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All Journal Vocatech : Vocational Education and Technology Journal The Indonesian Journal of Computer Science
Fatma Susilawati Mohamad
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Mechanical Engineering

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KLASIFIKASI KEMATANGAN TOMAT DENGAN MODEL WARNA YANG BERBEDA MENGGUNAKAN LINEAR DISKRIMINAN ANALISIS (LDA) Nica Astrianda; Hayatun Maghfirah; Fatma Susilawati Mohamad
VOCATECH: Vocational Education and Technology Journal Vol 3, No 2 (2022): April
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v3i2.75

Abstract

AbstractQuality of fruits depend heavily on the right time of plucking plus the right stage of ripeness to ensure its highest quality before selling. Tomatoes are one of the fruits that have a relatively fast maturity process. So that the classification of  tomato maturity has an  important role to reduce the risk of spoilage of tomato. Color is one of the attributes that can be used to identify the ripeness of tomato and it is one of the most distinctive characteristic of the fruits and vegetables that grow in tropical climates. In this study, the goal is to classify tomatoes maturity using color based predominant images. Linear Discriminant Analysis (LDA) is used to classify the ripeness classes based on three color models (HSV, YCbCr and CIElab). Comparisons are made between these color models for system accuracy and running time. For the highest accuracy of 95% achieved with a running time of 3,425 seconds with the CIElab color model, and a low of 67% with a running time of 3,526 seconds using the YcbCr color model, and 85% with the fastest system running time of 3,253 seconds obtained by the HSV color model.Keywords:Keywords: Linear Discriminant Analysis, HSV, YCbCr, CIELab, ripeness,  Tomatoes __________________________ AbstrakKualitas buah sangat bergantung pada waktu yang tepat untuk memetik ditambah tahap kematangan yang tepat untuk memastikan kualitas tertinggi sebelum dijual. Tomat adalah salah satu buah yang memiliki proses kematangan yang relatif cepat. Sehingga klasifikasi kematangan tomat memiliki peran penting untuk mengurangi resiko pembusukan tomat. Warna adalah salah satu atribut yang dapat digunakan untuk mengidentifikasi kematangan tomat dan itu adalah salah satu karakteristik yang paling khas dari buah-buahan dan sayuran yang tumbuh di iklim tropis. Dalam penelitian ini, tujuannya adalah untuk mengklasifikasikan kematangan tomat menggunakan gambar dominan berbasis warna. Linear Discriminant Analysis (LDA) digunakan untuk mengklasifikasikan Tingkat kematangan berdasarkan  tiga model warna berbeda yaitu  HSV, YCbCr dan CIElab. Perbandingan dibuat antara model warna ini untuk akurasi dan running time sistem. Untuk akurasi  tertinggi 95% dicapai dengan running time 3.425 detik dengan menggunakan model warna CIElab, dan terendah 67% dengan running time 3.526 detik menggunakan model warna YcbCr, dan 85% dengan running time sistem tercepat 3.253 detik diperoleh oleh model warna HSV.Kata Kunci:Kata kunci: Analisis Diskriminan Linier, HSV, YCbCr, CIELab, kematangan, Tomat 
Machine Learning Techniques for Early Detection and Diagnosis of Breast Cancer Prediction Al-Duais, Mohammed; Abdualmajed A.G. AL- Khulaidi; Fatma Susilawati Mohamad; Walid Yousef; Belal AL-Futhaidi; Murshid Al-Taweel; Mumtazimah Mohamad; Mohd Nizam Husen
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4690

Abstract

Currently breast cancer is considering very serious disease of death among women. The main reason for this cause is late of detected and diagnosis. The early detected and diagnosis help women for longer on live. Machine learning techniques is providing a best technique for early detected, diagnosis and predication of breast cancer. The objective of this study applied and compare two different techniques of machine learning (ML) to determent which give superior performance for predication for breast cancer. The method focuses on to achieve the objectives of this study, there are many steps has been done such as: Data collection and data preprocessing, design the proposed model. Two techniques have been used traditional and ensemble machine learning techniques. The traditional includes several algorithm such as Support vector machine (SVM), Naïve Bayes(NB), Logistic Regression (LR), K-Nearest Neighbor (KNN), and decision tree(DT) while the ensemble ML techniques covers several algorithm such as Random frost (RF), XGBoost and Adaboot.’ To evaluate the performance of these techniques, this study used several measurements such as accuracy, precision, recall, Fl scores for evaluation the performance . The results show that the ensemble ML technique gives superior classification than traditional ML technique. However, the average accuracy of the ensemble ML technique is 0.97, while the average accuracy of Traditional ML techniques is 0.96.Conclusion: The ensemble machine learning techniques outperform than traditional ML technique for detection diagnosis and prediction of breast cancer.
Co-Authors Abdualmajed A.G. AL- Khulaidi Al-Duais, Mohammed Belal AL-Futhaidi Hayatun Maghfirah Mohd Nizam Husen Mumtazimah Mohamad Murshid Al-Taweel Nica Astrianda Walid Yousef