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Journal : Rekursif: Jurnal Informatika

Implementasi Metode Certainty Factor Dalam Sistem Pakar Diagnosis Awal Tanda Bahaya Gangguan Menstruasi Andreswari, Desi; Erlansari, Aan; Coastera, Funny Farady; Hasian Lumbanraja, Joi Pebrianty
Rekursif: Jurnal Informatika Vol 11 No 2 (2023): Volume 11 Nomor 2 November 2023
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v11i2.30654

Abstract

Menstrual disorders are disorders that occur in the menstrual cycle including changes that occur in the cycle, blood count, and other changes related to the menstrual cycle. Most women do not feel symptoms at the time of menstruation, but a small percentage feel pain. Menstrual disorders or also called abnormal uterine bleeding is a complaint that often causes a woman to come to the doctor for immediate treatment. Complaints of menstrual disorders vary from mild to severe. To help overcome these obstacles, expert systems can be a solution. This expert system was created using the certainty factor method consisting of 46 symptoms with 12 types of diseases. This system produces output in the form of a percentage of the type of possible diagnosis of the disease experienced by the user and suggestions based on the symptoms experienced by the user. The functionality testing process of this expert system went well using black box testing and resulted in 100% functional success. Evaluation of the accuracy of the certainty factor method for diagnosing menstrual disorders resulted in an accuracy rate of 84.61%. Keywords: Menstruation, Menstrual disorders, Certainty Factor, Expert System, Diagnosis
Analisis Komparatif Metode Peningkatan Kontras Citra Bawah Air Menggunakan HE, AHE, dan CLAHE Ernawati, Ernawati; Oktoeberza, Widhia KZ; Andreswari, Desi; Purnama Sari, Julia; Erlansari, Aan; Farady Coastera, Funny; Dwi Jayanto, Paksi
Rekursif: Jurnal Informatika Vol 13 No 1 (2025): Volume 13 Nomor 1 Maret 2025
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v13i1.42151

Abstract

significant challenge in the field of digital image processing due to poor lighting conditions and uneven intensity distribution. This study aims to compare three contrast enhancement techniques Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE) applied to underwater imagery. The evaluation was conducted using quantitative metrics including entropy, contrast (RMS), and Structural Similarity Index (SSIM) to assess the improvement in image detail, intensity distribution, and structural similarity to the original image. Experimental results indicate that AHE achieves the highest entropy values, reflecting a significant enhancement of local information. HE provides the highest contrast values but tends to compromise the structural integrity of the image. CLAHE demonstrates the most balanced performance, producing the highest SSIM scores while maintaining stable enhancements in both contrast and detail. Based on these findings, CLAHE is recommended as the most effective contrast enhancement technique for underwater images, as it improves visual quality while preserving the original image structure. Key words : Underwater image enhancement; Contrast enhancement; CLAHE; HE; AHE.