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All Journal International Journal of Electrical and Computer Engineering Jurnal Gamma Perfecting a Video Game with Game Metrics JUTI: Jurnal Ilmiah Teknologi Informasi CommIT (Communication & Information Technology) International Journal of Artificial Intelligence Research Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Abdimas Madani dan Lestari (JAMALI) Automata
Arrie Kurniawardhani
Universitas Islam Indonesia
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Education Electrical & Electronics Engineering Library & Information Science Other

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MULTI-DOCUMENT SUMMARIZATION BASED ON SENTENCE CLUSTERING IMPROVED USING TOPIC WORDS Lukmana, Indra; Swanjaya, Daniel; Kurniawardhani, Arrie; Arifin, Agus Zainal; Purwitasari, Diana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 12, No 2, Juli 2014
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.988 KB) | DOI: 10.12962/j24068535.v12i2.a317

Abstract

Informasi dalam bentuk teks berita telah menjadi salah satu komoditas yang paling penting dalam era informasi ini. Ada banyak berita yang dihasilkan sehari-hari, tetapi berita-berita ini sering memberikan konten kontekstual yang sama dengan narasi berbeda. Oleh karena itu, diperlukan metode untuk mengumpulkan informasi ini ke dalam ringkasan sederhana. Di antara sejumlah subtugas yang terlibat dalam peringkasan multi-dokumen termasuk ekstraksi kalimat, deteksi topik, ekstraksi kalimat representatif, dan kalimat rep-resentatif. Dalam tulisan ini, kami mengusulkan metode baru untuk merepresentasikan kalimat ber-dasarkan kata kunci dari topic teks menggunakan Latent Dirichlet Allocation (LDA). Metode ini terdiri dari tiga langkah dasar. Pertama, kami mengelompokkan kalimat di set dokumen menggunakan kesamaan histogram pengelompokan (SHC). Selanjutnya, peringkat cluster menggunakan klaster penting. Terakhir, kalimat perwakilan yang dipilih oleh topik diidentifikasi pada LDA. Metode yang diusulkan diuji pada dataset DUC2004. Hasil penelitian menunjukkan rata-rata 0,3419 dan 0,0766 untuk ROUGE-1 dan ROUGE-2, masing-masing. Selain itu, dari pembaca prespective, metode kami diusulkan menyajikan pengaturan yang koheren dan baik dalam memesan kalimat representatif, sehingga dapat mempermudah pemahaman bacaan dan mengurangi waktu yang dibutuhkan untuk membaca ringkasan.
KLASIFIKASI CITRA BATIK MENGGUNAKAN METODE EKSTRAKSI CIRI YANG INVARIANT TERHADAP ROTASI Kurniawardhani, Arrie; Suciati, Nanik; Arieshanti, Isye
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 12, No 2, Juli 2014
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3771.41 KB) | DOI: 10.12962/j24068535.v12i2.a322

Abstract

Untuk membantu proses pendokumentasian citra Batik, dibutuhkan sistem klasifikasi yang cukup handal dalam mengklasifikasi dan mengidentifikasi citra Batik. Salah satu kehandalan sistem klasifikasi yang dibutuhkan adalah invariant terhadap rotasi. Kehandalan tersebut dibutuhkan agar sistem dapat diaplikasikan untuk mengenali citra dari berbagai macam sumber, seperti internet. Kehandalan sistem klasifikasi tidak lepas dari kehandalan metode ekstraksi cirinya. Salah satu metode ekstraksi ciri yang invariant terhadap rotasi adalah LBPROT. Namun, LBPROT memiliki kekurangan yaitu mengabaikan karakteristik lokal dari kekontrasan atau nilai varian. Di lain pihak, Completed Local Binary Pattern (CLBP) dan Completed Robust Local Binary Pattern (CRLBP) memiliki ciri yang dapat merepresentasikan nilai varian lokal tanpa mengabaikan struktur spasial lokal, yaitu ciri magnitude-nya, CLBP_M dan CRLBP_M. Oleh karena itu, pada penelitian kali ini diusulkan metode klasifikasi yang invariant terhadap rotasi, dengan menggunakan metode ekstraksi ciri yang menggabungkan kelebihan metode LBPROT dan CLBP_M (rotCLBP_M), atau LBPROT dan CRLBP_M (rotCRLBP_M). Hasil ekstraksi ciri akan menjadi data masukan untuk sistem klasifikasi Probabilistic Neural Network (PNN). Kinerja sistem diukur menggunakan akurasi. Hasil uji coba menunjukkan bahwa sistem klasifikasi dengan metode ekstraksi ciri rotCRLBP_M, lebih unggul dibandingkan dengan metode rotCLBP_M. Sistem klasifikasi dapat mencapai akurasi maksimal sebesar 90.34% untuk dataset Batik. Sedangkan pada dataset Brodatz, sistem klasifikasi dapat mencapai akurasi sebesar 87,92%.
Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation Kurniawan, Rahadian; Muhimmah, Izzati; Kurniawardhani, Arrie; Indrayanti, Indrayanti
CommIT (Communication and Information Technology) Journal Vol 11, No 1 (2017): CommIT Vol. 11 No. 1 Tahun 2017
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v11i1.1957

Abstract

The automatic interpretation of Pap Smear image is one of challenging issues in some aspects. Accurate segmentation for each cell is an important procedurethat must be done so that no information is lost during the evaluation process. However, the presence of overlapping cells in Pap Smear image make the automated analysis of these cytology images become more difficult. In most ofthe studies, cytoplasm segmentation is the difficult stage because the boundaries between cells are very thin. In this study, we propose an algorithm that can segment the overlapping cytoplasm. First, the morphology operation and global thresholding to segment cytoplasm is done. Second, the overlapping area on cytoplasm region is separated using morphological operation and distance criteria on each pixel. The proposed method has been evaluated against the results of manual tracing by experts. The experiment results show that the proposed method can segment the overlapping cytoplasm as similar as experts do, i.e., 2:897 3:632 (mean std) using Hausdorff distance.
Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means Kurniawan, Rahadian; Muhimmah, Izzati; Kurniawardhani, Arrie; Kusumadewi, Sri
CommIT (Communication and Information Technology) Journal Vol 13, No 1 (2019): CommIT Vol. 13 No. 1 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i1.5119

Abstract

The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.
CBIR of Batik Images using Micro Structure Descriptor on Android Agus Eko Minarno; Yuda Munarko; Arrie Kurniawardhani
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 5: October 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (499.175 KB) | DOI: 10.11591/ijece.v8i5.pp3778-3783

Abstract

Batik is part of a culture that has long developed and known by the people of Indonesia and the world. However, the knowledge is only on the name of batik, not at a more detailed level, such as image characteristic and batik motifs. Batik motif is very diverse, different areas have their own motifs and patterns related to local customs and values. Therefore, it is important to introduce knowledge about batik motifs and patterns effectively and efficiently. So, we build CBIR batik using Micro-Structure Descriptor (MSD) method on Android platform. The data used consisted of 300 images with 50 classes with each class consists of six images. Performance test is held in three scenarios, which the data is divided as test data and data train, with the ratio of scenario 1 is 50%: 50%, scenario 2 is 70%, 30%, and scenario 3 is 80%: 20%. The best results are generated by scenario 3 with precision valur 65.67% and recall value 65.80%, which indicates that the use of MSD on the android platform for CBIR batik performs well.
Comparison of Methods for Batik Classification Using Multi Texton Histogram Agus Eko Minarno; Ayu Septya Maulani; Arrie Kurniawardhani; Fitri Bimantoro; Nanik Suciati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.7376

Abstract

Batik is a symbol reflecting Indonesian culture which has been acknowledged by UNESCO since 2009. Batik has various motifs or patterns. Because most regions in Indonesia have their own characteristic of batik motifs, people find difficulties to recognize the variety of Batik. This study attempts to develop a system that can help people to classify Batik motifs using Multi Texton Histogram (MTH) for feature extraction. Meanwhile, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithm were employed for classification. The performance of those classifications is then compared to seek the best classification method for Batik classification. The performance is tested 300 images divided into 50 classes. The results show the optimum accuracy achieved using k-NN with k=5 and MTH with 6 textons is 82%; however, SVM and MTH with 6 textons denote 76%. According to the result, MTH as feature extraction, k-NN or SVM as a classifier can be applied on Batik image classification.
Image Retrieval Based on Multi Structure Co-occurrence Descriptor Agus Eko Minarno; Arrie Kurniawardhani; Fitri Bimantoro
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.v14i3.3292

Abstract

This study present a new technique for Batik cloth image retrieval using Micro-Structure Co-occurence Descriptor (MSCD). MSCD is a developed method based on Enhanced Micro Structure Descriptor (EMSD). Previously, EMSD has been improved by adding edge orientation feature. In previous study, EMSD cannot achieve an optimal precision. Therefore, MSCD is proposed to overcome the EMSD drawback using global feature approach, namely Gray Level Co-occurrence Matrix (GLCM). There are 300 batik cloth images which contain 50 classes used for dataset. The performance result show that MSCD can retrieve Batik cloth images more effective than EMSD.
Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach Rahadian Kurniawan; Arrie Kurniawardhani; Izzati Muhimmah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 5: October 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i5.6817

Abstract

In order to obtain a diagnosis of cervical cancer information, the characteristics of each cell nucleus must be identified and evaluated properly through a Pap smear test. The presence of inflammatory cells in Pap smear images can complicate the process of identification of cell nuclei in the early detection of cervical cancer. Inflammatory cells need to be eliminated to assist pathologists in reading Pap smear slides. In this work, we developed a novel method to extract the inflammatory cells that allow detection of cell nuclei more accuracy. The proposed algorithm consists of two stages: extraction of inflammatory cells using the distance criterion and image transformation. This experiment applied to the 1358 cells comprising 378 nuclei cells and 980 inflammatory cells from 25 Pap smear images. The results showed that our method can significantly reduce the amount of inflammation that can disrupt the cell nuclei in the detection process. The proposed method has promising results with a sensitivity level of 97% and a specificity of 84.38%.
The Development of Hand Gestures Recognition Research: A Review Achmad Noer Aziz; Arrie Kurniawardhani
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.203 KB) | DOI: 10.29099/ijair.v6i1.236

Abstract

This paper contains a review of the literature published in the last 5 years that discusses the topic of hand gesture recognition. The focus in this paper leads the reader to see the development of research over the years in hand gesture recognition, in particular that discusses about performance, methods, and datasets used in hand gesture recognition. From this paper, hopefully it can attract researchers’ interest to develop technology more deeply, especially in the field of hand gesture recognition. Hand gestures are not only used as a medium of communication for people with disabilities. Hand gestures can also be used to interact with a computer without any special devices with the technology that is available today.
OPTIMASI TEKNIK KLASIFIKASI MODIFIED K NEAREST NEIGHBOR MENGGUNAKAN ALGORITMA GENETIKA Siti Mutrofin; Abidatul Izzah; Arrie Kurniawardhani; Mukhamad Masrur
Jurnal Gamma Vol. 10 No. 1 (2014): September
Publisher : Jurnal Gamma

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

OPTIMASI TEKNIK KLASIFIKASI MODIFIED K NEAREST NEIGHBOR MENGGUNAKAN ALGORITMA GENETIKAOptimization Techniques Modified K Nearest Neighbor Classification Using Genetic AlgorithmSiti Mutrofin, Abidatul Izzah, Arrie Kurniawardhani, Mukhamad MasrurJurusan Sistem Informasi, Fakultas Teknik, Unipdu, Kompleks Ponpes Darul ‘Ulum Peterongan Jombang, 61481ABSTRACTOne of the tasks of data mining is classifi cation, many researchers are already conducting research on the method of classification. Classification method used is k-Nearest Neighbor (kNN). kNN has several advantages, including the training is fast, simple and easy to learn, resistant to the training data that has noise, and effectively if the large training data. Meanwhile, the lack of kNN is the value of k bias, complex computing, memory limitations, and easily fooled by irrelevant attributes. One improvement is the Modifi ed kNN kNN (MKNN), which aims to improve the accuracy of the kNN, by adding the calculation of validity.because it is considered the weight calculations contained in kNN, have problems outlier. However, MKNN also have the same disadvantages as kNN k value bias and complex computing. Based on the MKNN problems, the author intends to make improvements in terms of, optimization Genentic value k using Algorithm (GA), because GA has proven it can be used to optimize the value of k for kNN. Furthermore, the algorithm will be called GMKNN algorithm (Genetic Modifi ed k Nearest Neighbor). Evaluation level of truth results will be based on the value of accuracy, either using kNN algorithm, MKNN and GMKNN use data UCI machinelearning.Keywords : kNN , Modified kNN , Genetic Algorithm , Genetic Modified kNN, UCI Machine LearningABSTRAKSalah satu tugas dari data mining adalah klasifikasi, banyak peneliti yang sudah melakukan penelitian tentang metode klasifikasi. Metode klasifikasi yang biasa digunakan adalah k-Nearest Neighbor (kNN). kNN memiliki beberapa kelebihan, diantaranya adalah pelatihan sangat cepat, sederhana dan mudah dipelajari, tahan terhadap data pelatihan yang memiliki derau, dan efektif jika data pelatihan besar. Sedangkan, kekurangan dari kNN adalah nilai k bias, komputasi kompleks, keterbatasan memori, dan mudah tertipu dengan atribut yang tidak relevan. Salah satu perbaikan kNN adalah Modifi ed kNN (MKNN), yang bertujuan untuk meningkatkan akurasi dari kNN, dengan menambahkan perhitungan validity, karena dianggap perhitungan bobot yang terdapat pada kNN, memiliki permasalahan outlier. Namun, MKNN juga memiliki kelemahan yang sama dengan kNN yaitu nilai k bias dan komputasi yang kompleks. Berdasarkan permasalahan MKNN tersebut, penulis bermaksud untuk melakukan perbaikan dalam hal, optimasi nilai k menggunakan Genentic Algorithm (GA), karena GA sudah terbukti dapat digunakan untuk melakukan optimasi pada nilai k untukkNN. Selanjutnya algoritma tersebut akan dinamakan algoritma GMKNN (Genetic Modifi ed k Nearest Neighbor). Evaluasi tingkat kebenaran hasil akan didasarkan pada nilai akurasi, baik menggunakan algoritma kNN, MKNN maupun GMKNN menggunakan data UCI machine learning.Kata Kunci : kebijakan publik, Supiory, daerah tertinggal dan pengembangan
Co-Authors Abidatul Izzah Achmad Noer Aziz Agus Eko Minarno Agus Zainal Arifin Ahmad Faris Akbar Ayu Septya Maulani Catur Singgih Daniel Swanjaya Dhomas Hatta Fudholi Diana Purwitasari Dimas Setyawan Ramadhansyah Ferdian Nursulistio Fitri Bimantoro Ihya Fathurrahman Fathurrahman Indra Lukmana Indrayanti Indrayanti Indrayanti, Indrayanti Irving V. Paputungan Isye Arieshanti Izzati Muhimmah Khoirunnisa Shofwatul Laili Mufti Leriansyah Muhammad Rizqi Az Zayyad Muhimmah, Izzati Mukhamad Masrur Nanik Suciati Nur Jati Lantang Marfu'ah Rahadian Kurniawan Rio Galang Jati Respati Salsabila Zahirah Pranida Setya Winarno Siti Mutrofin Sri Kusumadewi Yuda Munarko