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All Journal Bulletin of Electrical Engineering and Informatics Dinamika Informatika Martabe : Jurnal Pengabdian Kepada Masyarakat Diakronika Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Pendidikan dan Teknologi Indonesia Jurnal Dinamika Informatika (JDI) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Applied Science and Technology Research Journal International Journal of Information Technology and Computer Science Applications (IJITCSA) JTH: Journal of Technology and Health Jurnal Ilmiah Informatika dan Komputer
Muhammad Fairuzabadi
Informatika, Universitas PGRI Yogyakarta, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Social Sciences Other

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SENTIMENT ANALYSIS OF POTENTIAL 2024 PRESIDENTIAL CANDIDATES ON TWITTER SOCIAL MEDIA USING METHODS NAIVE BAYES MULTINOMIAL Muslikhah; Muhammad Fairuzabadi; Wibawa
JTH: Journal of Technology and Health Vol. 2 No. 1 (2024): July: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v2i3.247

Abstract

This research aims to conduct sentiment analysis regarding potential presidential candidates in 2024, so that we can identify candidates who have positive, neutral and negative images in the view of the public on Twitter social media. This sentiment analysis helps candidates understand People's aspirations and adapt their communications accordingly. This research uses the naïve Bayes multinomial method and utilizes crawling technology on Twitter social media, data is collected and analyzed efficiently. The results of this research obtained 6000 comment data with each candidate having 2000 comments. Ganjar Pranowo had the highest positive sentiment (39%), followed by Anies Baswedan (35.8%) and Prabowo Subianto (25.9%). Ganjar also leads in neutral sentiment (38.8%). The highest number of negative sentiments was held by Prabowo Subianto (39.3%), Anies Baswedan (30.5%), Ganjar Pranowo (21.3%). So from these results, Ganjar Pranowo has the best electability based on public comments on Twitter social media.
DECISION SUPPORT SYSTEM FOR BOARDING HOUSE RECOMMENDATIONS AROUND UPY BASED ON RULES AND SPATIAL WEB Ganang Prihatma Artha; Muhammad Fairuzabadi; Aditya Wahana
JTH: Journal of Technology and Health Vol. 2 No. 2 (2024): October: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v2i2.379

Abstract

Boarding houses, often referred to simply as "boarding houses," are essential for students studying away from their hometowns. These students seek temporary accommodations to serve as their domicile until they complete their education. When searching for a boarding house, students typically rely on recommendations from local residents or acquaintances living near the campus. Choosing a place to live in a new area involves several factors, including cost, distance, facilities, and the surrounding environment. A Decision Support System (DSS) can assist in this process by providing recommendations that match the desired criteria, making it easier for students to find suitable accommodations. A Rule-Based System is a structured decision-making method that utilizes IF-THEN rules to evaluate various criteria relevant to boarding house selection. By integrating a Decision Support System with Rule-Based Systems and Spatial Web technologies, this approach effectively assists UPY students in finding suitable boarding houses. The system provides comprehensive information and tailored recommendations based on the specific preferences of students or boarding house seekers.
DEVELOPMENT OF A RELIGIOUS TOURISM MANAGEMENT INFORMATION SYSTEM AT K.H.R BAGUS KHASANTUKA TOMB IN SENUKO SIDOAGUNG Aisyiyah Faj'ri Nur Jannah; Muhammad Fairuzabadi
JTH: Journal of Technology and Health Vol. 2 No. 4 (2025): April: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v2i4.406

Abstract

The management of religious tourism at the K.H.R Bagus Khasantuka Tomb in Senuko Sidoagung faces several challenges, particularly in financial recording, human resource management, and the documentation of history and oral traditions. This study aims to develop a Religious Tourism Management Information System to enhance operational efficiency and support cultural heritage preservation. The development methodology follows the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and maintenance. The system was evaluated using Black Box and Alpha Testing methods to assess functionality and user experience. The results indicate that the developed system improves financial transparency, facilitates visitor data recording, and supports historical and traditional documentation more effectively. This system modernizes and structures religious tourism management, with the potential to increase visitor engagement.
DECISION SUPPORT SYSTEM FOR MOUNTAIN CLIMBING SELECTION IN INDONESIA USING THE MULTI-FACTOR EVALUATION PROCESS (MFEP) METHOD Achmad Imam Dairobbi; Muhammad Fairuzabadi
JTH: Journal of Technology and Health Vol. 2 No. 3 (2025): January: JTH: Journal of Technology and Health
Publisher : CV. Fahr Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61677/jth.v2i3.416

Abstract

Mountain climbing is a popular activity among various groups; however, selecting a suitable mountain can be challenging for climbers. Several factors, such as trail difficulty, altitude, climbing duration, cost, and water availability, must be considered to ensure a safe and comfortable climb. Therefore, this study develops a web-based Decision Support System (DSS) to assist climbers in choosing a mountain that matches their preferences. This research employs the Multi-Factor Evaluation Process (MFEP) method to evaluate mountains based on predetermined criteria. The system assigns weights to each factor and calculates evaluation scores to generate the best mountain recommendations. The system is developed using PHP for the backend, MySQL for the database, and Bootstrap for a responsive user interface. The study results indicate that the system can provide objective and accurate mountain recommendations. System testing using the Black Box Testing method confirms that all features function as intended. Based on MFEP calculations, the system ranks mountains according to relevant factors, enabling climbers to make informed and safer decisions.
Comparative Analysis Of Artificial Intelligence Models For User Behavior Prediction In Big Data-Driven Information Systems Faqihuddin Al Anshori; Muhammad Fairuzabadi; Mohd Nawi, Mohd Nasrun
APPLIED SCIENCE AND TECHNOLOGY REASERCH JOURNAL Vol. 4 No. 2 (2025): Applied Science and Technology Research Journal
Publisher : Lembaga Penelitian dan Pengabdian Mayarakat (LPPM) Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/astro.v4i2.8428

Abstract

In the era of digital transformation, Artificial Intelligence (AI) plays a pivotal role in enabling intelligent, data-driven information systems. This study presents a comprehensive comparative analysis of AI models: Decision Tree (DT) and Artificial Neural Network (ANN), for user behavior prediction within simulated big data environments, specifically in the e-commerce domain. Using 1,000 synthetic sessions that mimic real-world user activities, the study evaluates model performance using classification metrics such as accuracy, precision, recall, and F1-score. ANN outperforms DT across all metrics, achieving 87.2% accuracy and demonstrating superior learning efficiency and generalization. To complement the evaluation, a Long Short-Term Memory (LSTM) model is employed for time-series prediction, yielding a low MAPE of 1.12%, confirming its effectiveness in capturing sequential patterns. The findings offer valuable insights into AI model selection for adaptive and predictive information systems, with implications for developers and researchers seeking to enhance system responsiveness and personalization.
Co-Authors Achmad Imam Dairobbi Adi, Angger Seta Aditya Wahana Aditya Wahana Agustia Rizki, Joyanda Aisyiyah Faj'ri Nur Jannah Akhmad Zaenal Abidin Amin, Meilan Khoirul Ardiansa, Picky Nanda Bertorio, Margala Juang Cahyo Putro Wardoyo Devi Andani Dhiniaty Gularso Eko Setyawan Elsya Amalia, Aqbrila Erizal Erizal Erizal, Erizal Faqihuddin Al Anshori Firdiyan Syah Ganang Prihatma Artha Hastono, Tri Irma Noviana Juang Bertorio, Margala Lahmi, Sulahmi Listyawan, Irfan Marti Widya Sari, Marti Mohd Nawi, Mohd Nasrun Muarrof, Muhammad Syafi’ Al Muhammad Iqbal Birsyada Muslikhah Nauval, Mukhamad Nor Azhari Azman, Mohamed Nur Jannah, Aisyiyah Faj’ri Nurhakim, Habib Akbar Picky Nanda Ardiansa Prahenusa Wahyu Ciptadi Priharyanta, Gilang Kurnia Puji Handayani Putri Puji Handayani Putri Putra, Widhi Arianto Rianto Rianto Rohmatulloh, Bimo Isya Sari, Eka Nugraha Setia Wardani Sunggito Oyama, Sunggito Syafi’ Al Muarrof, Muhammad Syah, Firdiyan Tb Ai Munandar, Tb Ai Tentua, Meilany Nonsi Tita Murti Cahyani Tri Hastono Wahyu Ciptadi, Prahenusa Warniasih, Kristina Wibawa Wibawa Wibawa Wibawa, Wibawa Wibowo, Bayu Ananto