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All Journal Teknika Jupiter Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal SIMADA (Sistem Informasi dan Manajemen Basis Data) Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences Jurnal Multimedia Android Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal Pengabdian Masyarakat Jurnal Teknoinfo
Arie Setya Putra
Universitas Mitra Indonesia
Religion Arts Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Social Sciences Other

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Performance Evaluation of Fuzzy Logic System for Dendrobium Identification Based on Leaf Morphology Arie Setya Putra; Admi Syarif; Mahfut Mahfut; Sri Ratna Sulistiyanti; Muhammad Said Hasibuan
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.224

Abstract

Dendrobium is the second-largest family of flowering plants in the world. There are several classes of Dendrobium, which usually identify by its, including leaves and flowers. Due to the similarity of its characteristics, identifying orchid types is complicated and usually can only be done by an expert. Moreover, those characteristics are typically non-deterministic; examining the orchid species is very challenging. This research aims to develop a novel fuzzy-based system to identify the species of orchid based on unprecise existing leaf characteristics. We used the main characteristics of Dendrobium leaves, including shape, length, width, and tips of the leaves. Based on the information from the expert, we develop the membership for each class of Dendrobium. By adopting this knowledge, we develop the system by using compatible programming with this case, and Borland Delphi as complex application development. The experiment is done by using 200 real datasets from the Liwa Botanical Gardens, West Lampung Regency, Lampung Province, Indonesia. The results are compared with those given by a Dendrobium expert. A confusion matrix is a valuable evaluation tool for measuring the performance of classification models. From the above results, we can determine the confusion matrix and calculate the TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative). The confusion matrix given from the experiments is shown in Table 6. This indicates that the system can provide the same results as experts recommended. It is shown that the system can identify orchid types with an accuracy value of 94,6 %.  Thus, this system will be beneficial for automatically determining the orchid genus.
Optimization of Machine Learning Algorithms in Breast Cancer Classification: A Performance Based Analysis Agus Wantoro; Arie Setya Putra; Ochi Marshella Febriani
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.17160

Abstract

Timely identification of breast cancer recurrence is closely associated with patient survival and the effectiveness of treatment. Inaccurate detection can contribute to greater disease severity, higher treatment costs, longer recovery, and reduced quality of care. For Machine Learning (ML)-based decision-support systems, two important challenges are the unequal distribution of medical-data classes and the large number of features, both of which may affect model accuracy and computational efficiency. This study evaluates an approach that combines feature selection with class-imbalance handling to improve breast cancer detection performance. Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Relief-F are used to rank features according to their weights, while the Synthetic Minority Over-Sampling Technique (SMOTE) is applied to improve representation of the underrepresented class. Seven ML classifiers, namely k-Nearest Neighbor (k-NN), Tree, Support Vector Machine (SVM), Naive Bayes, AdaBoost, Random Forest (RF), and Neural Network (NN), are tested and assessed using confusion-matrix-based accuracy, precision, recall, and computational time. The experimental results indicate that incorporating class-imbalance handling improves the predictive performance of the ML algorithms. Among the evaluated combinations, Information Gain with Random Forest (IG+RF) provides the optimal result in this case. These findings highlight the value of integrating class-balancing and feature-selection procedures when developing machine-learning systems for breast cancer detection.
Co-Authors abrani dewi oktavia Admi Syarif Agus Wantoro Ahmad Abilah Pramana Aminuyati Bintang Kusuma Jakti Bobby Bachry Danang Kurniawan Fadhilah Dirayati Fatimah Fahurian Iin Marliana M Budi Hartanto M Teuku Faawati Mahfut Mahfut Melania Fitriyanti Muhammad Said Hasibuan Ochi Marshella Ochi Marshella Febriani Ochi Marshella Febriani Rosyana Fitria Purnomo Sri Ratna Sulistiyanti Yodhi Yuniarthe