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Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : -     EISSN : 25973584     DOI : -
Core Subject : Science,
Computer Science & IT
Seminar Nasional Sistem Informasi dan Teknologi (SISFOTEK) merupakan ajang pertemuan ilmiah, sarana diskusi dan publikasi hasil penelitian maupun penerapan teknologi terkini dari para praktisi, peneliti, akademisi dan umum di bidang sistem informasi dan teknologi dalam artian luas.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 471 Documents
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Bibliometrik Analisis: Brand Awareness Program Studi Diploma 3 Pada Database Scopus Bani Nurhakim; Dadang Sudrajat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This research aims to analyze the factors influencing brand awareness in the Diploma 3 program and to develop effective marketing strategies to enhance that awareness. The background of this study is based on the importance of brand awareness in influencing prospective students' decisions and public perception of the quality and reputation of educational institutions. This research employs survey and interview methods involving students, prospective students, and marketing staff from several higher education institutions in Indonesia. The data obtained were analyzed using statistical methods to identify the main factors affecting brand awareness. The results indicate that digital marketing and social media marketing play a significant role in increasing brand awareness of the Diploma 3 program. Consistent, innovative, and effective marketing strategies through social media have been proven to enhance recognition and appeal of the study program in the eyes of prospective students. This research makes an important contribution to the development of educational marketing strategies and offers new approaches to enhancing brand awareness of the Diploma 3 program. Thus, the results of this study are expected to assist educational institutions in increasing enrollment and retaining students by improving effective brand awareness
Bibliometric Analysis: Machine Learning untuk Blended Learning Agus Bahtiar; Mulyawan
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Blended learning, which combines face-to-face learning methods with digital technology, has grown rapidly thanks to advances in information technology. Along with that, machine learning technology offers great potential to improve personalization and adaptation in blended learning. This research aims to explore the application of machine learning in blended learning systems through bibliometric analysis. By analyzing SCOPUS indexed publications from 2019 to 2024, this study identifies trends, challenges and opportunities in the integration of machine learning with blended learning. The methods used include search keyword definition, initial data collection, refinement of search results, statistical compilation, and data analysis. The main findings show that there is a significant increase in the number of publications on this topic, with the highest peak in 2022. The wide distribution of publications indicates significant international collaboration. Citation analysis indicates that the quality and impact of research is also increasing, with recent publications gaining more citations. This research highlights the importance of applying machine learning in blended learning to improve educational effectiveness and support the development of more adaptive learning methods. The findings provide valuable insights for academics and practitioners to encourage further innovation and improve the quality of education in the digital era.
Bibliometrik Analysis: Signal Preprocessing Techniques for Kualitas Sinyal Electrogram Odi Nurdiawan; Dadang Sudrajat; Fathurrohman; Ade Rizki Rinaldi
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study explores electroencephalogram (EEG) signal preprocessing techniques used in the early detection and diagnosis of epilepsy, aiming to enhance the quality and reliability of data used in clinical applications. Effective signal preprocessing techniques are crucial for minimizing artifacts and noise, which can obscure critical information in EEG signals. More accurate EEG signal processing allows for the identification of abnormal patterns associated with various neurological conditions, such as epilepsy, which heavily relies on this signal analysis for precise diagnosis. This study conducted a bibliometric analysis using a descriptive approach to identify research trends, geographic distribution, institutional contributions, and key authors in this field. Data was collected from the Scopus database using the keywords "electroencephalogram AND signal AND processing AND epilepsy". The analysis results show a significant increase in the number of publications related to EEG signal preprocessing techniques over the past five years, with major contributions from countries like China, India, and the United States, reflecting the high global interest and focus on this topic. Additionally, deep learning and machine learning techniques emerged as the most dominant methods in this research, indicating future trends in the development of increasingly sophisticated EEG signal processing technologies. The findings also suggest that using techniques such as artificial neural networks, convolutional neural networks (CNN), and deep learning can enhance the accuracy of epilepsy diagnosis and prediction, making a significant contribution to modern clinical practice. Moreover, this study emphasizes the importance of developing and integrating more advanced preprocessing techniques to improve the effectiveness of EEG signal detection and classification, which is expected to enhance diagnostic outcomes and patient management with neurological disorders. This study provides valuable contributions to the development of medical diagnostic technologies, particularly for neurological disorders such as epilepsy, and highlights the need for further research to optimize these techniques for broader clinical application.
Analisis Internet Network Performance Menggunakan Parameter Quality of Service Pada Jaringan STMIK IKMI Cirebon Martanto; Dian Ade Kurnia; Fathurrohman; Irfan Ali; Yudhistira Arie Wijaya
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of the internet for employees is a crucial need to support the completion of their work. STMIK IKMI Cirebon also provides internet facilities for its employees. However, the internet facilities provided are not yet optimal, as evidenced by frequent connection disruptions. This study aims to measure the performance of the internet connection at STMIK IKMI Cirebon. The method used is Quality of Service (QoS), which is a method to assess how well the installed network functions and its ability to define the attributes of the network services provided. QoS is necessary to calculate the parameters that determine the quality of an internet network. The steps in this study include recording network traffic using Wireshark, followed by calculating parameters such as bandwidth, packet loss, delay, throughput, and jitter. The study results indicate that during data upload from 08:00 to 12:05 at STMIK IKMI Cirebon, the throughput percentage achieved was 31% with an index of 1 "POOR," delay was 9.444 ms with an index of 4 "VERY GOOD," jitter was 8.444 ms with an index of 3 "GOOD," and packet loss was 0% with an index of 4 "VERY GOOD." During data download from 15:00 to 19:05, the throughput percentage achieved was 132% with an index of 4 "VERY GOOD," delay was 14.052 ms with an index of 4 "VERY GOOD," jitter was 13.052 ms with an index of 3 "GOOD," and packet loss received an index of 4 "VERY GOOD." Based on these results, it can be concluded that these values have met the TIPHON standard for both upload and download, indicating that the internet connection at STMIK IKMI Cirebon is still suitable for use
Bibliometrik Analysis: Kontruksi Sosial Masyarakat Mengenai Teknologi AI Pada Data Base Scoupus 2014-2024 Nisa Dienwati Nuris; Khaerul Anam; Dadang Sudrajat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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This research investigates the social construction of ChatGPT technology in society by identifying and analyzing the factors that influence its adoption and utilization. Through this analysis, we aim to identify recent research trends, gaps, and future research opportunities. The study utilizes data from various international scientific journals indexed by Scopus to explore the application of social construction of technology techniques and their societal impact. The method used in this research is bibliometric analysis to uncover patterns in the study of the social construction of ChatGPT technology in society. The results show that user perceptions of ChatGPT are influenced by digital readiness, technological literacy, as well as perceptions of benefits and risks. Additionally, ChatGPT is closely related to the development of critical skills among students, supporting the enhancement of analytical and critical abilities. The research focus in the field of AI, particularly concerning social and economic impacts, is expanding. This study emphasizes the importance of AI in various aspects of life and its contribution to sustainable development, especially in higher education, where AI technology integration is involved. Educational institutions are encouraged to design policies to support learning and skill development through AI. This research has limitations, particularly in terms of sample size and methodology, which can be addressed in future studies by expanding the scope and methods of the research. Overall, this study enriches the understanding of the impact of AI technology, particularly ChatGPT, in higher education and provides a foundation for further research.
Bibliometrik Analysis: Optimasi Regresi Linear untuk Estimasi Big Data pada Database Scopus Tahun 2013-2024 Ahmad Faqih; Riri Narasati
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This bibliometric analysis investigates the optimization of linear regression for big data estimation, focusing on publication trends, citation metrics, geographic distribution, and research innovations from 2013 to 2024. The publication trend analysis reveals a significant increase in research on linear regression optimization, peaking in 2023, followed by a decline in 2024. Citation analysis shows that although this topic is relatively new, it has gained increasing scientific recognition, indicating its growing relevance. The geographic distribution highlights China, the United States, and the United Kingdom as the leading contributors to research on linear regression optimization for big data. Key innovations in this field include the application of quantum algorithms and advanced optimization techniques, which have significantly improved computational efficiency and the accuracy of linear regression models in handling large and complex datasets. These findings underscore that linear regression optimization will continue to evolve and make important contributions to big data analytics.
Bibliometrik Analysis: Pembelajaran Speaking Session Menggunakan Instagram Pada Data Base Scopus 2014-2024 Riri Narasati; Ahmad Faqih; Dadang Sudrajat
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

In today's digital age, social media plays an important role in various aspects of life, including education. This research explores the use of Instagram as a learning tool to improve English speaking skills. Through bibliometric analysis, this study identifies previous research trends and evaluates the effectiveness of Instagram-based learning methods. It also reviews existing literature to understand how Instagram has been used in language learning contexts, as well as identifying existing research gaps. The results show that the use of Instagram in English language learning can increase student motivation and participation. Interaction through video, story and dialog-based content on Instagram proved effective in improving speaking skills. The study also found that the use of project-based tasks on Instagram can help students in developing their confidence and speaking ability. In addition, this study highlights the importance of structured guidance and feedback in maximizing the benefits of learning through Instagram. This research makes a novel contribution to the literature by offering a more in-depth approach to the use of Instagram in English language learning. By exploring effective learning strategies and their impact on students' speaking skills, this study explores the role of Instagram in English language learning.
Analisa Pengaruh Jumlah Penerima dan Penyaluran Pinjaman melalui Finansial Teknologi (fintech) terhadap Pertumbuhan Ekonomi Masyarakat melalui Regresi Linear Sri Farida Utami; Willy Prihartono; Mohamad Alif Dzikry
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

This study looks into the relationship between the number of loans received and the amount funnelled by the technology financial platform and the economic growth of a population. Fintech. The method used in this study is linear regression. In recent years, fintech has grown to be a significant component of the financial system, particularly in emerging nations like Indonesia where traditional financial services are still unavailable. The study begins with the hypothesis that fintech can improve financial inclusion; theoretically, this will raise economic growth by improving the distribution of financial resources and the ease with which credit can be obtained. Important variables that were examined in the study were the total number of borrowers, the distribution of loans overall, and measures of economic growth. The outcomes of the linear regression demonstrated a strong positive association between the quantity of borrowing, the availability of fintech loans, and the population's economic expansion. The report emphasizes the significance of rules that enable healthy and inclusive fintech growth and offers pertinent policy implications for decision-makers and players in the fintech industry. The study's finding supports the claim that, by facilitating better access to credit and a more fair distribution of credit, fintech may significantly contribute to economic growth. According to the report, in order to maximize fintech's beneficial effects on the economy, policies that foster its growth are necessary.
Implementasi Algoritma Fisher Yates Pada Media Pembelajaran Aksara Lontara Berbasis Augmented reality Mirfan; Muhammad Yaumi; Erwin Akib
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The teaching and learning process carried out using the lecture method without the support of learning media, will result in students getting bored so that students do not have the enthusiasm to learn. Meanwhile, the impact when the questions are not randomized in the learning media causes monotonous learning so that students will face the same sequence of questions every time. they learn, causing boredom and reducing interest and involvement in the learning process. Increased Memorization Without Understanding: Users may only memorize the sequence and answers to questions without truly understanding the material, reducing the effectiveness of learning and in-depth understanding of concepts. This research aims to Design Literacy Learning Media Lontara Based on Augmented Reality and Implementing the Fisher-Yates Algorithm Lontara Script Learning Media Based on Android Augmented Reality. The research design used is Unified Modeling Language (UML) which is designed in a structured manner consisting of use case diagram model designs, activity diagrams, sequence diagrams and class diagrams, software applications using Unity, Vuforia using the Fisher-Yates Algorithm. Results of the research This is the Fisher-Yates algorithm ensuring the order of the questions is always random every time it is run. This makes the user's learning experience more dynamic and interesting. This algorithm is very effective in creating variations in the presentation of questions
Kombinasi Algoritma KNN, HSV dan LBP Pada Pengolahan Citra Digital untuk Membedakan Kematangan Pisang Mirfan; Sudriawan; Ulfa Laela R; Mila Jumarlis
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of carbide in ripening bananas can result in chemical contamination of bananas. This can have a negative impact on the health of consumers who consume these bananas, so this research aims to design a system to differentiate between naturally ripe bananas and carbonated ripe bananas with Digital Images using the Hue Saturation Value (HSV), Local Binary Patterns (LBP) and K-Nearest Neighbor (K-NN). In this research, the system development used is UML (Unified Modeling Language). Meanwhile, making software in this system uses PHP, HTML, CSS, Java script software and for the database uses MySql. This research collects data obtained through observation, interviews and literature study. The method used to create this system is Hue Saturation Value (HSV) to extract color features, Local Binary Patterns (LBP) to extract texture features and the K-Nearest Neighbor (K) algorithm. -NN) for classification of plantain types. The digital image classification system distinguishes naturally ripe or carbitant plantains and can display classification results well so that it can help the public in distinguishing naturally ripened bananas or carbitants. The system created has been able to implement the Hue Saturation Value (HSV), Local Binary Patterns (LBP), and K-Nearest Neighbor (KNN) methods well and the system can differentiate naturally ripe bananas and carbonates well with a level of accuracy for the k= value 3, namely 100%, k=5 96.67%, k=7 93.33% and k=9 with an accuracy of 96.67% from 30 testing data using 200 training data.

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