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All Journal Jurnal Manajemen Informatika JUKI : Jurnal Komputer dan Informatika Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Anshori, Muhammad Iqbal
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Computer Science & IT Engineering Mechanical Engineering Transportation

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Klasifikasi Jenis Jerawat Secara Otomatis Dengan Convolutional Neural Network Menggunakan Arsitektur Resnet-50 Anshori, Muhammad Iqbal; Zafar Sidiq, Muhammad Ali; Yaqin, Rifki Ainul; Prasetyo Agung, Ignatius Wiseto
Jurnal Manajemen Informatika JAMIKA Vol 15 No 1 (2025): Jurnal Manajemen Informatika (JAMIKA)
Publisher : Program Studi Manajemen Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jamika.v15i1.13712

Abstract

Acne is a common skin problem that requires different treatments based on its type, such as blackheads, conglobata, and papulopustular. This research develops an automatic acne type classification system using deep learning-based Residual Network (ResNet-50) architecture. With its 50 layers, ResNet-50 is effective in image classification. The objective of of this research is to classify the type of acne from skin images on the face, so that it can help diagnosis and treatment. face, so that it can help diagnosis and treatment. The method used in this research includes several main stages, namely the collection of the dataset, model training using CNN with ResNet-50 architecture, model testing, and performance evaluation. model, and performance evaluation. The dataset was obtained from Roboflow, consisting of three classes: acne-comedonica, acne-conglobata, and acne-papulopustulosa. The process involves image preprocessing, data augmentation, and model parameter adjustment, including Adam's dropout and optimizer techniques. The model can achieve 98.35% accuracy with loss of 0.0489 and the highest validation accuracy of 92.86% with a validation loss of 0.1976. In addition, confusion matrix analysis shows an accuracy result of 93%, which indicates the performance of the model in distinguishing between acne classes effectively. These results show that the model is effective in classifying the types of acne and can have a significant impact in assisting a more accurate and faster diagnosis. more accurate and quicker diagnosis.
Penerapan Arsitektur Monolitik Pada Aplikasi Jasa Service Online Tekku Berbasis Web Sidiq, Muhammad Ali Zafar; Anshori, Muhammad Iqbal; Yaqin, Rifki Ainul
JUKI : Jurnal Komputer dan Informatika Vol. 6 No. 1 (2024): JUKI : Jurnal Komputer dan Informatika, Edisi Mei 2024
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53842/juki.v6i1.418

Abstract

This article discusses the application of monolithic architecture in Tekku web-based online service application. The research uses the System Development Life Cycle (SDLC) method to analyze, design, implement, test, and maintain the application. The results include key features such as register, login, technician search, booking, payment, and service status. Testing was conducted using the black-box method to test the functionality of the program, with positive results on the login, logout, accept order, and update service status features. The advantages of monolithic architecture include ease of development and good performance, but the disadvantages are difficulty in developing complex features and difficult scalability. A maintenance phase is conducted to receive feedback and errors from users.
Stock Price Forecasting Using LSTM with Cross-Validation Rifki Ainul Yaqin; Anshori, Muhammad Iqbal; Angel, Reddis; Agung, Ignatius Wiseto Prasetyo; Arifin, Toni; Junianto, Erfian
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 3 No. 1 (2026): (In Progress)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v3i1.45130

Abstract

Stock price prediction remains challenging due to the market’s nonlinear, volatile nature, influenced by diverse economic and behavioral factors. Traditional models often suffer from overfitting and limited generalizability. This study addresses these limitations from prior research by other researchers for integrating Long Short-Term Memory (LSTM) with k-Fold Cross-Validation to improve prediction robustness. The proposed framework systematically evaluates model performance across varying market conditions. This methodological contribution enhances forecasting accuracy and stability, offering a more reliable approach to complex financial time series prediction. This study employs LSTM with one to two layers of 64–128 units, trained using Adam and dropout regularization, to capture long-term dependencies in stock price data. The workflow integrates feature selection, Min-Max scaling, and k-Fold Cross-Validation for robust evaluation. Model performance is assessed using RMSE, with reconfiguration applied to address underfitting or overfitting. The proposed model demonstrated substantial performance gains, achieving an average RMSE improvement of approximately 78.40% across all tested stocks compared to prior research. These enhancements are attributed to optimal hyperparameter tuning, consistent use of the Adam optimizer, and the implementation of k-Fold Cross-Validation, which reduced overfitting and provided more stable evaluations. Furthermore, findings revealed that simpler feature sets, such as using only closing prices, can outperform multiple technical indicators when normalization is inadequate, underscoring the importance of robust preprocessing and validation strategies. This study concludes that integrating LSTM with k-Fold Cross-Validation and optimized hyperparameters significantly improves stock price prediction accuracy.
Co-Authors Angel, Reddis Erfian Junianto Ignatius Wiseto Prasetyo Agung Prasetyo Agung, Ignatius Wiseto Rifki Ainul Yaqin Sidiq, Muhammad Ali Zafar Toni Arifin Yaqin, Rifki Ainul Zafar Sidiq, Muhammad Ali