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CCIT (Creative Communication and Innovative Technology) Journal
Published by UNIVERSITAS RAHARJA
ISSN : 19788282     EISSN : 26554275     DOI : 10.33050/ccit
Core Subject : Science,
Computer Science & IT
CCIT (Creative Communication and Innovative Technology) Journal adalah jurnal ilmiah yang diterbitkan olehSekolah Tinggi Manajemen Informatika dan Komputer Raharja. CCIT terbit dua kali dalam satu tahun, Setiap Bulan Februari dan Agustus.
Arjuna Subject : -
Articles 420 Documents
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File Upload Security: Essential Practices for Programmers Anrie Suryaningrat; Desi Ramayanti; Glen Maxi Taberima; Panji Pratama Kurniawan
CCIT (Creative Communication and Innovative Technology) Journal Vol 17 No 2 (2024): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v17i2.3172

Abstract

File upload processes, while convenient, introduce data security risks that must be addressed. Potential malware infections, sensitive information leaks, and data corruption necessitate the implementation of effective strategies and solutions. Data integrity refers to the accuracy, consistency, and reliability of stored and processed data. Human error, system failures, and cyberattacks can compromise data integrity, necessitating effective preventive and mitigation measures. Data security and file upload integrity are critical aspects of modern web applications. Given the escalating cyber threat landscape, implementing best practices to safeguard user data and ensure the authenticity of uploaded files is paramount. This paper delves into the implementation of best practices for protecting user data and ensuring file upload authenticity. The study proposes the implementation of best practices for secure file upload mechanisms in web applications, including the innovative application of hash-salting techniques. By adhering to the guidelines presented, developers can bolster the defenses of their web applications against a wide range of cyber threats, enhance user trust, and protect sensitive data. This approach also serves as an effective solution for improving efficiency, security, and compliance in digital records management.
User Satisfaction of Artificial Intelligence Air Quality Detection: UTAUT2 Approach Untung Rahardja; Putri Marewa Oganda; Sri Watini; Nesti Anggraini Santoso
CCIT (Creative Communication and Innovative Technology) Journal Vol 17 No 2 (2024): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v17i2.3236

Abstract

The Artificial Intelligence (AI)-based air quality detection application is a technology that can assist the public in monitoring the air conditions around them. This application provides information on pollution levels, health implications, and recommendations for appropriate actions based on air quality. However, the usage of this application is still constrained by various factors that influence user satisfaction. This study aims to examine the impact of elements within the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model on user satisfaction with AI-based air quality detection applications. The UTAUT2 model comprises 9 constructs. This research employs an online survey method with a sample of 150 respondents who have used AI-based air quality detection applications. Data were analyzed using the PLS-SEM (Partial Least Square Structural Equation Modeling) technique using SmartPLS4. The research findings indicate that only Performance Expectancy and Behavioral Intention significantly influence usage intention and behavior of the application. These findings highlight the critical role of user intention and performance expectations in determining usage behavior and user satisfaction. The practical implications and theoretical of this study, including recommendations for application developers and future researchers, are further discussed in this research.
Conceptual Design of an Integrated Smart Home System with PV Solar Power for Traditional Minahasa Wooden House Tulangi, Gideon F.; Rumokoy, Stieven Netanel; Atmaja, I Gede Para; Pangemanan, Daisy D. G.; Dodie, Stanley B.; Langie, Maureen B.
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3263

Abstract

The traditional Minahasa wooden house is an integral part of Indonesia's cultural heritage. However, in this modern era, the challenge of preserving cultural authenticity while introducing it to the technological era is becoming increasingly urgent. This research aims to design a Smart Home System concept integrated with Photovoltaic (PV) Solar Power Plant for traditional Minahasa wooden houses. The objective of this study is to identify the potential for integrating smart home technology and PV Solar Power Plant in Minahasa wooden houses and to develop a concept that combines traditional elements with modern technology. The research methodology will involve a literature review to understand the concepts of smart homes, PV Solar Power Plant, and the characteristics of traditional Minahasa wooden houses. Additionally, descriptive research through interviews will be conducted in collaboration with industry partners to obtain data and examples of traditional Minahasa wooden houses to be used as benchmarks in the concept design. This research can contribute to integrating modern technology into Indonesia's cultural traditions while enhancing the competitiveness of Minahasa wooden house products in the market. The result of this research is a conceptual design for the implementation of a Smart Home System integrated with a PV rooftop solar power plant system in Minahasa wooden houses. The utilization of solar energy as electrical energy is predicted to achieve an annual energy yield of 16,775 kWh.
Comparative Analysis of Time Series Methods LSTM and ARIMA for Predicting Inventory Availability (Case Study: PT XYZ) Kartawijaya, Edi; Munawar, Munawar; Firmansyah, Gerry; Tjahjono, Budi
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3443

Abstract

Product availability plays a crucial role in supply chain management, directly impacting all aspects of business operations, from production to distribution. This study analyzes the optimization of product availability at PT. XYZ, a frozen and chilled food trading company in Indonesia, focusing on four main commodities: beef, buffalo meat, chicken, and potatoes. Utilizing historical transaction data from 2020 to July 27, 2024, this research compares the performance of two forecasting models: ARIMA (AutoRegressive Integrated Moving Average) and Long Short-Term Memory (LSTM), in predicting product availability The traditional ARIMA model has proven effective in time series data analysis but has limitations in capturing complex patterns and non-linear fluctuations. LSTM, as a machine learning technique, demonstrates superiority in capturing long-term temporal relationships. This study finds that the LSTM model consistently outperforms ARIMA for beef, buffalo meat, and chicken categories, although there is a slight increase in error for the potatoes category. Model performance evaluation is conducted using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results indicate that the LSTM model exhibits lower errors compared to ARIMA, proving its effectiveness in predicting dynamic demand patterns. With a better understanding of product availability, the company is expected to reduce operational costs, avoid losses, and enhance customer satisfaction through more efficient supply chain management. This research provides significant insights for PT. XYZ and similar industries in implementing more accurate forecasting methodologies
Business Process Analysis in the Financial System of PT. Oti Eya Abadi With Business Process Modelling and Notation (BPMN) Method Hanama, Ikhsan Wahyudin; Pratama, Septiano Anggun; Joefrie, Yuri Yudhaswana; Lapatta, Nouval Trezandy
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 2 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3487

Abstract

Every company certainly needs an information system for ease of work management in a company organization. As in the problems faced by the company PT. Oti Eya Abadi who needs an information system that can create and print company cash as efficiently as possible so that leaders and employees who manage the company's financial cash do not make manual formats in excel anymore and obtain cash data formats automatically from the information system. In order to achieve a sequential but still efficient system flow in the use of information systems, an analysis is made related to the business process flow of the information system with a description in the form of Business Process Modelling and Notation (BPMN). By describing BPMN, the creation of an information system can be made more directed in each access feature so that when the information system is completed it can be used with access that is easier for users to understand and more efficient in using an information system, including the financial information system of PT. Oti Eya Abadi.
Sentiment Analysis of Twitter Data on the 2024 Indonesian Presidential Election Using BERT Roihan, Ahmad; Atmojo, Tito Tri; Wardoyo, Rizky A; Saputra, Muhamad Stabil Tanwin
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3210

Abstract

Social media platforms, particularly Twitter, are frequently employed by individuals to articulate their opinions on various subjects in textual form. The proliferation of viewpoints from diverse sources can influence public perceptions on these topics. The greater the popularity of a topic, the more abundant the opinions generated. Currently, the most widely discussed topic is the 2024 Indonesian presidential election. Sentiment analysis, or opinion mining, is an academic discipline that examines sentiments towards a given entity, while text mining involves the extraction of information through processing, classifying, and analyzing extensive datasets. This study will utilize data crawling techniques to gather data from Twitter which will subsequently undergo preprocessing and cleaning. Following this, the cleaned data will be classified by sentiment (positive, negative, or neutral) using a pre-trained language model (BERT) and Natural Language Toolkit (NLTK). The classified data will then be visualized with tools such as Matplotlib and Wordcloud to elucidate the data distribution.
Automating Internet Distribution with Script-Driven Provisioning and Load Balancing Methods Albadri, Aldhi; Nasution, Mahyuddin K. M.; Sutarman, Sutarman
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3321

Abstract

The utilization of software-based automation technology in the internet network distribution process is currently relatively expensive, while conventional configuration methods cause inefficient use of time, cost, and energy. The time spent is about 5 minutes for each configuration process. The waiting time for a queue of 5 customers with 1 technician is 20 minutes. This problem can be solved by applying the concept of network automation using the Zero Touch Provisioning method, which can increase time efficiency to 5 seconds for each configuration process. Additionally, the use of Priority and Round-Robin algorithms is very helpful in overcoming queue management problems, allowing the server to work according to the desired process logic. The results showed an average wait time of 7.6 seconds with a quantum value of 10. This value was obtained in the process of 5 customer queues with 1 server.
Comparison of Data Mining for Classifying Student Graduation Levels Using Naive Bayes, Decision Tree, and Random Forest Methods (Case Study of The Undergraduate Program at Mitra Indonesia University) Destanto, Tri; Nugroho, Handoyo Widi
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3409

Abstract

This study aims to apply data mining techniques to classify student graduation rates in the Undergraduate Program at Mitra Indonesia University. The methods used in this study include Naive Bayes, Decision Tree, and Random Forest. The data used includes student academic data, such as grades, attendance, and other demographic information. The research steps include data collection, data cleaning, data analysis, and the application of data mining algorithms. The results of the study show that the Random Forest method provides the highest accuracy compared to Naive Bayes and Decision Tree in predicting student graduation rates. The Random Forest method achieved an accuracy of 85%, while the Decision Tree achieved 80%, and Naive Bayes achieved 75%. These findings are expected to help Mitra Indonesia University identify students at risk of not graduating on time, so appropriate interventions can be provided to improve graduation rates
Implementation of Data Mining for Classifying Student Graduation Levels Using Naive Bayes, Decision Tree, Random Forest, Support Vector Machines and Neural Networks Methods (Case Study of The Undergraduate Program at Mitra Indonesia University) Hartanto, M. Budi; Destanto, Tri; Yuniarthe, Yodhi; Winarko, Triyugo
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3441

Abstract

This study aims to classify student graduation levels using five data mining methods: Naive Bayes, Decision Tree, Random Forest, Support Vector Machines, and Neural Networks. Conducted as a case study at Mitra Indonesia University, the research utilizes academic data, including GPA, course completion rates, and attendance records, to predict graduation success. The results reveal that Random Forest and Neural Networks exhibit the highest accuracy, making them the most suitable methods for predicting student outcomes. These findings contribute to the development of early intervention programs for students at risk of delayed graduation, providing valuable insights for higher education institutions.
Analysis of CSR Program Against Regional Inequality in Bogor Regency Using K-Means and Random Forest Algorithms Rizkiyanto, Muhamad Ardiansyah; Sabirin, Sahril; Wibowo, Arief
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3518

Abstract

Bogor Regency is vast and has significant economic and social potential. Collaboration between businesses and local governments is essential to achieve regional development goals. Corporate Social Responsibility (CSR) plays a role in sustainable economic, enhancing the quality of life for the community. CSR can be implemented independently by companies or supported by the CSR Support Group (TF-TJSL). The Bogor Regency is divided into three development areas, the Western, Central and Eastern, with regional inequality reflected in a Williamson index of 0.731. CSR has the potential to reduce these inequalities through positive contributions. This study analyzes CSR programs on regional inequality in Bogor Regency using data mining technology with K-Means and Random Forest algorithms. The K-Means algorithm shows the optimal result with the best silhouette score at K=2 with a score of 0.76268, reflecting a clear separation between clusters representing regional inequality. The Random Forest algorithm shows excellent classification ability with an accuracy of 0.985 and other evaluations of precision, recall, and f1-score are almost perfect, which indicates its effectiveness in classifying data into three clusters according to development areas. The regression model evaluation results are also good, with a very low MSE (0.003961), indicating minimal prediction error.

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