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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.
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Articles 10 Documents
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Search results for , issue "Vol 18 No 2 (2025): CCIT JOURNAL" : 10 Documents clear
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.
Safety Analysis: Rooftop PV System Training Equipment Rumokoy, Stieven Netanel; Warokka, Adriyan; Atmaja, I Gede Para; Gumilar, Lang-lang; Monika, Dezetty
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.v18i1.3538

Abstract

The implementation of PV Rooftop Power Plant systems as an alternative energy source has become increasingly widespread. The growing use of these systems is accompanied by the need for skilled workers with competencies in their installation. To support the development of such competencies, various training equipment designs have been created to enhance workers' skills in rooftop PV installation. This article aims to evaluate Occupational Health and Safety (OHS) aspects in the designed rooftop PV training equipment. The methodology used in this research includes risk analysis through hazard identification, risk assessment, and mitigation recommendations. Data collection was conducted through a literature review related to OHS standards, direct observation of the equipment's use, and interviews with instructors and technicians involved. The research results show that the main risks include Falling Hazards, Electrical Hazards, Mechanical Hazards, Tripping or Snagging Hazards, and Ergonomic Hazards. Risk control measures were implemented by ensuring that the trainees understood work-related risks, using Personal Protective Equipment (PPE), and monitoring the condition of the work area and equipment.
Information System Audit for Tangerang Live Application Using Cobit 5 Domain EDM dan APO Framework Dwi, Rosmawati; Afrijaldi, Rizki; Darmawan, Aden Andre; Alamsyah, Muhammad Luki; Auliya, Nur
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.v18i2.3224

Abstract

The Central Government has the ambition to make Indonesia the fourth-largest in the world's economic power through the vision of Indonesia Emas 2045. To achieve this goal, there is a need for the improvement of public services and rapid restructuring of population administration. Within this framework, Tangerang Live, introduced by the Tangerang City Government on August 17, 2016, is designed to provide online services in the fields of population, education, and healthcare. The success of public services is measured as an indicator of Good Governance, with more than 500 thousand users currently, and downloads show an increase influenced by the quality of services and initiatives from the Tangerang City Communication and Informatics Agency. This research aims to evaluate the implementation of IT governance in Tangerang Live, with data collection using Literature Review and Questionnaire through Gform with 24 respondents. This research method refers to the COBIT 5 model, focusing on two COBIT 5 domains, namely EDM and APO, consisting of five subdomains: EDM02, EDM03, APO04, APO05, and APO11. The results of the information system audit on Tangerang Live show an average Maturity Score of 3.77, with details of EDM02 3.74, EDM03 3.75, APO04 3.94, APO05 3.61, and APO11 3.82. In conclusion, the scores generated are at level 4 (managed), indicating that the overall implementation of information technology is quite good, although there are some recommendations for improvement, especially in the APO05 subdomain related to user data management and the efficiency of data usage. These improvements are necessary to enhance the quality of services for Tangerang Live users.
Design of a Portable Power Plant Using Solar and Wind Energy with Hybrid Charge Control Method Vrathama, Brenanda Kristian; Silalahi, Lukman Medriavin
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.v18i2.3450

Abstract

New Renewable Energy is an environmentally friendly solution for electricity generation, particularly through the utilization of wind and solar energy. In Indonesia, which has a tropical climate, both energy sources can reduce dependence on fossil fuels and carbon emissions. This research aims to design a portable power plant that integrates solar and wind energy using the Hybrid Charge Control method.[1] Testing was conducted at two different locations: Pantai Indah Kapuk 2 (PIK2) and VI Rusun Ujung Menteng. The results showed that in PIK2, the solar power plant reached a peak power of 3.459 W at 1:00 PM, while the wind source produced a peak power of 0.66 W at 5:00 PM. At the Rusun Ujung Menteng location, the highest recorded power from the solar panel was 3.048 W at 2:00 PM, and the wind source reached 0.82 W at 5:30 PM. The use of a DC motor in this system increased the voltage to 4 Volts but also presented issues requiring attention in torque management and cable material selection. The Charge Control method was applied to equalize the battery voltage at a level of 12.4 V. The researchers demonstrated the significant potential of portable renewable energy-based power plants for sustainable energy needs in Indonesia.
Comparison of Naive Bayes, Decision Trees and SVM Algorithms for Sentiment Classification of JMO Applications Nasrulloh, Anas; Yusuf, Muhamad; Mas’ud, Ibnu; Toifur, Tubagus; Ikhwanudin, Aolia; Syamhalim, Agianto
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.v18i2.3510

Abstract

In this study, the researchers found that SVM achieved a precision of 0.75 for negative sentiment and 0.93 for positive sentiment, with recalls of 0.86 and 0.94, and f1-scores of 0.80 and 0.94, and an overall accuracy of 0.88. Naive Bayes showed similar results with a precision of 0.74 for negative and 0.93 for positive, recalls of 0.87 and 0.94, f1-scores of 0.80 and 0.94, and an accuracy of 0.88. Meanwhile, Decision Tree had the lowest precision for negative (0.71) and positive (0.91) sentiment, with recalls of 0.73 and 0.93, f1-scores of 0.72 and 0.92, and an accuracy of 0.85. These findings suggest that SVM and Naive Bayes offer excellent performance in sentiment classification, while Decision Tree, while still effective, performed slightly lower. These results provide valuable guidance in selecting the right algorithm for sentiment analysis on app data. This study compares the effectiveness of three machine learning algorithms—Naive Bayes, Decision Trees, and Support Vector Machine (SVM)—in sentiment classification of JMO apps using review data taken from Google Play Store via web scraping and processed with a Python application. The evaluation is done based on precision, recall, f1-score, and accuracy metrics.
Implementation of Lean UX to Improve the Quality of User Experience (Case Study: PT. XYZ) Yusuf, Muhamad; Nasrulloh, Anas; Ikhwanudin, Aolia; Toifur, Tubagus; Ramadhan, Aditya Duta; Mas’ud, Ibnu
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.v18i2.3530

Abstract

The importance of websites in the modern digital world encourages various companies to develop effective user interfaces (UI) and user experiences (UX). This study aims to design the UI/UX design of PT. XYZ's website using the Lean UX method, which focuses on active collaboration with users in developing a Minimum Viable Product (MVP). The Lean UX method involves four main stages: Declare Assumptions, Create MVP, Run Experiments, and Feedback and Research. Testing was carried out using the System Usability Scale (SUS) to measure the level of usability. The results of the study showed that the new UI/UX design significantly improved efficiency and user satisfaction, with a SUS value of 80, which is included in the "Excellent" category. This study makes a significant contribution to website development in the digital sector, especially in designing user-friendly interfaces that are centered on user needs.
Optimization of Student Grade Data Management Using RESTful API and Microservices Architecture : Case Study at Universitas Mitra Indonesia hartanto, M. Budi; Fawaati, Teuku Muhammad; Fahurian, Fatimah; Yunita, Hilda Dwi; Zuhri, Khozainuz
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.v18i2.3603

Abstract

The management of student grade data is a critical component of academic information systems that require high efficiency and reliability. At Universitas Mitra Indonesia, the old monolithic system faced challenges in scalability, security, and accessibility. This study proposes the implementation of RESTful API and microservices architecture to optimize the management of student grade data. RESTful API serves as the primary interface for inter-service communication, while microservices allow for independent management of modules such as user authentication, grade data processing, and academic service provision. Implementation results demonstrate a 35% increase in data access speed, a 25% reduction in server load, and enhanced security through token-based authentication. This study significantly contributes to modernizing academic information systems, especially in improving the performance and scalability of digital academic services.
CNN Algorithm for Herbal Leaf Classification Using MobileNetV2 and ResNet50V2 Pagiu, Harry T.; Kasim, Anita Ahmad; Lapatta, Nouval Trezandy; Pratama, Septiano Anggun; Laila, Rahma
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.v18i2.3776

Abstract

Indonesia is home to over 30,000 types of herbal plants, with approximately 1,200 species utilized as raw materials for alternative and traditional medicine. Leaves play a crucial role in herbal medicine preparation. However, many people struggle to identify different herbal leaves due to their similar appearances, making classification difficult. Each leaf possesses unique characteristics such as shape, size, midrib, stalk, blade, and type, which can be used for differentiation. To assist in identifying herbal leaves, a classification system based on image recognition is essential. Convolutional Neural Networks (CNN) are deep learning algorithms designed for processing two-dimensional image data. Model performance can be enhanced through transfer learning, with MobileNetV2 and ResNet50V2 being widely used architectures. These pretrained models have been trained to recognize images with high accuracy. This study focuses on classifying herbal plants based on leaf shape using CNN architectures from MobileNetV2 and ResNet50V2. The evaluation results show that the MobileNetV2 architecture, with a 90%:10% data split, achieved an accuracy of 98.51%, precision of 98.92%, recall of 98.51%, and an F1-score of 98.56%. These findings indicate that CNN with transfer learning can effectively classify herbal leaves with high accuracy.
Implementation of the K-Nearest Neighbor Algorithm for Classifying Immigration Residence Permit Applicants at the Class I Special Immigration Office TPI Soekarno-Hatta Azizah, Nur; Henderi, Henderi; Raja, Berisno Hendro Pardamean Manik
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.v18i2.3577

Abstract

This study aims to apply the K-Nearest Neighbor (KNN) algorithm in classifying immigration residence permit applicants at the Class I Special Immigration Office TPI Soekarno-Hatta, focusing on the algorithm's effectiveness and accuracy in categorizing residence permit applicants based on the types of residence permits: Visit Stay Permit (ITK), Limited Stay Permit (ITAS), and Permanent Stay Permit (ITAP). This study employs a quantitative, experiment-based approach utilizing a dataset of 17,212 residence permit applicant records consisting of 11 key attributes, such as nationality, visa type, residence permit type, gender, and age group.The research process began with data preprocessing stages, including data cleaning, normalization, and dataset splitting into training and testing sets with 80:20 and 70:30 partitioning scenarios. The KNN algorithm was implemented using a parameter of k=5k = 5k=5, chosen based on experimentation to achieve optimal performance. The model's performance evaluation was conducted using accuracy, precision, and recall metrics derived from a confusion matrix. The findings reveal that the KNN algorithm successfully classifies data with the highest accuracy of 96.95% in the 80:20 dataset partition scenario and 96.84% in the 70:30 scenario. The Visit Stay Permit (ITK) class demonstrated the best performance with a precision of 97.46% and a recall of 99.97%, whereas the Permanent Stay Permit (ITAP) class showed the lowest performance with a recall of 59.79%, indicating challenges in recognizing patterns for this class. This study also identifies the advantages of the KNN algorithm, including its simplicity of implementation, flexibility in handling multiclass data, and effectiveness for low-dimensional datasets. However, the algorithm has limitations, such as sensitivity to imbalanced data distributions and high computational time for large datasets.
Neural Network Approach Using PyTorch to Predict the Growth of Various Types of Plants Silaban, Freddy Artadima; Firdausi, Ahmad
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.v18i2.3884

Abstract

In the era of rapid technological advancement, agriculture faces increasing challenges in optimizing production efficiency and managing resources sustainably. In Indonesia, various plant types are essential agricultural commodities, yet their productivity is often disrupted by erratic weather, poor land management, pest infestations, and land-use change. This study proposes a predictive model for plant growth using a neural network implemented in the PyTorch framework, integrating multiple environmental features such as temperature, humidity, soil moisture, nutrients, pH, and NPK levels. Unlike previous works that typically focus on specific crops or limited variables, this research introduces a multivariate approach combining diverse agro-environmental data to classify plant types accurately. The model architecture was tuned using GridSearchCV, resulting in optimal hyperparameters (e.g., batch size 32, learning rate 0.001, activation: tanh), achieving high performance with Area Under the Curve (AUC) values nearing 1.0 across most classes. Visualization of network weights reveals how input features are transformed through hidden layers, providing interpretability and transparency in decision-making. The proposed system demonstrates strong generalization capability, as validated on unseen data, and offers real-time prediction feasibility for deployment on edge devices such as NVIDIA Jetson Nano. This work contributes a novel, data-driven approach to smart agriculture by enabling precise growth prediction across multiple plant types, enhancing strategic planning for resource allocation and crop management. Future work includes model adaptation for time-series forecasting and validation with live sensor inputs in real-world agricultural environments.

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