     EISSN : 28072391     DOI : -
Core Subject :
Sriwijaya Journal of Informatics and Applcations (SJIA) is a scientific periodical researchs articles of the Informatics Departement Universitas Sriwijaya. This Journal is an open access journal for scientists and engineers in informatics and Applcations area that provides online publication (two times a year). SJIA offers a good opportunity for academics and industry professionals to publish high quality and refereed papers in various areas of Informatics e.q., Machine Learning & Soft Computing, Data Mining & Big Data Analytics, Computer Vision and Pattern Recognition and Automated Reasoning, and Distributed and security System
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Articles 5 Documents
 1 
Search results for , issue "Vol 1, No 1 (2020)" : 5 Documents clear
Face Detection Using Randomized Hough Transform (RHT) with Various Ellipses Segmentations Muhammad Fachrurrozi; Saparudin Saparudin; Mardiana Mardiana; Desty Rodiah; Winda Agusthia
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

Face detection is one of earlier phase in face recognition process. This research aims to get the faces area on digital image without being affected by face orientation, lights condition, background and the expression. The detected face area is usually shaped by a rectangle. Many pixels on the rectangle are not part of face, especially at the four of the image corners. This research use an ellipse as replacement a rectangle. The detected face is shaped by ellipses with various sizes and orientations. The digital image segmentations is used to detect face candidates area. The ellipse is formed by using Randomized Hough Transform (RHT) method, which is influenced by the center point of ellipse candidates. RHT found three random pixels on segmented image. The rate of success of RHT is determined by segmentation results. The research result is tested by using various thresholds, and get the best accuracy at 74.4%. The rate of accuracy is measured by comparing between RHT ellipses shape and circle shape on OpenCV library as ground truth.
Urea Fertilizer Quality Testing with Chi-Squared Automatic Interaction Detection (CHAID) Algorithm Ahmad Nauvan Zikri Al Ghifran; Yunita Yunita; Desty Rodiah
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

PT. XYZ has a Laboratory section in each of its factories that performs its duties manually to determine the quality of the fertilizers to be produced. This manual method is most likely at risk of human error and causes errors in the results of determining the quality of urea fertilizer. An expert system was built using the Chi-Squared Automatic Interaction Detection (CHAID) algorithm which can test the quality of urea fertilizer. The CHAID algorithm applies the decision tree technique where the technique will always branch off two or more as a basis in establishing rules. The system takes the values of the urea fertilizer test parameters as attributes. These attributes are processed to produce the most significant values that will be branches in the decision tree. The parameters used include Nitrogen, Biuret, Moisture, Free Ammonia, Iron, Oil Content, Crushing Strength, and Size Distribution. CHAID algorithm is suitable to be used to test the quality of urea fertilizer because in this study produced 4 different decision trees with an accuracy value of 99% using as much as 100 test data. This number influenced by the amount of training data used to build the rules.
Spelling Checker using Algorithm Damerau Levenshtein Distance and Cosine Similarity Nur Hamidah; Novi Yusliani; Desty Rodiah
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

Writing is an embodiment of the author's ideas that are to be conveyed to others. A writer often experiences typos in typing the script, so that it can influence the meaning of the text. Therefore, a system is needed to detect word errors. In this study, checking is done by using the Dictionary Lookup method and giving the candidate words using the Damerau Levenshtein Distance algorithm. Candidates will then determine the ranking by breaking the word into Bigram form and calculating the similarity value using the Cosine Similarity algorithm. The test results based on the data used yield different Mean Reciprocal Rank (MRR) values for each type of error. The type of error deletion produces an MRR value of 88.89%, the type of insertion error produces an MRR value of 97.78%, the type of substitution error produces an MRR value of 88.89%, the type of transposition error produces an MRR value of 89%
Effect of N-Gram on Document Classification on the Naïve Bayes Classifier Algorithm Fitria Khoirunnisa; Novi Yusliani, M.T.; Desty Rodiah, M.T.
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

News has become a major need for everyone, with news we can get the information needed. News can be distributed in the form of print mass media, electronic mass media and online media. The means of spreading the news now have grown very rapidly, making the amount of information being managed are bigger and word management classified also not small.  herefore, we need a system for classifying documents that are not structured. In this study, word processing in a document is done by N-Gram as a feature generation. The document classification process is carried out using the Naïve Bayes Classifier algorithm. This study examines the effect of N-Gram on document classification on the Naïve Bayes Classifier algorithm. The results of the classification accuracy of documents by applying N-Gram is 32.68% and without applying N-Gram is 84.97%. A decrease in the classification results occurs the number of features that result from solving N-Gram that is unique or dominant to another category. The accuracy of the results obtained shows that the application of N-Gram in the classification of documents using the Naïve Bayes Classifier algorithm gives a decreased effect on the performance of the classification
Multilabel Classification for News Article Using Long Short-Term Memory Winda Kurnia Sari; Dian Palupi Rini; Reza Firsandaya Malik
Sriwijaya Journal of Informatics and Applications Vol 1, No 1 (2020)
Publisher : Fakultas Ilmu Komputer Universitas Sriwijaya

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Abstract

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited when there is small labeled data and leads to the difficulty of capturing semantic relationships. In this case, it requires a multi-label text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multi-label text classification techniques. By comparing the seven proposed Long Short-Term Memory (LSTM) models with large-scale datasets by dividing 4 LSTM models with 1 layer, 2 layer and 3-layer LSTM and Bidirectional LSTM to show that LSTM can achieve good performance in multi-label text classification. The results show that the evaluation of the performance of the 2-layer LSTM model in the training process obtained an accuracy of 96 with the highest testing accuracy of all models at 94.3. The performance results for model 3 with 1-layer LSTM obtained the average value of precision, recall, and f1-score equal to the 94 training process accuracy. This states that model 3 with 1-layer LSTM both training and testing process is better.  The comparison among seven proposed LSTM models shows that model 3 with 1 layer LSTM is the best model.

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