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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal Informatika Universitas Pamulang PeTeKa IKRA-ITH ABDIMAS Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Tekinkom (Teknik Informasi dan Komputer) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JUKI : Jurnal Komputer dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Review of Practical Innovation, Technology and Green Energy (IRPITAGE) International Conference on Health Science, Green Economics, Educational Review and Technology (IHERT) Jurnal Penelitian Sistem Informasi Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi
Pakpahan, Victor Maruli
Universitas Mahkota Tricom Unggul
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Social Sciences Other

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Journal : Modem : Jurnal Informatika dan Sains Teknologi

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Sistem Pakar Mendiagnosa Penyakit Pada Tumor Otak Menggunakan Metode Case Based Reasoning (CBR): Studi Kasus : RSUD Dr.R.M. Djoelham Mhd Arif Permata; Yani Maulita; Victor Maruli Pakpahan
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.253

Abstract

This research aims to develop an expert system that can diagnose diseases related to brain tumors using the Case Based Reasoning (CBR) method. The CBR method works by comparing new cases with previous cases that have been stored in the database to provide appropriate diagnoses and treatment recommendations. This system is designed to assist medical personnel in analyzing patient symptoms, thus speeding up the process of identifying the type of brain tumor. In addition, the system is also equipped with a knowledge base obtained from real cases that have been validated by medical experts. Test results show that the diagnostic accuracy of this system reaches a fairly high level, especially in detecting frequently encountered types of brain tumors. Thus, this system has the potential to be an effective tool in the medical diagnosis process, especially in patients who show symptoms of brain tumors.
Diagnosa Penyakit Epilepsi Menggunakan Metode Bayes Ade Rahayu; Achmad Fauzi; Victor Maruli Pakpahan
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.231

Abstract

Epilepsy, or apoplexy, is a chronic disease characterized by recurrent seizures and impaired consciousness due to disorders of the central nervous system. In developing countries, including in RSU Putri Bidadari, epilepsy management is often hampered by high consultation costs, resulting in suboptimal quality of treatment and patient recovery. To overcome this challenge, a system is needed that can facilitate the diagnosis and treatment of epilepsy more efficiently. By using this method, RSU Putri Bidadari can improve the precision of epilepsy diagnosis and determine more appropriate treatment steps, despite limited resources. The Bayes method, as a statistical approach, offers a potential solution to improve the accuracy of diagnosis through data-based probability estimation of diseases and symptoms reported by patients such as frequent hunger, thirst, urination, weight loss, vaginal infections, easy fatigue, tingling legs, and blurred vision. The analysis results of the system show an estimated probability of 73% for patients suffering from generalized epilepsy. The Bayes method-based system is expected to help RSU Putri Bidadari in providing more effective treatment and improving the overall quality of life of epilepsy patients.
Co-Authors ACHMAD FAUZI Ade Rahayu Artika Suri Ayangda Danianty Miranda Br. Bangun Darjat Saripurna Debora Tambunan Dewi Rafiah Pakpahan Doris Yolanda Saragih Elsida Aritonang Farida Gultom Fauzi, Achmad Frans Gideon Sinuhaji Glen Bagasta Simanjuntak Grestin Ekalina Turnip Hendra Jonathan Sibarani Hesty Vitara Jen Peng Huang Jovanka Arnelita Jubelando O Tambunan Marto Sihombing Mhd Arif Permata Muammar Khadapi Muhammad Yunus Nezha Febriyan Nova Erawati Sidabalok Novi Yunanda Putri Ramanda, Dika Rusmin Saragih, Rusmin Salim Efendi Sanjaya Pinem Saut Matedius Situmorang Saut Matedius Situmorang Sibarani, Hendra Jonathan Sinuhaji, Frans Gidion Sitorus, Dompak Sri Dewi Novita Tony Blayer Simangunsong Viktor Edison Hutagaol Widya Natasya Yani Maulita