Articles
<![CDATA[Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging ]]>
<![CDATA[Edio da Costa]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Supeno Djanali]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v8i3.pp1671-1683
<![CDATA[To overcome pests and diseases of rice farming, farmers always rely on information and knowledge from agricultural experts for decision making. The problem is that experts are not always available when the farmers need and the cost is quite high. Pests and diseases elimination is hard to be done individually since the farmers are lack of knowledge about the pest types that attack the rice fields. The objective of this study is to build a knowledge-based system that can identify pests and diseases interactively based on the information that has been told by the farmers using SMS communication services. The system can provide a convenience way to the farmers in delivering pests and disease problem information using a natural language. The text mining method performs tokenizing, filtering and porter stemming that used to extract important information sent by a SMS service. The method of Jaccard Similarity Coefficient (JSC) was used to calculate similarities of each pest and disease based on symptoms that are sent by the farmers through SMS. The corpus database usedin this study consists of 28.526 root words, 1.309 stop wordsand 180 words list. Pest and disease database reference in this study was obtained from the Ministry of Agriculture and Fisher (MAF) Timor-Leste. The result of the experiment shows that the system is able to identify the symptoms based on the keywords identified with the accuracy of 81%. The result of pest and disease identification has the accuracy of 86%.]]>
<![CDATA[Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentation of Fetal Ultrasound Images ]]>
<![CDATA[Fajar Astuti Hermawati]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Nanik Suciati]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v8i3.pp1747-1757
<![CDATA[Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.]]>
<![CDATA[Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets ]]>
<![CDATA[Nenden Siti Fatonah]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Chastine Fatichah]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijece.v8i3.pp1731-1740
<![CDATA[The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method.]]>
<![CDATA[Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors ]]>
<![CDATA[Isye Arieshanti]]>;
<![CDATA[Yudhi Purwananto]]>;
<![CDATA[Handayani Tjandrasa]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 4: December 2013 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v11i4.1203
<![CDATA[ The identification of ovarian cancer using protein expression profile (SELDI-TOF-MS) is important to assists early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle those difficulties, a novel ovarian cancer identification model is proposed in this study. The model comprises of One-Pass Clustering and k-Nearest Neighbors Classifier. With simple and efficient computation, the performance of the model achieves Accuracy about 97%. This result shows that the model is promising for Ovarian Cancer identification.]]>
<![CDATA[Optic Nerve Head Segmentation Using Hough Transform and Active Contours ]]>
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Ari Wijayanti]]>;
<![CDATA[Nanik Suciati]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v10i3.833
<![CDATA[Optic nerve head is part of the retina where ganglion cell axons exit the eye to form the optic nerve. Glaucomatous changes related to loss of the nerve fibers decrease the neuroretinal rim and expand the area and volume of the cup. Therefore optic nerve head evaluation is important for early diagnosis of glaucoma. This study implements the detection of the optic nerve head in retinal fundus images based on the Hough Transform and Active Contour Models. The process starts with the image enhancement using homomorphic filtering for illumination correction, then proceeds with the removal of blood vessels on the image to facilitate the subsequent segmentation process. The result of the Hough Transform fitting circle becomes the initial level set for the active contour model. The experimental results show that the implemented segmentation algorithms are capable of segmenting optic nerve head with the average accuracy of 75.56% using 30 retinal images from the DRIVE database.Optic nerve head segmentation using hough transform and active contours]]>
<![CDATA[Robot Motion Control Using the Emotiv EPOC EEG System ]]>
<![CDATA[Sandy Akbar Dewangga]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Darlis Herumurti]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v7i2.678
<![CDATA[Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.]]>
<![CDATA[Robot Motion Control Using the Emotiv EPOC EEG System ]]>
<![CDATA[Sandy Akbar Dewangga]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Darlis Herumurti]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v7i2.678
<![CDATA[Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.]]>
<![CDATA[Perbandingan Performa antara Imputasi Metode Konvensional dan Imputasi dengan Algoritma Mutual Nearest Neighbor ]]>
<![CDATA[Azwar Rizal Alfarisi]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Isye Arieshanti]]>
<![CDATA[ Jurnal Teknik ITS Vol 2, No 1 (2013) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v2i1.2735
<![CDATA[Missing value adalah sebuah permasalahan yang sering terjadi pada dataset riil. Kekurangan ini biasanya mempengaruhi akurasi saat dilakukan klasifikasi dengan menggunakan dataset tersebut. Salah satu cara menyelesaikan masalah missing value tersebut adalah mengisi nilai baru atau dikenal dengan metode imputasi. Algoritma mutual nearest neighbor (MNN) adalah sebuah algoritma pengenalan pola yang menggunakan tetangga mutual terdekat suatu instance. Dalam studi ini, algoritma MNN digunakan sebagai metode imputasi. Performanya akan dibandingkan dengan metode imputasi konvensional yaitu mengisikan nilai mean atau modus data atribut ke missing value. Berdasarkan hasil uji coba, performa klasifikasi setelah dilakukan imputasi dengan algoritma MNN mengungguli performa klasifikasi dengan metode imputasi konvensional.]]>
<![CDATA[Deteksi Penyakit Epilepsi dengan Menggunakan Entropi Permutasi, K-means Clustering, dan Multilayer Perceptron ]]>
<![CDATA[Yunita Ardilla]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Isye Arieshanti]]>
<![CDATA[ Jurnal Teknik ITS Vol 3, No 1 (2014) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v3i1.5486
<![CDATA[Epilepsi didefinisikan sebagai kumpulan gejala dan tanda-tanda klinis yang muncul disebabkan gangguan fungsi otak secara intermiten, yang terjadi akibat lepas muatan listrik abnormal atau berlebihan dari neuron-neuron secara paroksimal dengan berbagai macam etiologi. Banyak pasien yang tidak menyadari adanya gejala epilesi dalam dirinya. Oleh karena itu diperlukan sistem yang bisa memprediksi apakah seseorang menderita epilepsi bebas kejang, atau epilepsi kejang. Dalam artikel ini diimplementasikan perangkat lunak pendeteksi penyakit epilepsi dengan menggunakan entropi permutasi, K-means clustering, dan multilayer perceptron. Hasil model dari algoritma multilayer perceptron akan digunakan dalam proses prediksi. Dataset yang digunakan dalam proses uji coba berisi lima himpunan (A-E) EEG dari manusia sehat dan yang menderita epilepsi yang tersedia online (''Klinik für Epileptologie, Universität Bonn''). Performa terbaik yang dihasilkan oleh model adalah akurasi sebesar 96,5%, specificity sebesar 95,45%, dan sensitivity sebesar 97,97%.]]>
<![CDATA[Deteksi Penyakit Glaukoma pada Citra Fundus Retina Mata Menggunakan Adaptive Thresholding dan Support Vector Machine ]]>
<![CDATA[Ahmad Mustofa]]>;
<![CDATA[Handayani Tjandrasa]]>;
<![CDATA[Bilqis Amaliah]]>
<![CDATA[ Jurnal Teknik ITS Vol 5, No 2 (2016) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v5i2.18929
<![CDATA[Glaukoma adalah penyebab kebutaan kedua terbanyak setelah katarak. Berbeda dengan katarak, kebutaan yang disebabkan oleh glaukoma bersifat permanen. Hal ini karena glaukoma disebabkan oleh tekanan besar pada bola mata yang menyebabkan tersumbatnya pembuluh darah yang menuju ke syaraf mata sehingga syaraf mata tidak mendapatkan suplai darah yang cukup dan akhirnya akan mengalami kerusakan. Gejala glaukoma yang timbul biasanya tidak dapat dirasakan secara langsung. Sehingga perlu dilakukan pemeriksaan mata terlebih dahulu untuk mengetahui adanya glaukoma. Pada pengerjaan tugas akhir ini, dibangun sebuah perangkat lunak untuk mendeteksi penyakit glaukoma pada citra fundus retina mata. Tahap pertama dalam pengerjaan tugas akhir ini adalah proses preprocessing citra. Tahap preprocessing terbagi menjadi preprocessing optic cup, preprocessing optic disk, dan preprocessing pembuluh darah. Kemudian akan dilakukan proses segmentasi optic cup, optic disk, dan pembuluh darah dengan menggunakan metode adaptive thresholding. Setelah proses segmentasi selesai, maka fitur Cup to disk ratio (CDR), ISNT Neuro Retinal Rim (NRR), dan ISNT pembuluh darah akan diekstraksi dari masing-masing citra yang telah tersegmentasi. Ketiga fitur tersebut kemudian dijadikan masukan pada pengklasifikasi support vector machine dengan menggunakan metode pencarian hyperplane sequential minimal optimization dan fungsi kernel linear. Dengan menggunakan data yang diambil dari database RIM-ONE, didapatkan nilai akurasi rata-rata sebesar 80%.]]>
