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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Arcitech: Journal of Computer Science and Artificial Intelligence Malcom: Indonesian Journal of Machine Learning and Computer Science
Hasbi, Muhammad Adryan
Unknown Affiliation
Computer Science & IT

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Analisis Sentimen Dugaan Pelanggaran Pemilu 2024 Berdasarkan Tweet Menggunakan Algoritma Naïve Bayes Classifier: Sentiment Analysis of Alleged 2024 Election Fraud Based on Tweets Using the Naïve Bayes Classifier Algorithm Nugroho, Dafa Setyo; Hanif, Isa Faqihuddin; Hasbi, Muhammad Adryan; Fredianto, Fredianto; Saputra, Adrian Maulana; Zildjian, Rachmad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1496

Abstract

Twitter merupakan media sosial yang menjadi sumber informasi dan opini utama masyarakat tentang berbagai topik, termasuk  tragedi dan kejadian terkini. Dengan jumlah penggunanya yang besar, Twitter memberikan para peneliti wawasan berharga dalam menganalisis sentimen publik. Namun pelaksanaan pemilu pada tahun 2024 kali ini di warnai berbagai isu serta banyak masyarakat yang berpendapat bahwa telah terjadi adanya pelanggaran dalam proses penyelenggaraan pemilu tahun 2024. Dengan adanya berbagai pendapat masyarakat akan hal ini, maka kami mencoba membuat sebuah artikel yang di dalamnya memuat analisa sentimen masyarakat. Analisis sentimen menggunakan data Twitter telah menjadi pendekatan yang populer, terutama menggunakan algoritma klasifikasi Naïve Bayes, mengekstraksi sentimen dari teks yang terbukti efektif dalam melakukan analisis sentimen. Penelitian ini bertujuan untuk menganalisis sentimental masyarakat atas adanya dugaan pelanggaran terhadap penyelenggaraan pemilu 2024 dengan menggunakan data Twitter. Tujuannya adalah untuk mendapatkan wawasan lebih dalam mengenai opini masyarakat atas dugaan pelanggaran yang terjadi dalam proses penyelenggaraan pemilu di Indonesia.
Designing an Chatbot with NLP Technology in a Website-Based New Student Admission Information System Fauzan, Muhammad Fathan; Imanda, Rahmi; Hasbi, Muhammad Adryan
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8489

Abstract

In the fast pace of digitalization, student admission information system websites face the challenge of providing responsive and quality services to applicants. One emerging solution is the use of chatbots, which enable automated interaction with customers. Technology continues to transform over time. At SMK Insan Teknologi (InTek), the service process is still manual, such as physical archives for student registration, incomplete information, and the absence of an official website. To improve administration and data access, a web-based information system is offered. While the Chatbot helps in interactive services and time efficiency to answer registrants' questions, NLP is used to make the conversation in the chat more natural and easy to understand by registrants. The results of testing the system show that the system functions properly in responding to messages sent through the chatbot on the website both from the message text according to the intent, as well as abstract text and not according to the pattern with an accuracy rate of 87,5%. It is hoped that this research can improve the quality of service and administrative efficiency at SMK Insan Teknologi and can be applied in other educational institutions.
Implementasi Chatbot Berbasis Large Language Model Untuk Pencarian Skripsi Mahasiswa Terintegrasi dengan Whatsapp Hasbi, Muhammad Adryan; Imanda, Rahmi; Fathan Fauzan, Muhammad
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 5 No. 1 (2025): June 2025
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v5i1.13974

Abstract

Students often face difficulties in finding relevant thesis references, which can hinder the completion of their final projects and delay graduation. This study aims to develop a chatbot using a Large Language Model (LLM) integrated with WhatsApp as an interactive and efficient solution for academic reference search. A total of 795 classified thesis documents were collected from the Faculty of Industrial Technology and Informatics, UHAMKA. The system was built using the LangChain framework, including Setting Table Schema, Semantic Search, Rank Result, and Natural Language Interface for Databases. Implementation results showed that the chatbot successfully responded to natural language queries with 100% accuracy. User Experience Questionnaire (UEQ) evaluations indicated strong positive responses, with Clarity (2.08) and Accuracy (2.00) achieving “Excellent” ratings indicate high levels of efficiency in conducting thesis searches. This research demonstrates the effective application of LLMs in conversational academic search systems and offers a foundation for the development of similar services in other higher education institutions.
Co-Authors Fathan Fauzan, Muhammad Fauzan, Muhammad Fathan Fredianto Fredianto Hanif, Isa Faqihuddin Nugroho, Dafa Setyo Rahmi Imanda Saputra, Adrian Maulana Zildjian, Rachmad