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All Journal JITCoS : Journal of Information Technology and Computer System Jurnal Ilmu Teknologi Informasi Indonesia (JITIFNA)
Dedek
Universitas Islam Negeri Sumatera Utara
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Engineering Industrial & Manufacturing Engineering

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Prediksi Curah Hujan Bulanan Di Medan Menggunakan Metode Long Short-Term Memory (LSTM) Dedek; Lailan Sofinah Harahap; Muhammad Rayhans Adrian
Jurnal Ilmu Teknologi Informasi Indonesia Vol. 2 No. 1 (2026): JITIFNA - Januari
Publisher : CV. SINAR HOWUHOWU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70134/jitifna.v2i1.969

Abstract

This study aims to predict monthly rainfall in Medan City using the Long Short-Term Memory (LSTM) method. The data utilized in this research comprises monthly rainfall figures and the number of rainy days for the 2015–2023 period, obtained from the Meteorology, Climatology, and Geophysics Agency (BMKG) Region I Medan via official publications of the Central Statistics Agency (BPS) of North Sumatra Province. The pre-processing stage involves data cleaning, normalization, and the construction of a time series dataset using a sliding window structure. The LSTM model was developed with two hidden layers and optimized using the Adam algorithm. Evaluation results indicate that the LSTM model effectively captures seasonal patterns and rainfall trends, as evidenced by a low Root Mean Square Error (RMSE) value. This study is expected to serve as a reference for hydrometeorological disaster mitigation in the Medan region.
Comparative Analysis of K-Means Clustering and Otsu Thresholding Methods in Digital Fingerprint Image Segmentation Rifqi Arrafi; Dedek; Fauhan Alfarizi Saragih; Muhammad Rayhans Adrians; M. Haraji
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.115

Abstract

Image segmentation is an important stage in digital image processing that aims to separate the main object from the background so that image information can be analyzed more accurately. This study compares the performance of Otsu Thresholding and K-Means Clustering methods in digital fingerprint image segmentation using a fingerprint image from the FVC2000 dataset. Prior to segmentation, preprocessing was performed using Gaussian Blur and Histogram Equalization to improve image quality. The evaluation was conducted using Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR). Experimental results on 20 fingerprint images from the FVC2000 dataset showed that the K-Means Clustering method achieved a lower average MSE value (5162.54) and a higher average PSNR value (11.34 dB) compared to Otsu Thresholding, which obtained an average MSE value of 8593.21 and an average PSNR value of 9.56 dB. Furthermore, Otsu Thresholding demonstrated significantly faster computational performance. These results indicate that K-Means provides better segmentation quality, whereas Otsu Thresholding is more computationally efficient.
Co-Authors Fauhan Alfarizi Saragih Lailan Sofinah Harahap M. Haraji Muhammad Rayhans Adrian Muhammad Rayhans Adrians Rifqi Arrafi