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All Journal International Journal of Advances in Intelligent Informatics INFORMAL: Informatics Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Hidayat, Muhamad Arief
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Computer Science & IT Electrical & Electronics Engineering

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Segmentasi Citra Tanda Tangan Menggunakan Fitur Titik SURF (Speeded Up Robust Features) dan Klasifikasi Jaringan Syaraf Tiruan hidayat, muhamad arief; retnani, windy eka yulia; Firmansyah, Diksy Media; Santika, Gayatri Dwi; Furqon, Muhammad ‘Ariful
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.53514

Abstract

Signature image classification is an important field of image processing. One of the stages of signature classification is segmentation. The segmentation process aims to detect image pixels that are part of the signature and separate them from text or logo pixels in a document image. There is a signature segmentation technique using interest points extracted using the SURF (Speeded Up Robust Features) algorithm [1] In this technique, a connected component pixel will be considered part of the signature if it has more SURF points in common with the database connected component pixel signature. Compared to the similarity with the database connected component non-signature pixels. This method is able to provide good accuracy results for signature pixel segmentation. However, the recall value is relatively low, namely 56%. This is because many connected component logos are considered as connected component signatures. In this study, signature segmentation was carried out using SURF points by adding two things: 1) using internal connected component characteristics as additional classification atributs: extent, solidity, ratio, and circularity 2) using an Artificial Neural Network classification algorithm to assist the segmentation process. The test results show that the proposed method improves segmentation quality by an average of 20.7% for an increase in accuracy, an average of 22.4% for an increase in precision, and an average of 18.6% for an increase in recall. When compared with the results reported in (Ahmed et al., 2012), the recall has increased by 38.3% - 42.8%
Gender classification performance optimization based on facial images using LBG-VQ and MB-LBP Hakim, Faruq Abdul; Dharmawan, Tio; Hidayat, Muhamad Arief
International Journal of Advances in Intelligent Informatics Vol 11, No 1 (2025): February 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i1.1827

Abstract

In the computer vision and machine learning field, especially for gender classification based on facial images, feature extraction is one of the inseparable parts. Various features can be extracted from images, including texture features. Several prior studies show that the Linde Buzo gray vector quantization (LBG-VQ) and Multi-block local binary pattern (MB-LBP) methods can extract texture features from images. The LBG-VQ produces less optimal performance in gender classification on the FEI facial images dataset. On the other hand, the MB-LBP produces more optimal performance when applied to the FERET facial images dataset. Therefore, this study was conducted to discover the gender classification performance when the LBG-VQ and MB-LBP methods are implemented independently or in combination on the FEI facial images dataset. Three preprocessing stages are involved before extracting images' features: noise removal, illumination adjustment, and image conversion from RGB to grayscale. The extracted features are then used as training material for several classification methods, namely Naïve Bayes, SVM, KNN, Random Forest, and Logistic Regression. Then, the K-Fold Cross Validation method is used to evaluate the trained models. This study discovered that the implementation of MB-LBP tends to show a performance improvement compared to the LBG-VQ. Furthermore, the most optimal classification model, with a performance of 91.928%, was formed by implementing Logistic Regression with MB-LBP on LBG-VQ quantized images. In conclusion, this study successfully formed an optimized gender classification model based on the FEI facial images dataset.
Optimasi Model Rekomendasi Topik Skripsi berdasarkan Performa Akademik Mahasiswa menggunakan SMOTE Adiwijaya, Nelly Oktavia; Al Abror, Muhammad Farhan; Dharmawan, Tio; Hidayat, Muhamad Arief
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7055

Abstract

Sekitar 68% mahasiswa mengalami keterlambatan dalam menyelesaikan skripsi yang mengindikasikan adanya kesulitan dalam penentuan topik penelitian sesuai dengan minat dan keahlian.Ketidaksesuaian ini seringkali disebabkan kurangnya pemahaman mahasiswa terhadap kemampuan akademik yang dimiliki. Hal ini berdampak signifikan pada keterlambatan kelulusan mahasiswa.Penelitian ini bertujuan mengatasi permasalahan tersebut dengan membangun model klasifikasi untuk membantu mahasiswa dalam menentukan topik skripsi berdasarkan kemampuan akademik mereka. Indikator yang digunakan berupa transkrip nilai mata kuliah mahasiswa dari semester 1 hingga semester 6. Penelitian ini menggunakan metode Feature Selection dan SMOTE sebelum dilakukan pemodelan untuk meningkatkan kualitas data. Dua algoritma Support Vector Machine (SVM) dengan kernel RBF dan Naive Bayes tipe kategorikal digunakan untuk membangun model klasifikasi. Berdasarkan hasil analisis yang diperoleh bahwa penerapan SMOTE untuk penanganan data sebelum diklasifikasi berpengaruh sangat baik terhadap hasil akurasi. Algoritma Support Vector Machine dengan kernel RBF memberikan akurasi tertinggi sebesar 96.81% sedangkan Naive Bayes tipe Categorical menghasilkan akurasi 83.75%. Hasil penelitian ini memberikan solusi praktis bagi mahasiswa dalam memilih topik skripsi yang relevan dengan kemampuan mereka dimana mata kuliah yang terkait dengan setiap topik skripsi dapat berbeda-beda untuk masing-masing mahasiswa.
Comparative Analysis of LSTM, GRU and Meta Prophet Stock Forecasting Methods with Var-Es Risk Evaluation Wijaya, Anggito Karta; Pandunata, Priza; Hidayat, Muhamad Arief
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7259

Abstract

This study compares the performance of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Prophet models in predicting real estate stock prices on the Indonesia Stock Exchange (2019–2024) and evaluates investment risks using Value at Risk (VaR) and Expected Shortfall (ES). Historical stock data underwent normalization and dataset splitting (ratios of 70:30, 80:20, and 90:10), with time steps of 40, 60, and 100, and three dense layers (25 and 50 neurons). Performance was evaluated using MSE, RMSE, MAE, and MAPE. Results indicate that GRU achieved the highest accuracy, especially for PWON, ASRI, and DILD stocks, with the lowest MSE values (PWON: 120.7436, ASRI: 26.3150, DILD: 28.9713). LSTM showed competitive performance, while Prophet had the lowest accuracy for short-term predictions. Risk analysis revealed Prophet had the lowest historical risk but the highest risk for 150-day forecasts. LSTM demonstrated superior long-term risk mitigation. Comparison with actual prices revealed that LSTM and GRU more accurately captured stock price fluctuations than Prophet, particularly during sharp price changes. GRU provided the closest predictions in the 150-day forecast scenario, making it the most effective model for real estate stock forecasting. This study offers valuable insights for investors and portfolio managers in understanding stock price movements and managing investment risks in the real estate sector.
Gender Classification Using Viola Jones, Orthogonal Difference Local Binary Pattern and Principal Component Analysis Mukminin, Muhammad Amirul; Dharmawan, Tio; Hidayat, Muhamad Arief
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 23 No. 3 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v23i3.3879

Abstract

Facial recognition is currently a widely discussed topic, particularly in the context of gender classification. Facial recognition by computers is more complex and time-consuming compared to humans. There is ongoing research on facial feature extraction for gender classification. Geometry and texture features are effective for gender classification. This study aimed to combine these two features to improve the accuracy of gender classification. This research used the Viola-Jones and Orthogonal Difference Local Binary Pattern (OD-LBP) methods for feature extraction. The Viola-Jones algorithm faces issues in facial detection, leading to outliers in geometry features. At the same time, OD-LBP is a new descriptor capable of addressing pose, lighting, and expression variations. Therefore, this research attempts to utilize OD-LBP for gender classification. The dataset used was FERET, which contained various lighting variations, making OD-LBP suitable for addressing this challenge. Random Forest and Backpropagation were employed for classification. This research demonstrates that combining these two features is effective for gender classification using Backpropagation, achieving an accuracy of 93%. This confirms the superiority of the proposed method over single-feature extraction methods.
Co-Authors Al Abror, Muhammad Farhan Dharmawan, Tio Firmansyah, Diksy Media Furqon, Muhammad Ariful Gayatri Dwi Santika Hakim, Faruq Abdul Mukminin, Muhammad Amirul Oktavia, Nelly Priza Pandunata retnani, windy eka yulia Wijaya, Anggito Karta