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All Journal Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
B. Herawan Hayadi
Magister of Computer Science, Potensi Utama University
Computer Science & IT

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Sentiment Analysis of IDAHOBIT Celebrations using Naïve Bayes and Decision Tree Algorithms Jaka Kusuma; Hartono Hartono; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.33

Abstract

The development of LGBTIQ in Indonesia reflects the shift in culture and the emergence of this phenomenon has attracted the attention of the Indonesian people. The use of NLP, ML, and statistics technology in tweet analysis can be used to identify sentiments contained in tweets. This study compares Naïve Bayes algorithm and Decision Tree in sentiment analysis classification, in which the multilingual sentiment analysis method is used in the labeling process of training data. Naïve Bayes results give the best classification with 100% accuracy, precision, and recall, and the number of positive sentiments is 385, negative sentiments are 3117, and neutral sentiments are 899. It looks that the negative class is the most superior compared to other classes. This proves that the Indonesian people have an unfavorable response to the IDAHOBIT celebration.
Sentiment Classification on Twitter Social Media Using K-Means Clustering, C4.5 and Naive Bayes (Case Study: Blocking Paypal by Kominfo) Muhammad Zulkarnain Lubis; Hartono Hartono; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.37

Abstract

Kominfo (Ministry of Communication and Information) requires all PSEs (Electronic System Providers) to register themselves so that their access is not blocked, as shown in the case of Paypal and several other PSEs. The blocking case reaps mixed opinions from netizens, especially Twitter social media users. We use the sentiment values obtained from the content of tweets collected through the crawling process and employ the K-Means Clustering to group them into clusters. Finally, we use these clusters as the target in a dataset and classify them using the C4.5 and Naive Bayes algorithms. Of the 1000 netizen tweets studied, we found that 6.5% of netizens supported the blocking action, 75.4% did not care or felt that the blocking action had no effect on them, and 15.4% did not support the blocking by Kominfo. The classification results in this study resulted in a 98.2% accuracy value, a 95% precision value, and a 95.5% recall value.
Smart Security in Smart City Using Naïve Bayes and RSA Junaidi Junaidi; R Roslina; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.41

Abstract

As all major cities adopt the concept of smart cities, concerns arise among the public regarding data security and privacy. The constant threat of attacks on confidentiality, integrity and accessibility of data makes it vulnerable to cyber attacks. The increasing use of IoT devices also increases the potential for cyber attacks that can harm all IoT users. Therefore, it is crucial for city governments to be aware of data security issues related to smart spaces, services, and citizen security, and to provide solutions to existing problems by making maximum policies related to the implementation of smart city concepts. From the above explanation, the author is taking the analysis step with the title "Analysis of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) Combination in Smart Security in the Implementation of Smart City in Pemko Medan" where the benefits that can be obtained are to gain deeper understanding of Smart Security level, obtain information about the Smart Securty level, and classify the stage of Smart Securty using the combination of Naive Bayes Classifier and Rsa (Rivest Shamir Adleman) in the implementation of Smart City in Pemko Medan.
Combination Of SqueezeNet And Multilayer Backpropagation Algorithm In Hanacaraka Script Recognition Yuni Franciska br Tarigan; Teddy Surya Gunawan; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.51

Abstract

Javanese script is one of Indonesia's cultural heritages that are increasingly rarely used today. The difficulty of recognizing the shapes of letters, let alone writing them, is the main obstacle in using the Hanacaraka script. This research offers an alternative to Hanacaraka script recognition using a combination of image feature extraction and machine learning, where we utilize a pre-trained SquzeeNet model and Multilayer Backpropagation algorithm. Of the 18 models built using ReLu, Sigmoid, and Tanh activation functions, we found that the Tanh activation function, using the combination of 50-50-100 neuron configuration and 25 epochs, was the most optimal function used to classify the training data with accuracy, precision, and recall values of 93.8%. Meanwhile, the Tanh activation function, using the 50-100-50 neuron configuration and 50 epochs, is the most optimal function to classify the testing data, with accuracy, precision, and recall values of 89.1%, 89.5%, and 89.5%. All built models show a training and testing performance ratio below 10%. From this result, we conclude that all models have good reliability in the training and testing classification process.
Predicting Non-Performing Loan's Risk Level Using KMeans Clustering and K-Nearest Neighbors Muhammad Mizan Siregar; Roslina Roslina; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.55

Abstract

In data mining, clustering is an unsupervised learning technique often used to group data by similarity. Clustering, especially the K-means clustering algorithm, is a feasible tool for expanding a dataset label by increasing the cluster's number according to the label's categories. This research extends the credit loan label data set from two categories (non-performing and performing loans) to four risk levels (high risk, medium risk, low risk, and no risk). The combination of three K-nearest neighbor’s distance metrics, Euclidean, Manhattan, and Chebyshev distance, with four different K values (K = 3, K = 5, K = 7, and K = 9) produced the best model with accuracy, precision, and recall values of 90%, 90.53571%, and 90%, from the model using the Euclidean distance with K = 9
TEXT MINING IN ONLINE TRANSPORTATION USER SENTIMENT ANALYSIS ON SOCIAL MEDIA TWITTER USING THE MULTINOMIAL NAIVE BAYESIAN CLASSIFIER METHOD AND K-NEAREST NEIGHBOOR METHOD Sartika Mandasari; Roslina Roslina; B. Herawan Hayadi
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.56

Abstract

Text mining is the process of detecting information or something new and researching large information. Text mining can also usually perform an analysis of unstructured text. Social media users in Indonesia, which currently almost reach 200 million users, have resulted in a flood of data. This condition makes text mining a solution to extract knowledge from the flood of data [1] . In exploring knowledge, there are various techniques or methods that can be adopted including the Multinomial Naive Bayesian Clasifier and K-Nearest Neighbor methods. Both of these methods have several phases that are able to explore the potential knowledge of a flood of supervised and unsupervised learning data. It is hoped that the combination of these two methods will help analyze public sentiment or perception towards online motorcycle taxi users in Indonesia
Co-Authors Hartono Hartono Jaka Kusuma Junaidi Junaidi Muhammad Mizan Siregar Muhammad Zulkarnain Lubis Roslina, Roslina Sartika Mandasari Teddy Surya Gunawan Yuni Franciska br Tarigan