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All Journal Jurnal Cakrawala Informasi (JCI)
Martius Apun Heses
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Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science

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Klasifikasi Gambar Aksara Jawa dengan Optimasi Parameter KNN Menggunakan GridSearchCV Priyambodo, Aji; Martius Apun Heses; Kristiawan Nurdianto
Jurnal Cakrawala Informasi Vol 4 No 2 (2024): Desember : Jurnal Cakrawala Informasi
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) - Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jci.v4i2.497

Abstract

This study focuses on optimizing the K-Nearest Neighbors (KNN) algorithm for Javanese script classification using the Cosine Similarity metric. Through a grid search in cross-validation, the optimal combination of hyperparameters, including the number of neighbors, weighting functions, and distance metrics, was identified. The results demonstrate that the Cosine Similarity metric with a distance weighting scheme achieved the best accuracy of 99.99%. Furthermore, evaluation based on precision, recall, and f1-score revealed highly stable performance across various classes, with most achieving perfect scores. Compared to previous methods, such as LBP, CNN, and DCNN, this approach shows a significant accuracy improvement. These findings indicate that optimizing KNN with the Cosine Similarity metric is highly reliable for developing classification models for complex patterns.
PENERAPAN METODE SAW UNTUK SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN GURU TERBAIK PADA SMK TRIMULYA DI SEMARANG Rutna Anggaraeni; Oktaga, Andreas Tigor; Prihati; Martius Apun Heses; Kristiawan Nurdianto
Jurnal Cakrawala Informasi Vol 5 No 1 (2025): Juni : Jurnal Cakrawala Informasi
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) - Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jci.v5i1.587

Abstract

The purpose of this research is to reveal the advantages of the Simple Additive Weighting (SAW) method in measuring lecturer performance to produce the best lecturer assessment. This research uses Purposive Sampling technique in determining student participants in the SMK TRIMULYA to select five teachers who have provided learning to all participants. The criteria used are discipline, teaching, motivation and innovation. The results of this study obtained the calculation results, namely teacher 1 (G1) with a total of 0,62, teacher 2 (G2) with a total of 0,60, teacher 3 (G3) with a total of 0,80, teacher 4 (G4) with a total of 1,00, and teacher 5 (G5) with a total of 0,72. So it can be concluded that the decision support system using the SAW method shows that the highest score is teacher 4 (G4). The SAW method is expected to help SMK TRIMULYA to determine the best teachers in the following periods.
OPTIMASI PENGENALAN AKSARA JAWA MENGGUNAKAN COSINE SIMILARITY, K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE Priyambodo, Aji; Martius Apun Heses; Kristiawan Nurdianto
Jurnal Cakrawala Informasi Vol 5 No 1 (2025): Juni : Jurnal Cakrawala Informasi
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) - Institut Teknologi dan Bisnis Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jci.v5i1.597

Abstract

This study investigates the use of cosine similarity as a distance metric to enhance Javanese script (Hanacaraka) recognition. A dataset of 4,385 images representing 20 classes was processed using Histogram of Oriented Gradients (HOG) for feature extraction and classified using K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) optimized via GridSearchCV. The SVM with cosine similarity achieved an accuracy of 99.84%, outperforming traditional metrics like Euclidean and Minkowski distances, as well as prior methods such as CNN-SVM and LBP-SVM. Cosine similarity's emphasis on angular relationships, rather than magnitude, made it effective for handling high-dimensional and intricate patterns. ANOVA analysis confirmed significant differences among distance metrics, validating cosine similarity’s advantages. This research supports cultural preservation and advances pattern recognition technologies. Future studies should explore integrating cosine similarity with deep learning models like CNNs or Transformers to improve scalability and adapt to broader datasets, including other traditional scripts.
Co-Authors Aji Priyambodo Andreas Tigor Oktaga, Andreas Kristiawan Nurdianto Prihati Rutna Anggaraeni