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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik bit-Tech
Asista Ainun Jannatin
Universitas Islam Indonesia
Computer Science & IT Engineering

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AI sebagai Alternatif Solusi Manajemen Tingkat Stres Mahasiswa Hanuga Fathur Chaerulisma; Ihwan Dwi Rizky Fitriawan; Asista Ainun Jannatin; Fayruz Rahma
Prosiding Sains Nasional dan Teknologi Vol 13, No 1 (2023): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 2023
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v13i1.9244

Abstract

Stres merupakan keadaan tidak nyaman yang disebabkan oleh tuntutan tertentu. Kehidupan perkuliahan sering kali membuat mahasiswa mengalami stres. Dampak negatif stres dapat memberikan efek yang serius. Atas dasar ini, diperlukan sebuah solusi untuk membantu mahasiswa dalam mengatasi stres. Penelitian ini berfokus pada kecerdasan buatan (AI) sebagai alternatif solusi untuk manajemen stres mahasiswa. Metode penelitian yang digunakan adalah penelitian kuantitatif dengan menyebar kuesioner kepada 14 responden mahasiswa aktif. Harapannya, penelitian ini dapat memberikan dampak positif untuk kesehatan mental mahasiswa. Selain itu, penelitian ini dapat menjadi pertimbangan penggunaan AI oleh institusi pendidikan tinggi sebagai alat pendukung psikologis mahasiswa.
Personalized Skincare Recommendation System Based on Ontology and User Preferences Asista Ainun Jannatin; Chanifah Indah Ratnasari
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2857

Abstract

Personalized skincare product selection remains a complex but critically important challenge, as tailoring recommendations to individual skin profiles directly enhances treatment efficacy and fosters consumer trust. Traditional systems, such as content-based and collaborative-filtering, often fail to capture semantic interactions among skin types, concerns, and ingredients. To address these limitations, we propose an innovative ontology-based skincare recommendation system that integrates structured dermatological knowledge with semantic reasoning. Leveraging the Methontology framework, we developed an ontology composed of twelve core classes such as Product, Ingredient, Skin Type, and Skin Concern and more than twenty-five object properties to model interrelated concepts. The knowledge base was populated via web scraping from three prominent platforms (Sociolla, Beautyhaul, Skinsort), yielding over 3,800 products and 28,000 ingredients. We augmented this dataset with dermatological literature to ensure clinical validity. The architecture employs Apache Jena Fuseki and SPARQL for inference, with a React-Node.js web interface. Users input skin type, concerns, and sensitivities, which are translated into RDF triples and processed through semantic rules to generate personalized recommendations. An evaluation based on the Technology Acceptance Model (TAM) assessed Perceived Usefulness and Ease of Use. Ten diverse respondents rated the system with an average score of 4.5 out of 5 (SD=0.3) and endorsed the relevance of recommendations with a score of 4.8. Our findings demonstrate that semantic technologies can significantly enhance personalization and transparency in skincare solutions. This work lays a robust foundation for future innovations in beauty technology, clinical decision support, and consumer health platforms.
Co-Authors Chanifah Indah Ratnasari Fayruz Rahma Hanuga Fathur Chaerulisma Ihwan Dwi Rizky Fitriawan