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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer Jurnal INKOM Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUTI: Jurnal Ilmiah Teknologi Informasi IJoICT (International Journal on Information and Communication Technology) Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of Technology And Business Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) Jurnal Sistem Informasi Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Aniati Murni Arymurthy
Fakultas Ilmu Komputer, Universitas Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Computer Science & IT Control & Systems Engineering Dentistry Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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PENERAPAN SISTEM DATA MINING UNTUK DIAGNOSIS PENYAKIT KANKER PAYUDARA MENGGUNAKAN CLASSIFICATION BASED ON ASSOCIATION ALGORITHM Herwanto, Herwanto; Arymurthy, Aniati Murni
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 8, No 2, Juli 2010
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (19168.544 KB) | DOI: 10.12962/j24068535.v8i2.a312

Abstract

Aplikasi system data mining untuk mengidentifikasi atribut-atribut penting yang berguna membantu pengambilan keputusan dari basis data rumah sakit akan dibahas dalam paper ini. Data-data medis pasien yang beresiko menderita penyakit kanker payudara dimasukkan ke dalam data warehouse. Metodologi model klasifikasi didasarkan pada tiga tahapan, yaitu a) menangani data yang tidak lengkap melalui ekstraksi, b) merubah data yang bernilai kontinyu menjadi data yang bernilai diskrit serta c) rule mining dan klasifikasi. Algoritma yang digunakan untuk proses data mining adalah Classification Based on Predictive Association Rule (CPAR). Pada tahapan diskritisasi, terdapat masalah yang dikenal dengan istilah "sharp boundary". Paper ini mengusulkan proses optimalisasi menggunakan soft discretization, di mana fuzzy logic digunakan untuk mempartisi data. Ada 2.767 pasien yang terpilih, masing-masing diambil 8 atribut: sex, umur dan hasil pemeriksaan laboratorium yaitu Hemoglobin (HB), Lekosit (Leko), Trombosit (Tromb), Hemotokrit (HCT), Red blood cell distribution width (RDW) dan RDW-SD. Tingkat akurasi maksimum untuk positif kanker payudara adalah 67% dan negatif kanker payudara 97%.
Classification of LiDAR Images Fused With Aerial Optical Images Using Ensemble Classifier AdaBoost.MH and Post-processing BFS Prasvita, Desta Sandya; Arymurthy, Aniati Murni
International Journal of Technology And Business Vol 1 No 1 (2017): IJTB|International Journal of Technology And Business
Publisher : LPPM of STIMIK ESQ

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Abstract

The objective of this research is to propose a method in order to increase classification performance of LiDAR images fused with aerial optical images. Classification method in use is popular multiclass ensemble classifier method, AdaBoost.MH. The Weak classifier in use of AdaBoost.MH is Hamming Trees. Then, the post-processing method is conducted to remove the noise with Breadth First Search (BFS) algorithm. Post-processing increase the accuracy to 0.96% and able to reduce the noise of classification result. This research is able to increase the accuracy on previous research by 94.96%
Classification of LiDAR Images Fused With Aerial Optical Images Using Ensemble Classifier AdaBoost.MH and Post-processing BFS Prasvita, Desta Sandya; Arymurthy, Aniati Murni
IJTB (International Journal of Technology And Business) Vol 1 No 1 (2017): IJTB (International Journal Of Technology And Business)
Publisher : LPPM of STIMIK ESQ

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Abstract

The objective of this research is to propose a method in order to increase classification performance of LiDAR images fused with aerial optical images. Classification method in use is popular multiclass ensemble classifier method, AdaBoost.MH. The Weak classifier in use of AdaBoost.MH is Hamming Trees. Then, the post-processing method is conducted to remove the noise with Breadth First Search (BFS) algorithm. Post-processing increase the accuracy to 0.96% and able to reduce the noise of classification result. This research is able to increase the accuracy on previous research by 94.96%
CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System Kusumaningrum, Retno; Manurung, Hisar Maruli; Arymurthy, Aniati Murni
INKOM Journal Vol 8, No 2 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (251.469 KB) | DOI: 10.14203/j.inkom.409

Abstract

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.
Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera Edy Winarno; Agus Harjoko; Aniati Murni Arymurthy; Edi Winarko
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (435.818 KB) | DOI: 10.11591/ijece.v6i6.pp2818-2827

Abstract

The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).
A non-negative matrix factorization based clustering to identify potential tuna fishing zones Devi Fitrianah; Hisyam Fahmi; Achmad Nizar Hidayanto; Pang Ning-Tan; Aniati Murni Arymurthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5458-5466

Abstract

Many nonnegative matrix factorization based clusterings are employed in discovering pattern and knowledge. Considering the sparseness nature of our data set about the daily tuna fishing data, we attempted to utilize a clustering approach, which is based on non-negative matrix factorization. Adding sparseness constraint and assigning good initial value in the modified NMF method, a proposed algorithm Direct-NMFSC yielded better result cluster compared to other methods which are also utilizing sparse constraint to their approaches, SNMF and NMFSC. The result of this study shows that Direct-NMFSC has 5.376 times of iteration number less than NMFSC in average with 531.97 as the CH index result. The determination of potential fishing zones is one of the essential efforts in the potential fishing zone mapping system for tuna fishing. By means of this novel data-driven study to construct the information and to identify the potential tuna fishing zones is done. We also showed that utilizing the Direct-NMFSC can spot and identify the potential tuna fishing zones presented in red cluster that covers both the spatial and temporal information.
PENAMBANGAN CITRA INDERAJA MENGGUNAKAN INFORMASI SPASIAL DAN SPEKTRAL Sri Hartati Wijono; Aniati Murni
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2010
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Tulisan ini merupakan hasil penelitian yang bertujuan untuk menghasilkan sistem yang mampu menghasilkancitra dengan tekstur dan komposisi yang memiliki tingkat relevansi paling besar dengan citra kueri.Untuk mencapai tujuan tersebut, penelitian ini menggunakan 70 citra inderaja optis. Digunakan informasispasial dan spektral untuk mencari kemiripan citra hasil kueri dengan citra kueri. Informasi spasial yangdigunakan adalah vektor ciri yang diperoleh dari Gabor Wavelet Transform. Informasi spektral menggunakanvektor komposisi Land Cover Land Use yang diperoleh dengan klasifikasi Gaussian Maximum Likelihood.Vektor ciri Gabor akan diuji dengan kueri menggunakan citra yang mengalami proses skala 0.5 dan rotasi 90°.Dilakukan proses circular shift terhadap vektor ciri Gabor untuk menangani masalah rotation invariant.Hasil penelitian menunjukkan bahwa Gabor Wavelet Transform memiliki masalah scale invariant dan rotationinvariant, sehingga memerlukan proses normalisasi. Masalah rotation variant dapat ditangani dengan prosescircular shift terhadap vektor ciri Gabor dan terbukti handal untuk meningkatkan hasil. Penelitianmenggunakan berbagai macam ukuran jarak dan ukuran jarak Matusita yang paling tinggi menghasilkan citrarelevan. Penggunaan informasi spasial dan spektral memberikan hasil lebih baik jika digunakan secarabersamaan.Kata Kunci: citra inderaja, Gabor Wavelet Transform, Gaussian Maximum Likelihood, Land Cover Land Use
Random adjustment - based Chaotic Metaheuristic algorithms for image contrast enhancement Vina Ayumi; L.M. Rasdi Rere; Mohamad Ivan Fanany; Aniati Murni Arymurthy
Jurnal Ilmu Komputer dan Informasi Vol 10, No 2 (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 (695.372 KB) | DOI: 10.21609/jiki.v10i2.375

Abstract

Metaheuristic algorithm is a powerful optimization method, in which it can solve problemsby exploring the ordinarily large solution search space of these instances, that are believed tobe hard in general. However, the performances of these algorithms signicantly depend onthe setting of their parameter, while is not easy to set them accurately as well as completelyrelying on the problem's characteristic. To ne-tune the parameters automatically, manymethods have been proposed to address this challenge, including fuzzy logic, chaos, randomadjustment and others. All of these methods for many years have been developed indepen-dently for automatic setting of metaheuristic parameters, and integration of two or more ofthese methods has not yet much conducted. Thus, a method that provides advantage fromcombining chaos and random adjustment is proposed. Some popular metaheuristic algo-rithms are used to test the performance of the proposed method, i.e. simulated annealing,particle swarm optimization, dierential evolution, and harmony search. As a case study ofthis research is contrast enhancement for images of Cameraman, Lena, Boat and Rice. Ingeneral, the simulation results show that the proposed methods are better than the originalmetaheuristic, chaotic metaheuristic, and metaheuristic by random adjustment.
CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING Muhamad Soleh; Aniati Murni Arymurthy; Sesa Wiguna
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): 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 (547.427 KB) | DOI: 10.21609/jiki.v11i2.623

Abstract

Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.
EKSTRAKSI FITUR FRAKTAL DAN MORFOLOGI SINYAL ELEKTROKARDIOGRAM DAN PEMANFAATANNYA DALAM KLASIFIKASI DEEP SLEEP Aniati Murni Arymurthy; Edward Citrahadi; Tieta Antaresti
Jurnal Ilmu Komputer dan Informasi Vol 4, No 2 (2011): 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 (821.551 KB) | DOI: 10.21609/jiki.v4i2.169

Abstract

Detak jantung manusia dapat memberikan informasi yang berguna tentang aktivitas yang terjadi di dalam tubuh. Salah satu informasi yang dapat diperoleh dari rekaman detak jantung atau elektrokardiogram adalah tingkat keterlelapan tidur seseorang (sleep stages). Dari sinyal elektrokardiogram seseorang, tingkat keterlelapan tidurnya dapat dikenali dengan terlebih dahulu mengekstrak fitur yang merepresentasikan sinyal elektrokardiogram tersebut secara keseluruhan. Ekstraksi dilakukan agar dimensi data dapat tereduksi sehingga proses klasifikasi dapat lebih mudah dilakukan. Penelitian ini melakukan ekstraksi fitur fraktal dan morfologi dari sinyal elektrokardiogram yang diperoleh dari PhysioNet. Sebelum melakukan ekstraksi fitur morfologi dari sinyal elektrokardiogram, terlebih dahulu dilakukan “Wavelet Denoising” untuk menghilangkan noise yang terdapat pada sinyal. Human heart rate can provide useful information about the activities that occur in the body. One of information which may be obtained from recording the heart rate or electrocardiogram is commonly called a person's level of deep sleep (sleep stages). From a person's electrocardiogram signal, the level of deep sleep recognizable by extracting features that represent the electrocardiogram signal as a whole. Extraction is done so that the dimension of the data can be reduced so that the classification process can be more easily done. This study aims to extract fractal features and morphology of the electrocardiogram signal obtained from PhysioNet. Prior to the extraction of morphological features of the electrocardiogram signal, first performed “Wavelet Denoising” to remove the noise contained in the signal.
Co-Authors Achmad Nizar Hidayanto Ade Azurat Ade Azurat Agus Harjoko Ahmad Fikri Ahmad Sutanto Ahmad Sutanto, Ahmad Andie Setiyoko, Andie Andreas Febrian Anggreyni, Desynike Puspa Bambang Trisakti Bambang Trisakti Chahyati, Dina Desta Sandya Prasvita Devi Fitrianah Edina Putri Purwandari Edina Putri Purwandari Edina Putri Purwandari Edward Citrahadi Edy Winarno Eliza Margaretha Eliza Margaretha Eliza Margaretha Fanany, M. Ivan Fanany, Mohamad Ivan Fauzan Muhammad Febri Maspiyanti Herwanto Herwanto Hisar Maruli Manurung Hisyam Fahmi I Gusti Agung Gede Arya Kadyanan Indriatmoko Intan Nurma Yulita, Intan Nurma Irham Muhammad Fadhil Ivan Fanany Kardawi, Muhammad Yusuf Katmoko Ari Sambodo LM Rasdi Rere LM Rasdi Rere Mahdi Kartasasmita Manurung, Hisar Maruli Manurung, Maruli Margaretha, Eliza Maruli Manurung Maruli Manurung Maruli Manurung Maspiyanti, Febri Meira Parma Dewi Mohamad Ivan Fanany Mohamad Ivan Fanany Muhamad Soleh Muhammad Iqbal Izzul Haq Nurtami Soedarsono Oemar Syarief Wibisono Pang Ning-Tan Persada, Bayu Satria Purwandari, Edina Putri Rahadianti, Laksmita Ratih Dewanti Retno Kusumaningrum Rian Josua Masikome Saragih, Frederic Morado Sesa Wiguna Setiawan, Suryana Sri Hartati Wijono Sunarno Sunarno Susanto, Muhammad Rifqi Priyo Tieta Antaresti Vina Ayumi Wahyudi - Wijaya, Hans Timothy WIJAYA, JUAN THOMAS Winarko, Edi Yohanes Gultom Yuni Arti