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INDONESIA
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING
Published by Universitas Medan Area
ISSN : 25496247     EISSN : 25496255     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
JURNAL TEKNIK INFORMATIKA, JITE (Journal of Informatics and Telecommunication Engineering) is a journal that contains articles / publications and research results of scientific work related to the field of science of Informatics Engineering such as Software Engineering, Database, Data Mining, Network, Telecommunication and Artificial Intelligence which published and managed by the Faculty of Informatics Engineering at the University of Medan Area .
Arjuna Subject : -
Articles 412 Documents
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Tourist Classification Based on Consumer Behavior Using XGBoost Algorithm zalukhu, Jenius; Hasudungan Lubis, Andre
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14402

Abstract

This study discusses the application of the XGBosst Algorithm to Tourists based on consumer behavior. The purpose of this study is to predict or analyze tourist review data, and to help provide and understand needs so as to improve the quality of services offered. Indonesia has great tourism potential thanks to its natural beauty and cultural diversity. This sector plays an important role in the national economy by creating jobs and encouraging the creative industry and hospitality. The presence of tourists increases regional income through taxes and spending in sectors such as hotels, restaurants, and souvenir shops, as well as creating new jobs. In addition to tourists being able to increase income, there is a need for an understanding of each tourist behavior that is important for the development of adaptive and sustainable tourism.
Sentiment Towards Social Media Politeness Ambassadors: A Case Study Using the Naive Bayes Method Fikri, Ridho Ahmad; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14404

Abstract

Social media has had a significant impact on modern society, serving as a primary platform for sharing information and opinions. One intriguing phenomenon is the viral case of a female police officer, Putri Cikita, who earned the title "Ambassador of Courtesy" due to her actions in a video. This study aims to analyze public sentiment regarding this case on Twitter using the Naive Bayes Classifier (NBC) method. The research adopts a quantitative descriptive approach with sentiment analysis based on Text Mining, utilizing Python and Google Colab. The dataset consists of 2,000 Indonesian-language tweets collected from August to November 2024 using the keywords "Ambassador of Courtesy" and "Putri Cikita." The research stages include data collection, data preprocessing (case folding, tokenizing, filtering, stemming), and sentiment labeling into positive, negative, and neutral classes. The analysis results reveal that 11.55% of tweets express positive sentiment, 68.40% are neutral, and 20.05% are negative. The Naive Bayes method proves effective in classifying textual sentiment data. This research provides insights into public perceptions of viral events and underscores the importance of public image management in the digital era.
Automated Food Preserving System Utilizing NodeMCU ESP8266-Based Drying Methodology Marpaung, Maikel; Susilawati
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14425

Abstract

Food drying is one of the effective methods for preserving food and extending its shelf life by inhibiting bacterial growth. In Indonesia, many food products require sun drying to preserve them. However, this process often disrupts due to sudden rain showers, which can impede the drying process. Therefore, a automatic food cover system is needed to facilitate human work and protect the drying process from rain disturbances. This study designs an apparatus that can automatically cover dried food using FC-37 rain sensor controlled by NodeMCU ESP8266. The device also features an email notification feature to provide information on whether it's raining or not, allowing users to take action before the rain arrives. With this automatic food cover system, we expect to improve efficiency and quality of food drying results.
Classification of Hepatitis Disease Using The Fuzzy Mamdani Method Hidayani, Nurul; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14426

Abstract

In the modern world, almost everyone uses technology and information. This is evident in various fields, ranging from education and employment to entertainment. Society is too dependent on technology and information, to the point of neglecting their own health. There are many diseases caused by neglecting one's own health, one of which is hepatitis. This is because some people pay little attention and are reluctant to get it checked. Because hepatitis is very dangerous for human survival, treatment must begin as soon as the first symptoms appear and assist in the early diagnosis of hepatitis. This will allow for the identification of the type of hepatitis disease. The aim of this research is to apply the Mamdani fuzzy method for the classification of hepatitis diseases. The Mamdani fuzzy method has been successfully utilized in systems for diagnosing hepatitis diseases. In this system, it will provide instructions, namely to select which symptoms are experienced, then you can choose those symptoms by checking them off, and this system will provide a diagnosis based on the symptoms experienced. The diagnosis results include the type of hepatitis disease experienced, as well as treatment solutions. The results obtained for diagnosing hepatitis A disease using fuzzy Mamdani calculation shows that 68% , and the diagnosis of hepatitis B disease using fuzzy mamdani calculations shows 53% , and the diagnosis of hepatitis C disease using fuzzy mamdani calculations shows 59%.
Coffee Quality Classification Based on Customer Reviews Using C4.5 Algorithm Siahaan, Ricardo Fransdoli; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14427

Abstract

Coffee is a very popular commodity throughout the world, and its quality is oken evaluated through customer reviews. This research aims to classify coffee quality based on reviews given by consumers using the C4.5 algorithm. C4.5 is a machine learning algorithm used to generate decision trees, which allows decision making based on relevant attributes. In this research, the data used consists of customer reviews taken from e-commerce plaVorms and coffee discussion forums. The data is then processed with natural language processing (NLP) techniques to extract important features such as sentiment, keywords and term frequency. These features are used as input for the C4.5 algorithm, which builds a classification model based on patterns contained in the data. The results of the research show that the C4.5 model is able to classify coffee quality with high accuracy, reaching up to 85%. The factors that most influence quality classification include taste, aroma, and packaging, which are frequently mentioned in reviews. In addition, the analysis also shows significant differences in the quality of coffee produced from different coffee producing regions, which can provide insight for producers to improve their products.
Sensitivity of Weather Forecast Analysis in Comparison of Fuzzy Time Series And Artificial Neural Network Methods Fitra , Akbario; Muhathir, Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14428

Abstract

This research aims to produce a comparative level of sensitivity accuracy between fuzzy time series and artificial neural network methods in weather forecasting. The background to the problem identified is that weather conditions are always changing, so a system development is needed to help obtain accuracy values from weather forecasts by paying attention to the sensitivity of the comparison results between the two methods. The research results show that the Artificial Neural Network is effective in providing weather forecast values according to existing datasets, while the Fuzzy Time Series is able to produce sensitivity accuracy values based on existing datasets. This research also reveals that both methods are quite good in determining accuracy results on weather forecast sensitivity to meet user needs. The conclusion of this research is that both methods can provide the right solution for the development of a weather forecasting system that can be used by users.
Analysis of Recommendation System on Travel Platform Using Content-Based Filtering and Collaborative Filtering Algorithms at PT. Angkasa Tour & Travel Prasetyo , Dewo; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14429

Abstract

This study aims to evaluate the effectiveness of the recommendation system on the PT Angkasa Tour & Travel travel platform using content-based filtering and collaborative filtering algorithms. The background of the identified problem is the need to improve the accuracy and relevance of recommendations in the travel platform, which functions to assist users in choosing travel services that suit their preferences. This research method includes an analysis of the application of the content-based filtering algorithm that focuses on the characteristics of individual users and products, as well as the collaborative filtering algorithm that utilizes collective user behavior patterns. The results of the study indicate that content-based filtering is effective in providing recommendations based on specific user preferences and product attributes, while collaborative filtering is able to produce recommendations based on collective user behavior patterns. This study also reveals that the combination of the two approaches can improve the accuracy and relevance of recommendations, thus better meeting user needs. The conclusion of this study is that the integration of content-based and collaborative filtering in the recommendation system can provide a more comprehensive solution to meet user preferences and needs on the PT Angkasa Tour & Travel travel platform.
Mobilenetv2 Analysis in Classification Diseases On Mango Leaves Simangunsong, Roy Candra; Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14430

Abstract

This study aims to analyze the performance of the MobileNetV2 model in classifying diseases on mango leaves, consisting of three classes: capmodium, collectricu, and normal leaves. The dataset used contains 1500 images, with 80% allocated for training data, 10% for testing data, and 10% for validation data. The model was trained using a deep learning approach to identify mango leaf diseases based on the visual patterns present in each class. The results show that the MobileNetV2 model achieved an accuracy of 90%, a precision of 91%, a recall of 90%, and an F1-score of 89%. These findings highlight the potential of MobileNetV2 as an effective tool for automatically detecting mango leaf diseases. Therefore, this study is expected to contribute to the development of technology-based solutions in the agricultural sector, particularly in supporting farmers in identifying diseases quickly and accurately, thereby improving mango crop productivity.
The Implementation of Random Forest to Predict Sales a Case Study at Chatime Binjai Supermall Sandy, Boy; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14431

Abstract

In an increasingly competitive business environment, retail industries like Chatime Binjai Supermall must quickly adapt. Changes in consumer trends, preferences, and technological advancements significantly impact business strategies. To stay competitive, Chatime Binjai Supermall needs to optimize sales, marketing, and inventory management through accurate data analysis and prediction. Random Forest, a powerful machine learning algorithm, is used to process historical data and more accurately predict sales. This study evaluates the performance of Random Forest in predicting daily, weekly, and monthly sales. The analysis shows that products like "Jasmine Green Tea (L)" have the highest daily demand, "PEARL (L)" leads weekly sales, and there is an increase in demand for specific products monthly, such as "CT RAINBOW JELLY (L)." The implementation of the Random Forest algorithm at Chatime Binjai Supermall demonstrates significant potential in enhancing sales efficiency and data-driven decision-making, helping the company remain relevant and competitive amidst market changes.
The Application of Genetic Algorithm in Construction Project Planning System At Cv. Haza Mulia Engineering Harahap , Ryanda Fadillah; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14432

Abstract

Project scheduling is a crucial aspect in construction project management that aims to ensure that all tasks are carried out in an optimal sequence to maximize efficiency and reduce completion time. This study has three main objectives: (1) to build a web-based construction project planning system at CV. Haza Mulia Engineering, (2) to apply genetic algorithms to the construction project planning system at CV. Haza Mulia Engineering, and (3) to analyze the performance of genetic algorithms in generating optimal project schedules. This study was motivated by the need to complete a final assignment or thesis and used genetic algorithms as the main method. The research process begins with the identification of tasks and dependencies in a construction project. An initial population consisting of random schedules is generated and evaluated using a genetic algorithm. The selection, crossover, and mutation processes are carried out to gradually produce a new, better population. The fitness of each individual is calculated based on the number of unconnected activity dependencies, and the algorithm stops when the best mutually continuous schedule is found. The main result of this study is a web-based application built using PHP. This application is able to produce more efficient project scheduling compared to conventional methods. The schedule generated by genetic algorithm shows significant reduction in project completion time by minimizing unmet dependencies. The conclusion of this study confirms that the application of genetic algorithm in web-based project planning scheduling can avoid conflicts between activities and make the schedule more structured.

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