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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
Religion Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation

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Journal : Journal Of Artificial Intelligence And Software Engineering

 1 
Comparison of KNN and SVM Performance in 2024 Election Results Sentiment Analysis Bukit, M Iqbal Fahilla; Lubis, Andre Hasudungan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7659

Abstract

This study compares the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in sentiment analysis related to the 2024 election results using data from social media. The dataset used consists of 506 public opinion entries categorized into three sentiment labels: positive, negative, and neutral. The data processing involved preprocessing steps such as case folding, tokenization, stopword removal, and stemming, then represented using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The test results showed that both algorithms were able to classify with an accuracy of over 70%. The KNN algorithm produced an accuracy of 75.49%, precision of 71.36%, recall of 75.49%, and an F1-score of 72.88%, while the SVM algorithm showed slightly better performance with an accuracy of 77.45%, precision of 70.59%, recall of 77.45%, and F1-score of 72.15%. Based on the confusion matrix analysis, both models have a high ability to classify positive sentiments, but still face obstacles in recognizing negative and neutral sentiments due to the imbalance in data distribution. Overall, this study indicates that SVM is more suitable for election sentiment analysis on high-dimensional text data.
Clustering Culinary Locations Using the DBSCAN Algorithm Halawa, Anestin; Lubis, Andre Hasudungan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7512

Abstract

The culinary industry plays a vital role in driving creative economic growth and local tourism while also being an integral part of urban lifestyle. Given the high number and diversity of culinary locations, clustering techniques are needed to group them based on marketing characteristics, enabling more efficient decision-making for both consumers and businesses. This study aims to cluster culinary locations based on marketing-related attributes using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. Secondary data was obtained from Kaggle, consisting of restaurant information in Semarang City, with attributes such as rating, number of reviews, and operating hours. After preprocessing and exploratory analysis, DBSCAN was applied with adjusted parameters to generate optimal clusters. The results produced 41 clusters with diverse characteristics, including several outliers detected as noise. Performance evaluation using Silhouette Score and Davies-Bouldin Index showed that DBSCAN achieved more compact and well-separated clusters compared to K-Means. These findings demonstrate that DBSCAN is more adaptive for non-uniform culinary data with varying densities and is suitable for segmentation and strategic decision-making in the culinary 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