Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

5.19

P-Index

This Author published in this journals

Nanik Suciati

Departemen Informatika Institut Teknologi Sepuluh Nopember

Author-ID : 387460

Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

Published : 91 Documents Claim Missing Document

Claim Missing Document

Articles

4 5 6 7 8

Modified Convolutional Neural Network Architecture for Batik Motif Image Classification Ardian Yusuf Wicaksono; Nanik Suciati; Chastine Fatichah; Keiichi Uchimura; Gou Koutaki
IPTEK Journal of Science Vol 2, No 2 (2017)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Batik is one of the cultural heritages of Indonesia that have many different motifs in each region as well as in its usage. However, the Indonesians sometimes not knowing the batik motif that they’re wearing every day, and sometimes they have a batik image without knowing batik information contained in their batik image. With the growing number of images of batik and batik motifs, a classification method that can classify various motifs of batik is required to automatically detect the motif from the batik image. Image processing using the Deep Learning especially for image classification is widely used recently because it has good results. The most popular method in deep learning is Convolutional Neural Network (CNN) which has been proved robust in natural images. This study offers a batik motif image classification system using CNN method with new network architecture developed by combining GoogLeNet and Residual Networks named IncRes. IncRes merges the Inception Module with Residual Network structure. With the 70.84% accuracy, the system can be used to classify the batik image motif accurately.

Pemantauan Perhatian Publik terhadap Pandemi COVID-19 melalui Klasifikasi Teks dengan Deep Learning Novrindah Alvi Hasanah; Nanik Suciati; Diana Purwitasari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 1 (2021): Februari 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Monitoring public concern in the surrounding environment to certain events is done to address changes in public behavior individually and socially. The results of monitoring public attention can be used as a benchmark for related parties in making the right policies and strategies to deal with changes in public behavior as a result of the COVID-19 pandemic. Monitoring public attention can be done using Twitter social media data because the users of the media are quite high, so that they can represent the aspirations of the general public. However, Twitter data contains varied topics, so a classification process is required to obtain data related to COVID-19. Classification is done by using word embedding variations (Word2Vec and fastText) and deep learning variations (CNN, RNN, and LSTM) to get the classification results with the best accuracy. The percentage of COVID-19 data based on the best accuracy is calculated to determine how high the public's attention is to the COVID-19 pandemic. Experiments were carried out with three scenarios, which were differentiated by the number of data trains. The classification results with the best accuracy are obtained by the combination of fasText and LSTM which shows the highest accuracy of 97.86% and the lowest of 93.63%. The results of monitoring public attention to the time vulnerability between June and October show that the highest public attention to COVID-19 is in June.

PEMOTONGAN ROI OTOMATIS PADA DIGITAL MAMMOGRAM MENGGUNAKAN OPERASI MORFOLOGI Januar Adi Putra; Nanik Suciati; Arya Yudhi Wijaya
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 3 No. 1 (2016)
Publisher : Universitas Widyatama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33197/jitter.vol3.iss1.2016.120

[Id] Salah satu metode yang paling efektif untuk mendeteksi dan mengidentifikasi kanker payudara adalah melalui pemeriksaan mammogram. Keab-normalan kanker payudara dapat dikenali dengan keberadaan massa pada citra mammogram, hal ini dikarenakan massa memiliki tingkat resiko tertinggi dari pada tipe-tipe lainnya. Pada paper ini akan dikemukakan algoritma baru untuk merepresen-tasikan bentuk massa yang tampak pada citra mamogram menggunakan operasi morfologi pada pengolahan citra digital sehingga dapat digunakan untuk analisis kanker payudara. Algoritma disusun tahap demi tahap dengan tujuan memisahkan atau melokalisasi area yang dicurigai terdapat massa kanker payudara untuk mendapatkan Region of Interest (ROI). Telah dilakukan serangkaian ujicoba untuk menguji tingkat kebenaran dari algoritma pemotongan ROI yang diusulkan dimana algoritma usulan terbukti mampu mendeteksi dan mengektraksi ROI pada citra mammogram dengan sangat baik dengan nilai PSNR tertinggi sebesar 0.90 pada kanker payudara ganas dan 0.93 pada kanker payudara jinak. Kata Kunci: ROI, Digital Mammogram, Operasi Morfologi. [En] One of the most effective methods to detect and identify breast cancer is through mammograms. Breast cancer abnormalities can be identified by the presence of a mass on a mammogram image, this is because the masses have the highest risk level of the other types. In this paper we proposed a new algorithm for represent the mass forms that appear on a mammogram image so it can be used for the analysis of breast cancer. Algorithms are prepared step by step with the aim to separate or localize the suspected area there are masses of breast cancer to get a Region of Interest (ROI). Has conducted trials to test the performance of the proposed algorithm to cuts the ROI and the result of proposed algorithm is proven able to detect and extract ROI on a image mammogram with excellent that the high PSNR is 0.90 for malignant cancer and the0.93 for benign cancer. Keywords : ROI, Digital Mammogram, Morph-ological Operation.

EKTRAKSI FITUR MENGGUNAKAN DISCRETE WAVELET TRANSFORM DAN FULL NEIGHBOUR LOCAL BINARY PATTERN UNTUK KLASIFIKASI MAMMOGRAM Januar Adi Putra; Nanik Suciati; Arya Yudhi Wijaya
Jurnal Ilmiah Teknologi Infomasi Terapan Vol. 3 No. 2 (2017)
Publisher : Universitas Widyatama

[Id]Local binary pattern adalah sebuah kode biner yang menggambarkan pola tekstur lokal. Hal ini dibangun dengan lingkungan batas dengan nilai abu-abu dari pusatnya. Local binary pattern tradisional memiliki beberapa kelemahan yakni varian terhadap rotasi dan pada saat proses thresholding pixel sensitif terhadap noise. Pada penelitian ini diusulkan sebuah metode ektraksi fitur baru untuk mengatasi masalah tersebut, metode tersebut disebut full neighbour local binary pattern (fnlbp). Metode ini nantinya akan dikombinasikan dengan discrete wavelet transform untuk ektraksi fitur dari citra mammogram dengan metode klasifikasi adalah Backpropagation Neural Network (BPNN). Berdasar ujicoba yang telah dilakukan metode usulan mendapatkan rata-rata akurasi yang lebih baik daripada metode local binary pattern tradisional baik yang dikombinasi dengan discrete wavelet transform ataupun tidak. Performa metode usulan full neighbour local binary pattern dapat menghasilkan akurasi yang sempurna yakni 100% baik pada saat menggunakan discrete wavelet transform ataupun tidak, sedangkan akurasi terendah yang didapat adalah 90.49%.Kata Kunci: Ekstraksi fitur, local binary pattern, wavelet, klasifikasi mammogram.[En]Traditional local binary pattern have some disadvantages which is a variant of the rotation and during the thresholding process the pixel is sensitive to noise. At this study the authors proposed a new method of features extraction to solve that problem and this method called full neighbor local binary pattern (fnlbp). This method will be combined with discrete wavelet transform to extract the features of the mammogram image and the classification method is Backpro- pagation Neural Network (BPNN). Based on experiments the result of proposed method in an average accuracy is better than traditional methods of local binary pattern which combined with discrete wavelet transform or not. The performance of the proposed method of full neighbor local binary pattern can produce perfect accuracy that is 100%, this accuracy is reached when using discrete wavelet transform or not, while the lowest accuracy obtained is 90.49%.

Modifikasi Kombinasi Particle Swarm Optimization dan Genetic Algorithm untuk Permasalahan Fungsi Non-Linier Muchamad Kurniawan; Nanik Suciati
INTEGER: Journal of Information Technology Vol 2, No 2 (2017): September 2017
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.integer.2017.v2i2.177

Particle Swarm Optimization (PSO) is the population-based optimization algorithm and the generation of random values. The deficiency of the PSO algorithm is prematurely convergent, meaning it quickly finds solutions to local solutions. PSO tidak mampu untuk mencari ruang solusi lebih luas. PSO can not afford to search for wider solution space. In this study modification of the combination of PSO with Genetic Algortihm (GA) or we call M-PSOGA. The advantage of GA taken is to find a wider solution space. M-PSOGA is evaluated on non-linear function problem. The results obtained by M-PSOGA produce the best solution from its predecessor method, PSO and PSOGA. Better on the results of the solutions obtained and the convergent velocity on global solutions.Keywords: Particel Swarm Optimization, Genetic Algorithm, Non-Linier Function.

DETEKSI WILAYAH CAHAYA INTENSITAS TINGGI PADA CITRA DAUN MANGGA UNTUK KLASIFIKASI JENIS POHON MANGGA Eko Prasetyo; R. Dimas Adityo; Nanik Suciati; Chastine Fatichah
Seminar Nasional Teknologi Informasi Komunikasi dan Industri 2017: SNTIKI 9
Publisher : UIN Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (537.616 KB)

Masalah yang dihadapi pada citra daun mangga hasil akuisisi dalam klasifikasi jenis pohon mangga adalah adanya wilayah dalam citra yang terpapar cahaya tinggi. Jika wilayah ini tergabung dalam wilayah pembangkitan fitur warna dan tekstur maka nilai fitur yang dibangkitkan dapat terdistorsi dari hasil yang benar. Untuk menghindari masalah tersebut maka wilayah ini harus dipisahkan. Untuk mendeteksi wilayah cahaya intensitas tinggi penulis menggunakan dua threshold yang dikembangkan dari threshold T. Threshold T didapatkan dengan metode Otsu. Nilai threshold atas (Ta) didapat dengan menaikkan nilai T beberapa persen. Nilai threshold bawah (Tb) didapat dengan menurunkan nilai T beberapa persen. Dalam penelitian ini, penulis menggunakan Saturation sebagai basis deteksi, karena merupakan komponen yang memberikan informasi kekuatan warna yang dipengaruhi oleh cahaya. Nilai piksel rendah pada komponen ini menyatakan pengaruh cahaya yang tinggi. Dari hasil uji coba 30 citra, rata-rata dua nilai threshold, Ta dan Tb, masing-masing Ta = 0.9T atau T-10%T dan Tb = 1.7T atau T+70%T. Hasil yang didapatkan dari penelitian ini adalah wilayah intensitas tinggi pada citra daun mangga dapat dideteksi dengan cukup baik. Kinerja recall 0.78, ini berarti ada sekitar 22% wilayah yang gagal dideteksi, sedangkan precision 0.57 berarti sekitar 43% piksel bukan intensitas tinggi yang terdeteksi.

Indonesian sign language recognition using kinect and dynamic time warping Wijayanti Nurul Khotimah; Nanik Suciati; Tiara Anggita
Indonesian Journal of Electrical Engineering and Computer Science Vol 15, No 1: July 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v15.i1.pp495-503

Sign Language Recognition System (SLRS) is a system to recognise sign language and then translate them into text. This system can be developed by using a sensor-based technique. Some studies have implemented various feature extraction and classification methods to recognise sign language in the different country. However, their systems were user dependent (the accuracy was high when the trained and the tested user were the same people, but it was getting worse when the tested user was different to the trained user). Therefore in this study, we proposed a feature extraction method which is invariant to a user. We used the distance between two users’ skeleton instead of using the users’ skeleton positions because the skeleton distance is independent to the user posture. Finally, forty-five features were extracted in this proposed method. Further, we classified the features by using a classification method that is suitable with sign language gestures characteristic (time-dependent sequence data). The classification method is Dynamic Time Wrapping. For the experiment, we used twenty Indonesian sign languages from different semantic groups (greetings, questions, pronouns, places, family and others) and different gesture characteristic (static gesture and dynamic gesture). Then the system was tested by a different user with the user who did the training. The result was promising, this proposed method produced high accuracy, reach 91% which shows that this proposed method is user independent.

Selective local binary pattern with convolutional neural network for facial expression recognition Syavira Tiara Zulkarnain; Nanik Suciati
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6724-6735

Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.

Exposure Fusion Framework in Deep Learning-Based Radiology Report Generator Hilya Tsaniya; Chastine Fatichah; Nanik Suciati
IPTEK The Journal for Technology and Science Vol 33, No 2 (2022)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v33i2.13572

Writing a radiology report is time-consuming and requires experienced radiologists. Hence a technology that could generate an automatic report would be beneficial. The key problem in developing an automated report-generating system is providing a coherent predictive text. To accomplish this, it is important to ensure the image has good quality so that the model can learn the parts of the image in interpreting, especially in medical images that tend to be noise-prone in the acquisition process. This research uses the Exposure Fusion Framework method to enhance the quality of medical images to increase the model performance in producing coherent predictive text. The model used is an encoder-decoder with visual feature extraction using a pre- trained ChexNet, Bidirectional Encoder Representation from Transformer (BERT) embedding for text feature, and Long-short Term Memory (LSTM) as a decoder. The model’s performance with EFF enhancement obtained a 7% better result than without enhancement processing using an evaluation value of Bilingual Evaluation Understudy (BLEU) with n-gram 4. It can be concluded that using the enhancement method effectively increases the model’s performance.

Pemanfaatan E-commerce dan Media Sosial Guna Meningkatkan Ekonomi dan Proses Bisnis UMKM Koppontren NURILA Bangkalan Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Handayani Tjandrasa; Agus Zainal Arifin; Zakiya Azizah Cahyaningtyas; Yulia Niza; Evelyn Sierra; Daniel Sugianto; Kevin Christian Hadinata; Salim Bin Usman; Muhammad Fikri Sunandar; Fiqey Indriati Eka Sari
Sewagati Vol 6 No 4 (2022)
Publisher : Pusat Publikasi ITS

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran yang besar dalam bidang industri dan ekonomi suatu negara. Di era digital ini, pemanfaatan teknologi untuk meningkatkan produktifitas UMKM sudah marak dilakukan. Sayangnya pemanfaatan tekonologi ini belum diterapkan pada UMKM dari Koperasi Pondok Pesantren Addimyathy Nurul Iman Labang (Koppontren NURILA). Tim pengabdi berinisiatif melaksanakan pelatihan untuk meningkatkan produktifitas UMKM Koppontren NURILA. Kegiatan terbagi menjadi empat tahap yaitu persiapan, pelatihan, pendampingan, dan evaluasi. Kegiatan ini mengangkat topik tentang pemanfaatan e-commerce dan media sosial untuk peningkatan ekonomi dan proses bisnis UMKM. Pelaksanaan pelatihan dan pendampingan dilakukan secara hybrid, yaitu daring dan luring di lokasi UMKM Koppontren NURILA. Berdasarkan hasil evaluasi, peserta kegiatan merasa puas terhadap kualitas materi dengan nilai 4.35 dari skala 5.

Co-Authors Adhira Riyanti Amanda Adni Navastara, Dini Agus Eko Minarno Agus Priyono Agus Zainal Arifin Agus Zainal Arifin Ahmad Saikhu Ahmad Syauqi Ahmad Syauqi Akwila Feliciano Akwila Feliciano Akwila Feliciano Pradiptatmaka Alam Ar Raad Stone Aldinata Rizky Revanda Altriska Izzati Khairunnisa Hermawan Amelia Devi Putri Ariyanto Amirullah Andi Bramantya Andika Rahman Teja Anny Yuniarti Antonius Kevin Wiguna Ardian Yusuf Wicaksono Ari Wijayanti Aris Fanani Arrie Kurniawardhani Arsy Bilahi Tama Ary Mazharuddin Shiddiqi Arya Yudhi Wijaya Atika Faradina Randa Atikah, Luthfi Avin Maulana Awangditama, Bangun Rizki Ayu Kardina Sukmawati Ayu Septya Maulani Baso, Budiman Bryan Nandriawan Bui, Ngoc Dung Chastine Fatichah Chastine Fatichah Chilyatun Nisa' Damayanti, Putri Daniel Sugianto Darlis Herumurti Davin Masasih Diana Purwitasari Dimas Rahman Oetomo Dini Adni Navastara Dini Adni Navastara, Dini Adni Dion Devara Aryasatya Eko Prasetyo Eva Yulia Puspaningrum Evelyn Sierra Fairuuz Azmi Firas Faishal Azka Jellyanto Faizin, Muhammad 'Arif Fajar Astuti Hermawati Fandy Kuncoro Adianto Fandy Kuncoro Adianto Febri Liantoni, Febri Fiqey Indriati Eka Sari Fitri Bimantoro Ginardi, R.V. Hari Glenaya Gou Koutaki Gurat Adillion, Ilham Hafidz, Abdan Handayani Tjandrasa Handayani Tjandrasa Hani Ramadhan Haq, Arinal Hidayat, Ahmad Nur Hidayati, Shintami Chusnul Hilya Tsaniya Imagine Clara Arabella Imam Kuswardayan Imam Mustafa Kamal Irawan Rahardja, Agustinus Aldi Isye Arieshanti Isye Arieshanti Januar Adi Putra Januar Adi Putra Kautsar, Faiz Keiichi Uchimura Kevin Christian Hadinata Kevin Christian Hadinata M. Bahrul Subkhi Maulidan Bagus A.R Maulidiya, Erika Mawaddah, Saniyatul MIFTAHOL ARIFIN, MIFTAHOL Mochammad Zharif Asyam Marzuqi Muchamad Kurniawan Muchamad Kurniawan Muchamad Kurniawan, Muchamad Muhamad Nasir Muhammad 'Arif Faizin Muhammad Alif Satriadhi Muhammad Farih Muhammad Fikri Sunandar Mutmainnah Muchtar Nafa Zulfa Ni Luh Made ITS Novrindah Alvi Hasanah R Dimas Adityo R. Dimas Adityo Rachman, Rudy Rahma Fida Fadhilah Rangga Kusuma Dinata Rangga Kusuma Dinata Rayssa Ravelia Rizal A Saputra Rizal A Saputra, Rizal A Rohman Dijaya Romario Wijaya Safhira Maharani Safhira Maharani Salim Bin Usman Salim Bin Usman Salsabiil Hasanah Sarimuddin, Sarimuddin Septiana, Nuning Sherly Rosa Anggraeni Sherly Rosa Anggraeni Shintami Chusnul Hidayati Shofiya Syidada Sjahrunnisa, Anita Suastika Yulia Riska Sugianela, Yuna Surya Fadli Alamsyah Syavira Tiara Zulkarnain Tanzilal Mustaqim Tiara Anggita Tiara Anggita Tsaniya, Hilya Wahyu Saputra, Vriza Wan Sabrina Mayzura Wibowo, Della Aulia Wicaksono, Farhan Wijayanti Nurul Khotimah Yulia Niza Yulia Niza Yuna Sugianela Yuna Sugianela Yuslena Sari, Yuslena Yuwanda Purnamasari Pasrun Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas

Title

Found 91 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 91 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 91 Documents
Search