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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Medika: Medika Jurnal Riset Sistem Informasi
Refida Septiana Putri
Universitas Amikom Purwokerto
Computer Science & IT Dentistry Education Health Professions Medicine & Pharmacology Nursing Public Health Other

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Pendampingan e-Smart Early Warning untuk Peringatan Dini Banjir di Wisata Desa Karangsalam Lor Nandang Hermanto; Pungkas Subarkah; Dini Riandini; Refida Septiana Putri; Salma Ngarifatul Khofiyah; Bagus Adhi Kusuma; Primandani Arsi
Jurnal Medika: Medika Vol. 4 No. 4 (2025)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/0yyt8272

Abstract

The Juneng Mijil Community Self-Help Group (KSM) in Karangsalam Lor Village, Baturraden District, Banyumas Regency is a village tourism manager, one of which is Juneng Waterfall. The problem with the partners is that there is no technology used for early flood warning at the Juneng Waterfall and Twin Waterfall tourist sites, as well as low community literacy regarding early flood management. This activity aims to optimize the use of Android-based information technology and the Internet of Things (IoT) applied at Juneng Waterfall and Kembar Waterfall, through KSM Juneng Mijil in Karangsalam Lor Village. The implementation methods in this community service include the Pre-Implementation Stage, Implementation Stage, and Evaluation Stage. The results of the activity showed high enthusiasm among participants, as well as an increase in understanding and knowledge regarding the benefits, usage, and maintenance of the Internet of Things (IoT) and Android. This activity is important in the utilization of technology, particularly in optimal and safe flood warning systems for the community.
CERTAINFACE SISTEM PAKAR BERBASIS WEB UNTUK IDENTIFIKASI TIPE KULIT WAJAH MENGGUNAKAN METODE CERTAINTY FACTOR SEBAGAI MEDIA EDUKASI PERAWATAN KULIT Refida Septiana Putri; Iqrom Danang Putra; Sitaresmi Wahyu Handani
Jurnal Riset Sistem Informasi Vol. 3 No. 3 (2026): Juli : Jurnal Riset Sistem Informasi
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/ahwges69

Abstract

Identifying facial skin type is a crucial first step in selecting appropriate skincare products, yet many individuals lack access to affordable dermatological consultations. This study developed a web-based expert system for facial skin type identification using the Certainty Factor (CF) method, implemented via the Laravel framework. The system diagnoses five skin types normal, dry, oily, combination, and sensitive based on 13 symptoms derived from dermatological literature. CF values ​​were sourced from published studies and serve as the system's Knowledge Base. Users input their level of confidence regarding the symptoms they experience; these inputs are combined with expert CF values ​​to calculate confidence scores for each skin type, with the highest-scoring type designated as the diagnosis result. The system also provides educational skincare articles tailored to each skin type. Development results demonstrate that the CF method effectively handles the subjectivity and uncertainty inherent in symptom-based diagnosis, enabling the system to serve as an initial screening tool for the public prior to seeking professional consultation.
Sentiment Analysis of Skincare Product Reviews: A Comparison of Naïve Bayes and Support Vector Machine Algorithms Refida Septiana Putri; Reykha Putri Randika; Febi Dwi Sasmita; Pungkas Subarkah
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13227

Abstract

The rapid growth of the skincare industry has generated a massive volume of consumer reviews on e-commerce platforms, making manual sentiment analysis increasingly impractical. This study compares the performance of Multinomial Naïve Bayes and Support Vector Machine (SVM) for sentiment classification of skincare product reviews using the Sephora Products and Skincare Reviews dataset from Kaggle, consisting of 602,130 reviews. Unlike previous studies that often employ different datasets and experimental settings, this research evaluates both algorithms using a uniform pipeline, including automatic sentiment labeling based on rating values, text preprocessing, Bag of Words feature representation, and identical evaluation procedures. Model performance was assessed using Area Under Curve (AUC), Accuracy, Precision, Recall, F1-Score, and Matthews Correlation Coefficient (MCC). The results indicate that positive sentiment dominates the dataset (82.37%), resulting in a highly imbalanced class distribution. Multinomial Naïve Bayes achieved better performance than SVM on most evaluation metrics, with an AUC of 0.784, F1-Score of 0.764, Precision of 0.749, and MCC of 0.153, whereas SVM obtained an AUC of 0.497, F1-Score of 0.743, Precision of 0.695, and MCC of 0.000. The near-zero MCC and low AUC of SVM suggest that the model struggled to distinguish minority classes under extreme class imbalance despite achieving high accuracy. These findings highlight the importance of employing multiple evaluation metrics beyond accuracy when assessing classification performance on imbalanced datasets. Furthermore, the results indicate that Multinomial Naïve Bayes provided better performance than SVM under the dataset characteristics and experimental configuration used in this study.
Co-Authors Bagus Adhi Kusuma Dini Riandini Febi Dwi Sasmita Iqrom Danang Putra Nandang Hermanto Primandani Arsi Pungkas Subarkah Reykha Putri Randika Salma Ngarifatul Khofiyah Sitaresmi Wahyu Handani