{"title":"Brent Crude Oil Price Forecasting using the Cascade Forward Neural Network","authors":"F. Fauzi, Dewi Ratnasari Wijaya, T. W. Utami","doi":"10.29207/resti.v7i4.5052","DOIUrl":"https://doi.org/10.29207/resti.v7i4.5052","url":null,"abstract":"Crude oil is one of the most traded non-food products or commodities in the world. In Indonesia, crude oil will still be a contributor to the Gross Domestic Product in 2021. Excessive consumption of fuel oil (BBM) in Indonesia has resulted in a scarcity of crude oil, especially diesel. Forecasting the price of Brent crude oil is an important effort to anticipate fluctuations in the price of fuel oil. The Cascade Forward Neural Network (CFNN) method is proposed to forecast fuel prices because of its superiority in fluctuating data types. The data used in this research is the price of Brent crude oil in the period January 2008 to December 2022. The CFNN method will be evaluated using the Mean Absolute Percentage Error (MAPE) to choose the best architectural model. The best Architectural Model is used to predict the next 12 months. After 10 architectural model trials, 2-6-1 became the best model with a MAPE data training value of 6.3473% and MAPE data testing of 9.4689%. Forecasting results for Brent crude oil for the next 12 months tend to experience a downward trend until December 2023.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127904402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Ibrahim, Onkky Alexandera, Ken Dhita Tania, Pacu Putra, Allsela Meiriza, Ken Ditha
{"title":"Assessing User Experience and Usability in the OVO Application: Utilizing the User Experience Questionnaire and System Usability Scale for Evaluation","authors":"Ali Ibrahim, Onkky Alexandera, Ken Dhita Tania, Pacu Putra, Allsela Meiriza, Ken Ditha","doi":"10.29207/resti.v7i4.5137","DOIUrl":"https://doi.org/10.29207/resti.v7i4.5137","url":null,"abstract":"The OVO application, despite having a large user base in Indonesia, has received low ratings compared to other digital wallet apps on the Google Play Store and App Store. Users frequently complain about the user experience, which greatly affects their overall satisfaction. This study evaluates the user experience and usability of the OVO application using the User Experience Questionnaire (UEQ) and System Usability Scale (SUS). The UEQ results show that efficiency is excellent (1.55), while attractiveness, perspicuity, dependability, and stimulation are above average (1.56, 1.67, 1.33, and 1.16, respectively). However, the novelty aspect falls below average (0.64), indicating a need for improvement. The SUS score is 77.53, classifying the app as \"Acceptable\" with a \"C\" grade and an overall \"Good\" rating. Addressing the identified shortcomings can enhance the user experience and usability, ultimately improving user satisfaction. This study contributes valuable empirical data to the field, offering insights for researchers and practitioners in assessing the user experience and usability of mobile applications.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"9 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131919329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alvina Felicia Watratan, Ema Utami, A. D. Hartanto
{"title":"Comparison of Naive Bayes and PSO-Based Naive Bayes Algorithms for Prediction of Covid-19 Patient Recovery Data in Indonesia","authors":"Alvina Felicia Watratan, Ema Utami, A. D. Hartanto","doi":"10.29207/resti.v7i4.4893","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4893","url":null,"abstract":"A brand-new illness known as COVID 19 was identified in 2019 but has yet to infect humans (World Health Organization, 2019). This group of viruses can infect mammals including humans as well as birds and cause sickness. People commonly contract coronaviruses from the flu and other minor respiratory ailments, but they can also spread serious diseases like SARS, MERS, and the deadly COVID-19. So that there are no more casualties, this number must be decreased. It is crucial to understand the variables that can truly reduce the danger of death and gauge the propensity for recovery in Covid-19 patients. Several techniques in data mining can be used to forecast patient recovery rates depending on various characteristics. This study's criteria included gender, age, province, and status. The Naive Bayes (NB) and Pso-based Naive Bayes algorithms are compared in this study using patient datasets to determine whether strategy is more accurate. The findings of this study reveal that using the NB method has a 94.07% accuracy rate, a precision value of 14%, a recall value of 1%, and an AUC value of 0.613, according to the study's data. The accuracy rate of PSO-based Naive Bayes is 95.56%, the precision is 25%, the recall is 1%, and the AUC is 0.540. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115211413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mhd. Adhitiya Okta Riyandi, H. Santoso, Panca O. Hadi Putra
{"title":"The Application of Game Mechanics and Technological Trend in Game-Based Learning: A Review of the Research","authors":"Mhd. Adhitiya Okta Riyandi, H. Santoso, Panca O. Hadi Putra","doi":"10.29207/resti.v7i4.4928","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4928","url":null,"abstract":"The rapid development of information technology affects numerous aspects of human life, including education. An example of IT application in education is game-based learning. Game-based learning has been implemented in various fields or subjects on various platforms. This is due to the potential of game-based learning to enhance the student engagement in the learning process. Nevertheless, the effectiveness of this method is still needs to be studied further. This systematic literature review aimed to explore about game mechanics that applied on current game-based learning researches, accompanied by the trend of technological utilization in research paper published in this domain. This study covered 30 journal and conference proceeding papers published from 2012-2022. The review was conducted using the Kitchenham method. Selected papers were then analyzed to determine the engagement model used in each paper (Feedback Model, Incentive and Achievement Model and Progression Model). Findings included the trend of research in this field (technology applied to each research, online feature, study majors/subject) are displayed based on the time paper were published. The result of the study indicated that all previous research used at least one of the engagement models, with 12 papers using all three models. In terms of technology, it was found that the adoption of web-based technology has been increasing in recent years, including online features which have also increased, along with the study subjects that implemented game-based learning. In summary, game-based learning can be applied in a wide range of subjects and platforms with the support of its feature, making learning more flexible. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130545715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kristiawan Nugroho, E. Winarno, Eri Zuliarso, Sunardi
{"title":"Multi-Accent Speaker Detection Using Normalize Feature MFCC Neural Network Method","authors":"Kristiawan Nugroho, E. Winarno, Eri Zuliarso, Sunardi","doi":"10.29207/resti.v7i4.4652","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4652","url":null,"abstract":"Speaker recognition is a field of research that continues to this day. Various methods have been developed to detect the human voice with greater precision and accuracy. Research on human speech recognition that is quite challenging is accent recognition. Detecting various types of human accents with different accents and ethnicities with high accuracy is a research that is quite difficult to do. According to the results of the research on the data preprocessing stage, feature extraction and the selection of the right classification method play a very important role in determining the accuracy results. This study uses a preprocessing approach with normalizing features combined with MFCC as a method for performing feature extraction and Neural Network (NN) which is a classification method that works based on the workings of the human brain. Research results obtained using the normalize feature with MFCC and Neural Network for multi-accent speaker recognition, the accuracy performance reaches 82.68%, precision is 83% and recall is 82.88%. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"43 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121007487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of Object Mask Detection Using the Convolution Neural Network (CNN)","authors":"Yuhandri, Musli Yanto, Eka Naufaldi Novri","doi":"10.29207/resti.v7i4.5059","DOIUrl":"https://doi.org/10.29207/resti.v7i4.5059","url":null,"abstract":"The spread of Coronavirus Disease (Covid-19) is still a serious problem we are currently facing. The spread occurred very quickly through the process of face-to-face interaction. The process of face-to-face interaction that occurs both in public spaces and closed spaces has a great risk of transmitting the Covid-19 virus. One of the efforts to deal with the spread of the Covid-19 virus is by increasing the use of masks both in public and closed spaces. Based on this, this study aims to develop an Object Detection process in image processing techniques. Object Detection development using the Convolution Neural Network (CNN) method to provide optimal output. The CNN can process the input image which is converted into a pixel matrix and then forwarded to the convolution layer. The research dataset consists of 2000 images of face masks and not masks. The images were obtained from the open sources github.com and kaggle.com. The results of the study present a system capable of detecting masks in real time. CNN provides very good performance with an accuracy rate of 99.05%. With these results, the contribution of this research can be used for the process of monitoring public services for the community to increase the use of masks.","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132366988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Iwan Purnama, Ibnu Rasyid Munthe, K. Khairul, Ronal Watrianthos, Zulkifli
{"title":"Fire Detection System At Labuhanbatu University Based On Internet Of Things (IoT)","authors":"Iwan Purnama, Ibnu Rasyid Munthe, K. Khairul, Ronal Watrianthos, Zulkifli","doi":"10.29207/resti.v7i4.4899","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4899","url":null,"abstract":"Fire accidents are disasters that often occur compared to other fire disasters such as floods, landslides, earthquakes or tsunamis. Fires can occur at any time and no one knows for sure when a fire accident will occur. The impact of a fire disaster is not only material that can disappear human lives. The causative factors of fire disasters often occur due to human negligence and fires often occur in houses where the occupants have left them. Labuhanbatu University at night will be left by the owner and all lecturers and educational staff, only guarded by 2 security people with this condition it is very dangerous when a fire occurs in one of the buildings. The purpose of this research is to focus on making a fire detection system at Labuhanbatu University based on the internet of things to provide early warning on safety. The system uses three sensors namely temperature sensor, gas sensor, and fire sensor. This research is an R&D research using the ADDIE model with the following stages Analysis, Design, Development, Implementation, Evaluation. The results of the fire sensor test were 90% successful, the results of the sensor test as soon as possible were 90% successful and the temperature sensor test results were 90% successful. This fire detection system can minimize or minimize the occurrence of fire accidents and losses because it is based on the internet of things providing early information when a fire occurs to education staff and lecturers at Labuhanabtu University. Overall this fire warning system can function properly. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131136952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Implementation of Enhanced Spray Routing Protocol for VDTN On Surabaya Smart City Scenario","authors":"Agussalim, Agung Brastama Putra","doi":"10.29207/resti.v7i4.4494","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4494","url":null,"abstract":"The application of smart-city, which promises better city management in helping to improve people's quality of life, is still inhibited due to the high cost of infrastructure investment. In several Smart cities, it takes at least $ 30 - 40 billion to convert a conventional town into a smart city, Include for Data collection infrastructure. Alternatively, low-power wide-area networks (LPWAN) could be considered, but it needs more bandwidth to serve data transmission in a smart city. Vehicle Delay Tolerant Network (VDTN) is one part of DTN that employs vehicles as a communication infrastructure that allows communication in challenging conditions and could make it an alternative network for Data Collection in Smart City. This paper proposes a Surabaya Smart City scenario with VDTN as a data collection. The scenario consists of 40 wireless sensors and 50 to 200 vehicles (car and bus) with 5 Road Side Units that forward data from the sensor to the monitoring server. Furthermore, to increase the VDTN performance, we improve our proposed routing protocol, Spray and Hop Distance (SNHD), with two sprays method (Adaptive and Simple) and multiple sources and destinations data collection support. The evaluation was done by simulation-based comparison with an increase in the number of vehicles to determine the impact of vehicle density on data collection performance in terms of delivery probability, Latency Average, and Overhead Ratio. Based on the simulation results, the simple spray method in SNHD and A-SNHD outperformed the well-known VDTN routing protocol, i.e., Epidemic and Spray and Wait. Moreover, when the number of cars is increased from 50 to 200, the performance of VDTN does not increase significantly as network density increases. It means that VDTN only requires a small number of vehicles for use as a low-cost alternative network for smart city. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"37 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132193797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Agis Abhi Rafdhi, E. S. Soegoto, S. Luckyardi, C. N. Albar
{"title":"Tree Algorithm Model on Size Classification Data Mining","authors":"Agis Abhi Rafdhi, E. S. Soegoto, S. Luckyardi, C. N. Albar","doi":"10.29207/resti.v7i4.4572","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4572","url":null,"abstract":"The goal of this research is to use a tree algorithm to categorize student clothing in order to acquire an accurate size. This research is qualitative approach through descriptive analysis, while the analysis employed C.45 Tree algorithm classification. Manual calculations utilizing the tree algorithm formula revealed that the majority of students require XL-sized clothing. On the X5 (Shoulder length) characteristic, the maximum entropy and information gain values were obtained at 0.212642462. According to the forecast, the shoulder length attribute is the first calculation in developing a decision tree scheme since it has the largest entropy and information gain value. Lastly, the findings of this study analysis can be used as a mapping prediction to make decisions on the size of the student group's clothing. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125119110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Requirement Elicitation Modeling Using Knowledge Acquisition in Automated Specification Method","authors":"A. Aminudin, Hafiz Pradana, I. Nuryasin","doi":"10.29207/resti.v7i4.4464","DOIUrl":"https://doi.org/10.29207/resti.v7i4.4464","url":null,"abstract":"Errors often occur during the requirements elicitation stage, causing failure of the software development process as a whole so that the system built cannot be used optimally, this data is obtained from survey data from several large companies involved in technology development. To overcome this problem, this study tries to apply elicitation requirements using the KAOS method in the case study of the SMM Reseller ordering system to obtain system requirements that are in accordance with the goals and objectives of each existing stakeholder. Based on the elicitation of system requirements, functional requirements are generated which include, automatic orders, automatic payments, manage product sales, manage orders, manage payment methods, manage problem orders, manage customer data, manage company information, automatic email notifications, and sales statistics information. The results of this study are a table of functional requirements that have been declared valid and in accordance with the goals and requirements of each stakeholder after evaluating and validating the results for each stakeholder involved. \u0000 ","PeriodicalId":435683,"journal":{"name":"Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129365253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}