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All Journal JEECS (Journal of Electrical Engineering and Computer Sciences) Jurnal Ilmu Komputer dan Sistem Informasi
Lailan Sofinah Harahap
Universitas Muhammadiyah Sumatera Utara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Sentiment Analysis of UINSU Students' Comfort Towards Trans Metro Deli Services at Taman Budaya Bus Stop Using the Naive Bayes Method Nurhidayati Nurhidayati; Dea Syahfira Hasibuan; Lailan Sofinah Harahap
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.10

Abstract

The large number of bus passengers at the Taman Budaya bus stop is one of the public transportation problems. Finding that the Metro Deli Bus Organizer is still operating. They are considered capable of meeting the requirements for choosing a mode of transportation. The purpose of this study is to determine the passenger transportation factors on the Trans Metro Deli Bus. The Trans Metro Deli Bus Passenger Transportation Factor is the purpose of this study. Data Collection Techniques Using Questionnaires Student comfort factors when using the Trans Metro Deli bus service. This study's methodology starts with problem identification and moves on to problem-solving techniques and assessment procedures. Respondents were given a questionnaire to fill out to collect data. The author of this study used Google Forms. The author of this study solved the problem using the Naïve Bayes algorithm. The Naïve Bayes algorithm model produces results with an accuracy of up to 71.43%, which is quite good. The accuracy results of 71.43% and approaching 100% show how accurate the sentiment analysis is using the Naïve Bayes classification. The accuracy results of 71.43% and approaching 100% show how accurate the sentiment analysis is using the Naïve Bayes classification. 'The bus took a long time to arrive' and 'didn't get a seat' were the most common negative reviews, indicating that some students felt uncomfortable. The Naïve Bayes results of the study showed that people who reviewed the Trans Metro Deli Bus expressed more positive opinions, with the highest score of 71.43%.
Sentiment Analysis Regarding the Indonesian House of Representatives Rejecting the Constitutional Court Decision from Social Media Using Naive Bayes Fahar Abdul Aziz; Lailan Sofinah Harahap
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 10 No. 1 (2025): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v10i1.4

Abstract

This study analyzes public sentiment towards the HOR's rejection of the Constitutional Court's decision regarding the age limit for regional head candidates. Data was obtained from TikTok comments using scraping techniques with the Apify platform, resulting in 574 comments being analyzed. Sentiment labeling was automatically used VADER (Valence Aware Dictionary and Sentiment Reasoner), with positive, neutral, and negative sentiment categories. Text representation was carried out using TF-IDF, and sentiment classification using the Naive Bayes algorithm. The analysis results showed that most comments were neutral (42.0%) and positive (41.8%), while negative sentiment was only 16.2%. This study provides important insights into public perceptions of political issues involving the HoR and Constitutional Court decisions. By analyzing sentiment through comment data on TikTok, this study shows that lexicon-based approaches such as VADER can be used for automatic sentiment labeling, saving time compared to manual methods. In addition, classical algorithms such as Naive Bayes, combined with TF-IDF text representation, have proven effective in handling sentiment classification for short and informal texts such as social media comments.
Analisis klasifikasi Algoritma K-Nearest Neighboar (K-NN) pada struktur Daerah di Kota Medan Safa Nadia Bakri; Lailan Sofinah Harahap
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 4 No. 2 (2025): Mei 2025
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v4i2.165

Abstract

This research aims to analyze the application of the K-Nearest Neighbor (KNN) algorithm in classifying regional structures in Medan City. Medan, one of the largest cities in Indonesia, has a variety of characteristics of regional structure that requires an appropriate analysis approach for spatial management and spatial planning. The KNN algorithm was chosen because of its ability to categorize data based on its proximity to other data points, which is very suitable for the needs of spatial planning and management. other data points, which is very suitable for the needs of regional classification analysis. In this research, the data used includes various attributes of the regional structure such as structure attributes such as population density, land use, and infrastructure in each sub-district. infrastructure in each sub-district in Medan City. In this research, the method used is statistical data processing statistical data processing to group areas with similar characteristics, using the KNN algorithm as a classification method. The classification process process involves selecting the right parameters, calculating the distance between data points, and selecting the optimal number of nearest neighbors. data points, as well as selecting the optimal number of nearest neighbors. The expected results The expected results of this analysis will provide a clear picture of the distribution pattern of the distribution pattern of the regional structure in Medan City, as well as assisting in the planning and development of a more efficient and directed city. The accuracy of the KNN model in classifying the regions will also be compared with other algorithms to assess its effectiveness and reliability in the context of this study.
Co-Authors Dea Syahfira Hasibuan Fahar Abdul Aziz Nurhidayati Nurhidayati Safa Nadia Bakri