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All Journal TRANSIENT: Jurnal Ilmiah Teknik Elektro Eduvest - Journal of Universal Studies
Donny Zaviar Rizky
Jurusan Teknik Elektro, Universitas Diponegoro Semarang Jl. Prof. Sudharto, SH, Kampus UNDIP Tembalang, Semarang 50275, Indonesia
Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Health Professions Neuroscience Social Sciences

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KLASIFIKASI PENYAKIT DIABETES MELITUS BERDASAR CITRA RETINA MENGGUNAKAN PRINCIPAL COMPONENT ANALYSIS DENGAN JARINGAN SARAF TIRUAN Rizky, Donny Zaviar; Isnanto, R. Rizal; Hidayatno, Achmad
Transient: Jurnal Ilmiah Teknik Elektro TRANSIENT, VOL. 2, NO. 3, SEPTEMBER 2013
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (442.645 KB) | DOI: 10.14710/transient.2.3.552-557

Abstract

Abstrak Seiring dengan perkembangan teknologi dan jaman, teknik identifikasi secara konvensional dinilai sudah tidak praktis dan memiliki berbagai kelemahan. Hal ini menimbulkan ide untuk membuat suatu teknik klasifikasi penyakit diabetes mellitus menggunakan pengekstrasi ciri PCA berdasar ciri alami manusia. Salah satunya adalah dengan menggunakan retina mata manusia sebagai objeknya. Dalam penelitian ini akan digunakan metode ekstrasi ciri secara statistik  yang secara luas telah lama digunakan yaitu PCA (Principal Components Analysis). PCA atau Principal component analysis sebagai salah satu metode untuk pengolahan citra masih relatif  jarang digunakan sebagai pengekstraksi ciri pola retina mata. Pemilihan metode ekstraksi ciri yang tepat dan efisien sangat menentukan keberhasilan dari sistem klasifikasi secara keseluruhan. Pengujian bertujuan untuk mengklasifikasikan beberapa citra dari basisdata Messidor. Citra masukkan berformat TIFF dengan ukuran 680x452. Hasil analisis kemudian diolah dengan 5 variasi komponen utama dan 5 variasi jumlah neuron tersembunyi untuk dikombinasikan yang bertujuan untuk menghasilkan  tingkat keberhasilannya akurat. Dari hasil pengujian kombinasi variasi komponen utama dan jumlah neuron tersembunyi dengan 15 data latih dan 15 data uji memiliki tingkat keberhasilan terbaik yaitu 78,334%. Hal ini dapat disimpulkan bahwa kombinasi metode PCA dan jaringan saraf tiruan perambatan balik cocok untuk mengklasifikasikan penyakit diabetes mellitus. Kata Kunci: Retina, Principal component analysis, Jaringan Saraf Tiruan Perambatan Balik  Abstract Along with the development of technology and time, the conventional identification techniques is considered impractical and have various weaknesses. This has led to the idea to create a technique classification of diabetes mellitus using PCA based extraction characteristic traits of human nature. One is by use the human eye retina as its object. In this research will use statistical characteristic extraction method that has long been widely used that is PCA (Principal Components Analysis). PCA or Principal component analysis as a method for image processing is still relatively rarely used as extracting characteristic patterns retina. The selection of appropriate feature extraction methods and efficiently determine the success of the classification system overall.Tests aim to classify some images from database Messidor. Insert TIFF image format with 680x452 size. Results of the analysis are then processed with 5 variations of major components and the amount of variation in the amount of 5 hidden neurons to combined that aims to produce an accurate success rate. Combination of the results of testing the major components and the amount of variation in hidden neurons with 15 training data and 15 test data has the best success rate is 78.334%. It can be concluded that the combination of PCA and back propagation neural network suitable for classifying diabetes mellitus. Keywords : Retina, Principal component analysis, analysis neural network backpropagation
Classification of Health Index of Distribution Substations using Supervised Learning Analysis with SVM Method Rizky, Donny Zaviar; Suprijadi, Suprijadi
Eduvest - Journal of Universal Studies Vol. 5 No. 1 (2025): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i1.50323

Abstract

As the only electricity provider in Indonesia, PLN is required to be reliable in distributing electrical energy to customers, this is greatly influenced by several PLN assets in the form of distribution substations. The function of this distribution substation is quite crucial in carrying out PLN's business processes to distribute electrical energy. In this study, efforts were made to improve the reliability of distribution substations by knowing the health index in accordance with EDIR PLN No. 017 concerning Distribution Transformer Maintenance Methods Based on Asset Management Principles as the Basis of the Health Index. By knowing the health level of the transformer at the distribution substation, the substation that has substandard criteria can be prioritized for maintenance. The research carried out was to take a sample in 1 month, namely March 2024, from a total of 239 substations, which were then classified using the Support Vector Machine (SVM) method which was compiled in the Python programming language which had been labeled with criteria on each substation. The criteria used in accordance with PLN EDIR No. 017 PLN are Good, Sufficient, Less and Poor. By using Machine Learning according to the Support Vector Machine (SVM) method with Supervised Learning, after the data samples were labeled, then from 239 sample data, it was divided into 2 data, namely training data and test data. In this study, the experiment was carried out with changes in training data by 60%, 70%, 80% and 90% which were then evaluated for accuracy using libary from Python.
Co-Authors Achmad Hidayatno R. Rizal Isnanto Suprijadi Suprijadi