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All Journal Jurnal Simetris Creative Information Technology Journal SISFOTENIKA Information System for Educators and Professionals : Journal of Information System Jurnal Eksplora Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science International Journal of Computer and Information System (IJCIS) International Research on Big-data and Computer Technology (IRobot) Journal Computer Science and Informatic Systems : J-Cosys International Journal Software Engineering and Computer Science (IJSECS) Bulletin of Network Engineer and Informatics (BUFNETS) Tamilis Synex: Multidimensional Collaboration Humanitis (Jurnal Humaniora Sosial dan Bisnis)
Patmawati Hasan
Universitas Sepuluh Nopember Papua
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data Hasan, Patmawati; Kiswanto, Rahmat H.; Lestari, Susi
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp419-429

Abstract

Malaria remains a serious public health problem in Indonesia, particularly in Papua Province, which accounts for 89% of national malaria cases. The similarity of malaria symptoms with other infectious diseases and limited laboratory facilities often lead to delays and inaccuracies in diagnosis. The study proposes an optimized hybrid model that combines forward chaining and certainty factor (CF) by integrating clinical and laboratory data to improve the accuracy of malaria diagnosis. The research design includes acquiring knowledge from medical experts, developing a rule-based system using forward chaining, and applying CFs to overcome uncertainty in symptom interpretation. The system is implemented using Python with support from libraries such as NumPy and PyKnow. The test results showed that the integration of laboratory data significantly improved diagnostic performance, with accuracy increasing from 81% malaria-positive using clinical data alone to 98% malaria-positive after combining with laboratory data. Expert testing to validate the accuracy of clinical and laboratory data results compared to expert validation results in an accuracy score of 98%. These findings show that the optimization of the hybrid forward chaining model and CF for malaria diagnosis based on clinical and laboratory data as a recommendation tool for early diagnosis of malaria in endemic areas.
Expert System for Disease Diagnosis in Pregnant Women Using Backward Chaining Method Yosefin Caltrin Odelia; Patmawati Hasan; Emy Lenora Tatuhey
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4498

Abstract

Healthcare providers treating pregnant women need reliable tools for accurate disease diagnosis. We created an expert system that assists doctors in identifying illnesses during pregnancy through backward chaining inference methods. The project involved collaboration with medical professionals at North Jayapura Health Center who shared their clinical expertise to develop the system. Our backward chaining method examines patient symptoms by working backward from potential diseases to find diagnostic matches. Through detailed interviews, healthcare professionals contributed their years of experience treating pregnant patients. We transformed their knowledge into diagnostic rules that help the system recommend possible diagnoses based on symptom patterns. The system serves as a decision support tool that helps doctors make quicker, more confident diagnostic choices. Many pregnancy-related diseases share similar symptoms, which can confuse even experienced practitioners. Our tool helps sort through these diagnostic challenges while preserving the doctor's authority in making final decisions. Evaluation showed the expert system significantly improved diagnostic performance in primary care settings. Many healthcare facilities lack access to specialists familiar with pregnancy complications. The system brings expert-level diagnostic knowledge to these underserved areas, improving patient care quality
PEMODELAN UML SISTEM INFORMASI PERPUSTAKAAN PADA USN PAPUA Nadia Nadia; Ghina Tripasha, Fheisyach Artianshal Karubun, Dwi andiyani, Grace Adelin Rumbairusy, grisye f silaho
HUMANITIS: Jurnal Homaniora, Sosial dan Bisnis Vol. 2 No. 11 (2025): HUMANITIS : Jurnal Humaniora, Sosial dan Bisnis
Publisher : ADISAM PUBLISHER

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

Abstract

Libraries are an important component in supporting the teaching and learning process in educational institutions, including at the University of Science and Technology (USN) Papua. To improve the efficiency of data management and information services, a structured and easily developed web-based library information system is needed. This study aims to design a library information system using the Unified Modelling Language (UML) approach as a modelling tool. The diagrams used include activity diagrams and class diagrams to illustrate business processes such as book data management, member management, borrowing, returning, and reporting. The modelling results show a systematic workflow and the interrelationships between entities in the system. With this modelling, it is hoped that the development of a library information system at USN Papua can be carried out more effectively and support digital transformation in academic services
Detection of Tinea Skin Disease Using Convolutional Neural Network (CNN) Method Patmawati Hasan; Nourman Satya Irjanto; Fifian Theresia Sibi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 17 No. 1 (2026): JURNAL SIMETRIS VOLUME 17 NO 1 TAHUN 2026
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v17i1.15220

Abstract

The skin disease tinea, caused by a dermatophyte fungal infection, is a significant health concern and can affect the quality of life of sufferers. Early detection of this disease is essential to prevent its spread, especially in areas with limited specialized medical personnel. This study aims to develop a tinea skin disease detection system using the Convolutional Neural Network (CNN) method, which can classify three types of tinea, namely Tinea Pedis, Tinea Manuum, and Tinea Corporis. The dataset used consists of 1,146 images of skin lesions equally divided into three categories, with each category containing 382 images representing different stages of disease symptoms. This dataset was processed through preprocessing techniques, including image cropping, scaling, contrast adjustment, and data augmentation to improve the training quality of the model. The developed CNN model has a structure of 8 convolutional layers and was trained using 80% training data and 20% validation data. The training results showed that the model achieved 75% accuracy on the training data and 85% on the validation data after 20 epochs, with consistent loss reduction. These results show that the CNN model can detect tinea skin disease with high enough accuracy and can be used as a diagnosis aid for medical personnel, especially in areas that lack specialists. The developed web-based application allows users to upload images and receive diagnosis results directly, providing convenience in early detection of tinea skin disease. This research makes an important contribution to the development of technological solutions in the improvement of health services in areas with limited medical resources.
Co-Authors Akrilvalerat Deainert Wierfi Alifyaa, Adhyndha Anjutami, Rianner Armehzan, Hazrin Asso, Agus Ayun, Beto Babut, Helena Caltrin, Yosefin Dahlan, Muhammad Imron Dalton, Timothy Deliana, Samanta Dony Ariyus Dumpel, Jessica Eka W Sholeha Eka Wahyu Sholeha Elia Mando Wanggai Elvis Pawan Elvis Pawan Elvis Pawan Ema Utami Estevina Carolina Bagre Fatagur, Karolina Fenetiruma, Elton Fifian Theresia Sibi Fonataba, Alan Gladis Dominica Ngaderman hababuk, Abril Vivi Yolanda Helson Matuan Herlina Lenora Yowei Hokoyoku, Venezuella Regina Joshina Imelda Numberi Irianti, Nurhaeni Irjanto, Norman S isawa, Charles sesera Islam, Fachrul Iwanggin, Wedes J.M, Esau Jenny Temba Jered Imanuel Wanda Kawana, Yuliana Dorce Khaihena, Albert Kimber, Petronela Kiswanto, Rahmat Haryadi Kondy, Sharon Telvie krimadi, semuel Kulwy, Agustinus Kusrini Lahallo, Jim Lito Klore, Skolastika Elisabeth Lizhau, Ting Lokobal, Luis M. Tomaula, Rudi Marcella Putri Pentury Mareyke Kalasina Yawa Maryen, Hana Rina Masyruah, Khoiratul Melki Sendoni Wondiwoi Minarni Muzaqi, Muhammad Iqbal Da'i Nadia Nadia Nadia Nadia Nangguar, Maria Loisa Nangguar, Rosita Nasiri, Asro Nawu, Jerom Neno, Friden Elefri Nourman Satya Irjanto Nur Atya Paiki, Gracella B Paulisen Matu Pawan, Elvis Pawan, Elvis Povay, Wama Albertho Prasetyaningrum, Eka Putra Immanuel Pali Rahmaddiyanto, Syarif Rahman, Fadil Rajsya, Indra Ramos, Victor Renyaan, Alfaris Salam Rumboirusi, Kartensia Firli Rumkorem, M Novran R Rusadin, Fina Alvionita Sabra, Isac Samon Sandra Keke Waromi Sari, Yuyun Purnama Sariyati H.Y. Bei Sibi, Fifian Theresia Simamora, Gysbi P Simatauw, Jeanet D Solo, Klemensia Dina I I Sulobua, Riski SUSI LESTARI SW, Febrian Ray Gere Tatuhey, Emy Lenora Tekam, Lodi Thamrin, Rosiyati M.H Theo W. M. L. K. Gusbager Tirsa Meira Pontoh Triyanto Triyanto Tuahuns, Wafiq Azizah Tulung, Revo Riantino Veny Cahya Hardita Viona Sharon Prilia Rombot Wahyu Wijaya Widianto Walangitan, Rafaely Hesky Wanggai, Maya Y Arsyad, Dhea Firda Y. A Numberi, Barbalina Y. Bei, Sariaty H. Yarangga, Abigael M I Yaruyap, Sertina Yosefin Caltrin Odelia Yulius Nahak tetik Yunita, Selviana