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Ronal Watrianthos
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Professional Organization - Ikatan Ahli Informatika Indonesia (IAII) / Indonesian Informatics Experts Association Jalan Jati Padang Raya No. 41 Jati Padang Pasar Minggu 12540 South Jakarta - Indonesia http://iaii.or.id/
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Journal of Systems Engineering and Information Technology
Published by Organisasi Profesi IKATAN AHLI INFORMATIKA INDONESIA
ISSN : -     EISSN : 2829310X     DOI : https://doi.org/10.29207/joseit.*
Core Subject : Science,
Computer Science & IT
International Journal of Systems Engineering and Information Technology (JOSEIT) is an international journal published by Ikatan Ahli Informatika Indonesia (IAII / Association of Indonesian Informatics Experts). The research article submitted to this online journal will be peer-reviewed. The accepted research articles will be available online (free download) following the journal peer-reviewing process. The language used in this journal is English. JOSEIT is a peer-reviewed, blinded journal dedicated to publishing quality research results in Computers Engineering and Information Technology but is not limited implicitly. All journal articles can be read online for free without a subscription because all journals are open-access.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol 2 No 1 (2023): March 2023" : 5 Documents clear
Analysis of the Effect Emotional Intelligence on Understanding Level in Programming Algorithm Learning Sri Handani Widiastuti; Nur Imansyah; Abdul Zain
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i1.4996

Abstract

The course Algorithms and Programming in the Informatics Engineering study program is given in the first and second semesters, as this course is a scientific course that serves as a basis for advanced programming courses in higher semesters. It is often the case that intelligent graduates obtain good academic grades during their studies and become outstanding students, but when working in their field of expertise, their performance is not as good as graduates who had lower academic grades. Success is not only determined by intellectual intelligence (IQ), but also by other types of intelligence. Intellectual intelligence or cognitive intelligence is one type of intelligence, while non-intellectual intelligence includes other types of intelligence outside of cognitive intelligence. One of this non-intellectual intelligence is emotional intelligence. Intellectual intelligence and non-intellectual intelligence play equally important roles in supporting individual success. This research discusses the analysis of the effect of emotional intelligence on the level of understanding of algorithms and programming using the linear regression method implemented with an application. The sample taken is students of the Informatics Engineering study program in the third and fourth semesters. With the results of the influence of emotional intelligence on the understanding of Algorithms and Programming, it can provide a solution to improve students' understanding of the Algorithms and Programming course and other computer courses in the scientific field of the Informatics Engineering study program. The application is created using the Python programming language.
Classification Analysis of Back propagation-Optimized CNN Performance in Image Processing Putrama Alkhairi; Agus Perdana Windarto
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i1.5015

Abstract

This study aims to optimize the performance of the Convolutional Neural Network (CNN) in the image classification task by applying data augmentation and fine-tuning techniques to a case study of mammal classification. In this study, we took a fairly complex image classification dataset and used the CNN model as a basis for training and evaluating the performance of the model compared to Back propagation. From this study, the CNN VGG16 architecture optimized with ADAM optimization has been compared with the Back propagation optimization of SGD. We also conducted a literature review on several related studies and basic concepts in CNN, such as convolution, pooling, and fully connected layers. The research methodology involves creating datasets using data augmentation techniques, model training using fine-tuning techniques, and testing model performance using a number of evaluation metrics, including accuracy, precision, and recall. The results of this study indicate that the techniques used have succeeded in improving the performance of the CNN model in complex image classification tasks with accuracy in identifying and monitoring animal species more accurately, with an accuracy of 91.18% for the best model. Model accuracy increased by 2% after applying data augmentation and fine-tuning techniques to the CNN model. These results indicate that the techniques applied in this study can be a good alternative in improving the performance of the CNN model in the image classification task.
Classification of Bullying Comments on YouTube Streamer Comment Sections Using Naïve Bayes Classification Ahlida Nikmatul H; Didih Rizki C; Christian S.K. Aditya
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i1.5016

Abstract

One of the social media crimes that is rampant in the current era is cyberbullying. Cyberbullying is a form of intimidation by someone to harass other people using technological devices. this research uses a design for information decision making that aims to get the expected results. the data collection process is carried out manually with a time frame of 1 week by watching the live broadcast of the online game YouTube streamer then sorting out some bullying and non-bullying comments in the comment’s column. Data labeling is done manually. The data obtained amounted to 1000 with 500 negative comments and 500 positive comments. The above test can be concluded that from the distribution of test data there are 90% - 10% have results that are superior to the results of other tests with an increase of 4% in the Naïve Bayes weighting Gain Ratio method. Based on the test data, the results of precision, recall, F1-score and accuracy of the Naïve Bayes classification method are obtained. The test analysis above can be concluded that from the distribution of test data, 90% - 10% have results that are superior to other test results with a 4% increase in the Naïve Bayes weighting Gain Ratio method. The existence of increased accuracy results is due to a randomized data processing process.
Research Growth of Engineering Faculty Universitas Negeri Padang: A Bibliometric Study of Journal Yose Indarta; Ronal Watrianthos; Agariadne Dwinggo Samala
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i1.5017

Abstract

This research aimed to conduct a comprehensive literature review of all papers from the beginning of time published by the Faculty of Engineering at Universitas Negeri Padang and indexed in the SINTA database using bibliometrics analysis. It started with a query to the DOI (Digital Object Identifier) database. The papers from this scholarly publication may be accessed here. A total of 1094 papers from 7 journals were collected. Each paper published by the Engineering Faculty at Universitas Negeri Padang receives an average of 0.42 citations. Given the insignificance of this average, it seems likely that the publication's contribution has yet to be fully recognized and has only had a little effect on the global state of knowledge. According to the findings, Universitas Negeri Andalas researchers collaborate the most with their peers around the country. Contrast this with the declining frequency with which multinational teams work together. With 38 articles published and ten citations obtained, Ahmaddul Hadi of the Informatics Department stood out as the most productive author. Research conducted by this faculty has been widely disseminated due to its publication in the reputable academic journal Inovasi Vokasi Dan Teknologi (INVOTEK).
Predicting ICO Prices Using Artificial Neural Network and Ridge Regression Algorithm Trầhn Kim Toại; Võ Thị Xuân Hạn; Võ Min Huân
Journal of Systems Engineering and Information Technology (JOSEIT) Vol 2 No 1 (2023): March 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/joseit.v2i1.5022

Abstract

An Initial Coin Offering (ICO) is a method of raising funds for digital currency projects. Investors purchase these coins at a very low initial price before they are released. These coins are then listed on the trading platform, and their prices may increase rapidly if the currency performs well. After six months of release, ICO evaluation is the expected time for investors to profit. A dataset consisting of 109 ICOs was constructed from reputable websites after data preprocessing. Correlation analysis of 12 inputs revealed issues of multicollinearity, leading to biased regression model results. Overfitting occurred when using the regression model. To address these limitations, the Ridge regression method resolved the issues with the ICO data. An artificial neural network model addressed the complex nonlinear relationships between inputs and ICO prices. By adjusting parameters to achieve the best performance according to the Root Mean Square Error, R-squares, and Mean Absolute Error metrics, the results showed that the Ridge regression algorithm with a test set of three ICOs achieved accuracy ranging from 63% to 92% of ICO prices, while the artificial neural network model predicted with 98% accuracy depending on the metric used.

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