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All Journal Jurnal Informatika Seminar Nasional Aplikasi Teknologi Informasi (SNATI) IPTEK Journal of Proceedings Series Perfecting a Video Game with Game Metrics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec REKAYASA Jurnal Ilmiah Pangabdhi Proceeding of the Electrical Engineering Computer Science and Informatics Network Engineering Research Operation [NERO] Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Layanan Masyarakat (Journal of Public Service)
Rima Tri Wahyuningrum
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Physics Social Sciences Other

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SEGMENTASI OBYEK PADA CITRA DIGITAL MENGGUNAKAN METODE OTSU THRESHOLDING Syafi?i, Slamet Imam; Wahyuningrum, Rima Tri; Muntasa, Arif
Jurnal Informatika Vol 13, No 1 (2015): MAY 2015
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (389.347 KB) | DOI: 10.9744/informatika.13.1.1-8

Abstract

Digital image has size and object in the form of foreground and background. To separate it, it is necessary to be conducted the image segmentation process. Otsu thresholding method is one of image segmentation method. In this research is divided into five processes, which are input image, pre-processing, segmentation, cleaning, and accuracy calculation. First process was input color images which consists of multiple objects. Second process was conversion from color image to grayscale image. Third process was automatically calculated threshold value using Otsu thresholding method, followed by binary image transformation. The fourth process, the result of third process is changed into negative image as the segmentation results, noise removal with a threshold value of 150, and morphology. The last accuracy calculation is conducted to measure proposed segmentation method performance. The experimental result have been compared to the image of Ground Truth as the direct user observation to calculate accuracy. To examine the proposed method, Weizmann Segmentation Database is used as data set. It conconsist of 30 color images. The experimental results show that 93.33% accuracy were achieved.
Rancang Bangun Aplikasi Video Streaming pada Portable · wireless dengan P latform Real Network Wahyuningrum, Rima Tri
Rekayasa Vol 1, No 2: Oktober 2008
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1102.632 KB) | DOI: 10.21107/rekayasa.v1i2.2180

Abstract

This paper would implement a video streaming application at wireless network. Before, it worked, main problem at wireless network is bandwidth. Special qualityfrom Real Network platform was be available facility at encoder side in which we could manage bandwidth that we need was suitable with network. Measurement in three places were city center with average power level -65 dbm, sub-urban with average power level -66 dbm and village with average power level-95 dbm, gave data for consideration matter at analysis video streaming that represent reality condition infield.
KOMBINASI KPCA DAN EUCLIDEAN DISTANCE UNTUK PENGENALAN CITRA WAJAH Wahyuningrum, Rima Tri
Rekayasa Vol 4, No 2: Oktober 2011
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.572 KB) | DOI: 10.21107/rekayasa.v4i2.2343

Abstract

Permasalahan machine learning dan pattern recognition bukan merupakan penelitian yang baru. Seiring dengan perkembangan teknologi, semakin berkembang pula teknik dan algoritma yang digunakan untuk menyelesaikan permasalahan machine learning dan pattern recognition. Pada penelitian ini telah berhasil melakukan pengenalan citra wajah menggunakan ekstraksi fitur Kernel Principal Component Analysis (KPCA) untuk menentukan karakteristik dari wajah dan Euclidean Distance sebagai metode klasifikasi berbasis statistik. Sedangkan uji coba telah dilakukan pada basis data citra wajah ORL, YALE dan BERN menggunakan kernel polynomial dan Gaussian, dengan reduksi dimensi menjadi v = 25 dan v = 50. Akurasi pengenalan citra wajah tertinggi dari ketiga basis data tersebut adalah menggunakan kernel Gaussian dan reduksi dimensi v = 50 dengan tujuh data pelatihan di setiap kelasnya. Pada basis data citra wajah ORL diperoleh akurasi pengenalan sebesar 98,50%, pada basis data citra wajah YALE diperoleh akurasi pengenalan sebesar 97,65%, dan pada basis data citra wajah BERN diperoleh akurasi pengenalan sebesar 97,95%. Dengan demikian, metode ekstraksi fitur KPCA yang dikombinasikan dengan metode klasifikasi Euclidean Distance sangat baik digunakan sebagai pengenalan citra wajah. Kata kunci: Kernel Principal Component Analysis (KPCA), Euclidean Distance, kernel polynomial, kernel Gaussian AbstractProblems of machine learning and pattern recognition are not a new research. Along with the development of technology, growing techniques and algorithms used to solve the problems of machine learning and pattern recognition. In this research has been successfully performed face recognition using Kernel Principal Component Analysis (KPCA) as feature extraction to determine the characteristics of the face and Euclidean Distance as the classification method based on statistics. While the experiments have been conducted on ORL face image database, YALE and BERN using polynomial and Gaussian kernel, the dimension reduction to v = 25 and v = 50. Highest recognition accuracy of three face image database is to use the Gaussian kernel and the reduction of dimension v = 50 with seven training data in each class. In the ORL face image database obtained recognition accuracy of 98,50%, on the basis of image data obtained YALE face recognition accuracy of 97,65%, and on the basis of image data obtained BERN face recognition accuracy of 97,95%. Thus, KPCA feature extraction methods are combined with Euclidean Distance classification method is best used as a facial image recognition. Key words: Kernel Principal Component Analysis (KPCA), Euclidean Distance, polynomial kernel, Gaussian kernel
PENGENALAN POLA SENYUM MENGGUNAKAN BACKPROPAGATION BERBASIS EKSTRAKSI FITUR PRINCIPAL COMPONENT ANALYSIS (PCA) Wahyuningrum, Rima Tri
Rekayasa Vol 4, No 1: April 2011
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (722.892 KB) | DOI: 10.21107/rekayasa.v4i1.2330

Abstract

Pada penelitian ini dilakukan pengenalan pola senyum menggunakan backpropagation berbasis ekstraksi fitur Principal Component Analysis (PCA). Penelitian ini bertujuan untuk mengembangkan penelitian tentang pengenalan ekspresi wajah, yaitu pola senyum seseorang yang diklasifikasikan menjadi lima macam (senyum manis, senyum mulut tertutup, senyum mulut terbuka, senyum mengejek, senyum yang dipaksakan). Data yang digunakan sebanyak 250 data, diambil dari 10 orang dengan lima macam pola senyum, masing-masing orang diwakili 25 data, sehingga masing-masing kelompok senyum terdapat lima data. Ukuran image wajah yang diolah adalah 100 × 100 pixel, kemudian dilakukan cropping pada bagian bibir sehingga ukuran image menjadi 39 × 25 pixel. Selanjutnya dilakukan proses grayscale sebelum dilakukan ekstraksi fitur menggunakan PCA. Tujuan penggunaan PCA adalah untuk mereduksi dimensi dari image yang diolah. Kemudian untuk pengenalannya menggunakan backpropagation. Pada penelitian ini digunakan teknik five cross validation supaya nilai akurasi yang dihasilkan bersifat objektif. Hasil akurasi pengenalan tertinggi diperoleh saat dilakukan uji coba menggunakan 10 hidden layer, dan nilai eigen 15 yaitu sebesar 82,67%. Kata kunci: pengenalan pola senyum, cropping, PCA, backpropagation. AbstractIn this study conducted a smile pattern recognition using feature extraction backpropagation-based Principal Component Analysis (PCA). This study aims to develop research on facial expression recognition, the pattern of a person’s smile is classified into five types (sweet smile, closed mouth smile, open mouth smile, smile taunting, a forced smile). Data used as many as 250 data, taken from 10 people with five kinds of smile patterns, each one represented by 25 data, so that each group contained five data smile. The size of the processed face image is 100 × 100 pixels, then do cropping on the lips so that the image size to 39 × 25 pixels. Grayscale process is then performed prior to feature extraction using PCA. Purpose of using PCA is to reduce the dimensions of the image is processed. Then for the introduction using backpropagation. In this study used five cross-validation technique so that the resulting accuracy values are objective. The results of the highest recognition accuracy obtained when conducted trials using 10 hidden layer, and eigenvalues 15 that is equal to 82.67%. Keywords: smile pattern recognition, cropping, PCA, backpropagation
PENGEMBANGAN MODEL SIMULASI KOMPUTER SISTEM ANGKUTAN PENYEBERANGAN LAUT Wahyuningrum, Rima Tri
Rekayasa Vol 3, No 2: Oktober 2010
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (327.077 KB) | DOI: 10.21107/rys.v3i2.2297

Abstract

Lama waktu entitas dalam sistem antrian ditentukan oleh waktu pelayanan dan waktu tunggu dalam angkutan penyeberangan merupakan waktu nonproduktif yang melibatkan kepentingan perusahaan angkutan penyeberangan dan kepentingan penumpang, karena waktu tunggu yang terlalu panjang akan mengurangi produktivitas bagi alat angkut maupun produktivitas kerja bagi penyeberang. Pada makalah ini digunakan pendekatan simulasi komputer untuk memodelkan sistem angkutan penyeberangan Kamal - Ujung Surabaya. Berdasarkan kriteria performansi minimasi lama entitas dalam sistem, pada model simulasi ini alternatif tindakan yang diusulkan adalah pengurangan batas kapasitas pick up resource. Hasil yang diperoleh dengan membandingan model as is dan model to be memberikan perbedaan yang siknifikan terhadap pengurangan rata-rata waktu entitas dalam sistem. Kata kunci: sistem antrian, model, simulasi komputer, angkutan penyeberangan. AbstractLength time of entity in quieng system of transportation is determined by service time and waiting time within system is non-productive time, that are involving shipping company and costumer/defector need. Because long waiting times will reduces the productivity of transport vehicle and productivity for the defector. This paper discuses the develponment of computer simulation approach to modeling the transportation system for crossing Kamal - Ujung Surabaya. Based on the criteria of minimizing time entities in the system, this simulation model of the proposed action alternative is a reduction in resource capacity limit pickup. Results obtained by comparing the model as is and model to be providing a significant difference to reduces mean time entities in the system. Keywords: quieng system, model, computer simulation, crossing tarnsportation.
PENGEMBANGAN SISTEM PEROLEHAN CITRA BATIK MADURA YANG EFEKTIF SEBAGAI UPAYA INVENTARISASI KEKAYAAN BUDAYA MADURA Wahyuningrum, Rima Tri; Rochman, Eka Mala Sari
Rekayasa Vol 5, No 2: Oktober 2012
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (610.772 KB) | DOI: 10.21107/rekayasa.v5i2.2128

Abstract

Gaya hidup manusia yang serba praktis, cepat dan akurat menuntut pertumbuhan teknologi yang pesat untuk memenuhi kebutuhan. Salah satunya adalah kebutuhan akan data dan informasi yang akurat. Salah satu teknik yang digunakan adalah Sistem Perolehan Citra berbasis Isi (SPCI) atau Content Based Image Retrieval (CBIR), yaitu suatu teknik pencarian citra yang mirip dengan melakukan perbandingan antara citra query dengan citra database. Obyek yang digunakan pada penelitian ini yaitu batik Madura dengan tujuan sebagai upaya inventarisasi kekayaan budaya Madura. Pada penelitian ini telah dibuat pengembangan Sistem Perolehan Citra Batik yang efektik menggunakan fitur warna, tekstur dan bentuk. Metode yang digunakan adalah kombinasi Fuzzy Color Histogram (FCH), Gray Level Co-occurrence Matrix (GLCM) dan Moment Invariants. Untuk mengukur kemiripan antar citra batik didasarkan pada perhitungan modifikasi Euclidean Distance. Dengan menggabungkan ketiga metode untuk ekstraksi fitur tersebut telah diperoleh tingkat akurasi perolehan citra yang tinggi, akurat dan efektif sesuai dengan yang diharapkan. Untuk mengevaluasi performansi dari sistem yang dikembangkan menggunakan Recall dan Precision. Berdasarkan hasil uji coba ditunjukkan bahwa dengan jumlah data yang ditampilkan sebanyak 10 pada nilai recall = 0.2 maka nilai precision yang dicapai cukup tinggi yaitu 0.93 yaitu saat jumlah data pelatihan yang digunakan sebanyak 160 dan jumlah data uji yang digunakan sebanyak 40 data.
Learning based on Virtual Class using combination of Second Life and Learning Managment System Irhamni, Firli; Siradjuddin, Indah Agustien; Kurniawati, Arik; Kusumaningsih, Ari; Triwahyuningrum, Rima
IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2014i1.449

Abstract

Learning is the most important aspect for increasing the quality of youth generation in a nation. Therefore many learning methods are developed in order to make the youth more qualified. Virtual class based on the second life learning method is proposed in this research. The objective of this learning method is to make learning process more organize and interesting for the youth. There are four stages to build virtual class based on the second life. First, create course material for the learning process. Second, make the virtual class scenario, i.e. student registration, evaluation, and the graduation. Third, design the avatar and the classroom hence the student can get in touch with each other and understand the course material easily. Fourth, build the virtual class based on second life and the scenarios. In this research second life is combined with moodle for learning hence the learning process more interesting and more understandable to the youth
Multi-Criteria in Discriminant Analysis to Find the Dominant Features Arif Muntasa; Indah Agustien Siradjuddin; Rima Tri Wahyuningrum
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3472

Abstract

A crucial problem in biometrics is enormous dimensionality. It will have an impact on the costs involved. Therefore, the feature extraction plays a significant role in biometrics computational. In this research, a novel approach to extract the features is proposed for facial image recognition. Four criteria of the Discriminant Analysis have been modeled to find the dominant features. For each criterion is an objective function, it was derived to obtain the optimum values. The optimum values can be solved by using generalized the Eigenvalue problem associated to the largest Eigenvalue. The modeling results were employed to recognize the facial image by the multi-criteria projection to the original data. The training sets were also processed by using the Eigenface projection to avoid the singularity problem cases. The similarity measurements were performed by using four different methods, i.e. Euclidian Distance, Manhattan, Chebyshev, and Canberra.  Feature extraction and analysis results using multi-criteria have shown better results than the other appearance method, i.e. Eigenface (PCA), Fisherface (Linear Discriminant Analysis or LDA), Laplacianfaces (Locality Preserving Projection or LPP), and Orthogonal Laplacianfaces (Orthogonal Locality Preserving Projection or O-LPP). 
Double Difference Motion Detection and Its Application for Madura Batik Virtual Fitting Room Rima Triwahyuningrum; Indah Agustien Siradjuddin; Yonathan Fery Hendrawan; Arik Kurniawati; Ari Kusumaningsih
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 4: December 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i4.2236

Abstract

Madura Batik Virtual Fitting Room using double difference algorithms motion detection is proposed in this research. This virtual fitting room consists of three main stages, i.e. motion detection, determination of region of interest of the detected motion, superimposed the virtual clothes into the region of interest. The double difference algorithm is used for the motion detection stage, since in this algorithm, the empty frame as the reference frame is not required. The double difference algorithm uses the previous and next frame to detect the motion in the current frame. Perception Test Images Sequences Dataset are used as the data of the experiment to measure the performance accuracy of this algorithm before the algorithm is used for the Madura batik virtual fitting room. The accuracy is 57.31%, 99.71%, and 78.52% for the sensitivity, specificity, and balanced accuracy, respectively. The build Madura batik virtual fitting room in this research can be used as the added feature of the Madura batik online stores, hence the consumer is able to see whether the clothes is fitted to them or not, and this virtual fitting room is also can be used as the promotion of Madura batik broadly.
Efficient Kernel-based Two-Dimensional Principal Component Analysis for Smile Stages Recognition Rima Tri Wahyuningrum; Fitri Damayanti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 1: March 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i1.766

Abstract

 Recently, an approach called two-dimensional principal component analysis (2DPCA) has been proposed for smile stages representation and recognition. The essence of 2DPCA is that it computes the eigenvectors of the so-called image covariance matrix without matrix-to-vector conversion so the size of the image covariance matrix are much smaller, easier to evaluate covariance matrix, computation cost is reduced and the performance is also improved than traditional PCA. In an effort to improve and perfect the performance of smile stages recognition, in this paper, we propose efficient Kernel based 2DPCA concepts. The Kernelization of 2DPCA can be benefit to develop the nonlinear structures in the input data. This paper discusses comparison of standard Kernel based 2DPCA and efficient Kernel based 2DPCA for smile stages recognition. The results of experiments show that Kernel based 2DPCA achieve better performance in comparison with the other approaches. While the use of efficient Kernel based 2DPCA can speed up the training procedure of standard Kernel based 2DPCA thus the algorithm can achieve much more computational efficiency and remarkably save the memory consuming compared to the standard Kernel based 2DPCA.
Co-Authors Ahmad Irsyad Amru Rosyidi Angkoso, Cucun Very Ari Kusumaningsih Arif Muntasa Arik Kurniawati Bagus Fajariyanto Bahrur Rosyid Bain Khusnul Khotimah Budi Dwi Satoto Fitri Damayanti Hendrawan, Yonathan Ferry I Ketut Eddy Purnama Irhamni, Firli Khozaimi, Ach. Kurniawan Eka Permana Mauridhi Hery Purnomo Mochammad Kautsar Sophan Rochman, Eka Mala Sari Siradjuddin, Indah Agustien Syafi?i, Slamet Imam Yonathan Ferry Hendrawan Yonathan Fery Hendrawan