Article Per Year (5 Year)
p-Index From 2021 - 2026
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This Author published in this journals
All Journal JAIS (Journal of Applied Intelligent System) Journal of Soft Computing Exploration
Dadan Saepul Ramdan
Politeknik TEDC Bandung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Facial Skin Color Segmentation Using Otsu Thresholding Algorithm Aris Haris Rismayana; Henny Alfianti; Dadan Saepul Ramdan
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5513

Abstract

The development of technology and information is currently very fast. One of the fields of technology and information that is experiencing development is the field of digital image processing. There are many technologies today that utilize digital images such as facial recognition, object detection and many others. Skin is one of the largest components of the human body. Currently, technology in the identification of skin color is widely used in recognizing the human race. In this study, skin color detection uses the YCbCr color space, which in this study only uses the range of Cb and Cr values, and ignores the Y value. Where Y is the lighting in the image. So if not changed, the image will contain light effects that can change the characteristics of skin color. However, problems were found because the detected images were not segmented properly, such as clothes and hair from the tested images were still detected as skin. Therefore, the HCbCr color space method is proposed where the Hue value will represent the color of visible light. While the Otsu Thresholding method will separate the background from the object in the digital image.
Expert System of Facial Skin Type Diagnosis and Skincare Recommendation Based on Certainty Factor Dadan Saepul Ramdan; Castaka Agus Sugianto; Rizqy Dimas Monica
Journal of Applied Intelligent System Vol 7, No 3 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i3.7150

Abstract

Facial treatment is an important need for everyone because the first sight of meeting someone is to see their face. Generally, facial skin type is just normal skin. However, several factors such as the environment, air, food, facial hygiene, and so on can affect the type of human facial skin. In this experiment, there were 5 types of facial skin, namely normal skin, dry skin, oily skin, combination skin, and sensitive skin. With the existence of various skin types, it makes some people confused in determining the type of facial skin. This also affects the selection of skincare or facial care according to the indications of each facial skin. Therefore an expert system was created to diagnose facial skin types. An expert system is a man-made system that is used to solve problems like an expert with knowledge from human to computer, although it does not give 100% absolute results, but expert systems are still helpful.
Improving intrusion detection performance using bayesian hyperparameter optimization for supervised network traffic classification Dahlan Dahlan; Dadan Saepul Ramdan
Journal of Soft Computing Exploration Vol. 7 No. 2 (2026): June 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i2.87

Abstract

The rapid growth of networked systems has increased the complexity of network traffic and the risk of cyber-attacks, making intrusion detection more challenging. Machine learning approaches have been widely used to address this issue; however, their performance often depends on appropriate hyperparameter settings. This study examined the effect of Bayesian-based hyperparameter optimization on the performance of supervised machine learning models for network traffic classification. A publicly available dataset was used, consisting of various traffic-related features and labeled instances indicating normal or malicious activity. Several machine learning models, including Random Forest, Decision Tree, AdaBoost, Logistic Regression, Gradient Boosting, and Naïve Bayes, were evaluated. Each model was tested using default parameters and then optimized using Bayesian Optimization. The performance was assessed using accuracy, precision, recall, and F1-score. The results showed that ensemble-based models, particularly Gradient Boosting and Random Forest, achieved the best performance after optimization, with accuracy values above 89% and strong F1-scores. However, the findings also revealed a trade-off between precision and recall, where higher precision was often associated with lower detection of certain attack instances. In contrast, simpler models such as Logistic Regression showed lower performance, indicating their limitations in capturing complex patterns. Overall, the study demonstrated that Bayesian-based hyperparameter optimization contributed to improving model performance and provided a more reliable approach for network traffic classification.
Co-Authors Aris Haris Rismayana Castaka Agus Sugianto Dahlan Henny Alfianti Rizqy Dimas Monica