Expert system for diagnosing EDC cash register malfunctions using the Decision Tree method
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Abstract
Problems with the use of Electronic Data Capture (EDC) machines at Jinjja Chicken Center Point Medan restaurant pose a significant challenge, especially since EDC machines not only function as a means of cashless payment, but also as part of the cashier's operational system. Frequent disruptions include program errors, display errors, total EDC shutdowns, line idles, and “please try again” messages. Until now, the process of reporting and repairing EDC malfunctions has been done manually by submitting a request to the bank, which often makes the diagnosis and repair process slow and inefficient. This is exacerbated by the limited technical information available to restaurant operators when customers experience disruptions. To overcome these problems, this study aims to develop an expert system for diagnosing EDC cash register malfunctions using the Decision Tree method, which is capable of mimicking the way an expert diagnoses EDC problems quickly and accurately. The Decision Tree method was chosen because it is capable of mapping the decision-making process based on attributes or symptoms that arise, to produce a conclusion in the form of the type of malfunction. This system was built using the PHP programming language and run locally using XAMPP as a web server. The research was conducted in a limited setting at the Jinjja Chicken Center Point restaurant in Medan, with five main malfunction categories as variables: Program Error, Display Error, EDC Completely Dead, Line Idle, and Please Try Again. The final result of this system development is expected to provide practical, efficient solutions that approximate the capabilities of an expert, as well as make a real contribution to the utilization of expert system technology to assist in the diagnosis of digital device damage in the service sector.
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Al-Ayubi, M. A. (2024). SISTEM PAKAR UNTUK MENDETEKSI GANGGUAN PADA SEPEDA MOTOR DENGAN PENDEKATAN DEMPSTER-SHAFER STUDI KASUS BENGKEL KENZIE MOTOR. Universitas Islam Sultan Agung Semarang.
Aldo, D., Nur, Y. S. R., Hulqi, F. Y. A., Lanyak, A. C. F., & Hikmah, R. N. (2022). Buku ajar sistem pakar. Dasril Aldo.
Aorora, D. P. (2022). Pelaksanaan Perlindungan Hukum Terhadap Pengguna Kartu Kredit Dalam Transaksi Pembayaran Menggunakan Kartu Melalui Mesin Electronic Data Capture (EDC) Di Bank Mandiri Di Kota Pekanbaru. Universitas Islam Riau.
Chogugudza, M. (2022). The classification performance of ensemble decision tree classifiers: A case study of detecting fraud in credit card transactions. Identifier: Vital, 69317.
Fadhilah, A. N., Husnah, H., & Risnawati, R. (2023). Faktor Penghambat Dan Pendukung Pembayaran Nontunai Menggunakan Mesin Electronic Data Capture (Edc) Pada Pt. Bumi Nyiur Swalayan Pusat. Jurnal Riset Manajemen Dan Ekonomi (Jrime), 1(2), 48–60.
Fadlu Rahman, I., Ayu Shestia, W., Delti Rama, S., Wenny, S. A., & Agung Dermawan, A. (2024). Klasifikasi Diagnosa Pasien Di Klinik Sri Dengan Metode Decision Tree. Jurnal Teknik Ibnu Sina (JT-IBSI), 9(01), 74–82. https://doi.org/10.36352/jt-ibsi.v9i01.890
Fernaldo, A. P., & Sani, A. (2023). SISTEM PAKAR DIAGNOSA GANGGUAN MESIN EDC MENGGUNAKAN METODE FORWARD CHAINING PADA BANK INDONESIA. EBID: Ekonomi Bisnis Digital, 1(2), 231–240.
Ghiasi, M. M. (2020). Implementing Decision Tree-Based Algorithms in Medical Diagnostic Decision Support Systems. In Masters thesis: Memorial University of Newfoundland. Memorial University of Newfoundland. https://research.library.mun.ca/14472/%0Ahttps://research.library.mun.ca/14472/1/thesis.pdf
Hari Agus Prastyo, E., Suhartono, S., Faisal, M., Yaqin, M. A., & Firdaus, R. A. J. (2024). Naive Bayes Classification Untuk Prediksi Cacat Perangkat Lunak. JIPI (Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika), 9(2), 782–791. https://doi.org/10.29100/jipi.v9i2.5508
Hatta, H. R., Syam, R., Cahyadi, D., Septiarini, A., Puspitasari, N., & Wati, M. (2021). Diagnosis of Aglaonema Plant Disease Using Forward Chaining and Naive Bayes Methods. 2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021, 168–172. https://doi.org/10.1109/ICAIBDA53487.2021.9689714
Hesananda, R., Noviani, I. A., & Zulfariansyah, M. (2024). Implementasi YOLOv5 untuk Deteksi Objek Mesin EDC: Evaluasi dan Analisis. BIOS : Jurnal Teknologi Informasi Dan Rekayasa Komputer, 5(2), 104–110. https://doi.org/10.37148/bios.v5i2.127
Korobeynikova, O. (2021). Digital Transformation Of The Retail Payment Market. European Proceedings of Social and Behavioural Sciences, 573–581. https://doi.org/10.15405/epsbs.2021.04.61
Labadie, C. (2021). Essays on Data Democratization & Protection in the Data-driven Enterprise. Université de Lausanne, Faculté des hautes études commerciales.
Pokhrel, S. (2024). Klasifikasi Paper Bertema Teknologi Informasi Dengan Menggunakan Metode Naive Bayes Classifier. In Αγαη (Vol. 15, Issue 1, pp. 37–48). Universitas Islam Negeri Maulana Malik Ibrahim.
Rachman, R. (2019). Penerapan Sistem Pakar Untuk Diagnosa Autis Dengan Metode Forward Chaining. Jurnal Informatika, 6(2), 218–225. https://doi.org/10.31311/ji.v6i2.5522
Sapriadi, S., Hayati, N., Eko Syaputra, A., Septi Eirlangga, Y., Manurung, K. H., & Hayati, N. (2023). Sistem Pakar Diagnosa Gaya Belajar Mahasiswa Menggunakan Metode Forward Chaining. Jurnal Informasi Dan Teknologi, 5(3), 71–78. https://doi.org/10.60083/jidt.v5i3.381
Sulistyowati, M.Kom, Sunarto, M. K. umar khairul afif. (2024). Implementasi Machine Learning Dalam Sistem Pakar Diagnostik. Lpm Mandiri-S1 Informatika., 97–110. http://repository.iti.ac.id/jspui/handle/123456789/2276
Tan, Q., Mu, X., Fu, M., Yuan, H., Sun, J., Liang, G., & Sun, L. (2022). A new sensor fault diagnosis method for gas leakage monitoring based on the naive Bayes and probabilistic neural network classifier. Measurement: Journal of the International Measurement Confederation, 194, 111037. https://doi.org/10.1016/j.measurement.2022.111037
Voytovych, N., Smolynets, I., & Hirniak, K. (2020). The role of technology innovation in food systems transformation. Calitatea, 21(174), 128–134.
WILSON, L. (2024). Enhancing Safety Surveillance during Clinical Trials: The Development of a Safety Data Review Handbook to Support Pharmacovigilance Clinicians. DNP Scholarly Projects. https://scholars.unh.edu/scholarly_projects/117
Witoelar, F., Wicaksono, T. Y., & Mangunsong, C. (2021). Binding constraints on digital financial inclusion in Indonesia: An analysis using the decision tree approach. Center for Global Development.
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