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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 40 Documents
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Search results for , issue "Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi" : 40 Documents clear
Implementation of MeCUE 2.0 Method in Evaluating MyTelkomsel User Experiencel Dela Arum, Hestiana; Ibrahim, Ali
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This research aims to evaluate the user experience of MyTelkomsel application using the Modular Evaluation of Key Components of User Experience (MeCUE) 2.0 method. The growth of telecommunications technology and increased smartphone usage highlights the importance of apps that efficiently meet user needs. Although MyTelkomsel offers a wide range of services, many users have reported difficulties using certain features and dissatisfaction with the service, as seen in reviews on platforms like the Play Store, indicating challenges with user experience. This research focuses on identifying where the app meets user needs and where improvements are necessary. The findings show that while the app performs well in terms of usability and usefulness (scoring 6.31 and 6.25, respectively), it scores lower in commitment (4.64) and intention to use (4.74). These results suggest that while the app is functionally effective, improvements are still needed to enhance user loyalty and engagement.
Deep Learning-based Gold Price Prediction: A Novel Approach using Time Series Analysis Zangana, Hewa Majeed; Obeyd, Salah Ramadan
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4651

Abstract

This paper presents a deep learning-based system for predicting gold prices using historical data. The system leverages Long Short-Term Memory (LSTM), a specialized recurrent neural network architecture, to capture temporal dependencies and patterns in the time series data of gold prices. A comprehensive dataset of historical gold prices is used, and the model is trained on a sequence of past data points to predict future prices. The data is preprocessed using normalization techniques to improve the performance of the model. Experimental results demonstrate the effectiveness of the proposed model in providing accurate price predictions, offering potential utility in financial forecasting and decision-making processes. The system's performance is evaluated through visualization and statistical metrics, illustrating its capacity to track gold price trends and predict future market movements. This work contributes to the growing field of time series forecasting by applying deep learning techniques to financial markets.
Recognizing Cow Muzzle Patterns using the Convolution Neural Network (CNN) Algorithm Zamroni, Sulthon; Wiriasto, Giri Wahyu; Kanata, Bulkis
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4598

Abstract

In today's digital era, any task or problem can be solved with minimal effort, especially in livestock identification such as cattle. Numerous systems and algorithms have been developed to recognize cattle, ranging from body shape, fur patterns, to specific parts of the cattle. This research aims to develop a cattle muzzle identification system using convolutional neural networks method with Alexnet architecture and to identify the factors that can decrease the accuracy of prediction results. The results of this research can help cattle farmers manage their livestock data more effectively, as traditional identification methods can cause discomfort and stress to the cattle. This research also serves as a reference for future researchers in developing cattle recognition research. Additionally, this research can be used to support insurance programs such as Cattle Farming Insurance (AUTS) to protect farmers from losses due to cattle theft and death. Cattle recognition through their muzzles using the CNN method can produce relatively high results. By slightly modifying the AlexNet architecture, this system can recognize cattle with an accuracy of 85%..
Analysis of Factors Influencing Viu Application User Satisfaction using the End User Computing Satisfaction (EUCS) and DeLone & McLean Sabrina, Dea Fitri; Indah, Dwi Rosa; Firdaus, Mgs Afriyan; Gumay, Naretha Kawadha Pasema
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4765

Abstract

One area of telecommunications technology that is also undergoing development in the context of entertainment is that of video streaming. Viu is a video streaming service that provides a range of premium content free of charge. Nevertheless, the Viu application continues to elicit a range of grievances, with the application’s rating remaining relatively low. It is of great importance to consider user satisfaction with an application. This research employs two methods, namely End User Computing Satisfaction (EUCS) and Delone & McLean, to ascertain the factors influencing user satisfaction with the Viu application. A sample of 244 respondents was obtained for the purposes of this research. The data obtained was analysed using the PLS-SEM technique with the Smart-PLS 4 tool. The final results obtained were that the variables of content, ease of use, timeliness, system quality, and finally information quality have a significant effect on user satisfaction.
Mapping of Flood and Landslide Prone Areas using Composite Mapping Analysis Method Based on Geographic Information System in East Aceh Maulita, Maya; Nurdin, Nurdin; Taufiq, Taufiq
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4483

Abstract

Disaster is an event that causes great losses to the community. Disasters are destructive, very detrimental, and require a long time to recover. To overcome the impact of natural disasters on the community in East Aceh Regency, research is needed related to the mapping system for multi-disaster prone areas (floods and landslides) in East Aceh Regency. The application used for the mapping process is ArcGIS Desktop and the research methodology used for mapping is Composite Mapping Analysis which consists of the process of determining the class of each parameter, determining the weight of each parameter by combining each parameter. The method of combining them consists of a scoring process for each parameter, then overlaying the parameters used, calculating and producing relative weights or spatial means, and combining spatial means to produce a value from the weight of each parameter for floods and landslides. The results of the study showed that the percentage of area for the class very prone to flood disasters was 232,156.13 Ha (42.3%), the vulnerable class had an area of 228,634.01 Ha (41.7%), and the non-vulnerable class had an area of 87,687.40 Ha (16%). The percentage of area for the class that is very vulnerable to landslides is 49,998.13 Ha (9.5%), the vulnerable class has an area of 301,863.93 Ha (57.2%), and the non-vulnerable class has an area of 175,542.56 Ha (33.3%). The contribution of this research is to provide information on disaster-prone areas, causal factors, characteristics of vulnerability to natural disasters such as floods and landslides and provide a basis for more effective decision-making in disaster mitigation and management efforts. This approach offers a new contribution to the technology of mapping and classifying disaster-prone areas.
Enhanced Image Security through 4D Hyperchaotic System and Hybrid Key Techniques Farandi, Muhammad Naufal Erza; Winarno, Sri; Fauzyah, Zahrah Asri Nur
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4675

Abstract

This study develops a digital image encryption method using a 4D hyperchaotic system combined with a hybrid key to maximize data security. By generating a random and uniform pixel distribution, the method makes decryption significantly harder for unauthorized access. Evaluations are conducted through histogram analysis, robustness tests, NPCR, UACI, and information entropy. The findings reveal that the method effectively breaks pixel correlation, rendering the encrypted image unrecognizable. Histogram analysis confirms a uniform pixel distribution, while robustness tests show the system can maintain image quality despite manipulations or attacks. NPCR and UACI tests highlight the method’s high sensitivity to even minor changes in the original image, further enhancing security. Information entropy demonstrates a higher level of randomness compared to other encryption techniques. This 4D hyperchaotic and hybrid key-based approach holds considerable promise for applications requiring highly secure image transmission and storage, ensuring reliable data protection in sensitive environments.
Digital Transformation and Performance Optimization at XYZ Higher Education through TOGAF-Based Enterprise Architecture Febriyani, Widia; Samsudin, Samsudin
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4630

Abstract

Information technology (IT) has become an indispensable component of the corporate landscape in the digital age, reshaping how organizations operate. The rapid development of IT has revolutionized traditional business methods, transforming them into more efficient, technology-driven operations. Companies and institutions worldwide increasingly focus on digitalization to integrate business processes, streamline workflows, and enhance productivity. XYZ University in Indonesia, as an educational institution, is not immune to this shift. To stay competitive and efficient, the university aims to implement fundamental principles of coordination and integration within its internal structure and with external partners. A central goal of XYZ University’s digital transformation efforts is to streamline its business processes and bolster its cybersecurity framework. These initiatives are critical to the university’s broader strategy of developing a robust Enterprise Architecture (EA). This research focuses on the Academic Directorate and addresses three fundamental domains: business processes, data and information management, and application systems. By concentrating on these areas, the research aims to develop an EA blueprint to provide a comprehensive framework for enhancing the university’s digital capabilities. This blueprint is intended to serve as a strategic solution for integrating information systems, ensuring smooth data flow across departments, and improving the overall efficiency of business processes within the institution. It will help XYZ University achieve its digital transformation goals in the long term, leading to better coordination, more robust security, and higher operational efficiency.
Assessing User Experience of ChatGPT Website Employing the User Experience Questionnaire (UEQ) Putri, Dila Okta Dwi; Lestarini, Dinda
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4566

Abstract

One form of information technology advancement is the use of AI in website development, one of which is ChatGPT website. ChatGPT has a poor user experience in various aspects, so it is necessary to evaluate the user experience on the ChatGPT website using User Experience Questionnaire (UEQ). The attractiveness variable obtained a positive evaluation value of 1.503. The perspicuity variable obtained a positive evaluation value of 1.661. The efficiency variable obtained a positive evaluation value of 1.615. The dependability variable obtained a positive evaluation value with an overall average value of 1.286. The stimulation variable obtained a positive evaluation value of 1.182. The novelty variable obtained a positive evaluation value of 0.942. The ChatGPT website has shown good quality because it has a positive evaluation value from user assessments. However, in the attractiveness, dependability, and novelty variables, there are still several items that get neutral ratings. So product improvements are still needed to increase user satisfaction.
Classification of Beef, Goat, and Pork using GLCM Texture-Based Backpropagation Neural Network Saraswati, Irma; Fahrizal, Rian; Fauzan, Anugrah Nuur; Yudono, Muchtar Ali Setyo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4715

Abstract

Identifying different types of meat is crucial for preventing fraudulent activities and improving food safety. This research aims to create a classification system for various meat types (beef, goat, and pork) using the Gray Level Co-occurrence Matrix (GLCM) for extracting texture features, followed by classification through a Backpropagation Neural Network (BPNN). The methodology utilizes 60 images of beef, goat, and pork, achieving a remarkable accuracy of 100% in the training phase, which highlights the model's capability to effectively recognize patterns. However, when tested with new data, the system exhibits a sensitivity of 90% and a specificity of 95%, with some misclassifications occurring between goat and beef due to their similar textures. The findings of this study suggest that GLCM is an effective tool for deriving relevant statistical parameters necessary for classification. This research makes a significant contribution to developing a meat identification system that safeguards consumers and promotes awareness of food safety issues. The results are anticipated to provide a solid foundation for advancing meat type recognition and practical applications in the marketplace, ultimately boosting public trust in the meat products they purchase.
Utilizing Google Trends Data to Examine the Impact of Unemployment Rates on Indonesia's Gross Domestic Product Jane, Giani Jovita; Hasabi, Rafif; Purnatadya, Sinatrya Dwi; Kartiasih, Fitri
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.3603

Abstract

Abstract Data related to the economy have varying frequencies and have delays in publication time. Such as data on the Open Unemployment Rate (OUR) with a semi-annual frequency and Gross Domestic Product at Constant Prices (riil GDP) according to expenditure with a quarterly frequency. So, frequency conversion is required to conduct simple regression modelling using these data. On the other hand, big data such as Google Trends is an additional predictor to estimate OUR and GDP data to overcome delays in publication time. Then the estimated data is modelled to investigate the effect of OUR on GDP. Data conversion uses the Chow-Lin method, while estimation with Google Trends data uses robust regression. The study shows that the estimation results using Google Trends as an additional predictor provide more accurate results than without Google Trends data for OUR and GDP data. Based on the robust regression results, it can be concluded that the OUR has a negative and significant effect on GDP. The findings provide valuable insights for supporting sustainable economic policy and further research on economic analysis.

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