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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat
Naufal, Muhammad Thoriq
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Development of Attendance Information System For Teacher Attendance at Pertiwi Elementary School Bandung Dewi, Irma Amelia; Miftahuddin, Yusup; Triseptiyadi, Fahmi; Vito, Nicola; Naufal, Muhammad Thoriq; Fahreza, Algi
REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Vol 5, No 2 (2024): REKA ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/rekaelkomika.v5i2.135-144

Abstract

The advancement of technology particularly in telecommunications, has led to the creation of various software applications that are beneficial to human life. One such application is modern attendance management systems. The objective of this research is to construct and implement a QR-code-based attendance information system for teachers and staff at SD Pertiwi Kota Bandung. The research methodology comprises socialization, counseling, implementation, and monitoring and evaluation. The responses indicate that the application has met or exceeded user expectations in terms of functionality and accessibility. Of the respondents, 30.74% gave "Normal" responses, 41.85% gave "Baik" responses, and 27.41% gave "Sangat Baik" responses. Additionally, the responses from program partners, SD Pertiwi, Bandung City, indicate positive evaluation and feedback.. The results of this development are intended to improve the accuracy of the attendance process, facilitate teachers and staff in reporting attendance, and minimize fraud and human error.
Klasifikasi Sentimen Pada Buzzer Politik Jelang Pemilu 2024 menggunakan Metode Lexicon-based NURHASANAH, YOULLIA INDRAWATY; NAUFAL, MUHAMMAD THORIQ
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 9, No 2 (2024): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v9i2.166-178

Abstract

ABSTRAKPemilu merupakan sebuah momen dimana masyarakat memiliki peran untuk berpartisipasi dalam pemerintahan. Media sosial, khususnya media sosial X menjadi salah satu yang diminati untuk menyebarkan informasi. Informasi yang tersebar dalam jumlah banyak sangat sulit untuk disaring dengan baik oleh masyarakat. Mempengaruhi opini publik dengan menggunakan kata-kata kasar, memanipulasi informasi, dan membuat konten negatif merupakan peran buzzer politik di dalam media sosial. Sehingga dibutuhkan alat untuk filterisasi sentimen publik yang beredar. Alat yang digunakan adalah analisis sentimen. Penelitian bertujuan untuk melakukan klasifikasi sentimen buzzer politik dalam media sosial X menggunakan model lexicon-based yang ditingkatkan kamusnya sesuai dengan studi kasus yang dilakukan (corpus-based). Dengan menggunakan 1031 data, didapatkan sentimen positif (bukan buzzer) sebesar 63.69%,  sentimen negatif (buzzer politik) sebesar 31.94%, dan sentimen netral sebesar 4.37%. Sehingga disimpulkan bahwa model lexicon-based mampu menjelaskan sentimen X.Kata Kunci: Analisis sentimen, Lexicon-based, Buzzer, X, PemiluABSTRACTElection is a moment where people have a role to participate in the government. Social media, especially X social media, is one of the most popular ways to disseminate information. Information that is spread in large quantities is very difficult for the public to filter properly. Influencing public opinion by using harsh words, manipulating information, and creating negative content is the role of political buzzers in social media. So a tool is needed to filter public sentiment that is circulating. The tool used is sentiment analysis. The research aims to classify the sentiment of political buzzers in social media X using a lexicon-based model that is enhanced by the dictionary according to the case study conducted (corpus-based). By using 1031 data, positive sentiment (not buzzer) is 63.69%, negative sentiment (political buzzer) is 31.94%, and neutral sentiment is 4.37%. So it is concluded that the lexicon-based model is able to explain X sentiment.Keywords: Sentimen analyzed, Lexicon-based, Buzzer, X , Election
Co-Authors DEWI, IRMA AMELIA Fahreza, Algi Miftahuddin, Yusup Nurhasanah, Youllia Indrawaty Triseptiyadi, Fahmi Vito, Nicola