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All Journal Jurnal Teknoin Jurnal Teknologi Jurnal Informatika dan Teknik Elektro Terapan INFORMAL: Informatics Journal Proceeding SENDI_U JURNAL MEDIA INFORMATIKA BUDIDARMA IJIS - Indonesian Journal On Information System JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat AMALIAH: JURNAL PENGABDIAN KEPADA MASYARAKAT JMAI (Jurnal Multimedia & Artificial Intelligence) Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Innovation and Future Technology (IFTECH) Jurnal Informatika dan Rekayasa Perangkat Lunak JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) jurnal syntax admiration Jurnal Sistem Informasi dan Sistem Komputer Jurnal Pengabdian Nasional (JPN) Indonesia Jurnal Teknik Informatika (JUTIF) Jurnal Cahaya Mandalika Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Buletin Poltanesa Jurnal Pendidikan dan Teknologi Indonesia Bulletin of Computer Science Research Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi Journal Of Information System And Artificial Intelligence Innovative: Journal Of Social Science Research TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Teknomatika: Jurnal Informatika dan Komputer Jurnal Pengabdian Nasional (JPN) Indonesia
Sidiq Purnomo, Agus
Universitas Mercu Buana Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences Other

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Implementasi Metode Certainty Factor dan Bayesian dalam Sistem Pakar Diagnosa Inkontinensia Urine Lansia Putri Taqwa Prasetyaningrum; Mutaqin Akbar; Agus Sidiq Purnomo; Irfan Pratama; Imam Suharjo
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp191-199

Abstract

Urinary incontinence is a medical condition commonly experienced by the elderly, requiring prompt and accurate diagnosis for effective treatment. This study aims to develop and compare the performance of two methods in expert systems for diagnosing urinary incontinence in the elderly: Certainty Factor and Bayesian. The developed expert system is web-based and utilizes a symptom dataset collected from the Santa Monika Boro Nursing Home. The findings reveal that the Certainty Factor method excels in diagnostic processing speed, while the Bayesian method offers higher accuracy in diagnostic predictions. This comparison provides valuable insights into selecting appropriate approaches for expert system applications in medical settings.
Analisis Pendekatan MCDM Berbasis Surrogate Weighting Procedures dan TOPSIS untuk Rekomendasi Motor Listrik Maya Putri Nur Fajri; A. Sidiq Purnomo
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9734

Abstract

Jumlah kendaraan listrik yang semakin meningkat di pasaran dengan spesifikasi yang beragam membuat konsumen kesulitan untuk menentukan pilihan yang tepat berdasarkan kebutuhan. Penelitian ini mengembangkan sistem pendukung keputusan untuk merekomendasikan pemilihan motor listrik roda dua di Indonesia menggunakan pendekatan Multi-Criteria Decision Making (MCDM) dengan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Sistem ini mengevaluasi 30 alternatif motor listrik berdasarkan lima kriteria: harga, jarak tempuh, waktu pengisian, kapasitas baterai, dan daya maksimum motor. Penelitian ini menerapkan Surrogate Weighting Procedures yang terdiri dari Equal Weight (EW), Rank Sum (RS), Rank Reciprocal (RR), dan Rank Order Centroid (ROC) untuk menentukan bobot dan TOPSIS untuk peringkat. Hasil penelitian menunjukkan bahwa variasi metode pembobotan menghasilkan perubahan distribusi bobot yang memengaruhi nilai preferensi dan posisi peringkat alternatif. Penelitian ini memberikan kontribusi melalui sistem evaluasi motor listrik yang mampu menghasilkan rekomendasi objektif dan stabil bagi konsumen dengan menguji berbagai skenario kepentingan kriteria. Charged Rimau dan Charged Anoa menunjukkan tingkat konsistensi tertinggi karena memiliki kombinasi spesifikasi yang relatif seimbang pada seluruh kriteria, khususnya jarak tempuh tinggi, waktu pengisian yang lebih singkat, kapasitas baterai besar, serta harga yang masih kompetitif dibandingkan alternatif lain. Sementara itu, Polytron Fox-R juga menunjukkan kestabilan peringkat karena menawarkan keseimbangan antara harga, jarak tempuh, dan kapasitas baterai tanpa memiliki nilai ekstrem pada kriteria tertentu.
Expert System for Stress Level Diagnosis in Final Semester Students Using Certainty Factor Method Based on Web Genoveva Seniyati Moruk; Agus Sidiq Purnomo
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3838

Abstract

Academic stress is a common problem experienced by final semester students during the thesis preparation process. However, many students are unaware of their stress levels due to limited access to campus counseling services and social stigma regarding mental health issues. This study aims to develop a web-based expert system for diagnosing stress levels in final semester students using the Certainty Factor method. The research methodology adopts the Turban model consisting of four stages: intelligence, design, choice, and implementation. Data were collected through an online questionnaire distributed to 60 final semester student respondents from various universities in Indonesia. The system was developed using the PHP programming language with a MySQL database and implements the Certainty Factor (CF) method for diagnosis calculation. The CF method works by combining expert CF values and user CF values iteratively until a final CF value is obtained, which is classified into three stress level categories: mild, moderate, and severe. The results showed that the system successfully diagnosed 54 students (90.0%) with severe stress, 2 students (3.3%) with moderate stress, and 4 students (6.7%) with mild stress. Black Box Testing on 16 scenarios showed that all system functions were valid (100%). Comparison of system diagnoses with psychologist expert diagnoses on 60 respondents showed an accuracy rate of 100%. This system can be used as an early detection tool for academic stress independently while maintaining user privacy.
An Expert System for Insomnia Diagnosis Among Generation Z Students Using the Certainty Factor Method Sisilia buik; A.Sidiq Purnomo
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3839

Abstract

Insomnia among Generation Z students shows a high prevalence, exacerbated by academic pressure and excessive use of digital devices. Existing diagnostic systems have not specifically accommodated the characteristics of insomnia symptoms in this group transparently and clinically explainable. This study aims to design, implement, and test a web-based expert system for diagnosing insomnia using the Certainty Factor method, capable of mimicking psychological reasoning in diagnosing insomnia among Generation Z students. The basic knowledge was obtained from interviews with experts, covering three types of insomnia, namely transient, acute, and chronic insomnia, sixteen symptom indicators, and thirty five inference rules with Certainty Factor values directly determined by experts. The system was developed using Hypertext Preprocessor programming language and MySQL database, and data were collected through questionnaires from seventy Generation Z students. Testing was conducted through Black Box Testing and validation of system diagnostic results against expert diagnoses. Black Box Testing showed a one hundred percent functional success rate. Validation of system diagnostic results against expert assessments on seventy cases demonstrated one hundred percent agreement, proving that the system can produce diagnoses identical to expert assessments. All Generation Z students were diagnosed with chronic insomnia, with Certainty Factor values ranging from zero point two five eight one to one point zero zero zero zero, confirming the high prevalence of chronic insomnia among Generation Z students. This system contributes as an accurate, transparent, and accessible self-screening instrument without barriers of cost or social stigma. Keywords : expert system, insomnia diagnosis, generation Z students, certainty factor method, web
Development of a Web-Based Expert System of Human Eye Disease Diagnosis Based on Symptoms Using the Certainty Factor Method Dionata Pin Asa; Agus Sidiq Purnomo
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3840

Abstract

Limited access to eye health services in Indonesia, the scarcity of ophthalmologists, and low public health literacy regarding early symptoms of eye diseases are major problems that drive the need for technology-based solutions. This study aims to develop a web-based expert system for diagnosing human eye diseases based on symptoms using the Certainty Factor method. The research methodology uses Turban's decision-making model consisting of four phases: intelligence, design, choice, and implementation. The intelligence phase includes observation, literature study, and interviews with eye experts to obtain data on six types of eye diseases (Low Vision, Cataract, Conjunctivitis, Glaucoma, Keratitis, Blepharitis) along with 16 clinical symptoms and expert CF values. The design phase includes knowledge base design, system flow, database, and user interface. The choice phase performs manual and system CF calculations. The implementation phase builds a web-based system using PHP and MySQL. Testing results on 40 patient data showed that the system achieved 100% accuracy according to expert diagnosis. A total of 77.5% of patients had confidence levels in the High and Very High categories. Manual and system calculations showed identical results in all cases. In conclusion, the Certainty Factor method is effective and reliable for web-based eye disease diagnosis.
Implementation of Naïve Bayes Method in a Web-Based Expert System for Diagnosing Diarrheal Diseases in Children Maria Anggelina Klau; Agus Sidiq Purnomo
Poltanesa Vol 27 No 1 (2026): June 2026
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v27i1.3842

Abstract

Diarrheal disease in children remains a major public health problem in Indonesia, particularly among children under five years old. Limited access to healthcare services and medical experts in several regions often causes delays in the diagnosis and treatment process, increasing the risk of dehydration and mortality. Previous studies on expert systems in the healthcare field have generally focused on common diseases and have rarely discussed web-based diagnosis systems specifically designed for childhood diarrheal diseases using the Naïve Bayes method and real medical record data. Therefore, this study aims to develop a web-based expert system capable of assisting the early diagnosis of diarrheal diseases in children using the Naïve Bayes classification method. The research process involved literature studies, expert interviews, medical record data collection from 50 patients at Depok III Public Health Center, system design using Unified Modeling Language (UML), implementation using PHP and MySQL, and system validation testing. The Naïve Bayes method was applied to calculate the probability of seven categories of diarrheal diseases based on user-selected symptoms. The testing results demonstrated that the developed system achieved an accuracy level of 94% compared to expert diagnoses. These findings indicate that the proposed expert system can provide fast and accurate preliminary diagnoses and can support parents and healthcare workers in identifying diarrheal diseases in children more effectively.
Co-Authors Abdulrahman, Firmansyah A Ade Fitriadin Agung Prinato Ahmad Baehaqi, Ahmad Alfian Romadhon Ali, Ficky Septian Andi Atmaja Kusuma Wijaya Andres Anief Fauzan Rozi Anief Fauzan Rozi Anief Fauzan Rozi Anief Fauzan Rozi Ari Cahyono Arif Wiji Setiyanto Arifi Zulaika Aris Susanto Bagas Irvan Bagaskara Barbo Bero Berita Estu Widodo Bima Pangestu, Danang Bowo Nugroho Dany Suktiawan Irman Fiano Dede Widiyanto, Dede Widiyanto Dewi, Mellania Metta Dionata Pin Asa Disantis Dwiki Kurniawan, Yohanes Edwin Rafiza Pradana Nasution Elisabeth Helsi Nggebu Emi Agustina Erlangga Samudera Kencana Fandi Azis Fauzan Rozi, Anief Fauzyah, Luthfia Feby Kristina Butar Butar Fendy Nugraha, Arbiana Fernando Bayu Andika Gaputra, Raygo Genoveva Seniyati Moruk Gita Prastianingrum Hafiid Alfayed, Muhammed Herdiansyah, Moch Rizal Hukom, Jessy Indah Susilawati Irfan Pratama Ismunu, R. Sumarwan Jery Mechael Pentagon Lumbantoruan Jeseka Bintar Laksana Jevi Ariyanti Kadek Ayu Puspita Dewi Kali, Steven Kamto, Kevin Arsan Khotimah, Rosmawatul Letsoin, Amrul Louis Fernando Sinaga M. Ridwan Nur Septian Maharani, Zelvia Olga Malela, Prabu Aji Maria Anggelina Klau Maria Mitro Wid Eko, Antonius Marsela, Dwi Maya Putri Nur Fajri Mokoagow, Mohamad Akbar Mutaqin Akbar Muthia Gidriani Maelan Na'imah, Alifatun Nadafi'ah Hari Fitri Natalia Anjela Sagat Nisriina Nuur Hasanah Octy Kartika Dewi Prasetyaningrum, Putri Taqwa Putra, Andhita Firman Syah Putri, Novita Anggraini Rahmandhita Fikri Sannawira Rahmandhita Fikri Sannawira, Rahmandhita Fikri Ridi Ferdiana Rika Handayani Robi Adi Saputra Rosmeri Maramba Santoso, Muhammad Iqbal Rafid Sembiring, Kristian Eykman Sisilia buik Stefanus M Patanduk Subardjo, Ratna Yunita Setiyani Suharjo, Imam Supatman Supatman Supriyantoo Susanto, Handy Tri Astuti Prihatin Venola Onibala, Injili Wahyu A, Shella Widatin Mayasari Wijayanti, Berlian Rezki Wulan Sari Kaslumin Yasser Yazid Mohammad Yuliana Rahayaan Yulisa Safitri