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All Journal Innovative: Journal Of Social Science Research
Ginting, Alex Elanta
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Humanities Computer Science & IT Law, Crime, Criminology & Criminal Justice Public Health Other

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Analisis Sentimen Pengguna Medsos Terhadap Program Makan Siang Gratis Menggunakan Algoritma VSM Fathoni, Muhammad Hafidhatul; Azdar, Qowiyyu; Jabbar, Fiqri Abdul; Ginting, Alex Elanta; Rahmaddeni, Rahmaddeni
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.18725

Abstract

The free lunch program is one of the populist policies launched by the government with the aim of improving the nutrition of elementary and secondary school students. However, in the digital era full of community participation, the community's response to this policy is important to understand in more depth. This study aims to analyze the sentiment of social media users, especially Twitter, towards the free lunch program using the Vector Space Model (VSM) algorithm. Data were collected from 2,330 relevant tweets using keyword-based data retrieval techniques. The analysis process includes text preprocessing, feature extraction with TF-IDF, and vector similarity measurement using cosine similarity to classify sentiment into positive, negative, and neutral categories. The results of the analysis show that 63.7% of tweets are negative, 31.5% are positive, and 4.7% are neutral. Negative sentiment generally contains criticism of policy transparency, budget effectiveness, and indications of program politicization, while positive sentiment pressures the benefit program on child nutrition. These findings indicate that the VSM algorithm can be used effectively to capture public opinion patterns based on social media text. Furthermore, these results provide important meaning for policy design to consider public perception in designing and communicating social programs in a more inclusive and responsive manner.
Implementasi Deep Learning untuk Pengenalan Plat Nomor Kendaraan Ginting, Alex Elanta; Azdar, Qowiyyu; Jabbar, Fiqri Abdul; Ramadhani, Yurifa; Junadhi
Innovative: Journal Of Social Science Research Vol. 5 No. 4 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i4.20358

Abstract

Automatic license plate recognition is a vital component in the development of intelligent transportation systems and security management based on digital imagery. This study examines the implementation of deep learning using algorithm to detect and recognize vehicle license plates from visual images. Using 472 sample images of license plates taken under varying lighting conditions, camera angles, and background complexities, the research involved manual data labeling and trained an end-to-end object detection model. Google Colab was employed to train the model, allowing efficient and cost-free GPU computation. After training, the system was tested for its ability to detect license plate regions, followed by character extraction using Optical Character Recognition (OCR). Experimental results show that the model accurately detects license plate regions with a detection accuracy exceeding 90%, and successfully reads most alphanumeric characters, despite challenges such as image blur and partial occlusion. These findings demonstrate that the a reliable solution for license plate recognition systems powered by artificial intelligence. Furthermore, this research offers potential for integration into automated edge devices and intelligent traffic management systems.
Co-Authors Azdar, Qowiyyu Fathoni, Muhammad Hafidhatul Jabbar, Fiqri Abdul Junadhi Junadhi Rahmaddeni Rahmaddeni Ramadhani, Yurifa