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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Sistem Informasi dan Aplikasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Information Systems and Informatics JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Science, Engineering, Technology, and Education Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal IPTEK Bagi Masyarakat Journal of Computer Science and Informatics Engineering Edu Society: Jurnal Pendidikan, Ilmu Sosial dan Pengabdian Kepada Masyarakat
Fakhriza, M.
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Public Health Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Decision Support System for Sunscreen Selection Based on Facial Skin Concerns Using the Analytic Network Process Sitorus, Andriani; Fakhriza, M.
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10112

Abstract

Exposure to ultraviolet (UV) radiation is one of the primary causes of premature skin aging and various facial skin problems. However, selecting an appropriate sunscreen product remains challenging due to limited consumer knowledge and the overlapping nature of facial skin concerns. This study proposes a decision support model using the Analytic Network Process (ANP) to determine the most suitable sunscreen product based on six common skin problems: acne-prone skin, very dry skin, outdoor-induced dullness, aging, hyperpigmentation and acne scars, and general dullness. These criteria were derived from literature and validated by a certified skincare expert. Nine sunscreen alternatives from the Wardah brand—chosen due to their wide usage in the Indonesian market and varying SPF, PA levels, and formulations—were evaluated. Expert judgment was used in pairwise comparisons, with Consistency Ratio (CR) used to ensure reliability. The ANP model was developed using unweighted, weighted, and limit supermatrices. Results showed that Wardah UV Shield Aqua Fresh Sunscreen Serum SPF 50 PA++++ had the highest global priority score. A prototype web-based system was built using PHP and MySQL to deliver personalized sunscreen recommendations. The novelty of this study lies in its integration of expert dermatological insights and the use of ANP to address interrelated skin concerns, which are rarely explored in prior skincare decision support research.
Image Classification of Red Dragon Fruit Ripeness Levels Using HSV Color Moments and the K-NN Algorithm Br Sembiring, Nadia; Fakhriza, M.
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10206

Abstract

Accurately determining the ripeness level of red dragon fruit (Hylocereus polyrhizus) is crucial for ensuring post-harvest quality and distribution efficiency. This study proposes a method for classifying red dragon fruit ripeness using color moment features in the HSV color space combined with the K-Nearest Neighbor (K-NN) algorithm. The dataset consists of 2,881 images of dragon fruit with a resolution of 800×800 pixels, categorized into three classes: ripe (886 images), unripe (1,241 images), and rotten (754 images). All images were captured under natural lighting conditions and underwent pre-processing to enhance color value consistency. Color features were extracted by calculating the mean, standard deviation, and skewness of the Hue, Saturation, and Value channels. The K-NN model was trained and tested on data randomly split in an 80:20 ratio. The testing results showed that the model achieved 100% accuracy in classifying the ripeness levels, demonstrating the effectiveness of this non-destructive method in distinguishing fruit ripeness. This approach holds strong potential to support efficient and consistent decision-making in the agricultural sector.
Co-Authors Abdul Aziz Abdul Halim Hasugian Andini Wulandari Angga Ardiansyah Aninda Muliani Armanda, Akbar Agustama Astuti, Andriani Dwi Baihaqi, Abdullah Afif Baratih, Ira Iranda Isyha Br Sembiring, Nadia Fadilla, Nurul Haerani, Sintia Hafidz, Hasbanur Harahap, Putri Sari Febrianty Hasibuan, Maria Hikmah Heri Santoso Hidayati, Lily Ilka Zufria Irawati, Cici Lubis, Desy Ramadhani Manday, M Irsyan Antony Manurung, Haidar Azra Mhd Furqan Muchain, Alfira Nafhan Muhammad Dedi Irawan Nasution, Muhammad Adnan Buyung Raissa Amanda Putri Rangkuti, Syawaliah Putri Ritonga, Fazrul Umri Salim, Putra Silalahi, Dinah Makhroza Sinaga, Imam Adlin Sitorus, Andriani Situmeang, Monica Syaqila, Saidatus Tarigan, Putri Amelia Triutami, Anggun