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Siti Maesaroh
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siti.maesaroh@mercubuana.ac.id
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collabits-fasilkom@mercubuana.ac.id
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Journal Collabits
Published by Universitas Mercu Buana
ISSN : 30628601     EISSN : 30466709     DOI : http://dx.doi.org/10.22441/collabits
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal Collabits adalah jurnal yang membahas strategi keamanan cyber untuk meningkatkan kinerja dan keandalan dalam implementasi teknologi kecerdasan buatan (AI), kecerdasan bisnis (BI), dan sains data, yang di kelola oleh Fakultas Ilmu Komputer (FASILKOM) terdiri dari dua prodi yaitu Teknik Informatika (TI dan Prodi Sistem Informasi (SI). Dengan pertumbuhan pesat dalam penggunaan teknologi ini, keamanan cyber menjadi semakin penting dalam menjaga integritas, kerahasiaan, dan ketersediaan data. Tulisan ini mengeksplorasi berbagai pendekatan, alat, dan praktik terbaik dalam mengamankan sistem AI, BI, dan sains data, termasuk deteksi ancaman, enkripsi data, manajemen akses, dan pemulihan bencana. Jurnal ini juga menganalisis dampak kebijakan keamanan cyber pada inovasi teknologi dan memberikan rekomendasi untuk meningkatkan keamanan dalam ekosistem digital yang terus berkembang
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 49 Documents
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CONSTRUCTION AND MANAGEMENT OF A COSMOS-BASED OPERATING SYSTEM USING VISUAL STUDIO DEVELOPMENT AND VMWARE VIRTUALIZATION TECHNOLOGY Yusuf, Mohamad; Fahrezi, Zidane; Hidayah, Rafif Syari; Istanto, Yudha Andika; Saputra, Gilas Adi
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25559

Abstract

Operating systems play an important role in bridging hardware and software on various computing devices. This research focuses on building an operating system based on Cosmos, an open source project that allows the creation of operating system kernels quickly and efficiently. In the process, we leverage Visual Studio development tools to develop and maintain the kernel, while VMware virtualization technology is used to test and manage development. This research contributes to further understanding of the development of Cosmos-based operating systems with optimal use of Visual Studio development tools and VMware virtualization technology
Implementation of The Naive Bayes Algorithm on Online Game Addiction and Its Impact on Students Akbar, Shafrizal Fadillah; Angwarmasse, Thanel Richard
Journal Collabits Vol 1, No 3 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i3.27301

Abstract

This research aims to implement the Naive Bayes algorithm in analyzing online game addiction and its impact on individuals. Online gaming addiction has become a global phenomenon with significant psychological, social, and academic implications. For this reason, an effective analytical tool is needed to identify the factors that contribute to this addiction and its impact. The Naive Bayes algorithm was chosen because of its ability to carry out classification based on probability, which is very suitable for handling complex and diverse data. This research collects data from questionnaires that cover demographic aspects, frequency of play, duration of play, and perceived impact. The analysis results show that the Naive Bayes algorithm has quite high accuracy in classifying individuals who are addicted to online games. In addition, this study identified several key factors that are closely related to addiction, such as age, gender, and motivation to play. The most prominent impacts of this addiction include decreased academic performance, disturbed sleep patterns, and problems with social relationships. With the implementation of the Naive Bayes algorithm, it is hoped that it can contribute to prevention and early intervention efforts against online game addiction. This research also opens up opportunities for further development in the use of other machine-learning techniques for digital behavior analysis.
Optimization of Education through Artificial Intelligence: Exploration of Types of AI and Their Contribution in Education Wibowo, Aditya Dwi; Mudhohi, Ahmad Faqih; Wulandari, Dapita Apriani; Milah, Eli Agus; Vinanti, Caterina
Journal Collabits Vol 1, No 2 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i2.27233

Abstract

Artificial Intelligence (AI) is an increasingly developing technology that plays a crucial role in various fields, including the education system. AI can enhance the efficiency and effectiveness of the education system through various methods such as adaptive learning, predictive analysis, intelligent tutoring systems, natural language processing, and gamification. Artificial Intelligence (AI) is expected to continue to evolve. As AI has great potential to optimize and improve many aspects of human life, including education, health, business, technology, and more. Therefore, this idea is adopted by researchers to further optimize AI capabilities in the world of education and contribute to the efficiency and effectiveness of the education world. This research discusses the role of artificial intelligence in improving the efficiency and effectiveness of the education system. A literature review technique is used to collect and analyze information on this topic. The research shows that artificial intelligence can increase the efficiency and effectiveness of the education system by accelerating and facilitating the learning process, providing personalized recommendations, predicting student behavior, and improving data management.
Measuring Quality of Service (QoS) on Ki Hajar Dewantoro High School Internet Networks Maesaroh, Siti; Hakim, Lukman; Erliyani, Ita; Hidayat, Khairul
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25564

Abstract

At SMA Ki Hajar Dewantoro Tangerang, the recurring issues with the internet network are attributed to several problems, including Throughput, Packet loss, Delay, and Jitter. By employing the Quality of Service (QoS) method, the values of these parameters can be measured to assess the performance of the existing internet services. The measurement of parameter values revealed the highest throughput during peak hours for ISP 1, with an average value of 1813 kbps falling into the 'Good' category. Conversely, the lowest throughput was observed during non-peak hours for ISP 2, with an average value of 300 kbps categorized as 'Poor.' The highest packet loss occurred during non-peak hours for ISP 2, with an average value of 1%, classified as 'Very Good.' On the other hand, the lowest packet loss was recorded during peak hours for ISP 1, with an average of 0.036%, categorized as 'Very Good.' Regarding delay, the highest average delay was noted during non-peak hours for ISP 2, with a value of 21.5ms falling into the 'Very Good' category. Conversely, the lowest delay was observed during peak hours for ISP 1, with an average value of 4.25ms categorized as 'Very Good.' For jitter, the highest average jitter occurred during non-peak hours for ISP 2, with a value of 21.5ms in the 'Good' category. In contrast, the lowest jitter was recorded during peak hours for ISP 1, with an average value of 4.25ms categorized as 'Good.'
Web-based Application Mockup Design for Student Activity Unit Registration at Mercu Buana University Andrean, Rifky; Soemantri, Syauqi Adli; Salamah, Umniy; Arrachman, Abdul Kholiq
Journal Collabits Vol 2, No 1 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i1.31238

Abstract

The development of the Student Activity Unit (UKM) registration application aims to streamline the registration process for students interested in joining extracurricular activities at educational institutions. This application offers a digital solution to replace the manual registration process, improving efficiency, data accuracy, and user experience.The design and implementation of the application follow a systematic approach, including needs analysis, system design, coding, testing, and deployment. The application features a user-friendly interface, secure data management, and real-time notifications, ensuring a seamless experience for both students and administrators.By integrating cloud-based technology, the application ensures scalability and accessibility, allowing users to register anytime and anywhere. Administrators can manage registrations, track member data, and generate reports more effectively. The study results show that the application significantly reduces administrative workload and enhances the engagement of students in UKM activities.This project highlights the potential of digital solutions in improving operational processes and fostering student participation in extracurricular activities. Future developments may include expanding features such as online payment integration and analytics for tracking member participation trends.
Development E-Archive Web System for District Office Letters Management Alfiah, Fifit; Setiadi, Ade; Amalia, Andiena
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25401

Abstract

Every company, business entity, or agency, both private and government, requires archiving activities because they are very necessary, given the importance of the role of archives. Therefore, to carry out archiving tasks properly, there must be improvements and optimal improvements so that they can function properly and achieve the goals that have been set. set. The availability of complete and accurate data and information will become a fundamental requirement in any organization, private or government. Understanding the significance of filing in terms of assisting leaders in making decisions or dealing with problems at the Larangan District Office in Tangerang City. Especially in the general section, where filing and processing document activities are still done traditionally, work takes a long time. The process of selecting data and reporting is difficult to manage because the data is distributed in multiple locations and various forms. Observation, interviews, and a literature review were used following the PIECES method. After analyzing, the authors propose a website-based Electronic Archive system for archiving incoming and outgoing mail, which also serves as a data storage area. As a result, the existence of a new system can reduce errors during data processing, and make the search process faster, and the reports generated follow the existing data
Comparative Analysis of Performance Between KNN and C5.0 Algorithms in Lung Cancer Disease Detection Maesaroh, Siti; Fatcha, Ibka Anhar; Ramadhan, Fikri
Journal Collabits Vol 1, No 3 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i3.27285

Abstract

Lung cancer is a disease characterized by the growth of abnormal cells in the lungs that can spread to other parts of the body. In practice, medical teams will usually evaluate a patient's symptoms conventionally, which is highly inefficient and time-consuming, especially if there are a large number of patients. This manual evaluation process can cause delays in diagnosis and treatment, and increase the risk of errors. Therefore, this research will discuss lung cancer detection using the K-Nearest Neighbors (KNN) algorithm and the C5.0 algorithm in order to solve the problems previously described. The use of the K-Nearest Neighbors (KNN) algorithm and the C5.0 algorithm was chosen because these two algorithms have the ability to process complex data and produce accurate models. The results of this study will show a comparison of which performance is much better used to accurately detect lung cancer based on the amount of training data available, and it can be known that the lung cancer detection process can be done more quickly and efficiently, using the K-Nearest Neighbors (KNN) or C5.0 algorithm to improve diagnosis accuracy. The results show that the KNN algorithm is superior to the C5.0 algorithm specifically for lung cancer detection.
Linear Regression Algorithm in Pulse Purchase System Simple Using Python Afiyati, Afiyati; Ayu, Kurnia Gusti; Roza, Yuni; Sakhrassalam, Haytsam; Syafiq, Nur Muhammad Zihni
Journal Collabits Vol 1, No 2 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i2.27256

Abstract

In today's digital era, the online credit purchase system has become an integral part of everyday life. The use of linear regression algorithms in this context is becoming increasingly relevant, as it provides a powerful approach to analyzing and predicting pulse buying patterns. This research proposes a simple pulse purchase system that implements a linear regression algorithm, using the Python programming language. The purpose of this study is to develop a predictive model that can estimate the amount of credit to be purchased based on certain variables, such as the time of purchase, the number of previous transactions, and the value of prior purchases. By analyzing historical transaction data, the system can take into account possible purchase patterns and estimate future credit needs with an adequate level of accuracy. The implementation of linear regression algorithms in Python allows users to easily access and use this pulse purchase system. Through a simple but intuitive interface, users can enter their transaction parameters and the system will predict the required number of pulses. Experiments were conducted to test the performance of this system in producing accurate predictions. The results of the experiment show that this system can provide estimates close to the real value, with a high degree of accuracy. This indicates that the use of linear regression algorithms in pulse purchase systems has great potential to improve efficiency and reliability in online transactions. In addition, the implementation of this algorithm also has a positive impact on transaction security. By analyzing purchasing patterns, the system can detect anomalies or suspicious activities that may occur, thereby increasing the level of security in the process of buying credit online. Overall, this study shows that the use of linear regression algorithms in pulse purchase systems has significant benefits in improving the efficiency, reliability, and security of online transactions. The practical implementation of this algorithm in the Python programming language opens the door for further development in the analysis and optimization of future pulse purchase system.
Design of Production Results Reporting Management System Case Study UD Konveksi Tangerang Sari, Novita; Warsito, Bayu Ronggo; Aryansah, Muhammad Okky; Al Farez, Muhammad Seftyan Pikri
Journal Collabits Vol 2, No 1 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i1.32609

Abstract

UD. Konveksi Tangerang is a company that operates in the field of ready-to-wear production. Every day there are many requests from customers to produce clothes, jackets, alma maters and t-shirts. The data collection system for incoming orders, production results and current production results reports at UD Konveksi Tangerang is still carried out using conventional methods, namely using paper documents and Microsoft Excel. The current system still has shortcomings, including lack of monitoring of the production process so that the production results are not completed on time, errors occur when inputting the copy of SO and the amount of stock produced, resulting in product discrepancies, making reports takes a long time because the PPC admin has to recapitulate production data from each section one by one. This research produces a production results reporting system which functions to help the production head to record production results reports every day. The research methods used in this research are the analysis method using PIECES, the waterfall development method, creating programs using the PHP programming language and MySQL database, system testing using Blackbox Testing.
Transfer Learning Implementation on Bi-LSTM with Optimizer to Improve Non-Ferrous Metal Prices Prediction Pratomo, Adji; Jatmika, Muhammad O.; Kerim, Bedine; Budiarto, Rahmat
Journal Collabits Vol 1, No 1 (2024)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v1i1.25468

Abstract

Over the past few years, the implementation of renewable energy or go-green has intensified along with the rapid development of its technology and increasing uncertainty of natural conditions that cause the prices of non-ferrous metals such as copper, aluminum, nickel, etc. used as main components for developing renewable energy devices, e.g.: battery, experience instability price in the commodity futures market. Economic players who trade metals in the futures market certainly need to be careful and must evaluate the state of the world economy. This study proposes a prediction engine as a combination of Bidirectional Long-Short Term Memory (BiLSTM), with three optimization algorithms, i.e.: Adam, Root Mean Squared Propagation (RMSProp), and Stochastic Gradient Descent (SGD), and transfer learning to make model training better. Experiments on four historical data on nickel, lead, aluminum and copper prices in the commodity futures market are conducted. The selected features are: open price, close price and volume price. Twelve models will be created to find the model that best predicts the metal prices. The top 3 models with the best performance were selected, they are: model 4 RMSProp with R2 value of 0,99029 and MSE 0,00076 as the first ranking, model 3 Adam with R2 value of 0,98877 and MSE 0,00074 as the second ranking, and model 4 Adam with value of R2 0,98522 and MSE 0,00115 as the third ranking.

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