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All Journal JAIS (Journal of Applied Intelligent System) Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Pengabdian Masyarakat (ABDIRA) Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Komputer
Suamanda Ika Novichasari
Teknologi Informasi, Universitas Tidar
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Journal : Jurnal Ilmu Komputer

 1 
Identifikasi Pola Tanda Tangan Berbasis Jaringan Syaraf Tiruan Dengan Metode Learning Vector Quantization Yoannes Romando Sipayung; Suamanda Ika Novichasari
Multimatrix Vol. 1 No. 1 (2018)
Publisher : Universitas Ngudi Waluyo

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Abstrak - The introduction of signature patterns is one of the fields of pattern recognition that is currently developing. Each person's signature is generally identical but not the same. LVQ is a method of artificial neural networks to conduct learning in a supervised competitive layer. There are previous studies that use this method, but in these studies do not include the processing time needed to identify signature patterns. This research will test using this method. In this study, used image data with a size of 433 x 276 pixels as many as 300 pieces from 30 people, where each person was taken 10 signatures. For training data, the data is 180 signatures, while 120 test data are used for the test data. This study uses Canny edge detection to obtain an edge in the signature image. During the training process and LVQ testing, the process was carried out 3 times. The results of the training and testing with the LVQ metodel indicate that the method can identify the signature pattern well. Keywords:  Signature Patterns, Artificial Neural Network, Learning Vector Quantization 
PSO-SVM Untuk Klasifikasi Daun Cengkeh Berdasarkan Morfologi Bentuk Ciri, Warna dan Tekstur GLCM Permukaan Daun Suamanda Ika Novichasari; Yoannes Romando Sipayung
Multimatrix Vol. 1 No. 1 (2018)
Publisher : Universitas Ngudi Waluyo

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Abstract— Of the two types of superior varieties cultivated cloves, clove types of zanzibar is the best kind. However, when not flowering of the three types of clove leaves indistinguishable from the image. This study uses 4 morphological features of shape, 3 color features and 10 most commonly used GLCM features and apply SVM for classification with Particle Swarm Optimization (PSO) optimization method to improve the accuracy of clove plant classification based on leaf surface image. Results of research on the top surface image classification leaf clovers, PSO-SVM method proposed is shown to have a higher accuracy compared with PSO-SVM method than previous research (Novichasari, S.I., 2015) with an accuracy of 90.5% and AUC 0.944. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SVM
Pembobotan Atribut PSO Untuk Klasifikasi Data Kinerja Akademik Mahasiswa Sri Mujiyono; Suamanda Ika Novichasari
Multimatrix Vol. 1 No. 1 (2018)
Publisher : Universitas Ngudi Waluyo

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An educational database containing information about students is useful for predicting student academic performance. Mujiyono, Sri in 2017 has proven that PSO improves SVM performance for predicting student academic performance. This study aims to prove that PSO can improve the performance of the NBC, C4.5, SVM and NN classification methods for the classification of student academic performance. The results of this study prove that PSO can improve the performance of all the classification methods used. With PSO optimization, NN defeats the accuracy of SVM.
OPTIMASI KLASIFIKASI DATA KINERJA AKADEMIK MAHASISWA MENGGUNAKAN SVM BERBASIS ALGORITMA GENETIKA Suamanda Ika Novichasari
Multimatrix Vol. 1 No. 2 (2019)
Publisher : Universitas Ngudi Waluyo

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For a college, especially a private university, students are the main component that supports the survival of the college. An educational database containing information about students is useful for predicting student academic performance. Several studies on the classification of academic performance have been conducted, it is clear that classification problems generally exist in the number of attributes, too many unnecessary attributes will increase computational time and reduce accuracy. The combination of PSO + SVM has proven to be more effective than SVM in various types of datasets. Therefore, this study will try to compare SVM-GA for the classification of student academic performance so that students who have good and bad academic performance can be seen. The data used is the academic performance data of the midwifery students of Ngudi Waluyo University, 2012-2014. The highest accuracy of SVM-GA is the accuracy of 93.55% and AUC 0.977. The previous SVM method had an accuracy of 90.51% and AUC 0.963. Based on the AUC value, the performance of the proposed SVM-GA method is in the "Perfect" group. 
SEGMENTASI FUZZY C –MEANS DAN NEURAL NETWORK UNTUK MEMBANTU IDENTIFIKASI KUALITAS BUAH JERUK BERDASARKAN WARNA DAN UKURAN Iwan Setiawan Wibisono; Suamanda Ika Novichasari
Multimatrix Vol. 2 No. 1 (2019)
Publisher : Universitas Ngudi Waluyo

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Buah jeruk adalah buah yang kaya akan kandungan vitamin C yang tinggi. Selain itu buah jeruk siam jawa ini manis juga mempunyai rasa yang menyegarkan. Untuk mendapatkan kesegaran serta rasa yang manis maka perlu dipilih buah jeruk yang telah matang. Tingkat kematangan buah jeruk siam jawa terlihat dari tekstur kulit serta warna kulitnya. Buah yang telah matang biasanya mempunyai tekstur kulit yang halus, tipis dan mengkilat serta warna yang cenderung tegas. Banyak permasalahan yang timbul ketika melakukan identifikasi kematangan buah jeruk secara tradisional. Bagi petani jeruk, tingkat kematangan ini sangat mudah mereka bedakan, tetapi bagi orang awam tentu akan mengalami banyak kesulitan. Masalah ini akibat sifat manusia yang memiliki beberapa kelemahan, diantaranya adalah kelemahan yang diakibatkan keterbatasan fisik maupun faktor kelelahan. Dengan semakin majunya teknologi komputer membuat kerja manusia semakin cepat dan mudah. Masalah mengklasifikasikan kualitas buah jeruk dapat diselesaikan dengan menerapkan ilmu computer vision, memungkinkan piranti dapat mengenali serta menganalisa obyek berupa gambar yang diambil dalam mengenali kondisi kematangan buah jeruk.  Kemampuan ini jelas akan sangat membantu khususnya bagi mereka yang tidak memiliki pengetahuan tentang pemilihan kematangan buah jeruk. Kematangan buah biasanya ditentukan oleh beberapa parameter, diantaranya adalah dari parameter ukuran, berat, ciri warna, keharuman dari buah tersebut, dan lain-lain. Parameter kematangan buah dari sisi warna kulit buah merupakan salah satu faktor penting didalam identifikasi kematangan buah. Dalam penelitian ini digunakan metode Fuzzy C-Means dan Neural Network (NN PSO) untuk mengklasifikasikan kualitas Buah jeruk, berdasarkan. ciri. fisiknya. yaitu, menggunakan. analisis. tekstur warna, ukuran dan berat yang merupakan salah satu dari ciri fisik buah jeruk. Penelitian ini menggunakan 50 buah jeruk siam jawa yang terdiri dari 25 jeruk siam jawa matang dan 25 jeruk siam jawa mentah. Tujuan penelitian ini untuk membuktikan berapa presentase keberhasilan pengenalan dengan metode Fuzzy C-Means dan membandingkan tingkat presentase keberhasilan yang lebih baik antara algoritma Fuzzy C-Means dengan algoritma Nueral Network. Berdasarkan hasil uji coba yang telah dilakukan pada penelitian ini, terbukti bahwa metode yang diusulkan dapat digunakan untuk mengkalsifikasi jeruk siam jawa. Akan tetapi tingkat akurasi masing cukup memeuaskan, yaitu 87%. Maka untuk penelitian berikutnya metode dapat dikembangkan lagi. Mungkin dapat dimaksimalkan lagi pada proses prapengolahan dan ekstraksi ciri citra. Keywords— Klasifikasi Jeruk. Image Processing, FC-M, NN-PSO
KELAYAKAN KREDIT BANK MENGGUNAKAN C4.5 BERBASIS PSO Suamanda Ika Novichasari; Sri Mujiyono
Multimatrix Vol. 2 No. 1 (2019)
Publisher : Universitas Ngudi Waluyo

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Abstract— Credit success in a bank plays a role in maintaining the survival of a bank. Therefore it is very necessary to measure creditworthiness accurately to classify customers with good credit and bad credit. Based on these conditions the right data mining technique to use is classification. One of the data mining classification techniques is Naïve Bayes Classifier (NBC), but the accuracy is still less than the C4.5 algorithm and the neural network. This final report describes the steps of research using the Particle Swarm Optimizatin (PSO) algorithm to weight attributes to increase the accuracy value of C4.5. This study uses data set public German Credit Data. The validation process uses tenfold-cross validation, while testing the model using confusion matrix and ROC curve. The results show that the accuracy of C4.5 increased from 72.3% to 75.50% after being combined with PSO. Keywords: Credit, German Credit Data, C4.5-PSO. Keywords— Leaf image classification, cloves, shape, color, GLCM, PSO-SVM 
Co-Authors Abdillah, Mokhammad Nurkholis Adam, Otmar Shah Alifia Revan Prananda Bagus Fatkhurrozi Beta Estri Adiana D Jayus Nor Salim Damar Wicaksono Dhanang Suwidagdho Eka Legya Frannita Ibrahim Nawawi Imam Adi Nata Iwan Setiawan Wibisono Kartika Yuni Purwanti Majidah, Zharifa Nur Otmar Shah Adam Rakhmawati, Restu Setya Wijaya, Vania Amelia Sri Mujiyono Sri Mujiyono Swantyka Ilham Prahesti Syifa Fauziah Syifa Fauziah Vania Amelia Setya Wijaya Wardhani, Olivia Wibowo, Fadhila Syahida Wibowo, Rheza Ari Yoannes Romando Sipayung Zulmi Roestika Rini