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All Journal Scientific Journal of Informatics Jurnal Bisnis, Manajemen, dan Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Elektro dan Telkomunikasi INFOKUM Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences International Journal of Engineering, Science and Information Technology Journal of Informatics Management and Information Technology INCODING: Journal of Informatics and Computer Science Engineering Bulletin of Information Technology (BIT) EXPLORER Prosiding Snastikom Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Journal of Computing and Informatics Research Journal Of Artificial Intelligence And Software Engineering Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Andre Hasudungan Lubis
Universitas Medan Area
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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
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.
Grouping of Tourism Locations in Indonesia Using Distance Variations in the K-Means Algorithm Farida, Juni Irsan; Lubis, Andre Hasudungan
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.14528

Abstract

Indonesia is home to a diverse range of tourist destinations, yet the classification and mapping of these locations remain a challenge in tourism management. This study aims to cluster tourist destinations in Indonesia by applying the K-Means algorithm with three distance metric variations: Euclidean Distance, Manhattan Distance, and Canberra Distance. The dataset was sourced from public data repositories and underwent preprocessing steps, including data normalization. The optimal number of clusters was determined using the Elbow Method, while the clustering results were evaluated using the Silhouette Score and Davies-Bouldin Index. The findings indicate that Manhattan Distance produced the highest Silhouette Score (0.321463), suggesting superior clustering performance compared to the other two metrics. The results of this study provide valuable insights for stakeholders in formulating strategic tourism promotion and infrastructure development efforts.
Comparison of Support Vector Machine (SVM) and Naïve Bayes Algorithm Performance in Analyzing Garuda Bird Design Sentiment in IKN Moh Hafiz Raja Pratama , Munthe; Andre Hasudungan , lubis
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.14830

Abstract

The Government's policy in moving the Indonesian Capital City (IKN) is considered controversial, this has given rise to various responses from the public, especially on social media X. This research aims to analyze tweet sentiment related to IKN and compare the two algorithms. In this experiment, we succeeded in collecting 5128 tweet data regarding IKN in the X application, the total amount of IKN data was classified into positive sentiment as 2598 1659 negative data and sentiments. Research objectives, methods used, main results, and implications. This research aims to measure public sentiment towards the design of the Garuda bird as the main symbol of the Indonesian Capital City (IKN) by using a comparison of the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in analyzing the sentiment of the Garuda bird design in the IKN. main results, for example: the proportion of positive, negative and neutral sentiment, as well as the factors that most influence sentiment. Implications of research results for government, designers and society.
CatBoost Algorithm Implementation for Classifying Women's Fashion Products Madani, Fadillah; Lubis, Andre Hasudungan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15604

Abstract

The rapid growth of the women's fashion industry in the digital era has intensified the need for data-driven approaches to understand customer preferences. This study aims to classify women’s clothing products based on customer reviews by applying CatBoost, a gradient boosting algorithm known for its strong performance with categorical features. The dataset, consisting of 23,486 entries and 11 attributes, was obtained from Kaggle and processed through data cleaning, normalization, exploratory analysis, and model training. Hyperparameter optimization was conducted using Grid Search. Model performance was evaluated using accuracy, precision, recall, and F1-score, and benchmarked against four traditional classifiers: Decision Tree (C4.5), Naïve Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The results show that CatBoost achieved an accuracy of 93.70%, an F1-score of 0.9606, and an AUC of 0.9691, indicating excellent and balanced classification performance. This study demonstrates the effectiveness of CatBoost in handling customer review data and contributes to the development of intelligent product classification systems in the fashion industry
Co-Authors Alex Simanungkalit Bukit, M Iqbal Fahilla Dian Noviandri Dinda Rizky Aprillya Elveny, Maricha Farida, Juni Irsan Fauziah Rahman Gulo, Senang Hati Halawa, Anestin Hartono Hartono Hesti Sabrina Idrus, Syed Zulkarnain Syed Indah Sari Gaurifa Irwannia, Fira Juanda Hakim Lubis Juanda Hakim Lubis Krisdayanti, Sabina Madani, Fadillah Manurung, Dinikxon Moh Hafiz Raja Pratama , Munthe Muhammad Fikri Riandra Muhathir, Muhathir Nurul Khairina Pakpahan, Josua Prayuda Pariyandani, Ayu Rahmad Syah, Rahmad Ronal Maruli Marusaha Sianipar, Carmenita Sihombing, Stevi Freshia Siregar, Erlina Sucitra Dewi SUTRISNO Sutrisno, Nos Utami, Widya Rizki Wan Rizca Amela Wanti, Eka Pirdia zalukhu, Jenius Zealtiel, Billiam Zulfikar Sembiring Zulhikmah Marpaung