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All Journal Sistemasi: Jurnal Sistem Informasi E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Majalah Ilmiah UPI YPTK Journal Of Human And Education (JAHE) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Komtekinfo Internet of Things and Artificial Intelligence Journal Journal of Vocational Education and Information Technology (JVEIT) J-Icon : Jurnal Komputer dan Informatika Jurnal Informatika Ekonomi Bisnis Marsipature Hutanabe: Jurnal Pengabdian Kepada Masyarakat Teknologi : Jurnal Ilmiah Sistem Informasi
Aldo Eko Syaputra
Universitas Adzkia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Social Sciences Other

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Journal : Internet of Things and Artificial Intelligence Journal

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Implementation of a Forward Chaining Expert System in Diagnosing Laptop Damage Sakinah, Putri; Hendra, Yomei; Satria, Budy; Rahman, Zumardi; Maulana, Fajar; Syaputra, Aldo Eko
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.791

Abstract

Laptops have become a primary need for almost everyone, but the damage rate is also high. Manual diagnosis of laptop damage requires special expertise and is prone to errors that can exacerbate damage. The purpose of this study was to develop an expert system based on the forward chaining method to diagnose laptop damage. Data obtained through expert interviews, literature study, and the internet comprised 13 symptoms and five main types of laptop damage. Relate data in tables to form IF-THEN rules of the forward chaining method. The test results on six symptoms indicate that the system can diagnose IC Power damage with 100% accuracy, which is the highest diagnosis. In conclusion, the forward chaining method can diagnose laptop damage based on emerging symptoms.
Decision Support System For Student Activity Unit Selection Using Certainty Factor Method Manurung, Kiki Hariani; Hayati, Nova; Shofia, Alima; Syaputra, Aldo Eko
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.794

Abstract

In various fields, including the selection of Student Activity Units on campus, Decision Support Systems (DSS) have become an important tool to assist the decision-making process. SPK provides information and analysis that is structured and easy to understand, thereby helping decision-makers to choose SMEs that best suit their interests, talents, and goals. Choosing the right Student Activity Units for students can contribute to the development of their personal qualities and help develop a variety of social and professional skills. Using the Certainty Factor method in creating a Decision Support System to assist students in choosing Student Activity Units that are most relevant to their desired interests and talents. The Certainty Factor method is an artificial intelligence technique that can overcome uncertainty in data and provide a level of confidence in every decision. Based on trials carried out on several interest and talent characteristics using the Certainty Factor method, percentage results were obtained with a confidence level of 80.26%. Based on the test results, it can be concluded that the expert system created can make it easier to determine talent interests that match student desires.
Co-Authors Adawia, Rahmiza Ainil Mardiah Alima Shofia Ardi, Abdullah Aufar, Yazid Budiarti Rahayu Putri, Perra Budy Satria Dari, Rahmatia Wulan Fajar Maulana Fatwa Rachmansyah, Haikal Hariani Manurung, Kiki Hasanah Nasta, Nurul Hayati , Nova Hayati, Nova Hendra, Yomai Hendra, Yomei Hidayat, Teguh Husnatun Hasanah, Tria Kiki Hariani Manurung Manurung, Kiki Hariani Maulana, Fajar Mufid, Muhammad Fauzan Muhammad Fajri Muhammad Fauzan Mufid MUHAMMAD THORIQ Muhammad Thoriq Nurul Hasanah Prima, Wahyu Rahmadhani, Firda Rahman, Zumardi SAKINAH, PUTRI Sapriadi, Sopi Saputra, Memed Sopi Sapriadi Thoriq, Muhammad Yesri Elva Yofhanda Septi Eirlangga Yomei Hendra