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Putra Wanda
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Faculty of Science and Technology, Universitas Respati Yogyakarta Yogyakarta, Indonesia Phone: 0274-488781 Email: ijicom@respati.ac.id
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INDONESIA
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Published by Universitas Respati Yogyakarta
ISSN : 29856914     EISSN : -     DOI : https://doi.org/10.35842/icostec
Core Subject : Science,
Computer Science & IT
ICoSTEC is an annual forum for international researchers and students to exchange ideas on current studies and research topics. The international conference will discuss several sub-topics, including innovation in information science and technology and leveraging globalization.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 57 Documents
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Classification of Basurek Batik Using Pre-Trained VGG16 and Support Vector Machine Meli Handayani; Rika Rosnelly; Hartono Hartono
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.v2i1.31

Abstract

By introducing Indonesian batik motifs, we know that the island of Sumatra, especially Bengkulu and Jambi provinces, has a distinctive batik called Basurek batik. This research aims to classify the two batik motifs using the Support Vector Machine (SVM) algorithm. First, we extract the image of the batik motif with a pre-trained VGG-16 model and then use them as a dataset for the SVM classification process. The classification process itself uses linear, polynomial, and sigmoid kernels. We divided the data 90:10 and used 10-fold cross-validation to analyze each training and testing data classification result. The results of this study are the highest values of accuracy, precision, and recall of 76.4%, 76.5%, and 76.4% produced by the linear kernel for the training data classification. For the testing data classification, both the linear and polynomial kernels generate the best accuracy, precision, and recall values of 87.5%, 90%, and 85.5%. On average, incorporating the training and testing classification results, we found that the linear kernel is the best function for classifying the Basurek batik motif using the collected images from the internet.
COMPARISON OF SGD, RMSProp, AND ADAM OPTIMATION IN ANIMAL CLASSIFICATION USING CNNs Desi Irfan; Teddy Surya Gunawan; Wanayumini Wanayumini
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.v2i1.32

Abstract

Many measures have been taken to protect endangered species by using "camera trap" technology which is widespread in the field of technology-based nature protection field research. In this study, a machine learning-based approach is presented to identify endangered wildlife images with a data set containing 5000 images taken from Kaggle and some other sources. The Gradient Descent optimization method is often used for Artificial Neural Network (ANN) training. This method plays a role in finding the weight values that give the best output value. Three optimization methods have been implemented, namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam on the Artificial Neural Network system for animal data classification. In some of the studies reviewed there are differences in the results of SGD and ADAM, which on the one hand SGD is superior, and on the one hand ADAM is superior with the appropriate learning rate. The results of this study show that the CNN method with the Adam optimization function produces the highest accuracy compared to the SGD and RMSprop optimization methods. The model trained using Adam's optimization function achieved an accuracy of 89.81% on the test, showing the feasibility of the approach.
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.
HYPERTEROID DISEASE ANALYSIS WITH BACKPROPAGATION ARTIFICIAL NEURAL NETWORK Ela Roza Batubara; Muhammad Zarlis; Rika Rosnelly
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.v2i1.34

Abstract

In the era of technology 4.0, a system is needed to support the development of a company, both in industry, education, and others to help solve problems. In this study, the authors used the Backpropagation Neural Network Algorithm in recognizing hyperthyroid disease patterns. In this study, in the recognition of hyperthyroid disease patterns. The author uses 11 data variables that will be trained using the backpropagation algorithm where the weighting is done randomly and the second data is trained using the backpropagation algorithm. In this study using Matlab application for processing. From the results of testing data derived from kaggle, namely hyperthyroid disease data above, we can see in the 2-2-1 architecture which shows that the target is reduced by the jst output that the SSE is 0.06571 which indicates that there is an increase in hyperthyroid disease activity in humans. From the data obtained, that the performance of artificial neural network calculations with the Backpropagation Algorithm is 86%. Can be seen by comparing the desired target with the prediction target.
Sentiment Classification on Mandalika MotoGP Event Using K-Means Clustering and Random Forest Khairul Fadhli Margolang; Muhammad Zarlis; Hartono Hartono
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.v2i1.35

Abstract

As one the most famous world-class motorcycle racing competition, MotoGP is an event broadcast live on television with millions of viewers on each race. Indonesia, especially the Pertamina Mandalika Circuit, will hold this prestigious racing event in the 19th series of 2022. This event sparks Indonesian netizens' reactions on social media, especially on Twitter. This research aims to analyze the public sentiment and emotional value regarding this event, with the data collected from Twitter social media. With the features of sentiment and emotion values extracted from the contents of this tweet, we use K-means clustering to generate sentiment clusters as targets for the classification using the Random Forest (RF) algorithm. From the evaluation using the 5-fold and 10-fold cross-validation, we get the highest accuracy of 0.99, the highest precision of 0.990175, and the highest recall of 0.99 from the RF model with ten trees configuration. We also get the lowest accuracy, precision, and recall values of 0.96, 0.960934, and 0.96 from the RF models with 15 and 20 trees configuration, with the 10-fold evaluation
Sentiment Analysis on Hotel Ratings Using Dynamic Convolution Neural Network Novendra Adisaputra Sinaga; Teddy Surya Gunawan; Wanayumini Wanayumini
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.v2i1.36

Abstract

Currently, the role of information technology is very important in everyday life because heavy workloads can become easier, communication time can be made shorter and data processing can be faster and more accurate. Hotel ranking sentiment analysis can provide important information for hotel owners and managers to improve the quality of service and guest experience. It can also be used by prospective guests to make the right booking decisions. Sentiment analysis can identify positive or negative feelings from guest reviews. There are 694,213 data reviews about hotels using English which are used as training data. The data was preprocessed and 76,905 vocabularies were obtained by utilizing Word2Vec. The training data was carried out at the encoding stage. The DCNN model is given a K-Max-Polling value of 2. The model is trained for 20 epochs. The model that has been formed is tested with 173,554 data and obtained an accuracy rate of 95%.
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.
Analysis of Machine Learning Algorithms in Predicting the Flood Status of Jakarta City Irwan Daniel; Hartono Hartono; Zakarias Situmorang
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.v2i1.38

Abstract

By mining the information in the dataset, we can solve a prediction problem, especially flood status prediction based on floodgate levels, using machine learning algorithms. This research employs three machine learning algorithms (K-Nearest Neighbor, Naive Bayes, and Support Vector Machine) for predicting the flood status using a dataset containing the data of DKI Jakarta's floodgate levels. Using a 5-fold, 10-fold, and 20-fold cross-validation evaluation, we get the highest accuracy (85.096%), f-score (85.1%), precision (85.641%), and recall (85.096%) from the model using the SVM algorithm with a polynomial kernel. Average performance-wise, the K-NN algorithm performs better than the other algorithm with an average accuracy of 83.147%, an average f-score of 83.156%, an average precision of 83.566%, and an average recall of 83.147%
Analysis Of Solar Energy Utilization Of Hybrid Systems For Freezing Mango Fruit Yudhy Kurniawan; M. Idrus Alhamid; Ardiyansyah Yatim
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.v2i1.39

Abstract

The use of vapour compression refrigeration systems is growing both on a domestic and commercial scale. The impact of electrical energy consumption in this sector is quite large compared to the need for electrical energy in other electronic equipment. Various efforts were made to be able to reduce the consumption of electrical energy for the cooling system. One of the efforts made by utilizing the natural potential of energy around is solar energy and wind energy as a source of electrical energy in the freezing unit of mango fruit. This fruit is one of the main commodities in West Java, especially in Indramayu district. The purpose of this study is to find out the needs of electricity consumption and how efficient the use of alternative energy is for freezing mango fruit. Inthe process freezing is carried out at temperatures below the freezing point of the food. Good freezing usually ranges from -12 oC to -24 oC. With this temperature, food can last up to 3 to 12 months. In this study, the method was carried out for freezing mangoes by utilizing solar energy which was assembled hybrid with wind energy in the vapour compression refrigeration system for the freezing. The design method is carried out by taking into account the cooling load on the freezer to find out the cooling capacity is 1/2 PK. The test results can be analyzed comparing the performance of the utilization of the hybrid energy system with the design and use of PLN electricity, the length of time to reach the freezing temperature of a unit.
Comparative Analysis of Support Vector Machine And Perceptron Algorithms In Classification Of The Best Work Programs In P2KBP3A Jaka Tirta Samudra; Rika Rosnelly; Zakarias Situmorang
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.v2i1.40

Abstract

With the rapid growth of government agencies that are required to carry out an activity in every aspect that publishes and carries out obligations every year, it is required to be held accountable and also implemented for every device that receives such as fostered villages by utilizing the available APBD funds to maximize the work program that has been designed. so that it can be implemented as much as possible. That way, to get the best from all aspects of every work program implementation, there must be an important point from the annual work program design that is made without exception. Data mining itself can help P2KBP3A in analyzing each work program that is designed before being implemented in the future for the annual work program by looking at various aspects of past work program data and grouping work programs in the form of classification. In designing the work program, this research builds a classification model by adding a sigmoid activation function that uses SVM and perceptron to compare the accuracy results of the algorithm used to get the best work program design. From the various classifications used, the best value for classifying the dataset of the best P2KBP3A work programs can be seen from the average accuracy value of 87.5%, F1 value of 82.2%, the precision value of 80.2%, and recall value of 87.5%

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