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All Journal CCIT (Creative Communication and Innovative Technology) Journal Journal Sensi: Strategic of Education in Information System
Toifur, Tubagus
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Visual Analysis Of Body Signals In Smoker Data To Understand Health Impacts On Python Nasrulloh, Anas; Yusuf, Muhamad; Mas’ud, Ibnu; Toifur, Tubagus; Ikhwanudin, Aolia; Syamhalim, Agianto
Journal Sensi: Strategic of Education in Information System Vol 11 No 1 (2025): Journal SENSI
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/sensi.v11i1.3764

Abstract

In this study, researchers found that 44.4% of people had blood pressure less than 120/80, 58.2% had fasting blood sugar <= 99, 47.7% had hemoglobin > 17.2, and 62% had oral problems and teeth caused by smoking. This research was carried out by visualizing the impact of smoking on blood pressure, fasting blood sugar, hemoglobin and the mouth and teeth in the body using body signal of smoking data obtained from Kaggle which was analyzed and presented in the form of a pie chart using Python.
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.
Co-Authors Ikhwanudin, Aolia Mas’ud, Ibnu Muhamad Yusuf, SE., M.Si. Nasrulloh, Anas Ramadhan, Aditya Duta Syamhalim, Agianto