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All Journal International Journal of Advances in Applied Sciences IPTEKMA CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Jurnal Informatika Upgris JOIN (Jurnal Online Informatika) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Eksplora Informatika JMM (Jurnal Masyarakat Mandiri) JURIKOM (Jurnal Riset Komputer) Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Global Science Society Journal Of Information And Technology Unimor (JITU) Jurnal Minfo Polgan (JMP) Jurnal Energi Elektrik AMMA : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering JURUTERA (Jurnal Umum Teknik Terapan) CHAIN: Journal of Computer Technology, Computer Engineering and Informatics Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Masyarakat Berdikari dan Berkarya (MARDIKA)
Akram, Rizalul
Program Studi Informatika , Universitas Samudra
Religion Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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Penerapan Fuzzy Logic Dan Case-Based Reasoning Pada Sistem Pakar Diagnosis Penyakit Gizi Balita di Puskesmas Manyak Payed Novianda .; Rizalul Akram; Dela Fitria
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 8, No 1 (2025): Januari
Publisher : Akademi Ilmu Komputer Ternate

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v8i1.298

Abstract

Abstrak: Status gizi balita berdampak signifikan terhadap kesehatan dan perkembangan mereka, dengan gizi buruk sebagai salah satu penyebab utama kematian balita. Deteksi dini penyakit gizi menjadi kunci untuk meningkatkan kualitas pertumbuhan dan perkembangan anak. Penelitian ini mengembangkan sistem pakar berbasis Fuzzy Logic dan Case-Based Reasoning (CBR) untuk mendiagnosis tujuh penyakit gizi pada balita, termasuk defisiensi vitamin A, kekurangan yodium, anemia, stunting, marasmus, kwashiorkor, dan obesitas, dengan mempertimbangkan 47 gejala. Data dikumpulkan melalui wawancara, observasi, dan studi literatur di Puskesmas Manyak Payed. Sistem dikembangkan menggunakan PHP untuk logika aplikasi, MySQL untuk basis data, dan dirancang menggunakan diagram ERD. Fuzzy Logic digunakan untuk menentukan tingkat keparahan gejala (rendah, sedang, tinggi), sedangkan CBR menilai kemiripan kasus baru dengan data sebelumnya. Hasil pengujian menggunakan 20 data kasus menunjukkan akurasi diagnosis sebesar 100%, dengan tingkat keparahan dan relevansi masing-masing 85%. Penerapan Fuzzy Logic dan Case-Based Reasoning (CBR) dalam sistem pakar ini telah terbukti efektif dalam meningkatkan akurasi dan relevansi diagnosis penyakit gizi pada balita. Sistem ini efektif dalam mendukung deteksi dini penyakit gizi dan membantu tenaga kesehatan memberikan intervensi yang lebih cepat dan tepat, sehingga dapat meningkatkan kesehatan serta kualitas hidup balita.Kata kunci: balita; case-based reasoning; fuzzy logic; penyakit gizi; sistem pakarAbstract: The nutritional status of toddlers significantly impacts their health and development, with malnutrition being one of the leading causes of toddler mortality. Early detection of nutritional diseases is crucial to improving children's growth and development. This research developed an expert system based on Fuzzy Logic and Case-Based Reasoning (CBR) to diagnose seven nutritional diseases in toddlers, including vitamin A deficiency, iodine deficiency, anemia, stunting, marasmus, kwashiorkor, and obesity, considering 47 symptoms. Data was collected through interviews, observations, and literature studies at Puskesmas Manyak Payed. The system was developed using PHP for application logic, MySQL for the database, and designed using an ERD diagram. Fuzzy Logic was used to determine the severity of symptoms (low, moderate, high), while CBR assessed the similarity between new cases and previous data. Testing results using 20 case data showed a diagnosis accuracy of 100%, with severity and relevance each reaching 85%. The implementation of Fuzzy Logic and Case-Based Reasoning (CBR) in this expert system has proven effective in improving the accuracy and relevance of diagnosing nutritional diseases in toddlers. This system is effective in supporting the early detection of nutritional diseases and assisting healthcare providers in delivering faster and more accurate interventions, thereby improving the health and quality of life of toddlers.Keywords: case-based reasoning; expert system; fuzzy logic; nutritional diseases; toddler
Klasifikasi Citra Bakteri Tuberkulosis pada Sampel Sputum Menggunakan Metode Backpropagation Neural Network Munawir, Munawir; Halimah, Nur; Akram, Rizalul
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8399

Abstract

Tuberculosis (TB) is a disease caused by the bacterium Mycobacterium tuberculosis, which was discovered by Robert Koch in 1882. The bacterium is rod-shaped, with a width of 0.3–0.6 μm and a length of 1–4 μm. It is transmitted through the air, for example, when an infected person coughs or sneezes. TB diagnosis is typically performed through microscopic analysis of sputum samples. TB is a serious infectious disease and remains a global health concern. Rapid and accurate diagnosis is crucial for effective treatment, yet conventional methods are often time-consuming and less precise. This study developed a TB bacterial image classification system for sputum samples using a Backpropagation Neural Network (BPNN). The system differentiates between single and clustered bacteria using length, endpoints, and branching features. The dataset consisted of 120 images, divided into 60 training and 60 testing samples. All images were processed using preprocessing techniques to enhance image quality. The length, endpoints, and branching features were extracted from the images and used as input to the BPNN. The results showed that the BPNN method could classify TB bacterial images with an accuracy of 86%. The system was also able to distinguish single and clustered bacteria more accurately, potentially contributing to improved TB diagnosis.
Sistem Pakar Diagnosa Penyakit Pada Kucing Anggora Menggunakan Metode Fuzzy Mamdani Berbasis Website Nur Aynun Siregar; Rizalul Akram; Nurul Fadillah
CHAIN: Journal of Computer Technology, Computer Engineering, and Informatics Vol. 1 No. 2 (2023): Volume 1 Number 2 April 2023
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/chain.v1i2.30

Abstract

Cats are the most popular mammals for many people, such as the Angora cat. Cat lovers don't look at age, most of these cats are liked by all people so it's not uncommon for cats to be found everywhere. The Angora cat has a beautiful physique with fur and a cute face. So it's not uncommon for the Angora cat to become the most preferred charmer of hearts. This hobby of keeping cats is not only seen from their stature but must be accompanied by their health. Some of the diseases that are often suffered by Angora cats include Feline Calicivirus, Helminthiasus, Dermatopytosis, Conjunctivitis, and Toxoplasmosis. This expert system is used by carers to diagnose early disease in Angora cats based on the symptoms experienced by cats and to help provide information about the cat's disease. Research data using laboratory tests, interviews and documentation were then processed using the Fuzzy Mamdani method and research design with UML which was designed in a use case diagram model to describe system activity. The stages are forming the Fuzzy Mamdani set, determining the rules, and defuzzification. The test results with 46 symptoms from 5 types of disease with an accuracy value of this system is 90.90%. The result of this research is that the system can help Anggora lovers from an early age to find out what are the symptoms of various types of cat diseases, as well as solutions to overcome them and can consult a doctor before proceeding to a more advanced stage.
Identification of Facial Wrinkles using Gabor Filters and the Naïve Bayes Algorithm Munawir, Munawir; Harahap, Siti Rafah Sa`dia; Akram, Rizalul
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Facial Wrinkles are one of the key indicators in identifying signs of aging on the skin. Detecting facial wrinkles poses a challenge in image processing due to their complex, coarse texture, which is often difficult for computers to recognize, especially under varying lighting conditions, camera angles, and facial expressions. This study focuses on the application of features using Gabor Filters for the texture feature extraction, with the final results determined by the Naïve Bayes classification algoritm. In this study, 200 facial images were used, divided into two clases, with 100 images per class serving as training data. For the test data, 100 facial images were used, consisting of 50 wrinkled facial images and 50 non-wrinkled facial images. Based on the test result using the Confusion Matrix, the accuracy was 74%, precision 80%, recall 64% and F1-Score 71%. These results indicate that the combination of Gabor filters and Naïve Bayes is quite effective in recognizing wrinkle patterns on the face based on extracted texture feature, and can serve as a faoundation for developing more accurate facial wrinkle detection systems in the future.
Co-Authors Abdul L. Mawardi Ahmad Ihsan Ahmad Ihsan al-azizi, khalid mahfudh Anam, M Khairul Ananta, Auliyah Balqis Asmita, Tesa Atmaja, Teuku Hadi Wibowo Cut, Banta Dela Fitria Dewiyana Fadhliani, Fadhliani Farid Syaumi Rizki Ginting Hapzi Ali Harahap, Siti Rafah Sa`dia Khairul Muttaqin Liza Fitria Marhamah Muhammad Khalid Muhammad Zaki Munawir Munawir Munawir Munawir Munawir Nanda, Novianda Nanda Novianda Novianda . Novianda Novianda Novianda Novianda Nur Aynun Siregar Nur Halimah Nurul Fadillah Pangestu, M Ridho Rozzi Kesuma Dinata S, Rahmansyah Salsabila, Unik Hanifah Seri Rati SRI WULANDARI Tesa Asmita Asmita Thoriq, Thoriq Thoriq Tsunami, Lindu wibowo atmaja, teuku Hadi Yona Fransiska Dewi Yulida Amri