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All Journal JURNAL PENDIDIKAN TAMBUSAI Ainet : Jurnal Informatika Zonasi: Jurnal Sistem Informasi Indonesian Journal of Data and Science Jurnal Teknologi Sistem Informasi Jurnal Informatika Progres JRIIN :Jurnal Riset Informatika dan Inovasi ARUS JURNAL SAINS DAN TEKNOLOGI
Lukman Lukman
Department of Electrical Engineering, Muhammadiyah University Makassar, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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IMPLEMENTASI METODE CERTAINTY FACTOR (CF) PADA APLIKASI SEHAT ORGANIK DALAM MENDIAGNOSA PENYAKIT Desi Anggreani; Lukman
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 1 (2024): Publikasi Artikel ZONAsi Periode Januari 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i1.17877

Abstract

Kesehatan merupakan aspek kunci keberhasilan hidup, dengan tubuh sehat sebagai tanda terhindar dari penyakit. Permasalah kesehatan mengalami transisi pola dimana penyakit yang pada awalnya didominasi oleh penyakit menular dan saat ini telah berpindah ke Penyakit Tidak Menular (PTM). Penyakit yang tidak menular berupa kanker, ginjal, hipertensi, diabetes dan lain sebagainya. Aplikasi sehat organik menghubungkan pasien dengan ahli kesehatan untuk melakukan konsultasi. Pembaruan dari penelitian ini aplikasi dilengkapi dengan informasi rinci pengobatan secara kimia dan pengobatan secara herbal dengan memanfaatkan tanaman obat-obatan. Metode Certainty Factor (CF)digunakan dalam proses diagnosa penyakit berdasarkan gejala yang dirasakan oleh pasien. Berdasarkan implemntasi dan pengujian yang dilakukan aplikasi sehat organik memiliki persentase nilai keyakinan diagnosa penyakit mencapai 91%. Menggunakan 10 pengguna berdasar pada penilaian pakar dan penilaian sistem diperoleh nilai akurasi dalam proses diagnosa mencapai 80%. Aplikasi ini diharapkan dapat memberikan manfaat dalam penanganan dini penyakit tidak menular dan memberikan pemahaman lebih mendalam tentang penyakit tersebut. Health is a pivotal determinant of life success, with a healthy body serving as a marker of disease prevention. Health issues are undergoing a transition where diseases, initially dominated by infectious ones, have now shifted towards Non-Communicable Diseases (NCDs). Non-communicable diseases encompass conditions such as cancer, kidney disorders, hypertension, diabetes, and more. An organic health application establishes a connection between patients and healthcare experts for consultations. An update in this research introduces a comprehensive application that provides detailed information on chemical and herbal treatments, utilizing medicinal plants. The Certainty Factor (CF) method is employed in the disease diagnosis process, relying on symptoms reported by patients. Through implementation and testing, the organic health application achieves a diagnostic confidence level of 91%. An evaluation involving 10 users, based on expert and system assessments, yields an 80% accuracy in the diagnostic process. This application is expected to offer benefits in the early management of non-communicable diseases and provide a deeper understanding of these health issues.
Menentukan Tingkat Kemiripan Judul Mahasiswa Fakultas Keguruan dan Ilmu Pendidikan Unismuh Makassar Menggunakan Metode Cosine Similarity Lukman; Wahyuni, Titin; Baba, Haedir
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/zzptwc89

Abstract

Plagiarism and duplicate thesis titles pose serious challenges to maintaining research originality among students at the Faculty of Teacher Training and Education (FKIP), Universitas Muhammadiyah Makassar. This study aims to implement the cosine similarity method to detect thesis title similarity and evaluate its performance using standard metrics. The research data comprised 1,000 thesis titles processed through preprocessing stages, TF-IDF feature extraction, cosine similarity calculation, and model evaluation. Results show the system can detect similarity with 87.33% accuracy, 100% precision, 58.70% recall, and 73.97% F1-score. Perfect precision indicates the system is highly reliable in identifying similar titles without false positives. However, the relatively low recall indicates that some similar titles remain undetected. This research provides practical contributions as a tool for verifying the authenticity of thesis titles and encourages the development of more sensitive similarity-detection systems in the future.
Co-Authors Abrah, Rezkytullah Baba, Haedir Bakti, Rizki Yusliana Baydowi, Wildi Desi Anggreani Dwi Saputra, Yoggi Fachrim Irhamna Rachman Hamdani Malik, Sayuti Muhammad Faisal Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Mujidah, Jihan Izzathul Nurmisba Perani Rosyani Salam, Abd Salsabila, Ismi Sulaeman Syamsuri, Andi Makbul Titin Wahyuni Yumi