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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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KLASIFIKASI FASE PERTUMBUHAN PADI BERDASARKAN CITRA HIPERSPEKTRAL DENGAN MODIFIKASI LOGIKA FUZZY Maspiyanti, Febri; Fanany, M. Ivan; Arymurthy, Aniati Murni
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 10 No. 1 (2013)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v10i1.3272

Abstract

Remote sensing is a technology that is capable of overcoming the problems of measurement data for fast and accurate information. One of implementation of remote sensing technology in the field of agriculture is in hyperspectral image data retrieval to find out the condition and age of the rice plant. It is necessary for the estimation of rice yield in order to support Government policy in conducting imports rice to meet food needs in Indonesia. To have a good prediction model in estimation of rice yield that has high accuracy must be preceded by the determination of the phase of the rice plant. The selection of the appropriate classifier must also supported the selection of just the right features to get the optimum accuracy. In this study, we conducted a comparison between Fuzzy Logic and Modified Fuzzy Logic to perform the classification on nine rice growth stages based on hyperspectral image. Modified Fuzzy Logic have the same procedure with Fuzzy Logic but with extra crisp rules given in Fuzzy Rules which is expected to increase the accuracy achievement. In this study, Modified Fuzzy Logic proved to be able to improve the accuracy of up to 10% compared to Fuzzy Logic.
PERBANDINGAN KLASIFIKASI BERBASIS OBJEK DAN KLASIFIKASI BERBASIS PIKSEL PADA DATA CITRA SATELIT SYNTHETIC APERTURE RADAR UNTUK PEMETAAN LAHAN Sutanto, Ahmad; Trisakti, Bambang; Arymurthy, Aniati Murni
Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital Vol. 11 No. 1 (2014)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/inderaja.v11i1.3300

Abstract

Utilization of remote sensing data for land mapping has long been developed. In Indonesia, as a tropical region, the cloud becomes a classic problem in observing the Earth’s surface using optical remotely sensor satellite. Synthetic Aperture Radar (SAR) sensor satellite has the ability to penetrate clouds so it can solve cloud cover problems. In this study, the ALOS PALSAR data were used to assess object-based and pixel-based classification techniques. This data was chosen due to its capacity for object recognition based on backscatter characteristics. Object-based classification using the methods of Statistical Region Merging (SRM) for the object segmentation process and Support Vector Machine (SVM) for the classification process, whereas the pixel-based classification using SVM method. In the classification stage, several features of Target Decomposition and Image Decomposition of ALOS PALSAR data have been tested. The accuracy assessment of the classification was conducted using confusion matrix of the Region of Interest (ROI) data using the QuickBird data. Implementation of the object-based classification produced better result comparing to pixel-based classification. The number of optimal features is seven which consisted of three features Freeman Decomposition (Red, Green, Blue), Entropy, Alpha Angle, Anisotropy and Normalized Difference Polarization Index (NDPI). Overall accuracy reached 73.64% for the result of the object-based classification and 62.6% for the pixel-based classification.
CP_SDUNet: road extraction using SDUNet and centerline preserving dice loss Persada, Bayu Satria; Susanto, Muhammad Rifqi Priyo; Rahadianti, Laksmita; Arymurthy, Aniati Murni
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp260-272

Abstract

Existing automatic road map extraction approaches on remote sensing images often fail because they cannot understand the spatial context of an image. Mainly because they could not learn the spatial context of the image and only knew the structure or texture of the image. These approaches only focus on regional accuracy instead of connectivity. Therefore, most approaches produce discontinuous outputs caused by buildings, shadows, and similarity to rivers. This study addresses the challenge of automatic road extraction, focusing on enhancing road connectivity and segmentation accuracy by proposing a network-based road extraction that uses a spatial intensifier module (DULR) and densely connected U-Net architecture (SDUNet) with a connectivity-preserving loss function (CP_clDice) called CP_SDUNet. This study analyzes the CP_clDice loss function for the road extraction task compared to the BCE Loss function to train the SDUNet model. The result shows that CP_SDUNet, performs best using an image size of 128×128 pixels and trained with the whole dataset with a combination of 20% clDice and 80% dice loss. The proposed method obtains a clDice score of 0.85 and an Interest over Union (IoU) score of 0.65 for the testing data. These findings demonstrate the potential of CP_SDUNet for reliable road extraction.
PENERAPAN SISTEM DATA MINING UNTUK DIAGNOSIS PENYAKIT KANKER PAYUDARA MENGGUNAKAN CLASSIFICATION BASED ON ASSOCIATION ALGORITHM Herwanto Herwanto; Aniati Murni Arymurthy
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%.
MSDFF-RCNet: A Combined Multi-Structure Data Fusion Framework and Recurrent Attention for Remote Sensing Scene Classification Hestrio, Yohanes; Persada, Bayu Satria; Saragih, Frederic Morado; Kardawi, Muhammad Yusuf; Jatmiko, Wisnu; Arymurthy, Aniati Murni
Jurnal Ilmu Komputer dan Informasi Vol. 19 No. 1 (2026): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v19i1.1475

Abstract

Remote sensing scene classification faces significant challenges in distinguishing visually similar land-use categories due to high intraclass variation and interclass similarity in high-resolution imagery. Although deep learning approaches have shown promise, single-architecture methods often fail to capture the diverse spatial and hierarchical features required for robust scene discrimination. This study proposes MSDFF-RCNet, a multi-structure data fusion framework combined with recurrent attention mechanisms to enhance remote sensing scene classification performance. The framework integrates complementary feature representations from AlexNet, ResNet50, and DenseNet161 architectures, while the recurrent attention mechanism focuses on discriminative spatial regions for improved classification accuracy. Comprehensive experiments conducted on four benchmark datasets demonstrate substantial performance improvements over the baseline ARCNet architecture: UC Merced (43.8% to 84.9%, +41.1%), AID (63.8% to 94.4%, +30.6%), NWPU-RESISC45 (61.5% to 95.4%, +33.9%), and OPTIMAL 31 (47.3% to 87.9%, +40.6%). Statistical significance analysis confirmed the reliability of these improvements (p < 0.01), while comprehensive evaluation across precision, recall, and F1-score metrics validated the framework’s robustness. Although the multi-structure approach requires substantial computational resources (25.6× parameter increase), the consistent and significant accuracy improvements across diverse datasets demonstrate the effectiveness of complementary feature fusion for remote sensing scene classification. The proposed framework provides a valuable contribution to automated Earth observation systems that require high-precision land-use classification capabilities.
New Generation Indonesian Endemic Cattle Classification: MobileNetV2 and ResNet50 Ahmad Fikri; Aniati Murni
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26659

Abstract

Cattle are an essential source of animal food globally, and each country possesses unique endemic cattle races. However, categorizing cattle, especially in countries like Indonesia with a large cattle population, presents challenges due to costs and subjectivity when using human experts. This research utilizes Computer Vision (CV) for image data classification to address this urgent need for automatic categorization. The main objective of this study is to develop a mobile-friendly model using CV techniques that can accurately detect and classify Indonesian endemic cattle races, such as Limosin, Madura, Pegon, and Simental. To achieve this, an object localization approach is employed to extract multiple features from distinct regions of each cattle image, including the head, ear, horn, and muzzle areas. The authors evaluate two CV algorithms, ResNet50 and MobileNetV2, to assess their performance in cattle race classification. The dataset used is facial photos of 147 cows. The results indicate that ResNet50 outperforms MobileNetV2, achieving a training data accuracy of 83.33% compared to MobileNetV2's 77.08%. Moreover, the validation accuracy of ResNet50 (76.92%) significantly surpasses MobileNetV2's (38.46%). The novel contribution of this research lies in developing a cost-effective and efficient solution for identifying endemic cattle breeds in Indonesia. The mobile-friendly model based on ResNet50 demonstrates superior accuracy, enabling cattle farmers and researchers to categorize cattle races with higher precision, reducing manual effort, and minimizing costs. In conclusion, this research provides a valuable advancement in automatic cattle categorization using CV techniques. By offering a practical and accurate model that considers Indonesia's specific cattle breeding conditions, this study contributes to the sustainable management and conservation of endemic cattle races while enhancing the efficiency of cattle farming practices.
Triangular fuzzy number for similarity measurement of Y chromosome DNA profile Meira Parma Dewi; Aniati Murni Arymurthy; Suryana Setiawan; Nurtami Soedarsono
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.5304

Abstract

This study measures the similarity of the short tandem repeat (STR) profile of human DNA. The similarity measurement had been done to the STR value of the allele loci in DNA profile between the query’s DNA to the reference’s DNA profile. The measurements were conducted on 27 DNA profile loci including the Y chromosome loci (YSTR). The YSTR loci were used as the main comparison of similarity measurements to determine the biological kinship relationship between the query DNA profile and the alleged male biological family. To measure the similarity of two STR values that have shifted due to several factors in the DNA source extraction process, a fuzzy similarity measure was used. The STR values of the DNA profile loci are described as triangular fuzzy numbers. Similarity value of the STR is the intersection of two isosecle that been compared. To conclude that the query has a biological relationship with the male reference, the similarity of the YSTR locus is equal or more than 0.75 and the similarity value of the other 24 DNA profile loci is greater or equal to 0.5. From the trial that have been done, 90% give the right results.
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 Hestrio, Yohanes Hisar Maruli Manurung Hisyam Fahmi I Gusti Agung Gede Arya Kadyanan Indriatmoko Intan Nurma Yulita 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 Sri Hartati Wijono Sunarno Sunarno Suryana Setiawan Susanto, Muhammad Rifqi Priyo Tieta Antaresti Vina Ayumi Wahyudi - Wijaya, Hans Timothy WIJAYA, JUAN THOMAS Winarko, Edi Wisnu Jatmiko Yohanes Gultom Yuni Arti