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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Techno.Com: Jurnal Teknologi Informasi Perfecting a Video Game with Game Metrics Journal of Computer Science and Informatics Engineering (J-Cosine) KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Insyst : Journal of Intelligent System and Computation Sewagati
Rarasmaya Indraswari
Departemen Sistem Informasi, Institut Teknologi Sepuluh Nopember, Kampus ITS, Keputih, Sukolilo, Surabaya 60111
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Social Sciences

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Fuzzy Region Merging using Fuzzy Similarity Measurement on Image Segmentation Wawan Gunawan; Agus Zainal Arifin; Rarasmaya Indraswari; Dini Adni Navastara
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 6: December 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (748.683 KB) | DOI: 10.11591/ijece.v7i6.pp3402-3410

Abstract

Some image’s regions have unbalance information, such as blurred contour, shade, and uneven brightness. Those regions are called as ambiguous regions. Ambiguous region cause problem during region merging process in interactive image segmentation because that region has double information, both as object and background. We proposed a new region merging strategy using fuzzy similarity measurement for image segmentation. The proposed method has four steps; the first step is initial segmentation using mean-shift algorithm. The second step is giving markers manually to indicate the object and background region. The third step is determining the fuzzy region or ambiguous region in the images. The last step is fuzzy region merging using fuzzy similarity measurement. The experimental results demonstrated that the proposed method is able to segment natural images and dental panoramic images successfully with the average value of misclassification error (ME) 1.96% and 5.47%, respectively.
Automatic image slice marking propagation on segmentation of dental CBCT Agus Zainal Arifin; Evan Tanuwijaya; Baskoro Nugroho; Arif Mudi Priyatno; Rarasmaya Indraswari; Eha Renwi Astuti; Dini Adni Navastara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.13220

Abstract

Cone Beam Computed Tomography (CBCT) is a radiographic technique that has been commonly used to help doctors provide more detailed information for further examination. Teeth segmentation on CBCT image has many challenges such as low contrast, blurred teeth boundary and irregular contour of the teeth. In addition, because the CBCT produces a lot of slices, in which the neighboring slices have related information, the semi-automatic image segmentation method, that needs manual marking from the user, becomes exhaustive and inefficient. In this research, we propose an automatic image slice marking propagation on segmentation of dental CBCT. The segmentation result of the first slice will be propagated as the marker for the segmentation of the next slices. The experimental results show that the proposed method is successful in segmenting the teeth on CBCT images with the value of Misclassification Error (ME) and Relative Foreground Area Error (RAE) of 0.112 and 0.478, respectively.
MULTI-CLASS REGION MERGING FOR INTERACTIVE IMAGE SEGMENTATION USING HIERARCHICAL CLUSTERING ANALYSIS Khairiyyah Nur Aisyah; Syadza Anggraini; Novi Nur Putriwijaya; Agus Zainal Arifin; Rarasmaya Indraswari; Dini Adni Navastara
Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (893.612 KB) | DOI: 10.21609/jiki.v12i2.757

Abstract

In interactive image segmentation, distance calculation between regions and sequence of region merging is being an important thing that needs to be considered to obtain accurate segmentation results. Region merging without regard to label in Hierarchical Clustering Analysis causes the possibility of two different labels merged into a cluster and resulting errors in segmentation. This study proposes a new multi-class region merging strategy for interactive image segmentation using the Hierarchical Clustering Analysis. Marking is given to regions that are considered as objects and background, which are then referred as classes. A different label for each class is given to prevent any classes with different label merged into a cluster. Based on experiment, the mean value of ME and RAE for the results of segmentation using the proposed method are 0.035 and 0.083, respectively. Experimental results show that giving the label on each class is effectively used in multi-class region merging.
A Bonferroni Mean Based Fuzzy K Nearest Centroid Neighbor Classifier Arya Widyadhana; Cornelius Bagus Purnama Putra; Rarasmaya Indraswari; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 14, No 1 (2021): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v14i1.959

Abstract

K-nearest neighbor (KNN) is an effective nonparametric classifier that determines the neighbors of a point based only on distance proximity. The classification performance of KNN is disadvantaged by the presence of outliers in small sample size datasets and its performance deteriorates on datasets with class imbalance. We propose a local Bonferroni Mean based Fuzzy K-Nearest Centroid Neighbor (BM-FKNCN) classifier that assigns class label of a query sample dependent on the nearest local centroid mean vector to better represent the underlying statistic of the dataset. The proposed classifier is robust towards outliers because the Nearest Centroid Neighborhood (NCN) concept also considers spatial distribution and symmetrical placement of the neighbors. Also, the proposed classifier can overcome class domination of its neighbors in datasets with class imbalance because it averages all the centroid vectors from each class to adequately interpret the distribution of the classes. The BM-FKNCN classifier is tested on datasets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and benchmarked with classification results from the KNN, Fuzzy-KNN (FKNN), BM-FKNN and FKNCN classifiers. The experimental results show that the BM-FKNCN achieves the highest overall average classification accuracy of 89.86% compared to the other four classifiers.
RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT Rarasmaya Indraswari; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 10, No 1 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (183.204 KB) | DOI: 10.21609/jiki.v10i1.410

Abstract

SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel is a frequently used classification method because usually it provides an accurate results. The focus about most SVM optimization research is the optimization of the the input data, whereas the parameter of the kernel function (RBF), the sigma, which is used in SVM also has the potential to improve the performance of SVM when optimized. In this research, we proposed a new method of RBF kernel optimization with Particle Swarm Optimization (PSO) on SVM using the analysis of input data’s movement. This method performed the optimization of the weight of the input data and RBF kernel’s parameter at once based on the analysis of the movement of the input data which was separated from the process of determining the margin on SVM. The steps of this method were the parameter initialization, optimal particle search, kernel’s parameter computation, and classification with SVM. In the optimal particle’s search, the cost of each particle was computed using RBF function. The value of kernel’s parameter was computed based on the particles’ movement in PSO. Experimental result on Breast Cancer Wisconsin (Original) dataset showed that this RBF kernel optimization method could improve the accuracy of SVM significantly. This method of RBF kernel optimization had a lower complexity compared to another SVM optimization methods that resulted in a faster running time.
Segmentasi Multi Proyeksi pada Citra Cone Beam Computed Tomography Gigi Menggunakan Metode Level Set Fahmi Syuhada; Rarasmaya Indraswari; Agus Zainal Arifin; Dini Adni Navastara
Journal of Computer Science and Informatics Engineering (J-Cosine) Vol 5 No 2 (2021): December 2021
Publisher : Informatics Engineering Dept., Faculty of Engineering, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jcosine.v5i2.413

Abstract

Segmentation of dental Cone-beam computed tomography (CBCT) images based on Boundary Tracking has been widely used in recent decades. Generally, the process only uses axial projection data of CBCT where the slices image that representing the tip of the tooth object have decreased in contrast which impact to difficult to distinguish with background or other elements. In this paper we propose the multi-projection segmentation method by combining the level set segmentation result on three projections to detect the tooth object more optimally. Multiprojection is performed by decomposing CBCT data which produces three projections called axial, sagittal and coronal projections. Then, the segmentation based on the set level method is implemented on the slices image in the three projections. The results of the three projections are combined to get the final result of this method. This proposed method obtains evaluation results of accuracy, sensitivity, specificity with values of 97.18%, 88.62%, and 97.61%, respectively.
Deteksi Penyakit Mata Pada Citra Fundus Menggunakan Convolutional Neural Network (CNN) Rarasmaya Indraswari; Wiwiet Herulambang; Rika Rokhana
Techno.Com Vol 21, No 2 (2022): Mei 2022
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v21i2.6162

Abstract

Pada tahun 2020, terdapat 1,1 milyar orang yang mengalami kehilangan penglihatan di seluruh dunia. Jumlah ini diproyeksikan akan terus bertambah hingga mencapai 1,76 milyar orang pada tahun 2050. Penyebab utama kebutaan untuk anak-anak dan remaja adalah penyakit mata, yang dapat dicegah apabila dilakukan deteksi dan penanganan lebih dini. Oleh sebab itu, pada penelitian ini diusulkan metode berbasis Convolutional Neural Network (CNN) untuk mendeteksi penyakit mata pada citra fundus. Metode yang diusulkan menggunakan metode transfer learning dengan arsitektur jaringan MobileNetV2 sebagai base model. Arsitektur head model yang diusulkan, yang terdiri dari lapisan global average pooling dan diikuti oleh 2 lapisan fully-connected, mampu memberikan akurasi yang paling tinggi dan efisiensi paling baik dibandingkan dengan arsitektur head model lainnya. Eksperimen pada dataset citra fundus yang terdiri dari 601 citra dengan berbagai macam penyakit mata menunjukkan bahwa metode yang diusulkan mampu memberikan performa yang baik dengan nilai akurasi sebesar 72%, precision sebesar 72%, recall sebesar 72%, dan F1-score sebesar 72%. Hasil eksperimen menunjukkan bahwa metode yang diusulkan dapat memberikan akurasi yang lebih tinggi dan lebih efisien dibandingkan dengan menggunakan arsitektur CNN lainnya, seperti ResNet50V2, InceptionV3, InceptionResNetV2, VGG16, dan VGG19.
Website Development for Publication and Marketing of ITS-Assisted Halal Product MSME Nur Aini Rakhmawati; Rarasmaya Indraswari; Eko Setiyo Budi Purnomo; Nadhif Ikbar Wibowo
KAIBON ABHINAYA : JURNAL PENGABDIAN MASYARAKAT Vol. 4 No. 2 (2022)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/ka.v4i2.4068

Abstract

The high and increasing number of MSMEs and the importance of socialization and guidance related to halal product policies is one of the factors for the establishment of the Center for Halal Studies (PKH) of Institut Teknologi Sepuluh Nopember (ITS). PKH ITS aims to assist MSMEs in obtaining halal certification and marketing their products. In this community service activity, it is proposed to develop the ITS PKH website (http://halal.its.ac.id/) for of publication and marketing of ITS-assisted MSME’s halal products. The stages in this community service activity include preparation, implementation, documentation, and reporting. The ITS PKH website that we developed contains a complete profile of ITS assisted MSMEs equipped with a QRCode. This unique QRCode leads to the MSME profile page on the ITS PKH website and has been utilized by ITS-assisted MSMEs by being pasted on the website/social media/product packaging of each MSME. This website also has a “Ask Halal” feature to help people search and check halal products. Currently, there are 126 MSMEs spread throughout Indonesia who use the ITS PKH website that we developed. With the features provided by the ITS Halal Study Center website, it is hoped that MSMEs will find it easier to market their products because users find it easy to get and find information related to MSME products.
Platform Knowledge-Based Website untuk Meningkatkan Visibilitas UMKM di Sektor Perikanan Ika Nurkasanah; Qolbi Salima Alami; Mahendrawathi ER; Rarasmaya Indraswari; Radityo Prasetianto Wibowo; Rahmatsyam Lakoro
Sewagati Vol 7 No 3 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1364.733 KB) | DOI: 10.12962/j26139960.v7i3.482

Abstract

Indonesia merupakan negara kepulauan dengan sumber daya alam yang banyak, salah satunya adalah sumber daya alam laut, khususnya ikan. Namun, dampak yang diberikan oleh COVID-19 cukup besar dalam industri perikanan. Usaha Mikro, Kecil, dan Menengah (UMKM) yang bergerak di sektor perikanan mengalami kendala dan penurunan transaksi dalam bisnisnya. Oleh karena itu, program pengabdian masyarakat ini bertujuan untuk meningkatkan visibilitas UMKM di sektor perikanan dengan mengembangkan knowledge-based website. Solusi knowledge-based website dipilih berdasarkan kondisi saat ini, dimana masyarakat gemar mencari informasi yang lengkap sebelum melakukan pembelian produk. Metode pelaksanaan program pengabdian masyarakat ini diawali dengan tahap analisis kebutuhan mitra dan pengguna, kemudian dilanjutkan dengan tahap desain arsitektur website, pengembangan website, uji coba website, dan diakhiri dengan tahap sosialisasi dan pendampingan penggunaan website kepada mitra. Hasil program pengabdian masyarakat ini mampu membantu UMKM di sektor perikanan untuk melakukan pemasaran secara online, memperluas jangkauan pasar, dan meningkatkan visibilitas layanan yang ditawarkan. Bagi calon pelanggan, knowledge-based website ini memungkinkan pencarian pengetahuan terkait kandungan, manfaat, efek samping, dan informasi lainnya terkait produk yang ditawarkan oleh mitra.
Metode Pembobotan Hibrida untuk Ekstraksi Frasa Kunci Bahasa Arab Evan Kusuma Susanto; M. Bahrul Subkhi; Agus Z. Arifin; Maryamah; Rizka W. Sholikah; Rarasmaya Indraswari
Intelligent System and Computation Vol 4 No 2 (2022): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v4i2.255

Abstract

Banyaknya informasi membuat proses pengindeksan dan pencarian inti dari dokumen menjadi permasalahan yang rumit. Sebagian besar dokumen yang tersedia tidak dilengkapi dengan kata kunci terkait. Hal ini sehingga memaksa pembaca untuk membaca seluruh dokumen untuk mendapat gambaran penuh dari konten seluruh dokumen. Ekstraksi frasa kunci otomatis yang menggunakan Algoritma YAKE memberi solusi cepat ekstraksi frasa kunci menggunakan fitur lokal dari sebuah dokumen. Namun, penggunaan fitur lokal saja membuat hasil ekstraksi menjadi kurang relevan karena diperlukan istilah signifikan yang muncul di dokumen lain. Masalah lain yang muncul adalah terdapat beberapa fitur lokal yang tidak dapat digunakan untuk bahasa Arab, misalnya huruf kapital. Pada penelitian ini, diusulkan metode pembobotan kata yang mengintegrasikan fitur statistik lokal dari sebuah dokumen dan fitur eksternal dari dokumen lain untuk sistem ekstraksi kata kunci. Metode ini dapat digunakan secara efektif pada bahasa Arab dan dapat digunakan pada bahasa lain yang tidak memiliki huruf kapital serta untuk dokumen-dokumen yang tidak terstruktur seperti berita atau karya ilmiah. Dari hasil uji coba telah dibuktikan bahwa performansi metode ini lebih baik daripada metode pembanding yaitu YAKE dan TF-IDF.
Co-Authors Agus Z. Arifin Agus Zainal Arifin Arif Mudi Priyatno Arya Widyadhana Baskoro Nugroho Cornelius Bagus Purnama Putra Dini Adni Navastara, Dini Adni Eha Renwi Astuti Eko Setiyo Budi Purnomo Evan Kusuma Susanto Evan Tanuwijaya Fahmi Syuhada Ika Nurkasanah Khairiyyah Nur Aisyah M. Bahrul Subkhi Mahendrawathi ER Maryamah Nadhif Ikbar Wibowo Novi Nur Putriwijaya Nur Aini Rakhmawati Qolbi Salima Alami Radityo Prasetianto Wibowo Rahmatsyam Lakoro Rika Rokhana Rizka W. Sholikah Syadza Anggraini Wawan Gunawan Wiwiet Herulambang