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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Sigit Priyanta
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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 1 
Detection Of Spam Comments On Instagram Using Complementary Naïve Bayes Nur Azizul Haqimi; Nur Rokhman; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 3 (2019): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.47046

Abstract

Instagram (IG) is a web-based and mobile social media application where users can share photos or videos with available features. Upload photos or videos with captions that contain an explanation of the photo or video that can reap spam comments. Comments on spam containing comments that are not relevant to the caption and photos. The problem that arises when identifying spam is non-spam comments are more dominant than spam comments so that it leads to the problem of the imbalanced dataset. A balanced dataset can influence the performance of a classification method. This is the focus of research related to the implementation of the CNB method in dealing with imbalance datasets for the detection of Instagram spam comments. The study used TF-IDF weighting with Support Vector Machine (SVM) as a comparison classification. Based on the test results with 2500 training data and 100 test data on the imbalanced dataset (25% spam and 75% non-spam), the CNB accuracy was 92%, precision 86% and f-measure 93%. Whereas SVM produces 87% accuracy, 79% precision, 88% f-measure. In conclusion, the CNB method is more suitable for detecting spam comments in cases of imbalanced datasets.
Adwords Keyword Set Selection Decision Support System Using AHP and TOPSIS Method Sholikin Ady Chandra; Edi Winarko; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.50731

Abstract

CV. Gitani Creative Agency is a company engaged in the field of creative agency providing digital marketing service. Google Adwords is a platform used by the company to run this service. Keyword set selection is critical to the performance of ads. However, finding the right keyword set is not an easy task. The company needs to consider various criteria to get the optimal advertising results. Decision support system (DSS) is needed as an objective reference in the process of keyword set selection. The criteria for decision-making are click, impressions, cost, and avg. CPC.AHP method is used to compare the value of each criteria and then generate priority weights of each criteria. While TOPSIS method is used for alternative ranking. The combination of these methods aims to improve the performance of TOPSIS method.The result of this study shows that the combination of AHP and TOPSIS methods can be used to determine the best keyword set for ads. Based on the testing results, DSS can do alternative ranking correctly in accordance with the results of manual calculation and it is also flexible to the changes in criteria and alternatives.
Aspect-Based Sentiment Analysis of Online Marketplace Reviews Using Convolutional Neural Network MHD Theo Ari Bangsa; Sigit Priyanta; Yohanes Suyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.51646

Abstract

Most online stores provide product review facilities that contain responses to a product. The number of reviews makes it difficult for potential customers to make conclusions, so that sentiment analysis is needed to extract information from these reviews. Most sentiment analysis is done at the document level, so the results were still lacking in detail because the classification is based on the entire sentence or document and does not identify the specific aspect discussed. This research aims to classify aspect-based sentiments from online store reviews using the convolutional neural network (CNN) method with the extraction of features using Word2Vec. The dataset used is Indonesian review data from the site bukalapak.com. The test results on the built system showed that CNN's method of Word2Vec feature extraction has a better score than the naive bayes method with an accuracy value of 85.54%, 96.12% precision, 88.39% recall, and f-measure 92.02%. Classification without using stemming preprocessing on the dataset increases the accuracy by 2.77%.
Comparison of Filter and Wrapper Based Feature Selection Methods on Spam Comment Classification Amalia Nur Anggraeni; Khabib Mustofa; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.66965

Abstract

The continuous growth of the internet has led to the use of social media for various purposes increase. For instance, some irresponsible parties take advantage of the comment feature on social media platforms to harm others by providing spam comments on the shared object. Furthermore, variation of comments creates many features to be processed, thereby negatively impacting the performance of a classification algorithm. Therefore, this study aims to solve the problem associated with spam comments by comparing filter and wrapper based feature selection using text classification techniques. Data collected from training and test data of 4944 and 100 comments showed that the best accuracy, precision, recall, and f-measure of MNB are 96%, 100%, 92%, and 95.8%. The best accuracy is achieved using feature selection by combining Chi-Square and Sequential Forward Selection methods with a subset of 500 features. Furthermore, the accuracy increase in the MNB and SVM classifications are 8% and 4%. This research concludes that the combination of feature selection improves the classification performance of Indonesian language spam comments.
Aspect-Based Sentiment Analysis of KAI Access Reviews Using NBC and SVM Huda Mustakim; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 2 (2022): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.68903

Abstract

The existence of KAI Access from PT. KAI prove their sincerity in serving consumers in this modern era. However, many negative reviews found in Google Play Store. There has been research on the review, but the analysis stage still at document level so the aspect related to the application is not known clearly and structured. So it is necessary to do an aspect-based sentiment analysis to extract the aspects and the sentiment. This study aims to do an aspect-based sentiment analysis on user reviews of KAI Access using Naive Bayes Classifier (NBC) and Support Vector Machine (SVM), with 3 scenarios. Scenario 1 uses NBC with Multinomial Naive Bayes, scenario 2 uses SVM with default Sklearn library parameter, and scenario 3, uses SVM with hyperparameter tunning, while the data scrapped from Google Play Store. The results show the majority of user sentiment is negative for each aspect, with most discussed errors aspect shows the high system errors. The test results gives the best model from scenario 3 with an average accuracy 91.63%, f1-score 75.55%, precision 77.60%, and recall 74.47%.
Tegal Tourism Object Selection Decision Support System Using Fuzzy Logic Dika Permana Putra; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 1 (2022): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.70226

Abstract

There are many agencies that have databases but are left without proper management. For example, in tourist attractions in Kota & Kab. Tegal, along with the rapid development of tourism technology, the tourism industry requires the tourism industry to apply information technology to provide convenience for tourists to find out tourist areas according to the cost, and the distance of the tourist attractions entered. The provision of tourism information helps tourists to consider and make decisions to travel. Tahani Fuzzy Logic was chosen because the concept of Fuzzy logic is easy to understand, flexible, and because the Tahani logic method is a form of decision support where the main tool is functional with the main input criteria determined by the user/tourist. This system was implemented using web programming and MySQL database, where the variables to be considered are Type of Tour, Number of Facilities, Price of Tour Tickets, Number of Tourist Visitors, Travel Distance from City Center. The results of this study are a decision support system for tourism selection in Tegal using Fuzzy Tahani which can recommend tourist attractions in Tegal which are determined by tourists depending on tourist criteria based on the firestrength of the selected variables
Automatic Essay Scoring Using Data Augmentation in Bahasa Indonesia Nur Fadilah; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 16, No 4 (2022): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.76396

Abstract

Essay is one of the assessments to find out the abilities of students in depth.  UKARA is an automatic essay scoring development that combines NLP and machine learning.  This study uses the datasets provided for the UKARA challenge which consists of 2 types, datasets A and B. The dataset provided is still small for the model creation  process so that it is one of the causes of the resulting model is not optimal. This research focuses on the process of adding or augmenting data using EDA (Easy Data Augmentation Techniques). There are four methods applied, namely Synonym Replacement (SR), Random Insertion (RI), Random Swab (RS), and Random Deletion (RD).  The data is used for model creation by using the BiLSTM method. Performa model evaluated using confusion matrix with nilai accyouracy, precision, recall dan f-measure.The results showed that the dataset A without augmentation using k-fold cross validation produced the highest accuracy value with a value of 85.07%. While the results in data B show EDA insert with k-fold cross validation of 72.78%.
Co-Authors Amalia Nur Anggraeni Dika Permana Putra Edi Winarko Huda Mustakim Khabib Mustofa MHD Theo Ari Bangsa Nur Azizul Haqimi Nur Fadilah Nur Rokhman Sholikin Ady Chandra Yohanes Suyanto