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

1 2 3 4 5

Multi-class K-support Vector Nearest Neighbor for Mango Leaf Classification Eko Prasetyo; R. Dimas Adityo; Nanik Suciati; Chastine Fatichah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.8482

K-Support Vector Nearest Neighbor (K-SVNN) is one of methods for training data reduction that works only for binary class. This method uses Left Value (LV) and Right Value (RV) to calculate Significant Degree (SD) property. This research aims to modify the K-SVNN for multi-class training data reduction problem by using entropy for calculating SD property. Entropy can measure the impurity of data class distribution, so the selection of the SD can be conducted based on the high entropy. In order to measure performance of the modified K-SVNN in mango leaf classification, experiment is conducted by using multi-class Support Vector Machine (SVM) method on training data with and without reduction. The experiment is performed on 300 mango leaf images, each image represented by 260 features consisting of 256 Weighted Rotation- and Scale-invariant Local Binary Pattern features with average weights (WRSI-LBP-avg) texture features, 2 color features, and 2 shape features. The experiment results show that the highest accuracy for data with and without reduction are 71.33% and 71.00% respectively. It is concluded that K-SVNN can be used to reduce data in multi-class classification problem while preserve the accuracy. In addition, performance of the modified K-SVNN is also compared with two other methods of multi-class data reduction, i.e. Condensed Nearest Neighbor Rule (CNN) and Template Reduction KNN (TRKNN). The performance comparison shows that the modified K-SVNN achieves better accuracy.

Optic Nerve Head Segmentation Using Hough Transform and Active Contours Handayani Tjandrasa; Ari Wijayanti; Nanik Suciati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.833

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

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

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.

Geometric Feature Extraction of Batik Image Using Cardinal Spline Curve Representation Aris Fanani; Anny Yuniarti; Nanik Suciati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 2: June 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i2.54

Batik is an Indonesian national heritage which has been recognized as a world cultural heritage (world heritage). Batik is widely used as clothing material. The advancement of technology allowed the material optimization in clothing design. Geometrical information of batik image is required in a modul for optimizing clothing design with batik as raw material. Geometric feature extraction of batik image is used to help computer to recognize batik's pattern or motif. This research proposes a method for geometric feature extraction of batik image by using cardinal spline curve representation. The method for geometric feature extraction is divided into 2 processes, i.e., feature extraction for Klowongan and feature extraction for Isen-Isen. Klowongan represents pattern of batik image, whereas Isen-Isen is content patterns of Klowongan. Feature extraction of Klowongan is performed by deleting collinear points from object boundaries until the dominant points are obtained. The dominant points are then used as control points. Feature extraction of Isen-Isen is performed by saving coordinate of every connected components which are also used as control points. Geometry feature of batik image is represented as a set of control points of klowongan and isen-isen. Batik image can be reconstructed by drawing cardinal spline curve using a set of control points in the geometric representation. The experiment shows that the reconstructed images is visually similar with the original batik image.

Batik Image Retrieval Based on Color Difference Histogram and Gray Level Co-Occurrence Matrix Agus Eko Minarno; Nanik Suciati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 12, No 3: September 2014
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v12i3.80

Study in batik images retrieval is still challenging until today. One of the methods for this problem is using Color Difference Histogram (CDH) which is based on the difference of color features and edge orientation features. However, CDH is only utilizing local features instead of global features; consequently it cannot represent images globally. We suggest that by adding global features for batik images retrieval, the precision will increase. Therefore, in this study, we combine the use of modified CDH to define local features and the use of Gray Level Co-occurrence Matrix (GLCM) to define global features. The modified CDH is performed by changing the size of image quantization, so it can reduce the number of features. Features that detected by GLCM are energy, entropy, contrast and correlation. In this study, we use 300 batik images which are consisted of 50 classes and 6 images in each class. The experiment result shows that the proposed method is able to raise 96.5% of precision rate which is 3.5% higher than the use of CDH only. The proposed method is extracting a smaller number of features; however it performs better for batik images retrieval. This indicates that the use of GLCM is effective combined with CDH.

Reduksi Data Latih pada K-Support Vector Nearest Neighbor Menggunakan Entropy Eko Prasetyo; R. Dimas Adityo; Nanik Suciati; Chastine Fatichah
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2018
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Pemilihan sebagian data latih atau reduksi data latih yang mempunyai pengaruh pada garis keputusan klasifikasi penting dilakukan. Tujuannya untuk mengurangi beban sistem pada tahap pelatihan. Sebagai metode reduksi data, K-Support Vector Nearest Neighbour (K-SVNN) mendapatkan hasil berdasarkan ketinggian nilai Significant Degree (SD) masing- masing data. Nilai SD dihitung menggunakan variabel LVRV (Left Value dan Right Value). Sayangnya, LVRV hanya dapat digunakan pada kasus klasifikasi biner. Penelitian ini melakukan uji coba penggunaan Entropy untuk menghitung SD. Secara konseptual, Entropy memberikan nilai kemurnian distribusi kelas data sehingga dimungkinkan penggunaan Entropy untuk menghitung SD pada kasus multi kelas. Pada makalah ini, disajikan analisis perbandingan perilaku nilai SD antara menggunakan LVRV dan Entropy. Hasil reduksi data menggunakan threshold (T) > 0, didapatkan akurasi yang sama pada kedua metode, sedangkan klasifikasi dengan reduksi data latih memberikan nilai akurasi lebih tinggi daripada tanpa reduksi. Hal ini membuktikan bahwa entropy dapat digunakan untuk menggantikan LVRV untuk menghitung SD.

FRACTAL DIMENSION AND LACUNARITY COMBINATION FOR PLANT LEAF CLASSIFICATION Mutmainnah Muchtar; Nanik Suciati; Chastine Fatichah
Jurnal Ilmu Komputer dan Informasi Vol 9, No 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Plants play important roles for the existence of all beings in the world. High diversity of plant’s species make a manual observation of plants classifying becomes very difficult. Fractal dimension is widely known feature descriptor for shape or texture. It is utilized to determine the complexity of an object in a form of fractional dimension. On the other hand, lacunarity is a feature descriptor that able to determine the heterogeneity of a texture image. Lacunarity was not really exploited in many fields. Moreover, there are no significant research on fractal dimension and lacunarity combination in the study of automatic plant’s leaf classification. In this paper, we focused on combination of fractal dimension and lacunarity features extraction to yield better classification result. A box counting method is implemented to get the fractal dimension feature of leaf boundary and vein. Meanwhile, a gliding box algorithm is implemented to get the lacunarity feature of leaf texture. Using 626 leaves from flavia, experiment was conducted by analyzing the performance of both feature vectors, while considering the optimal box size r. Using support vector machine classifier, result shows that combined features able to reach 93.92 % of classification accuracy.

Implementasi Metode Kombinasi Histogram of Oriented Gradients dan Hierarchical Centroid untuk Sketch Based Image Retrieval Atika Faradina Randa; Nanik Suciati; Dini Adni Navastara
Jurnal Teknik ITS Vol 5, No 2 (2016)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Teknik pencarian gambar yang saat ini umum digunakan masih berbasis teks atau text based search seperti pada mesin pencarian Google Image, Yahoo, dan lain sebagainnya. Namun metode ini masih kurang efektif karena nama dari sebuah file tidak dapat merepresentasikan isinya, oleh karena itu diperlukan pemilihan kata kunci yang benar-benar tepat agar hasil yang diinginkan dapat ditampilkan dengan baik. Salah satu teknik pencarian gambar yang saat ini sedang diteliti adalah Sketch-Based Image Retrieval (SBIR). Dengan teknik ini user dapat menginputkan sketsa gambar atau user dapat menggambarkan obyek pada area yang disediakan lalu sistem akan melakukan pencocokkan sketsa dengan database gambar. Untuk mengimplementasikan teknik ini digunakan metode kombinasi Histogram of Oriented Gradient dan Hierarchical Centroid. Tahapan implementasi teknik tersebut yaitu, yang pertama melakukan preprocessing pada gambar dengan cara mendeteksi tepi obyek lalu membuat citra menjadi hitam putih. Yang kedua melakukan ektraksi fitur menggunakan Histogram of Oriented Gradients dan Hierarchical Centroid dan menghasilkan fitur vektor. Yang terakhir menghitung jarak kedekatan antara gambar yang diuji dengan gambar yang terdapat dalam database menggunakan Euclidean Distance. Hasil Euclidean Distance kemudian diurutkan secara ascending dan dikembalikan sejumlah gambar yang jaraknya terdekat. Hasil temu kembali menghasilkan nilai Average Normalized Modified Retrieval Rank sebesar 0,35 dan nilai presisi dan recall sebesar 78 % dan akurasi sebesar 96%.

Rancang Bangun Aplikasi Piano Virtual Menggunakan Teknologi Augmented Reality dan Vuforia SDK Rahma Fida Fadhilah; Nanik Suciati; Wijayanti Nurul Khotimah
Jurnal Teknik ITS Vol 5, No 2 (2016)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Piano yang diketahui berbentuk besar, tidak portabel dan relatif mahal sehingga banyak masyarakat yang memanfaatkan kemajuan teknologi untuk belajar piano melalui teknologi komputer. Telah banyak aplikasi bermain piano yang ada, namun metode yang digunakan kurang menarik. Oleh karena itu, perlu dibuat sebuah aplikasi yang dapat menjadikan piano lebih menarik untuk dimainkan. Salah satu teknologi yang sedang berkembang saat ini adalah teknologi Augmented Reality (AR). AR mampu memberikan pengalaman yang interaktif dengan menampilkan objek 3 dimensi sehingga terlihat nyata kepada pengguna. Untuk mengembangkan tampilan AR, digunakan Library Vuforia SDK. Pengguna juga dapat menyentuh langsung tombol virtual atau Virtual Button yang merupakan fitur bawaan Library Vuforia SDK. Hasil pengujian aplikasi menunjukkan bahwa aplikasi dapat menampilkan objek piano dengan tampilan AR dan pengguna dapat menyentuh tombol virtual . maka dari itu, aplikasi yang dibangun berhasil memberikan pengalaman baru kepada pengguna dari segi tampilan dan permainan.

Rancang Bangun Pembangkit World Dinamis menggunakan Algoritma Recursive Backtracking pada Game 2D Platformer "Mine Meander" Faishal Azka Jellyanto; Imam Kuswardayan; Nanik Suciati
Jurnal Teknik ITS Vol 5, No 2 (2016)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Salah satu genre game yang cukup banyak diminati adalah genre 2D Platformer. Dalam perkembangan game 2D Platformer, kebanyakan game memiliki jenis map yang bersifat statis. Statis dalam hal ini artinya lingkungan atau area permainan akan tetap sama setiap dimainkan kembali. Dengan jenis map yang statis ini, tentunya bisa membuat beberapa orang merasa cepat bosan. Game Mine Meander adalah game 2D Platformer dengan fitur Dynamic World Generator dimana pemain bisa menjumpai bentuk world map yang berbeda dan juga menjumpai platform atau rintangan yang letaknya selalu berubah setiap kali dimainkan kembali. Dalam pembuatan layout map, metode yang digunakan adalah Algoritma Recursive Backtracking. Kemudian platform dan tangga ditata supaya world map selalu mempunyai jalan keluar bagi pemain untuk dapat menyelesaikan level. Dalam proses pengujian, dilakukan pengecekan apakah bentuk world map berubah saat memainkan level tertentu sebanyak 2 kali. Kemudian hasilnya menunjukkan bahwa bentuk world map dapat berubah dan selalu ada jalan keluar untuk menyelesaikan level tersebut.

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