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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Bina Insani ICT Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Information System for Educators and Professionals : Journal of Information System JIEET (Journal of Information Engineering and Educational Technology) Informatics for Educators and Professional : Journal of Informatics Jurnal Teknik Informatika UNIKA Santo Thomas Journal of Information System, Applied, Management, Accounting and Research Journal of Computer System and Informatics (JoSYC) Jurnal Kajian Ilmiah Jurnal Sistem Informasi Bulletin of Computer Science Research Journal of Students‘ Research in Computer Science (JSRCS) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System
Entin Sutinah, Entin
Universitas Bina Sarana Informatika
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences Other

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Sistem Pakar Berbasis Whatsapp Bot Dengan Metode Forward Chaining Untuk Diagnosis Dini Gangguan Mental Pada Perkembangan Anak Fadhilah, Rayhan; Revansa , Muhammad Fahri; Fakhriza, Giffari Ahmad; Sutinah, Entin; Kusumo, Aryo Tunjung
Journal of Students‘ Research in Computer Science Vol. 6 No. 2 (2025): November 2025
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/4b27ra56

Abstract

Mental disorders in children can hinder their development if not detected early. Limited public awareness and a shortage of professionals often lead to undiagnosed cases. This study developed an expert system based on a WhatsApp Bot using the Forward Chaining method to support early diagnosis of children's mental disorders. Built with Node.js and MySQL, the system can identify seven types of disorders: ADHD, anxiety, depression, autism, OCD, eating disorders, and PTSD. Black box testing showed the system functions well and provides accurate results. It is expected to assist parents and educators in early detection and awareness of mental health issues in children.
Penerapan Algoritma Decision Tree Untuk Memprediksi Pengelolaan Inventaris Sarana Pembelajaran Kampus Martini, Martini; Nani Agustina; Entin Sutinah
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.889

Abstract

UBSI as an educational institution that has learning support facilities must be able to manage campus inventory effectively. This study aims to determine the management of asset management that needs to be done, both in the form of routine maintenance and updating of goods. The UBSI Jatiwaringin branch campus only makes reports on the condition of inventory items, so it cannot determine whether the reported inventory data is updated or repaired, so far it is not known which items are prioritized based on their level of importance. The data will then be followed up by the main campus to check the inventory data report. The method used to determine inventory predictions is the Decision Tree Algorithm which has priority, location, condition, frequency, and prediction attributes. As targets in the decision tree are prediction attributes that have maintenance or renewal classes. Determination of inventory data predictions by calculating the entropy, gain, gain info, and gain ratio values ??of each attribute and resulting in the Priority attribute being the root node in the formed decision tree. This indicates that the priority attribute has a strong influence in determining whether an item is included in the maintenance or renewal class. Based on testing results using RapidMiner software with the K-Fold Cross Validation method, the Decision Tree algorithm can generate a decision model with an average accuracy of 86.67% in campus inventory management. The results of this study are expected to be useful for Jatiwaringin Campus administrators to conduct initial inspections without waiting for repairs from the main campus.
Co-Authors Achmad Baroqah Pohan Agustina , Nani Andi Setiawan Andre Gusti Hermawan Anwar , Rian Septian Asmoro, Randi Ashar Azima, Gustiyasyah Nurul Bangkit Anugrah Putra Barkah, Novalia Eni Heni H Eni Heni Hermaliani Fadhilah, Rayhan Fakhriza, Giffari Ahmad Gustiyasyah Nurul Azima Ibnu Alfarobi Imaduddin, Encep Fahmi Khoirun Nisa Kusumo, Aryo Tunjung Lina Rosina Limbong Martini Martini Martini Martini Martini Mega Priyanto Moch.Arif Wicaksono Nani Agustina Nani Agustina Nani Agustina - AMIK BSI Jakarta, Nani Agustina Nico Hermanto Novalia Barkah Odilia Rosdiana Simamora Redi Nurhidayat Revansa , Muhammad Fahri Reza Trimahardhika Rian Septian Anwar Riza Syahrial Rizal Gustiar Sandra Regina Syarif Hidayatulloh Sya’Roni