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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Jurnal RESISTOR (Rekayasa Sistem Komputer) Journal of Computer Networks, Architecture and High Performance Computing
Dewi, Ni Putu Dita Ariani Sukma
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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TPHerbleaf : Dataset Untuk Klasifikasi Jenis Daun Tumbuhan Herbal Berdasarkan Lontar Usada Taru Pramana Dewi, Ni Putu Dita Ariani Sukma; Kesiman, Made Windu Antara; Sunarya, I Made Gede; Indradewi, I Gusti Ayu Agung Diatri; Andika, I Gede
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 6 No. 2 (2023): Jurnal RESISTOR Edisi Agustus 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v6i2.1421

Abstract

Tumbuhan herbal ialah jenis tumbuhan yang dimanfaatkan dalam bidang kesehatan. Tumbuhan herbal umumnya dikenali dari daunnya karena daun mudah dibandingkan dengan bagian tumbuhan lainnya seperti bunga, buah, atau akarnya. Minimnya pengetahuan mengenai jenis tumbuhan herbal dan kemiripan jenis morfologi daun merupakan tantangan yang ditemui dalam pengenalan tumbuhan herbal, sehingga sulit untuk mengenali tumbuhan herbal terutama bagi orang yang tidak memiliki pengetahuan botani. Penelitian ini bertujuan untuk membuat dataset citra daun tumbuhan herbal bernama TPHerbleaf. Dataset ini akan digunakan untuk mengenali dan mengklasifikasikan jenis daun tumbuhan herbal berpedoman pada Lontar Usada Taru Pramana yang merupakan kearifan lokal masyarakat Bali dalam pengobatan tradisional dan telah dikaji secara ilmiah. Metode untuk klasifikasi tumbuhan herbal menggunakan EfficientNet B2 yang menghasilkan nilai akurasi 97,5% untuk training, 81,77% untuk validation, dan 83,49% untuk testing. Dengan menggabungkan pengetahuan tradisional dengan teknologi modern, penelitian ini diharapkan dapat memberikan kontribusi dalam meningkatkan pemahaman serta pelestarian warisan budaya melalui aplikasi praktis dalam bidang klasifikasi citra.
IDENTIFIKASI TANDA TANGAN MENGGUNAKAN PENGOLAHAN CITRA DIGITAL DAN METODE MACHINE LEARNING Putra, I Kadek Nurcahyo; Dewi, Ni Putu Dita Ariani Sukma; Pusparani, Diah Ayu; Mupu, Dibi Ngabe
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 7 No. 1 (2023)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v7i1.618

Abstract

Signature is used to legally approve an agreement, treaty, and state administrative activities. Identification of the signature is required to ensure ownership of a signature and to prevent things like forgery from happening to the owner of the signature. In this study, data signatures were obtained from 25 people over the age of 50. The signers provided 20 signatures and were free to choose the stationery used to write the signature on white paper. The total data obtained in this study was 500 signature data. The obtained signature was scanned to create a signature image, which was then pre-processed to prepare it for feature extraction, which can characterize the signature images. The HOG method was used to extract features, resulting in a dataset with 4,536 feature vectors for each signature image. To identify the signature image, the classification methods SVM, Decision Tree, Nave Bayes, and K-NN were compared. SVM achieved the highest accuracy, which is 100%. When K=5, the K-NN method achieved a fairly good accuracy of 97.3%. Meanwhile, Naive Bayes and Decision Tree achieved accuracy significantly lower than K-NN (61%). Because the HOG method produced a large feature vector for each signature, it is recommended that important features that represent signatures be optimized or extracted to produce smaller features to speed up computation without sacrificing accuracy, and that the HOG method be compared to other extraction feature methods to obtain a better model in future research.
The implementation of the Random Forest Algorithm with Resampling and Without Resampling on the Hepatitis C Disease Dataset Hendrayana, I Gede; Dewi, Ni Putu Dita Ariani Sukma; Aryasa, Jiyestha Aji Dharma; Prayoga, I Made Ade; Raharjo, Rizki Anom
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6089

Abstract

This study evaluates the performance of Random Forest models for Hepatitis C classification using a dataset from Kaggle, focusing on addressing class imbalance through resampling techniques. We compare three approaches: baseline Random Forest without resampling, Random Forest with SMOTE+ENN (Synthetic Minority Oversampling Technique + Edited Nearest Neighbors), and Random Forest with SMOTE+OSS (Synthetic Minority Oversampling Technique + One-Sided Selection). Results show that the baseline model achieved high accuracy (0.9837) but failed to detect minority classes (e.g., suspect Blood Donor recall=0.00). SMOTE+ENN significantly improved performance, achieving perfect classification (precision=1.00, recall=1.00) for Hepatitis, Fibrosis, and Cirrhosis, while maintaining high accuracy (0.9919) and ROC AUC (0.9999). In contrast, SMOTE+OSS showed limitations in detecting Hepatitis (recall=0.00) and yielded lower precision for Fibrosis (0.44), indicating its undersampling approach may be too aggressive. The study highlights SMOTE+ENN as the most effective method for balancing class distribution and enhancing model robustness in medical diagnostics. These findings underscore the importance of selecting appropriate resampling techniques to improve minority class detection in imbalanced datasets, with implications for developing reliable AI-based diagnostic tools for Hepatitis C.
Co-Authors Andika, I Gede Aryasa, Jiyestha Aji Dharma I Gede Hendrayana I Gusti Ayu Agung Diatri Indradewi I Made Gede Sunarya Indradewi, Gusti Ayu Agung Diatri Made Windu Antara Kesiman Mupu, Dibi Ngabe Prayoga, I Made Ade Pusparani, Diah Ayu Putra, I Kadek Nurcahyo Raharjo, Rizki Anom