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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Infotekmesin JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknik Informatika (JUTIF) Jurnal Abdimas Bina Bangsa Jurnal INFOTEL Infact: Jurnal Sains dan Komputer
Sunneng Sandino Berutu
Universitas Kristen Immanuel
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Uji Kinerja Metode Deep Convolutional Neural Networks Untuk Identifikasi Gangguan Daya Listrik Sunneng Sandino Berutu
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1541

Abstract

The identification model development of the power disturbance signals with the deep convolutional neural networks (CNNs) method involves a large amount of data. However, the real signal data is limited. Therefore, researchers employ synthetic signal data. These signals can be generated by the formula IEEE standardized. In these formulas, two categories have a similar formula i.e interruption and sag. The difference is only in the intensity parameter (α). This paper analyzed the model performance of identifying those disturbances where the intensity values are set differently for training and testing datasets based on the upper bound value α of sag and the lower bound value α of interruption. Several noise levels are included in the signals. So, there are several datasets with noises in this simulation. Furthermore, those datasets are trained using the model based on deep CNN. The test results show that the true positive (TP) of the model's performance in identifying the interruption signal is 93.54% and the sag signal is 78.78%. In addition, the performance of the model using a dataset without noise obtained a high percentage in accuracy, precision, and f1-score parameters with 92.4%, 97.4%, and 92,76%, respectively.
Pengembangan Model Klasifikasi Sentimen Dengan Pendekatan Vader dan Algoritma Naive Bayes Terhadap Ulasan Aplikasi Indodax Zendrato, Agus Dirgahayu; Berutu, Sunneng Sandino; Sumihar, Yo’el Pieter; Budiati, Haeni
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5050

Abstract

Cryptocurrency trading applications such as Indodax have grown rapidly, the understanding of user sentiment towards the platform is still lacking, so it is interesting to analyze user sentiment towards the platform. To measure sentiment, this research proposes a combined approach of Vader and Naïve Bayes methods. The data used is a collection of user comments on the google play store platform related to user experience using Indodax. The Vader method is used to analyze sentiment directly from the comment text, while Naïve Bayes is adopted to improve accuracy in sentiment classification. The sentiment analysis process involves various steps, starting from data preparation, data pre-processing, labeling of training and testing data and performance evaluation of the Naive Bayes model. At the sentiment analysis stage with the Vader Sentiment method, the positive category obtained the highest percentage of 63.5%, followed by the neutral category at 18.9% and negative at 17.6%. Meanwhile, based on the performance evaluation of the Naïve Bayes model, the accuracy value is 78% while the highest precision value is achieved by the negative sentiment category at 80% and recall in the positive sentiment category at 44%.
A Conversion of Signal to Image Method for Two-Dimension Convolutional Neural Networks Implementation in Power Quality Disturbances Identification Berutu, Sunneng Sandino; Chen, Yeong-Chin; Wijayanto, Heri; Budiati, Haeni
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1529

Abstract

The power quality is identified and monitored to prevent the worst effects arise on the electrical devices. These effects can be device failure, performance degradation, and replacement of some device parts. The deep convolutional neural networks (DCNNs) method can extract the complexity of image features. This method is adopted for the power quality disruption identification of the model. However, the power quality signal data is a time series. Therefore, this paper proposes an approach for the conversion of a power quality disturbance signal to an image. This research is conducted in several stages for constructing the approach proposed. Firstly, the size of a matrix is determined based on the sampling frequency values and cycle number of the signal. Secondly, a zero-cross algorithm is adopted to specify the number of signal sample points inserted into rows of the matrix. The matrix is then converted into a grayscale image. Furthermore, the resulting images are fed to the two-dimension (2D) CNNs model for the PQDs feature learning process. When the classification model is fit, then the model is tested for power quality data prediction. Finally, the model performance is evaluated by employing the confusion matrix method. The model testing result exhibits that the parameter values such as accuracy, recall, precision, and f1-score achieve at 99.81%, 98.95%, 98.84, and 98.87 %, respectively. In addition, the proposed method's performance is superior to the previous methods. 
Co-Authors Bobby Girsang Chen, Yeong-Chin Dachi, Christian Moris Febe Maedjaja Febe Maedjaja Fornieli Gulo Haeni Budiati Heri Wijayanto Indriyani Hulu Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika, Jatmika Jatmitka, Jatmitka Juli Supriyanto Gea Kambayo, Andreas Noprianto Kambayo Kristian Juri Damai Lase Laia, Yardiana Lase, Anan Sosmita Nauli, Sumitta Nehe, Pius Hermanto Nshimiyimana, Aristophane Rahman, Mosiur Reonardh Sibarani Sispianygala, Aprilia Sumihar, Yo’el Pieter Tri Elsa, Maria Tri Yuli Yanto Veren, Yolanda Yuan, Anita Zakaria, Immanuel Richie De Harjo Zendrato, Agus Dirgahayu