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Journal of Computer System and Informatics (JoSYC)
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 27147150     EISSN : 27148912     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering
Journal of Computer System and Informatics (JoSYC) covers the whole spectrum of Artificial Inteligent, Computer System, Informatics Technique which includes, but is not limited to: Soft Computing, Distributed Intelligent Systems, Database Management and Information Retrieval, Evolutionary computation and DNA/cellular/molecular computing, Fault detection, Green and Renewable Energy Systems, Human Interface, Human-Computer Interaction, Human Information Processing Hybrid and Distributed Algorithms, High Performance Computing, Information storage, Security, integrity, privacy and trust, Image and Speech Signal Processing, Knowledge Based Systems, Knowledge Networks, Multimedia and Applications, Networked Control Systems, Natural Language Processing Pattern Classification, Speech recognition and synthesis, Robotic Intelligence, Robustness Analysis, Social Intelligence, Ubiquitous, Grid and high performance computing, Virtual Reality in Engineering Applications Web and mobile Intelligence, Big Data
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 443 Documents
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Question Answering System at the Kingdom of Sumedang Larang with Naïve Bayes Method Richo Fedhia Saldhi; Z.K.A. Baizal; Ramanti Dharayani
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2079

Abstract

The Sumedang Larang Kingdom is one of the kingdoms in Indonesia which was founded by Prabu Tajimalela in 721 AD. The Sumedang Larang Kingdom is known as the national history of Indonesia. Still, most of the current generation does not know the history of the Sumedang Larang Kingdom, especially the younger generation. Therefore, we developed a question-and-answer system to seek information about the Sumedang Larang Kingdom. With the development of information technology, research on question answering systems is applied to research on Biomedical Questions to produce correct answers. Our system will help literacy about the Sumedang Larang Kingdom for the younger generation, especially students, and increase Indonesian cultural assets. The QA system aims to generate and provide precise short answers to user questions by automatically using information extraction and natural language processing methods. To collect and create questions, we use the concept of ontology. In addition, we use the Natural Language Naïve Bayes method to answer user questions. We built a QA system that can help students find information about the history of the Sumedang Larang Kingdom. Based on the accuracy of the results of testing the method we propose. In our evaluation, we involve the Decision Tree method as the base model. We note that the accuracy of the Naïve Bayes method is higher than that of the Decision Tree. The accuracy result of Naïve Bayes at the ratio of 8:2 and 7:3 is 67%, while the Decision Tree is only 56%.
Perancangan User Interface dan User Experience Adaptive Mobile Learning Untuk Siswa Sekolah Menengah Rakhma Shafrida Kurnia; Bayu Pujiarti
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2085

Abstract

This study aims to design and analyze the user interface (UI) and user experience (UX) of mobile-based learning (m-learning) applications that implement Adaptive learning. The design method is Design Science Research Methodology (DSRM). A poorly designed user interface can lead to a negative user experience, not using the software properly and continuously. On the other hand, UI can make using software easy and enjoyable, so a well-designed UI of learning apps can produce a pleasant learning experience for students. Adaptive learning is expected to be a method used as a means to balance learning needs to make it more efficient and effective to use. In the Evaluation stage, the User Experience Questionnaire (UEQ) is used which is grouped into six parameters, namely: attractiveness, clarity, Efficiency, accuracy, Stimulation , and Novelty. Based on the results of the Evaluation using UEQ, the mean values ​​obtained for all parameters are in the range of positive values ​​of more than 0.8. The user experience aspect in the form of Attractiveness is in the good category, the clarity aspect is in the above average category, the Efficiency aspect is in the good category, the accuracy is in the above average category, the Stimulation aspect is in the good category, and the Novelty aspect is in the above average category. The implementation of Adaptive learning on the UI and UX of the application has been well received by potential users based on the results of the Demonstration and Evaluation stages.
Employee Attrition Prediction Using Feature Selection with Information Gain and Random Forest Classification Sindi Fatika Sari; Kemas Muslim Lhaksmana
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2099

Abstract

Employee attrition is the loss of employees in a company caused by several factors, namely employees resigning, retiring, or other factors. Employee attrition of employees can have a negative impact on a company if it is not handled properly, including decreased productivity. The company also requires more time and effort to recruit and train new employees to fill vacant positions. This attrition prediction aims to help the human resources (HR) department in the company to find out what factors influence the occurrence of employee attrition. This research implements Random Forest while comparing Information Gain, Select K Best, and Recursive Feature Elimination feature selection methods to find which feature selection produces the best performance. The implementation of the aforementioned methods outperforms previous research in terms of accuracy, precision, recall, and f1 scores. In preparing this research, the first author collects data sets, makes programs, and compiles journals. The second author assists the first author in programming and preparing the journal. From the results of the tests that have been carried out, Information Gain produces the highest accuracy value of 89.2%, while Select K Best produces an accuracy value of 87.8% and Recursive Feature Elimination produces an accuracy value of 88.8%.
The The Study of UX on Students’ Perception and Attitude of Using Zoom During Covid-19 Pandemic Using User Centered Design Method Inacio Campos; Mira Kania Sabariah; Danang Junaedi
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2105

Abstract

The pandemic of COVID-19 resulted in the physical shutdown which has now transformed education into an exclusive "online learning" model. Zoom is being used to evaluate the perceived usability as the reference platform. The students find it less collaborative, less interactive, boring, and less collaborative. From this perspective, the Usability of the current online learning platforms is an important factor, particularly because no physical classes are present. The User-Centered Design (UCD) approach was chosen for this study and using the Usability Scale (SUS) method to evaluate the interface. The objective of this study is to analyze user experience, design solutions and evaluate user interfaces that can meet user needs. A pre-survey to evaluate the difficulties of the Zoom application based on user experience, and a post-survey to see if the upgraded design can help the students using the Zoom application for online learning. Then, use the System Usability Scale (SUS) questionnaire approach to measure the system's usability. After the UCD approach was completed, the researcher did a follow-up survey. The results showed that SUS ratings went up to 85.12. As a result, the previously low acceptability ranges have been raised to acceptable. Additionally, the grade scale has been reclassified B. The Zoom program now has more features and is easier to use, and fulfills the students' needs.
Self Organizing Maps (Kohonen) untuk Cluster Bidang Karya Ilmiah (Skripsi) Mahasiswa Berdasarkan Nilai-Nilai Matakuliah Pendukung Machine Learning Hery Sunandar; Yasir Hasan
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2108

Abstract

The time for the preparation of scientific papers is considered too fast and not suitable for students due to several things in the scope of its preparation. One of the reasons is that the student is unable to complete and the supporting aspects of the field of scientific work being done, many students whose topics of scientific work are in machine learning or the like, but these students are unable to complete their scientific work because they do not understand the theoretical supporting knowledge of the machine learning field. So that it can make the student depressed or even harder to repeat his scientific work the next semester. The scope of machine learning courses are statistics and probability, matrix and linear algebra, algorithms and programming, and data structures. This research was conducted to overcome the problems faced by students, namely knowing the suitability group for the field of student scientific work they will be working on. So that in its preparation students can be responsible in their trial and are of higher quality. The clustering test with the self organizing maps (SOM) algorithm is more stable because the input according to the data owned is not random, only the weighting is done randomly but based on the uniform low (mins) and high (max) limit values. The desired number of clusters is two namely cluster 0 is able to do scientific work based on machine learning and cluster 1 vice versa. The SOM process for 40 student data with a target of two clusters and the results are cluster 0 = 14 students, cluster 1 = 26 students. The result is obtained by increasing the radius = 1, which previously this achievement was not successful if radius = 0.
Kinerja Algoritma Yamamoto’s Recursive Code dan Algoritma Fixed Length Binary Encoding pada Kompresi File PDF Yulrismawati Sihura; Taronisokhi Zebua; Hukendik Hutabarat
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2109

Abstract

The use of files in digital form is currently growing and requires media to store them. The more files that store, the more storage space need. This encourages the development of file reduction techniques or data compression techniques with the aim of reducing the required storage space. The compression technique has several algorithms that can be used to compress files such as Yamamoto's recursive code algorithm and Fixed Length Binary Encoding (FLBE). These algorithms have different performance to produce quality compressed results, so they need to be compared. This study describes the analysis and comparison of the performance of the two algorithms based on the exponential method as measured by the quality of the compressed pdf file. The exponential comparison method is one of the methods of the Decision Support System (DSS) to determine the priority order of decision alternatives with multiple criteria. Compression quality parameters that are measured as an alternative comparison are the value of the ratio of compression, compression ratio, space saving. Based on the results of the comparison with Exponential Comparison Method (MPE), it was found that the fixed length binary encoding algorithm has a better performance with a value of 2.55% compared to the Yamomoto's recursive code algorithm with a smaller value of 2.22%.
Implementation of Dimensionality Reduction with SVD to Improve Rating Prediction in Recommender System M. Naufal Mu'afa; Z.K.A. Baizal
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2110

Abstract

Recommender system is widely implemented in various fields. Collaborative Filtering is one of the most used recommender system paradigms because it is easy to use. K-means clustering algorithm is widely use in Collaborative Filtering. This algorithm can predict the item rating that will be given by a user. Rating can be predicted by calculating the average rating of the item. The clustering performance of this algorithm is low because this algorithm selects initial centroid randomly. This causes high errors in the item rating prediction. To obtain lower error, we propose dimensionality reduction with Singular Value Decomposition (SVD). SVD is able to factorize the clustering result data and reduce dimensionality of the data. Dimensionality reduction with SVD can be carried out by removing non-dominant characteristics of the data. This study uses the result of factorization to calculate the similarity between clusters. The value of similarity between clusters is used to predict the rating of an item that will be given by a cluster. The experimental results show that the combined method of K-means and SVD can produces RMSE up to 8.936% lower than the K-means method.
Penerapan Normalisasi Data Dalam Mengelompokkan Data Mahasiswa Dengan Menggunakan Metode K-Means Untuk Menentukan Prioritas Bantuan Uang Kuliah Tunggal Muhammad Rafli Kusnaidi; Timotius Gulo; Soeb Aripin
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2112

Abstract

At Budi Darma University there are obstacles in providing UKT rocks where it is profitable and less targeted for students who get it. This happened because those who deserved this assistance were students who had difficulty in costs, therefore we needed a way by grouping student data based on their social level. In determining the students who deserve to get the rock, they can use the data of students who are undergoing their studies at Budi Darma University. By digging up information based on student data. So that the data can be used first, the data normalization is carried out in order to obtain more accurate data. Where student data can be grouped correctly, data normalization must be carried out. One of the normalization methods that are often used in normalizing data is the decimal scaling method which is a data transformation method with normalization to equalize the range of values ​​on each attribute with a certain scale by moving the decimal value from data in the desired direction After the data is normalized, the next process is to explore student data information by applying data mining. The application of data mining is carried out to obtain information in the form of student data groups that are used as a priority in obtaining UKT assistance. The method used in classifying student data is using the K-Means algorithm. The manual testing method is that there are 3 clusters where the number of clusters 0 cluster 1 and cluster 3 is the same as testing data mining applications, namely rapidminer so that those who deserve to be prioritized get tuition assistance based on the sample, namely cluster / grouping 0 which consists of 22 people. This study aims to see the effect of applying data normalization in the K-Means method to classify student data which is used as a recommendation in the selection of UKT assistance.
Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN Rizky Fauzi Ramadhani; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2117

Abstract

Jakarta is a city in Indonesia that has a high population density that must pay attention to its health condition. Good air quality provides positive benefits to support public health so that they can be more productive at work and create fresh and healthy air. This study uses Machine Learning to classify air based on certain attributes. Then, the development of a prediction model based on time data is designed to produce a predictive map of air pollution in Jakarta area for the next 3 years. The methods applied are Decision Tree and Artificial Neural Networks. As a result, the Decision Tree and Artificial Neural Network models show very good accuracy for predictions from 2024 to 2026. The Decision Tree and Artificial Neural Network models get an accuracy of 98% and 94%. In 2025 the Decision Tree and Artificial Neural Network models get 99% and 93% accuracy. In 2026 the Decision Tree and Artificial Neural Network models get an accuracy of 94% and 93% which can be seen from the Decision Tree model which is superior to the Artificial Neural Network with a difference of 1 - 6%.
Candlestick Patterns Recognition using CNN-LSTM Model to Predict Financial Trading Position in Stock Market Aditya Ramadhan; Irma Palupi; Bambang Ari Wahyudi
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2133

Abstract

Investors need analytical tools to predict the price and to determine trading positions. Candlestick pattern is one of the analytical tools that predict price trends. However, the patterns are difficult to recognize, and some studies show doubts regarding the robustness of the recognizing system. In this study, we tested the predictive ability of candlestick patterns to determine trading positions. We use Gramian Angular Field (GAF) to encode candlestick patterns as images to recognize 3-hour and 5-hour of 6 candlestick patterns with Convolutional Neural Network (CNN), coupled with the Long short-term memory (LSTM) model to predict the close price. The trading position consists of buying and selling position with a hold period of several hours. Our results show CNN successfully detected 3-hour and 5-hour GAF candlestick patterns with an accuracy of 90% and 93%. LSTM can predict the close price trend with 155.458 RMSE scores and 0.9754% MAPE with 10-hour look back. With a hold duration of three hours and CNN-LSTM as an additional model, the test data's 85 candlestick patterns are recognized with 82.7% accuracy, compared to 60% accuracy of profitable trading positions when CNN candlestick pattern recognition is used alone. Compared to employing CNN candlestick pattern identification alone, the CNN-LSTM model combination can improve the prediction power of candlestick patterns and offer more lucrative trading positions.

Page 14 of 45 | Total Record : 443

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