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Journal : CSRID

Sistem Pakar Diagnosa Penyakit Perokok Menggunakan Metode Backward chaining Subagio, Selamat; Rahmayani, Rahmayani; Samsir, Samsir; Azhar, Wahyu
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.340-353

Abstract

he rapid development of information and computer technology has had a significant impact on various fields, including healthcare. One of its applications is the expert system, a computer-based system utilizing Artificial Intelligence (AI) designed to imitate the reasoning and decision-making abilities of human experts. Expert systems are widely used to assist in diagnosing diseases based on symptoms experienced by patients, providing fast, efficient, and accurate solutions without requiring direct consultation with medical professionals. This study focuses on developing an Expert System for Diagnosing Smoking-Related Diseases among Lecturers at Universitas Al Washliyah Labuhanbatu. The system aims to help users, particularly active smokers, identify potential diseases caused by smoking habits. Based on preliminary studies and interviews conducted with the Health Department of Rantauprapat City, it was found that common diseases suffered by smokers include oral disease, lung disease, respiratory disorders, throat disease, and heart disease. These illnesses often develop unnoticed in the early stages, making early diagnosis essential for prevention and health awareness. The research applies the Backward Chaining inference method, which works by reasoning backward from a possible conclusion (disease) to find supporting facts (symptoms). The relationship between symptoms and diseases is represented through IF–THEN rules derived from expert knowledge. The system was developed using Macromedia Dreamweaver 8 as a web editor and MySQL as the database management system to store information on diseases, symptoms, and diagnostic results. The implementation results show that the system can provide early diagnoses quickly and accurately based on user-input symptoms. Furthermore, the system includes a confidence level feature that presents diagnostic certainty in percentage form. Hence, the developed expert system not only serves as a medical decision-support tool but also as a digital health education medium that promotes awareness of smoking dangers and the importance of maintaining a healthy lifestyle.
Co-Authors Adisty Garandina Ainsyah, Ainsyah Alya Sania Putri Amini, Hijratun Andi Uceng Aris Munandar Artika Pertiwi, Artika Asiah Ramadhani ASNIMAR ASNIMAR, ASNIMAR Aulia Dea Ananda Awaluddin Awaluddin Ayu Nadira Wulandari Azhar, Wahyu Baharuddin Baharuddin Batubara, Apriany Ramadhan Deliana, Deliana Dewiyani, Evi Dhea Ananda Br Barus Djam'an, Nurwati Dwi Wulan Joy Lumbantobing Ernita Pasaribu Eunike Gracia Sormin Fachrur Rozi Fahrur Rozi Fauzan Azima Fenny Rizky Amelia Fitra, Apriyus Hamzah, Diza Fathamira Harahap, Lila Mawaddah Hariyanti Hamid Hasymiah, Hasymiah Hidayati Ruslaini Husna Husna Husna, Malaul Iasyah Fakharany Ilda Zahratunisa Iskandar Iskandar Khairani Syahfitri Khairunnisa, Armelia Khoirun Nisa LUKMAN, LUKMAN Mansur, Yuwarman Marniati Marniati, Marniati Maryam, Nilam Meilina, Rulia Miani, Riza Miftahul Jannah Miranti, Fehmita Misriani, Misriani Muhammad Ilham Musdalifa Musdalifa Muthi’ah Syifa Isnaini Mutia Sari, Shinta Mutiah Mutadayyaniah Marbun Muttazimah, Muttazimah Nadia Dwi Utami Nadya Tesalonika Simbolon Natasya Ruth Panjaitan Novizar Nazir Nuraisyah Syahrun Nurdin Arsyad, Nurdin Nurlaila Nasution Nurwandri, Andri Putri Adhelia Br Damanik Qadri, Lailatul Rafiansyah, Rafiansyah Rahmawaty, Rahmawaty Ratna Wilis Ratno, Suyit Raudhatun Nuzul ZA, Raudhatun Riyadi, Alif Andaru Rosita Rosita RR. Ella Evrita Hestiandari Samsir Samsir, Samsir Santy, Putri Saraswati, Lucia Sasri Agustina Putri Sendy, Beby Sitepu, Lily Amanda Siti Annisa Sri Rahayu Ningsih Sri Rosita Subagio, Selamat Suci Dahlya Narpila Suci Salsabila Syahid, Abdur Rahman Tania Eviana Umi Salamah Yohana Oktaferin Situmorang Yuliana, Maya Yulidar Yulidar Yunita Yunita Yunita, Ayuwari Yusuf, Namira Za, Raudhatun Nurul Zetra Hainul Putra