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PENERAPAN EKSTRAKSI CIRI STATISTIK ORDE PERTAMA DENGAN EKUALISASI HISTOGRAM PADA KLASIFIKASI TELUR OMEGA-3 Liantoni, Febri; Santoso, Agus Adi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 9, No 2 (2018): JURNAL SIMETRIS VOLUME 9 NO 2 TAHUN 2018
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.806 KB) | DOI: 10.24176/simet.v9i2.2476

Abstract

Telur merupakan makanan yang memiliki gizi tinggi. Dijaman sekarang telah ada telur dengan omega-3 hasil rekayasa. Secara visual untuk membedakan telur ayam biasa dan telur ayam dengan omega-3 sangat sulit karena bentuk fisik dan warna telurnya terlihat sama. Bagian yang membedakan adalah kuning telur omega-3 agak kekuningan dan kuning telur biasa lebih kemerahan. Penelitian ini diciptakan sebuah sistem analis yang mampu mengenali telur berdasarkan tekstur dengan beberapa langkah dalam teknik pengolahan citra. Beberapa teknik pengolahan citra yang digunakan yaitu konversi citra RGB ke grayscale, perbaikan kualitas citra, menghilangkan noise dengan gaussian filter dan analisis citra menggunakan ekstraksi ciri statistik orde pertama dengan nilai parameter mean, standard deviasi. Berdasarkan pengujian diperoleh tingkat precision 87,93%, recall 96,22% dan accuracy 85% berdasarkan 140 data training dan 60 data uji.
ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION Liantoni, Febri; Kirana, Kartika Candra; Muliawati, Tri Hadiah
Jurnal Ilmu Komputer dan Informasi Vol 7, No 2 (2014): 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 (432.297 KB) | DOI: 10.21609/jiki.v7i2.260

Abstract

Ant Colony Optimization (ACO) is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.
Image Retrival Pada Obyek Lingga Yoni Di Situs Peninggalan Sejarah Trowulan Mojokerto Nugroho, Hendro; Liantoni, Febri
INTEGER: Journal of Information Technology Vol 1, No 1 (2016): Maret 2016
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.394 KB) | DOI: 10.31284/j.integer.2016.v1i1.55

Abstract

This study contains about Image Retrieval system image on Lingga Yoni at historical sites Trowulan. In the area Trowulan is a legacy of work Majapahit era where the majority of people it is a Hindu, so many relics found in the form of Linga Yoni which serves as the worship of Lord Shiva. Data retrieval image Yoni Linga Linga Yoni as many as 50 images using a digital camera, and the image size Lingga Yoni 200 x 300 pixels in BMP file format. Stages Image Retrieval system on the study include segmentation stages: (1) Smoothing using the method Pas Low Filter to soften the image of the noise; (2) the extraction step texture by using Region Growing by altering the RGB color image is converted into to facilitate the HSL color groups; (3) Region Region Merging Growing did for the incorporation of color image corresponding to the object Linga Yoni; (4) to get to extraction stage form was originally looking for edge detection using Canny edge; (5) the image is converted into binary form to the morphology using opening and closing. At Stages Image Retrieval 50 Linga Yoni image texture extraction step performed using the 4 corners of each feature value GLCM with Different Inverse Moment IDM to revise the results of Image Retrieval using methods Precision and Recall.
Modifikasi Ant Colony Optimization Berdasarkan Gradient Untuk Deteksi Tepi Citra Liantoni, Febri; Suciati, Nanik; Fatichah, Chastine
Jurnal Buana Informatika Vol 6, No 3 (2015): Jurnal Buana Informatika Volume 6 Nomor 3 Juli 2015
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (557.421 KB)

Abstract

Abstract. Ant Colony Optimization (ACO) is an optimization algorithm which can be used for image edge detection. In traditional ACO, the initial ant are randomly distributed. This condition can cause an imbalance ants distribution. Based on this problem, a modified ant distribution in ACO is proposed to optimize the deployment of ant based gradient. Gradient value is used to determine the placement of the ants. Ants are not distributed randomly, but are placed in the highest gradient. This method is expected to be used to optimize the path discovery. Based on the test results, the use of the proposed ACO modification can obtain an average value of the Peak Signal to Noise Ratio (PSNR) of 12.724. Meanwhile, the use of the traditional ACO can obtain an average value of PSNR of 12.268. These results indicate that the ACO modification is capable of generating output image better than traditional ACO in which ants are initially distributed randomly.Keywords: Ant Colony Optimization, gradient, Edge Detection, Peak Signal to Noise Ratio Abstrak. Ant Colony Optimization (ACO) merupakan algoritma optimasi, yang dapat digunakan untuk deteksi tepi pada citra Pada ACO tradisional, semut awal disebarkan secara acak. Kondisi ini dapat menyebabkan ketidakseimbangan distribusi semut. Berdasarkan permasalahan tersebut, modifikasi distribusi semut pada ACO diusulkan untuk mengoptimalkan penempatan semut berdasarkan gradient. Nilai gradient digunakan untuk menentukan penempatan semut. Semut tidak disebar secara acak akan tetapi ditempatkan di gradient tertinggi. Cara ini diharapkan dapat digunakan untuk optimasi penemuan jalur. Berdasarkan hasil uji coba, dengan menggunakan ACO modifikasi yang diusulkan dapat diperoleh nilai rata-rata Peak Signal to Noise Ratio (PSNR) 12,724. Sedangkan, menggunakan ACO tradisional diperoleh nilai rata-rata PSNR 12,268. Hasil ini menunjukkan bahwa ACO modifikasi mampu menghasilkan citra keluaran yang lebih baik dibandingkan ACO tradisional yang sebaran semut awalnya dilakukan secara acak.Kata Kunci: Ant Colony Optimization, gradient, deteksi tepi, Peak Signal to Noise Ratio
PEMANFAATAN HIERARCHICAL CLUSTERING UNTUK PENGELOMPOKKAN DAUN BERDASARKAN FITUR MOMENT INVARIANT Liantoni, Febri; cahyani, laili
EDUTIC Vol 3, No 2 (2017): MEI 2017
Publisher : Universitas Trunojoyo Madura

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

Abstract

AbstrakIlmu mengenai tanaman telah mengalami kemajuan yang pesat. Salah satunya cabang ilmu mengenai morfologi tanaman. Ilmu morfologi ini mempelajari susunan tubuh tanaman khususnya mengenai bentuk tepi daun. Pada penelitian ini akan dilakukan pengelompokkan daun berdasarkan bentuk tepi daun. Metode yang digunakan untuk melakukan pengelompokkan adalah metode Centroid Linkage Clustering yang merupakan bagian dari algoritma Hierarchical Clustering. Metode ini dikenal lebih memiliki beban komputasi yang relatif lebih ringan karena hanya cukup menghitung titik tengah antar cluster. Berdasarkan hasil uji coba yang dilakukan, penggunaan metode Centroid Linkage Clustering didapatkan nilai akurasi clustering sebesar 87%, sedangkan dengan menggunakan metode k-means didapatkan nilai akurasi clustering sebesar 81%. Hal ini menunjukkan bahwa kinerja metode Centroid Linkage Clustering lebih baik dibandingkan metode k-means. Kata Kunci: Morfologi, Centroid Linkage Clustering, Hierarchical Clustering, Cluster, K-mean.
Adaptive Ant Colony Optimization on Mango Classification Using K-Nearest Neighbor and Support Vector Machine Liantoni, Febri; Hermanto, Luky Agus
Journal of Information Systems Engineering and Business Intelligence Vol 3, No 2 (2017): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (726.262 KB) | DOI: 10.20473/jisebi.3.2.75-79

Abstract

Abstract— Leaves recognition can use an image edge detection method. In this research, the classification of mango gadung and manalagi will be performed. In the preprocess stage edge detection method using adaptive ant colony optimization method. The use of adaptive ant colony optimization method aims to optimize the process of edge detection of a mango leaves the bone image. The application of ant colony optimization method on mango leaves classification has successfully optimized the result of edge detection of a mango leaves the bone structure. Results showed edge detection using adaptive ant colony optimization method better than Roberts and Sobel method. The result an experiment of mango leaves classification with k-nearest neighbor method get accuracy value equal to 66,25%, whereas with the method of support vector machine obtained accuracy value equal to 68,75%.Keywords— Edge Detection, Ant Colony Optimization, Classification, K-Nearest Neighbor, Support Vector Machine
Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor Liantoni, Febri
Ultimatics : Jurnal Teknik Informatika Vol 7 No 2 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Program Studi Teknik Informatika UMN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (671.182 KB) | DOI: 10.31937/ti.v7i2.356

Abstract

Plants are the most important part in life on earth as oxygen supplier to breathe, groceries, fuel, medicine and more. Plants can be classified based on its leaves shape. Classification process is required well data extraction feature, so it needs fixing feature process at pre-processing level. Combining median filter and image erosion is used for fixing feature process. Whereas for feature extraction is used invariant moment method. In this research, it is used leaves classification based on leaves edge shape. K-Nearest Neighbor Method (KNN) is used for leaves classification process. KNN method is chosen because this method is known rapid in training data, effective for large training data, simple and easy to learn. Testing the result of leaves classification from image which is on dataset has been built to get accuracy value about 86,67%. Index Terms—Classification, Median Filter, Invariant Moment, K-Nearest Neighbor.
Pengenalan karakter angka menggunakan metode Integral Proyeksi Liantoni, Febri
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 2 (2017): July-December
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1197.719 KB) | DOI: 10.26594/register.v3i2.706

Abstract

 Saat ini dengan kemajuan teknologi membuat komputer memiliki kemampuan komputasi yang lebih tinggi untuk meningkatkan kemampuan dalam pengolahan data. Kemajuan teknologi ini juga berimbas pada kemampuan teknologi citra digital yang berhubungan dengan pengenalan karakter angka yang merupakan bagian dari pengenalan pola. Pengenalan karakter penting untuk pengolahan informasi yang memungkinkan proses identifikasi secara cepat dan otomatis. Pada penelitian ini dilakukan proses pengenalan karakter angka menggunakan metode Integral Proyeksi. Alasan menggunakan metode integral proyeksi karena mempunyai kelebihan pemrosesan yang sederhana dan cepat dalam mengidentifikasi suatu citra digital. Integral Proyeksi yang digunakan yaitu Integral Proyeksi vertikal dan Integral Proyeksi horisontal. Hasil penelitian menunjukkan pengenalan karakter angka mampu mengenali karakter dengan benar jika hasil praproses menghasilkan gambar yang baik. Pengenalan karakter angka akan kurang sempurna jika gambar yang diproses tidak baik, hal ini dikarenakan metode Integral Proyeksi bekerja dengan menghitung jumlah piksel tiap gambar untuk mengenai nilai gambar tersebut. Pengujian pengenalan karakater angka yang dilakukan terdapat 20 gambar uji menghasilkan nilai akurasi sebesar 65%.    Nowadays with the advancement of technology makes computers have higher computing capabilities to improve the capability of data processing. Advances in technology have also affected the ability of digital image technology related to the introduction of alphanumeric characters that are part of pattern recognition. Character recognition is important for information processing that allows rapid identification process automatically. In this research, numeric character recognition process using integral projection method. Reasons for using integral projection method for processing has the advantage of a simple and quick in identifying a digital image. The integral projection used is vertical projection and horizontal projection. The results showed numeric character recognition could recognize the characters correctly if the results of preprocessing produce good images. The introduction of the characters will be less than perfect if the images are processed is not good, this is because the integral projection method works by counting the number of pixels for each image to the value of the image. Testing the result of recognition from 20 image which is on dataset has been built to get accuracy value about 65%.
PENERAPAN EKSTRAKSI CIRI STATISTIK ORDE PERTAMA DENGAN EKUALISASI HISTOGRAM PADA KLASIFIKASI TELUR OMEGA-3 Liantoni, Febri; Santoso, Agus Adi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 9, No 2 (2018): JURNAL SIMETRIS VOLUME 9 NO 2 TAHUN 2018
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (271.806 KB) | DOI: 10.24176/simet.v9i2.2476

Abstract

Telur merupakan makanan yang memiliki gizi tinggi. Dijaman sekarang telah ada telur dengan omega-3 hasil rekayasa. Secara visual untuk membedakan telur ayam biasa dan telur ayam dengan omega-3 sangat sulit karena bentuk fisik dan warna telurnya terlihat sama. Bagian yang membedakan adalah kuning telur omega-3 agak kekuningan dan kuning telur biasa lebih kemerahan. Penelitian ini diciptakan sebuah sistem analis yang mampu mengenali telur berdasarkan tekstur dengan beberapa langkah dalam teknik pengolahan citra. Beberapa teknik pengolahan citra yang digunakan yaitu konversi citra RGB ke grayscale, perbaikan kualitas citra, menghilangkan noise dengan gaussian filter dan analisis citra menggunakan ekstraksi ciri statistik orde pertama dengan nilai parameter mean, standard deviasi. Berdasarkan pengujian diperoleh tingkat precision 87,93%, recall 96,22% dan accuracy 85% berdasarkan 140 data training dan 60 data uji.
Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor Liantoni, Febri; Hermanto, Luky Agus
Jurnal Buana Informatika Vol 8, No 4 (2017): Jurnal Buana Informatika Volume 8 Nomor 4 Oktober 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.249 KB) | DOI: 10.24002/jbi.v8i4.1443

Abstract

Abstract. Leaf is one important part of a plant normally used to classify the types of plants. The introduction process of mango leaves of mangung and manalagi mango is done based on the leaf edge image detection. In this research the conventional edge detection process was replaced by ant colony optimization method. It is aimed to optimize the result of edge detection of mango leaf midrib and veins image. The application of ant colony optimization method successfully optimizes the result of edge detection of a mango leaf midrib and veins structure. This is demonstrated by the detection of bony edges of the leaf structure which is thicker and more detailed than using a conventional edge detection. Classification testing using k-nearest neighbor method obtained 66.67% accuracy.Keywords: edge detection, ant colony optimization, classification, k-nearest neighbor.Abstrak. Pengembangan Metode Ant Colony Optimization Pada Klasifikasi Tanaman Mangga Menggunakan K-Nearest Neighbor. Daun merupakan salah satu bagian penting dari tanaman yang biasanya digunakan untuk proses klasifikasi jenis tanaman. Proses pengenalan daun mangga gadung dan mangga manalagi dilakukan berdasarkan deteksi tepi citra struktur tulang daun. Pada penelitian ini proses deteksi tepi konvensional digantikan dengan metode ant colony optimization. Hal ini bertujuan untuk optimasi hasil deteksi tepi citra tulang daun mangga. Penerapan metode ant colony optimization berhasil mengoptimalkan hasil deteksi tepi struktur tulang daun mangga. Hal ini ditunjukkan berdasarkan dari hasil deteksi tepi citra struktur tulang daun yang lebih tebal dan lebih detail dibandingkan menggunakan deteksi tepi konvensional. Pengujian klasifikasi dengan metode k-nearest neighbor didapatkan nilai akurasi sebesar 66,67%.Kata Kunci: deteksi tepi, ant colony optimization, klasifikasi, k-nearest neighbor.