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Intelmatics
Published by Universitas Trisakti
ISSN : -     EISSN : 27758850     DOI : https://doi.org/10.25105/itm
Core Subject : Science,
Computer Science & IT
The IntelMatics Journal is a scientific journal published by the department of informatics engineering at Trisakti University. The purpose and objective of the publication of the IntelMatics journal are as a means of dissemination of international standard science in the field of software engineering, information security, and business analysis in the scope of data intelligence and visualization. Journal will be published every sixth month
Arjuna Subject : Teknik (semua kategori) - Teknik (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol. 4 No. 2 (2024): Juli-Desember" : 6 Documents clear
Design of Study Program Performance Dashboard using Streamlit Ilham, Moch Ilham A; Siswanto, Teddy; Sugiarto, Dedy
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.20643

Abstract

To achieve certain performance goals, such as increasing the integration of study program performance data from various sources, such as student data, lecturers, curriculum, research, and so on, a performance dashboard is needed to monitor the performance of lecturers, students and curriculum to be more efficient. In this study, a dashboard was created with streamlit where the data was taken from the web scraping method. By utilizing the data visualization capabilities owned by Streamlit, study program performance information can be presented in the form of graphs, diagrams, or tables that are easier to understand and interpret.
Application of IOT Technology in The Control of Organic Waste Processing Machines with PT100 Sensors and Heaters for Fertilizer Healing and Animal Feeding Dharma, Ricardo; Budi Santoso, Gatot; Mardianto, Is
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.20913

Abstract

Waste is one of the major problems in Indonesia that still has to be resolved, because it has many negative impacts on the environment and health. Waste can be divided into two types: organic and inorganic waste. The increase in waste and the limited capacity of the Integrated Waste Disposal Sites (TPST) will cause waste to accumulate. Therefore, organic waste will have a negative impact on the environment if not managed properly, one of the efforts to reduce its impact is to process organic waste into fertilizer and animal food with new innovations in Internet of Thing (IOT) technology that can be used as an improvement in the agricultural sector. The manufacture of waste processing machines into fertilizer and animal food uses PT100 sensors as temperature control sensors from waste, PLC as data processing integration, HMI cloud and HMI haiwell are used as hardware that displays visual temperature data. This research shows that the use of PT100 sensors in waste processing machines has a significant effect on machine performance. In the process of making fertilizer, the PT100 sensor can regulate the temperature accurately, for example, when the temperature is set at 80℃ and exceeds the limit, the heater will turn off and the temperature decreases to 60℃. IoT technology allows real-time monitoring and control of temperature through mobile phones and HMIs, as well as providing Telegram notifications for high or low temperature warnings.
Analysis Of Topic Movement & Conversation Membership On Twitter Using K-Means Clustering Sediyono, Agung; Valentino Hutagalung, Josua; Solihah, Binti
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.21002

Abstract

Humans are born to socialize with each other. Social media is one of the media to be able to socialize with each other. Twitter is one of the social media that contains hundreds of millions of tweets where the tweet contains news, products that are currently popular, even about the daily life of users who can change. Social Context Analysis is a tool to analyze social changes and individual needs in society from time to time. In this study, the author uses the K-means Clustering method to group topics on Twitter. In its implementation, this research is expected to be able to see the occurrence of topic movements and membership movements on Twitter topics.
Implementation Enterprise Resource Planning (ERP) ODOO Version 15.0 Manufacturing Module at CV. Razzaq Berkah Mulia Setiawan, Ibnu Fajar; Siswanto, Teddy; Sugiarto, Dedy
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.21073

Abstract

In the modern era, digitalization has had a significant impact on various business fields, with technology being the main key in fascilitating company performance. Digitalization of company systems is now an obligation, data integration is essential for analysis that supports company progress. ERP is software that simplifies company operations by integrating various business application modules such as Inventory, Accounting, Sales, Point of Sales, Manufacturing, Contact, Website, and Purchasing. CV Razzaq Berkah Mulia, which has the potential to compete with international furniture companies, has not fully adopted information systems in its management. This lack of digitalization can lead to losses and recording errors that hinder their business performance. An optimized and valid implementation of Odoo ERP is essential to improve the quality and operational efficiency of CV Razzaq Berkah Mulia. This Implementation will go through several phases, namely Discovery and Planning, Design, Development, Testing, Deployment, and Support. At the Discovery and Planning stage, the company needs will be identified and an implementation strategy will be planned. The Design stage will design the system as per requirements, while the Development Stage will involve the creation and configuration of the system. At the testing stage, the system willl be tested to ensure its functionality, and the Deployment stage will involve launching the system into the operational environtment. Finally, the Support stage will provide post-implementation support.  The final result of this development can be accepted and implemented for the implementation of Odoo at CV. Razzaq Berkah Mulia through UTAUT with a satisfaction value of Performance Expectancy 88%, Effort Expectancy 88%, Supporting Facilities 88,4%, Facilitating Conditions 91%, Attitude Towards Technology 89,2%, Behavioral Intention 90,8%.
PERFORMANCE COMPARISON OF TWITTER SENTIMENT ANALYSIS USING FASTTEXT SVM AND TF-IDF SVM: A CASE STUDY ON ELECTRIC MOTORCYCLES Sulaba, Wishnu Abhinaya; Solihah, Binti; Sari, Syandra
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i2.18145

Abstract

Electric motorcycles are trending on Twitter as two-wheeled vehicles different from those using fossil fuels. Electric motorcycles rely on batteries charged using electricity. However, there are many opinions about electric motorcycles on social media, especially Twitter. Yet, tweets and comments on Twitter often contain irrelevant words that can affect sentiment analysis. In this study, sentiment analysis was conducted on 8,000 data from Twitter using FastText and TF-IDF as word embedding techniques, along with Support Vector Machine (SVM) as the classification technique. The aim of this research is to compare the performance of SVM using different feature extraction techniques, namely FastText and TF-IDF. The results of this study are expected to be beneficial for electric vehicle manufacturers and individuals interested in electric vehicles. In this comparison, the performance of TF-IDF and FastText feature extraction in sentiment classification with SVM will be evaluated. SVM performance is assessed based on accuracy, precision, recall, and F1-score for each feature extraction technique used. The test results show an average accuracy above 83%, with the highest values being 86% for accuracy, 79% for precision, 52% for recall, and 58% for F1-score.  
COMPARATIVE SENTIMENT ANALYSIS OF VISITOR REVIEWS FOR WATERBOM BALI TOURIST ATTRACTION ON TRIPADVISOR SOCIAL MEDIA USING RANDOM FOREST AND NAÏVE BAYES CLASSIFICATION Hilmi, Hilmi Abdul Gani; Solihah, Binti; Sari, Syandra
Intelmatics Vol. 4 No. 2 (2024): Juli-Desember
Publisher : Penerbitan Universitas Trisakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25105/v4i1.19278

Abstract

With the advancement of technology, especially the internet, the role of the internet as the primary source of information in global life is becoming increasingly crucial. This is particularly true in the context of searching for information about tourist destinations before visiting them. TripAdvisor is a website designed for searching travel destinations and attractions. On this platform, users can provide reviews and see comments from other travelers regarding various tourist destinations, including Waterbom Bali. To gain insights into visitors' perspectives and enhance services for them, the overwhelming number of reviews can be analyzed for sentiment to understand whether travelers' views tend to be positive, negative, or neutral. In this research, the Random Forest and Naïve Bayes methods are employed to conduct sentiment analysis. Scraping data from Waterbom Bali resulted in a dataset of 5750 entries. Despite data imbalance after labeling positive, negative, and neutral sentiments, class imbalance techniques will be applied. The sentiment analysis method, comparing Random Forest and Naïve Bayes, is implemented using the Word2Vec feature extraction method to evaluate its effectiveness. Experimental results show significant differences between the two methods. In Random Forest, after undersampling, an accuracy of 24% was obtained, while oversampling resulted in an accuracy of 98%. Meanwhile, for Multinomial Naïve Bayes, after undersampling, an accuracy of 36% was achieved, and oversampling yielded an accuracy of 97%.

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