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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Mahasiswa Ilmu Komputer Jurnal Komputasi
Ibrahim, M. Yhogha Ismail Ibn
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Rule-Based Expert System Model with Backward Chaining Algorithm for Symptom-Based Skin Disease Diagnosis Atim, Sandi Badiwibowo; Ibrahim, M. Yhogha Ismail Ibn
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4416

Abstract

A rule-based expert system was a computational model designed to emulate expert decision-making using a knowledge base and inference algorithms. This research developed a rule-based expert system model with a backward chaining algorithm to diagnose skin diseases based on clinical symptoms. Backward chaining, a goal-driven inference method, started with a disease hypothesis (e.g., psoriasis) and verified related symptoms (e.g., kemerahan, sisik keperakan), enabling efficient differentiation of skin diseases with overlapping symptoms, such as dermatitis, psoriasis, and scabies. The model provided advantages in handling uncertainty, produced accurate diagnoses, and supporting interactive symptom verification. Developed using a knowledge base from credible sources like WHO and AAD, the model was intended to assist in clinical decision-making. The results showed that the backward chaining algorithm effectively improved the accuracy and efficiency of diagnosing skin diseases based on patient-reported symptoms
Analisis Sentimen Ulasan Digital Menggunakan Naive Bayes dan Pengaruhnya terhadap Keputusan Calon Tamu: Studi Kasus Hotel Santika Premiere Bandara Palembang Ruswita, Istiana; Ibrahim, M. Yhogha Ismail Ibn
Jurnal Komputasi Vol. 14 No. 1 (2026)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v14i1.350

Abstract

Perkembangan teknologi informasi meningkatkan peran ulasan digital sebagai referensi utama dalam pengambilan keputusan pemesanan hotel. Penelitian ini bertujuan untuk menganalisis sentimen ulasan digital menggunakan metode Naive Bayes serta menguji pengaruhnya terhadap keputusan calon tamu pada Hotel Santika Premiere Bandara Palembang. Data penelitian terdiri dari 15.463 ulasan yang diperoleh dari Google Review dan Traveloka, serta responden kuesioner yang pernah membaca ulasan sebelum melakukan pemesanan. Tahapan penelitian meliputi preprocessing teks, klasifikasi sentimen, dan analisis regresi linier. Hasil menunjukkan bahwa 73,3% ulasan tergolong positif dengan tingkat akurasi model sebesar 88,2%. Analisis regresi menunjukkan bahwa sentimen ulasan digital berpengaruh positif dan signifikan terhadap keputusan calon tamu (p < 0,05) dengan koefisien determinasi (R²) sebesar 0,458. Temuan ini menegaskan bahwa reputasi digital berbasis e-WOM berperan penting dalam membentuk kepercayaan dan niat pemesanan, serta dapat dimanfaatkan sebagai dasar pengambilan keputusan manajerial berbasis data di industri perhotelan.
IMPLEMENTASI ALGORITMA NAÏVE BAYES UNTUK KLASIFIKASI KELAYAKAN PENERIMA PROGRAM INDONESIA PINTAR (PIP) PADA SD NEGERI X Ibrahim, M. Yhogha Ismail Ibn; Istiana Ruswita
Jurnal Mahasiswa Ilmu Komputer Vol. 7 No. 1 (2026): Jurnal Mahasiswa Ilmu Komputer March 2026
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ilmukomputer.v7i1.11457

Abstract

Abstrak : Efektivitas distribusi Program Indonesia Pintar (PIP) sangat bergantung pada akurasi proses seleksi di tingkat sekolah. Di SD Negeri X, mekanisme penentuan penerima bantuan masih menghadapi kendala akibat proses verifikasi manual yang cenderung subjektif dan tidak efisien. Penelitian ini mengeksplorasi penggunaan teknik data mining dengan algoritma Naïve Bayes untuk mengotomatisasi klasifikasi kelayakan siswa. Atribut yang diuji meliputi indikator ekonomi dan kondisi sosial keluarga siswa yang diolah menggunakan metode 10-Fold Cross Validation. Temuan penelitian menunjukkan bahwa model Naïve Bayes berhasil mencapai tingkat akurasi sebesar 78,20%. Walaupun nilai recall (52,68%) dan presisi (53,64%) dipengaruhi oleh karakteristik dataset yang ada, penggunaan algoritma ini memberikan landasan pengambilan keputusan yang lebih terukur dan transparan. Implementasi sistem ini diharapkan dapat meminimalisir kesalahan manusia dalam identifikasi penerima bantuan sehingga penyaluran PIP menjadi lebih tepat sasaran. Abstract: The effectiveness of the Indonesia Smart Program (PIP) distribution significantly depends on the accuracy of the selection process at the school level. At SD Negeri X, the mechanism for determining aid recipients still faces challenges due to manual verification processes that tend to be subjective and inefficient. This study explores the application of data mining techniques using the Naïve Bayes algorithm to automate student eligibility classification. The tested attributes include economic indicators and the social conditions of the students' families, which were processed using the 10-Fold Cross-Validation method. The research findings demonstrate that the Naïve Bayes model successfully achieved an accuracy rate of 78.20%. Although the recall (52.68%) and precision (53.64%) values were influenced by the characteristics of the existing dataset, the use of this algorithm provides a more measurable and transparent basis for decision-making. The implementation of this system is expected to minimize human error in identifying aid recipients, thereby ensuring that PIP distribution is more targeted.
Co-Authors Atim, Sandi Badiwibowo Istiana Ruswita Ruswita, Istiana