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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 394 Documents
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EFFECTIVITY IMPROVEMENT OF HYBRID PROJECT MANAGEMENT WATER-SCRUM-FALL WITH SIX SIGMA IMPLEMENTATION Aria Setyoko; Teguh Rahardjo; Nur Fitriani Anita
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5539

Abstract

In software development projects there is continuous development aimed at increasing the efficiency and effectiveness of the team in providing software quality and customer satisfaction. The problem is that many projects are planned using a waterfall approach by clients, therefore some tools are needed to balance this situation. This research aims to evaluate the effectiveness of hybrid methodologies in software development by uncovering the use of a combination of Waterfall and Agile Scrum methodologies with the application of Six Sigma. This hybrid methodology was deemed suitable for combining the plan- and contract-based characteristics of Waterfall with the flexibility and rapid iteration of Agile Scrum. The use of Six Sigma is used to focus on change, assist in systematically identifying and correcting process problems, and process improvement. In research methods, sample teams run different methodologies on similar software projects. Hybrid project management is carried out by applying the Waterfall approach in planning and contracts. At the same time, each phase in Waterfall is iterated using Agile Scrum to ensure flexibility and adaptability. The research results found that this hybrid method can increase team efficiency, reduce development cycle time, detect higher defects in each sprint, increase the final quality of the software, and finally increase the Sigma Index with a team comparison of σ=3.22 and σ= 3.11 and higher compared to teams that only use Agile-Scrum. In conclusion, the integration of Waterfall, Agile Scrum, and Six Sigma can be an effective strategy to face the challenges of modern software development.
LEARNING AUTOMATA-BASED AODV ROUTING PROTOCOL TO IMPROVE V2V COMMUNICATION IN URBAN TRAFFIC SIMULATION Ade Syahputra; Ketut Bayu Yogha Bintoro; Erssa Istary Yusuf; Michael Marchenko
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5551

Abstract

Network congestion, packet loss, and high latency in the AODV routing protocol are significant obstacles to achieving reliable vehicle-to-vehicle (V2V) communication. Consequently, an update to the AODV protocol is necessary. This research proposes the Learning Automata-based AODV (LA-AODV) routing protocol to address these issues. The LA-AODV protocol incorporates learning automata into the routing protocol by considering speed, acceleration, and x and y coordinates. The communication quality index with the nearest vehicles is measured before selecting a set of relay nodes until the maximum estimated time is reached. The primary objective of this study is to enhance the performance of V2V communications by reducing network congestion, packet loss, and latency. The results demonstrate that LA-AODV achieves a maximum packet delivery ratio (PDR) improvement of 4.0% and a throughput of up to 56.50 kbps, surpassing the performance of both AODV and DSDV protocols. These findings indicate the potential of LA-AODV to optimize V2V communications, thereby significantly improving transportation safety and efficiency. The research contributes to the field by providing a novel solution to enhance V2V communication quality in urban traffic scenarios, offering significant benefits in reduced latency, increased reliability, and overall better network performance.
COMBINATION OF LOGARITHMIC PERCENTAGE CHANGE AND GREY RELATIONAL ANALYSIS FOR BEST ADMINISTRATION STAFF SELECTION Sumanto Sumanto; Mochamad Wahyudi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5564

Abstract

The best administrative staff are individuals who are able to maintain the smooth operation of the organization with high efficiency and precision. One of the main problems is subjectivity in assessment that can cause dissatisfaction among employees. Sometimes, assessments are based more on personal relationships than objective performance, thus creating a sense of unfairness. The purpose of this study, using a combination of LOPCOW and GRA in determining the best administrative staff to develop a holistic and data-driven evaluation approach for the optimal administrative staff selection process. This process involves a comprehensive assessment based on various criteria, including work efficiency, accuracy, multitasking ability, and excellence in communication and problem solving. LOPCOW provides a strong objective basis by considering significant changes in performance data through logarithmic percentage changes, while GRA helps in identifying and understanding the relationship of similarities and differences between alternatives based on given criteria. By integrating these two methods, organizations can combine the advantages of LOPCOW's objectivity with the power of GRA's relational comparison analysis, resulting in a more comprehensive and accurate performance evaluation. The results of the ranking of the selection of the best administrative staff show that the first best administrative staff was obtained by Staff Name AH with a GRG value of 0.1666, the second best administrative staff was obtained by Staff Name RW with a GRG value of 0.1569, the third best administrative staff was obtained by Staff Name ES with a GRG value of 0.1266.
IMPLEMENTING RETRIEVAL-AUGMENTED GENERATION AND VECTOR DATABASES FOR CHATBOTS IN PUBLIC SERVICES AGENCIES CONTEXT Ibnu Pujiono; Irfan Murtadho Agtyaputra; Yova Ruldeviyani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5572

Abstract

Rapid developments in information technology, such as chatbots and generative artificial intelligence, have drastically lowered the cost of providing services to the society. This study aims to measure performance of developed chatbot using retrieval augmented generation and vector database. This research compares the performance of existing Large Language Modelling (LLM) in answering questions related to regulations concerning public service agencies.. Using a vector database, questions are assessed and answered by the LLM model, considering cosine similarity scores. The best-performing model, gpt-4, is selected for the deployment process which have average cosine similarity score 0,404. The use of LLM for chatbot creation at the prototyping stage can provide a good response to the question asked related to public service agencies with retrieval augmented generation (RAG) process through regulation-based document extraction.
PERFORMANCE OF THE YOLOV5 ALGORITHM TO DETECT HUMANS IN THE REAR EXCAVATOR AREA Hanna Naili Syifa'; Anan Nugroho
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 1 (2024): JITK Issue August 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i1.5576

Abstract

Work involving excavators carries a high risk of accidents that can result in fatalities, making Occupational Safety and Health (OSH) critically important. Most excavator accidents are caused by blind spots at the rear, where the operator's limited field of view increases the risk of hitting nearby objects or workers. Despite safety features such as reverse alarms and rear cameras, these technologies only display real-time video without automatically detecting workers, thus still posing a significant risk. This study aims to develop a human detection system for the rear area of excavators using the YOLOv5 algorithm based on image processing. The system's main features include real-time human detection, distance estimation, and audible warnings if a human is detected within a high-risk distance. The system was tested using three video recordings depicting human objects behind the excavator in different scenarios. Despite the limited number of video samples, the human objects provided sufficient complexity to evaluate the system's effectiveness. The test results showed an average accuracy of 80.5% and an F1-score of 87.78%. These findings indicate that the YOLOv5-based detection system performs well in various video conditions and shows potential effectiveness in real operational situations. Consequently, this system is expected to reduce the risk of work accidents with excavators caused by rear blind spots and improve on-site worker safety. This research contributes to the field of occupational safety by integrating image processing algorithms into the development of heavy equipment safety technology, thereby enhancing worker protection.
AUTOMATION OF THE BERT AND RESNET50 MODEL INFERENCE CONFIGURATION ANALYSIS PROCESS Medi Noviana; Sunny Arief Sudiro
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5053

Abstract

Inference is the process of using models to make predictions on new data, performance is measured based on throughput, latency, GPU memory usage, and GPU power usage. The models used are BERT and ResNet50. The right configuration can be used to maximise inference. Configuration analysis needs to be done to find out which configuration is right for model inference. The main challenge in the analysis process lies in its inherent time-intensive nature and inherent complexity, making it a task that is not simple. The analysis needs to be made easier by building an automation programme. The automation programme analyses the BERT model inference configuration by dividing 10 configurations namely bert-large_config_0 to bert-large_config_9, the result is that the right configuration is bert-large_config_2 resulting in a throughput of 12.8 infer/sec with a latency of 618 ms. While the ResNet50 model is divided into 5 configurations, namely resnet50_config_0 to resnet50_config_4, the result is that the right configuration is resnet50_config_1 which produces a throughput of 120.6 infer/sec with a latency of 60.9 ms. The automation programme has the benefit of facilitating the process of analysing the inference configuration.
DESIGNING 'KIDDOCARE' APPLICATION FOR PEDIATRIC NURSING PRACTICE WITH USER CENTERED DESIGN (UCD) Rifa Yanti; Dasril Aldo; Nursaka Putra; Dading Qolbu Adi; Miftahul Ilmi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5129

Abstract

This research aims to develop KiddoCare, a mobile-based pediatric nursing application designed using the User Centered Design (UCD) method. The methodology used in this study included interviews, questionnaire distribution, and documentation with the aim of filling the gap of tools and applications that specifically meet the unique needs in pediatric care. Interviews with health workers showed that one of the biggest challenges was the lack of efficient communication between nurses and parents, as well as difficult access to pediatric health information. Therefore, this app is designed to improve communication between nurses and pediatric patients and facilitate access to important health information. The UCD process includes understanding the user context, determining user needs, designing and producing solutions, and evaluating those needs. From the initial survey of 74 respondents, 50.56% agreed and 46.02% strongly agreed with the need for easier access to pediatric health information. A total of 74 respondents evaluated the app and gave positive feedback; 47.01% 'agreed', 41.17% 'strongly agreed' with the functionality of the app, and 11.82% 'moderately'. No respondents expressed 'disagree' or 'strongly disagree'. In conclusion, KiddoCare was rated as an effective and appropriate pediatric nursing tool, supporting flexible care that is adaptive to each child's individual needs.
SOCIAL MEDIA COMMENTS FOR GOVERNMENT INSTITUTION VIDEO CLASSIFICATION USING MACHINE LEARNING M. Faris Al Hakim; Subhan Subhan; Prasetyo Listiaji
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5187

Abstract

YouTube is a social media site that is quite familiar and is used as a means of disseminating video-based information. With a fairly high number of users, YouTube can become a communication medium for audiences, including government agencies. The user’s responses in comments reflect the nuance of the presented video. This research aims to determine the best algorithm for classifying video types based on user comments. Several machine learning algorithms used to carry out classification are Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression. K-Fold Cross Validation was chosen as a method to evaluate the performance of classification algorithms based on the accuracy values. of these algorithms in classifying YouTube videos based on comments. The first experiment with the highest ratio of training and test data for each algorithm was obtained at a ratio of 90:10, with respectively 78.99%, 86.21%, 84.01%, 72.72%, and 79.31%. In the second experiment with k-fold cross validation using a ratio of 90:10, the highest accuracy for each algorithm was obtained at a value of k = 10, which was respectively 74.39%, 81.34%, 78.05%, 85.21%, and 72.15%. From these results, it can be concluded that the most suitable algorithm for classifying YouTube videos based on comments is the Random Forest algorithm with a training and test data ratio of 90:10 and SVM with 10-cross-fold validation. These results show that a larger portion of data for learning has a positive impact on algorithm performance.
ARCHITECTURE OF SMART TOURISM APPLICATION: A DEVELOPING COUNTRIES’ PERSPECTIVE A CASE STUDY IN INDONESIA Ruci Meiyanti; Yuwan Jumaryadi; Riri Fajriah; Bagus Priambodo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5381

Abstract

Beautiful, comfortable, safe, and affordable tourist attractions are every tourist's dream. Meanwhile, tourism in developing countries, the authenticity and uniqueness of nature and culture are the main attractions for tourists. The lack of accurate information that can accommodate tourist locations, local culture, unique tourism, transportation, and accommodation of a tourist attraction for developing countries is an obstacle to the success of tourist visits. The role of technology and society can help the concept of smart tourism governance for developing countries. Therefore, a framework model is needed that can explore the advantages of tourism in developing countries. The success of Smart Tourism cannot be separated from the development of the application architecture that is used as the basis for developing the Smart Tourism system application. So, the purpose of this study is to create an informative, accurate, safe, and easy smart tourism application architecture. This study uses a qualitative method, namely with literature studies and in-depth interviews were also conducted with tourism and informatics experts. The Mc Farlan Matrix used in the Ward and Peppard method and the TOGAF framework are used in the formation of smart tourism applications. The results of this study are The results of this study are in the form of an application architecture that focuses on stakeholder interests in the form of Smart Tourism Service Application Architecture for various Stakeholders which is an integration of smart tourism organization, smart destination, smart service, smart decision, smart share, smart experience, smart recommendation.
UNDERSTANDING THE CONTINUANCE OF ELECTRONIC PAYMENTS USAGE AFTER COVID-19: A SURVEY IN INDONESIA Maulyta Noer Fadilla; Nori Wilantika; Arfive Gandhi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5492

Abstract

During the ongoing pandemic with elevated COVID-19 cases, efforts to minimize direct physical contact for virus prevention have been heightened. Consequently, there has been a strong emphasis on adopting non-cash transactions, particularly electronic payments. As the Indonesian government revoked the social restriction policy on December 30, 2022, people gradually resumed normal activities such as work, school, and shopping. The question arises whether the widespread adoption of electronic payments will persist after COVID-19. To understand this and the factors influencing the sustained use of electronic payments, this study utilized the UTAUT, Trust, and Perceived Security as the research model. The findings indicate that all 920 survey participants maintain their electronic payment usage after COVID-19. Through PLS-SEM analysis, key factors contributing to the sustained use of electronic payment after COVID-19 include the intention to use electronic payments, user trust, performance expectations, facilitating conditions, and perceived security. Additional variables proposed in this research, user trust and perceived security, are proven to have an influence on users' intentions to continue using electronic payments.

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