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All Journal International Journal of Electrical and Computer Engineering Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Kitektro JOURNAL OF INFORMATICS AND COMPUTER SCIENCE JURNAL NASIONAL TEKNIK ELEKTRO Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Syntax Literate: Jurnal Ilmiah Indonesia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi Journal of Electronics, Electromedical Engineering, and Medical Informatics JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Rekayasa elektrika Jurnal Pengabdian Rekayasa dan Wirausaha

Fitri Arnia

Department Of Electrical And Computer Engineering, Universitas Syiah Kuala, Indonesia

Author-ID : 233702

Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Neuroscience Social Sciences Transportation Other

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Penggunaan Gray Level Co-Occurance Matrix Dari Koefisien Aproksimasi Wavelet untuk Deteksi Cacat Tekstil Islamadina, Raihan; Arnia, Fitri; Munadi, Khairul
Jurnal Buana Informatika Vol 6, No 2 (2015): Jurnal Buana Informatika Volume 6 Nomor 2 April 2015
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (501.216 KB)

Pendeteksian cacat tekstil saat ini masih dilakukan secara manualmengakibatkan seseorang sulit mendeteksi lebih dari 60% dari cacat yang ada.Untuk itu, penelitian ini menerapkan metode deteksi cacat tekstil secara otomatismenggunakan Gray Level Co-Occurance Matrix (GLCM) dari koefisienaproksimasi wavelet yang bertujuan untuk mengevaluasi analisis kinerja metode.Tahapannya, sampel citra tekstil dibagi menjadi delapan bagian untukmendapatkan tekstur cacat yang lebih jelas. Bagian tersebut didekomposisikedalam dua level. GLCM dihitung dari koefisien aproksimasi wavelet level satudan dua untuk dijadikan fitur. Penelitian ini dilakukan empat set simulasi citradengan orientasi latar berbeda. Setiap set terdiri dari satu citra noncacat dan duajenis citra cacat. Setiap bagian citra noncacat dihitung jaraknya dengan semuabagian pada citra cacat pertama dan kedua menggunakan jarak euclidean. Hasilsimulasi menunjukkan bahwa GLCM dari koefisien aproksimasi wavelet levelkedua mampu mendeteksi lebih dari 70% dari cacat yang ada.

HISTOGRAM E QUALIZATION SMOOTHING FOR DETERMINING THRESHOLD ACCURACY ON ANCIENT DOCUMENT IMAGE BINARIZATION Dwipayana, Mahendar; Arnia, Fitri; Musliyana, Zuhar
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 2, No 2 (2016): Oktober 2016
Publisher : Universitas Ubudiyah Indonesia

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

Ancient documents are inheritance that must be preserved. The documents contain historical, scientific, social, religious information, etc. Converting ancient documents into digital image formats is one of ways to preserve the inheritance and can be stored into a computer. However, images of ancientdocuments have many blemishes caused by age, moisture, flood, etc. Therefore, special techniques are needed for those images to be restored and can improve the legibility of the ancient documentsâ images. In this study, the image restoration process uses separation of background and foreground/text on histogram equalization such as research conducted by Fitri Arnia in 2008. Through histogram equalizationimages can be seen the distribution of pixels from the intensity of black color "0" to white "1". The distribution of pixels on histogram equalization describes the curves of foreground/text and curves of background. Among the histogram curves, the determination of thresholdvalues can be done so as to clarify the foreground/text and background areas on images of ancient documents. The lowest point between the two curves is the lowest pixel (local minima) which is used as the threshold value. However, the selection of such threshold values in some cases is very difficult to determine because there are still many fluctuations in the curve at the lowest curve. Therefore, this study proposesa histogram smoothing method in the ancient documentsâ images to minimize curvature fluctuations and to determine more accurate threshold values. In this research, average filtering method is used for smoothing the histogram image. This filter successfully refines the histogram and makes the image of the restoration or binary image display the value of the ancient document image readability increases.Keywords: HistogramEqualization, Smoothing Histogram, Average Filtering, Thresholding

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

Ancient documents are inheritance that must be preserved. The documents contain historical, scientific, social, religious information, etc. Converting ancient documents into digital image formats is one of ways to preserve the inheritance and can be stored into a computer. However, images of ancientdocuments have many blemishes caused by age, moisture, flood, etc. Therefore, special techniques are needed for those images to be restored and can improve the legibility of the ancient documents? images. In this study, the image restoration process uses separation of background and foreground/text on histogram equalization such as research conducted by Fitri Arnia in 2008. Through histogram equalizationimages can be seen the distribution of pixels from the intensity of black color "0" to white "1". The distribution of pixels on histogram equalization describes the curves of foreground/text and curves of background. Among the histogram curves, the determination of thresholdvalues can be done so as to clarify the foreground/text and background areas on images of ancient documents. The lowest point between the two curves is the lowest pixel (local minima) which is used as the threshold value. However, the selection of such threshold values in some cases is very difficult to determine because there are still many fluctuations in the curve at the lowest curve. Therefore, this study proposesa histogram smoothing method in the ancient documents? images to minimize curvature fluctuations and to determine more accurate threshold values. In this research, average filtering method is used for smoothing the histogram image. This filter successfully refines the histogram and makes the image of the restoration or binary image display the value of the ancient document image readability increases.Keywords: HistogramEqualization, Smoothing Histogram, Average Filtering, Thresholding

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.Vol2.Iss2.733

Ancient documents are inheritance that must be preserved. Thedocuments contain historical, scientific, social, religious information, etc.Converting ancient documents into digital image formats is one of ways topreserve the inheritance and can be stored into a computer. However,images of ancientdocuments have many blemishes caused by age,moisture, flood, etc. Therefore, special techniques are needed for thoseimages to be restored and can improve the legibility of the ancientdocumentsâ€™ images. In this study, the image restoration process usesseparation of background and foreground/text on histogram equalizationsuch as research conducted by Fitri Arnia in 2008. Through histogramequalizationimages can be seen the distribution of pixels from the intensityof black color "0" to white "1". The distribution of pixels on histogramequalization describes the curves of foreground/text and curves ofbackground. Among the histogram curves, the determination ofthresholdvalues can be done so as to clarify the foreground/text andbackground areas on images of ancient documents. The lowest pointbetween the two curves is the lowest pixel (local minima) which is used asthe threshold value. However, the selection of such threshold values insome cases is very difficult to determine because there are still manyfluctuations in the curve at the lowest curve. Therefore, this studyproposesa histogram smoothing method in the ancient documentsâ€™ imagesto minimize curvature fluctuations and to determine more accuratethreshold values. In this research, average filtering method is used forsmoothing the histogram image. This filter successfully refines thehistogram and makes the image of the restoration or binary image displaythe value of the ancient document image readability increases.Keywords: HistogramEqualization, Smoothing Histogram, AverageFiltering, Thresholding

Penggunaan Histogram dari Koefisien Aproksimasi Wavelet untuk Deteksi Cacat Tekstil Arnia, Fitri; Saputra, Andika; Munadi, Khairul
JURNAL NASIONAL TEKNIK ELEKTRO Vol 3, No 1: Maret 2014
Publisher : Jurusan Teknik Elektro Universitas Andalas

Generally, textile defect inspection at textile industry is still conducted manually by human. This approach is susceptible to errors and tends to be inconsistent due to fatigue and inattentiveness. To guarantee the consistency and inspection quality, an automatic defect detection system is required. This research proposes the use of histograms generated from two-level waveletâ€™s approximation coefficients as features to detect textile defects. The Euclidian distance that is calculated between feature of reference textile (non-defective textile) and feature of defective one is used as an evaluation parameter. If the Euclidian distances of the features of textile images are higher than a predetermined threshold, the textiles are determined as defective ones, and vice versa. Simulations are conducted using four groups of textile defects. It turns out that the proposed method can achieve 100% detection rate for textile group with ink-spot and textile group with holes.Keywords : Wavelet coefficient histogram, Euclidean distance, Textile defect, industrial textiles, Image featuresÂ AbstrakPada industri tekstil, cacat produksi umumnya masih diperiksa secara manual oleh manusia. Pemeriksaan secara manual rentan terhadap kesalahan dan kurang konsisten, karena sifat manusia yang dapat lelah, lupa dan lain sebagainya. Untuk menjamin konsistensi dan kualitas pemeriksaan cacat kain, sebuah sistem deteksi otomatis perlu ada. Penelitian ini mengusulkan penggunaan histogram dari koefisien aproksimasi wavelet dua tingkat sebagai fitur untuk deteksi cacat tekstil. Jarak Euclidian yang dihitung diantara fitur tekstil citra referensi (berasal dari citra tidak cacat) dengan fitur tesktil citra cacat digunakan sebagai parameter evaluasi. Jika jarak Euclidian dari fitur suatu citra tekstil berada di atas nilai ambang yang telah ditentukan sebelumnya, citra tersebut dinyatakan cacat, dan sebaliknya. Penelitian dilaksanakan dengan menjalankan simulasi deteksi cacat tekstil, menggunakan empat kelompok cacat tekstil yang berbeda. Ditemukan bahwa metode usulan mencapai tingkat kebenaran deteksi sebesar 100% untuk citra kelompok cacat tinta dan kelompok cacat lubang. Â Â Â Â Â Â Â Â Â Â Â Kata Kunci : Histogram koefisien wavelet, Jarak Â Euclidean, Cacat tekstil, Industri tekstil, Fitur citraÂ Â

Deteksi Pemalsuan Citra dengan Teknik Copy-Move Menggunakan Metode Ordinal Measure dari Koefisien Discrete Cosine Transform ., Zulfan; Arnia, Fitri; Muharar, Rusdha
JURNAL NASIONAL TEKNIK ELEKTRO Vol 5, No 2: Juli 2016
Publisher : Jurusan Teknik Elektro Universitas Andalas

This article discusses a new method for the detection of forgery images generated by copy-move technique. Copy-move technique is one of image forgery techniques which taking a particular object from its original image and add it on that image for the purpose of increasing the number of or changing the same object in the original image. This study aims to detect the forged image generated by the copy-move techniques and copy-move forged image that has been modified by the rotation operation and histogram equalization. Detection feature used is Ordinal Measure of Discrete Cosine Transform coefficient (OM-DCT). Detection starts with division of the image into a block size of BXB (B = 16x16, 32x32 and 64x64) and two-dimensional DCT was performed to each of blocks. The feature distance from the original to the fake image, was calculated by the Euclidian distance and each feature has a distance of less than or equal to the threshold value (T) according to the observations will be marked as a forged part. The results show that there are blocks detected on the copy-move image, whether on the unmodified copy-move forge image or those which modified by the rotation operation and histogram equalization. The number of blocks that are found in the copy-move object varies according to the size of the detection block used.Key words: Discrete Cosine Transform (DCT), ordinal measure of DCT Coefficient, copy-move, rotation, histogram equalization.Abstrakâ€” Artikel ini membahas tentang metode baru untuk deteksi citra palsu yang dihasilkan dari teknik copy-move. Teknik copy-move merupakan salah satu teknik pemalsuan citra dengan cara mengambil objek tertentu dari citra asli dan menambahkannya pada citra tersebut dengan tujuan untuk menambah jumlah atau merubah objek yang sama pada citra asli. Penelitian ini bertujuan untuk mendeteksi citra palsu yang dihasilkan oleh teknik copy-move dan citra palsu copy-move yang telah dimodifikasi dengan operasi rotasi dan ekualisasi histogram. Fitur deteksi yang digunakan adalah Ordinal Measure dari koefisien Discrete Cosine Transform (OM-DCT). Pendeteksian dimulai dengan membagi citra ke dalam blok berukuran BxB (B = 16x16, 32x32 dan 64x64) dan DCT 2 dimensi dilakukan pada setiap blok tersebut. Jarak fitur citra asli dengan palsu dihitung dengan persamaan jarak Ecluidian dan setiap fitur yang memiliki jarak lebih kecil atau sama dengan nilai threshold (T) menurut pengamatan akan ditandai sebagai bagian yang dipalsukan. Hasil pendeteksian menunjukkan bahwa ada blok-blok yang terdeteksi pada objek citra yang di-copy-move baik pada citra palsu copy-move yang tidak dimodifikasi ataupun yang telah dimodifikasi dengan operasi rotasi dan ekualisasi histogram. Jumlah blok yang ditemukan pada objek copy-move bervariasi sesuai ukuran blok pendeteksian yang digunakan.Kata kunci : Discrete Cosine Transform (DCT), ordinal measure dari koefisien DCT, copy-move, rotasi, ekualisasi histogram.

Karakterisasi Kematangan Buah Kopi Berdasarkan Warna Kulit Kopi Menggunakan Histogram dan Momen Warna Syahputra, Hendri; Arnia, Fitri; Munadi, Khairul
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 1: March 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Conventionally, the coffee maturity level is determined by observing the fruit colour, and it is done manually. This approach may result in inconsistency in colour classification. Thus, an automatic colour classification method based on colour of coffee maturity level is required. This paper presents the characterization of coffee maturity level based on two colour features: colour histogram and colour moment. Characterization of coffee maturity level was grouped into four class: green for unripe coffee, greenish-yellow for half ripe coffee, red for ripe coffee, and dark red for too ripe coffee. The purpose of the research is to determine the colour features that can characterize the coffee maturity level based on computer simulation in extracting and calculating the statistical values of the colour histogram and colour moments. It turned out from 200 coffee images that the statistical values of colour histogram are more suitable for characterising the coffee maturity. The kurtosis values of hue histogram for each maturity level of coffee were different: kurtosis value of unripe coffee was 17.2-28.3, those of half ripe coffee, ripe coffee and too ripe coffee were 29.2-31.4, 32.7-83.5, and more than 84.2 respectively..Keywords : colour histogram kurtosis, colour moment, image processing.AbstrakSecara tradisional, tingkat kematangan buah kopi ditentukan dari warna kulitnya yang dikelompokan secara manual. Cara ini menghasilkan pengelompokan warna yang kurang konsisten, sehingga diperlukan sebuah metode otomatis pengelompokan buah kopi berdasarkan warna dari tingkat kematangannya. Penelitian ini memaparkan hasil karakterisasi kematangan buah kopi arabika menggunakan dua fitur warna citra, yaitu histogram dan momen warna. Karakterisasi kematangan dibagi menjadi empat kelompok: hijau untuk kopi muda, hijau kekuningan untuk kopi setengah masak, merah untuk kopi masak, dan merah tua untuk kopi tua. Tujuan penelitian ini adalah menentukan fitur warna yang dapat mewakili karakter kematangan buah kopi dengan melakukan simulasi komputer untuk mengekstrak dan menghitung nilai statistik dari histogram warna dan nilai momen warna dari empat kelompok buah kopi.Â Hasil penelitian menggunakan 200 citra kopi menunjukkan bahwa nilai statistik dari histogram warna lebih menggambarkan karakter kematangan buah kopi, dibandingkan dengan momen warna. Nilai kurtosis dari histogram hue memiliki nilai berbeda untuk setiap kategori kematangan buah kopi: kopi muda memiliki nilai kurtosis 17.2-28.3, kopi setengah masak 29.2-31.4, kopi masak 32.7-83.5dan kopi tua lebih dari 84.2.Â Â Kata Kunci : kurtosis histogram warna, momen warna, pengolahan citra.

Perbandingan Kinerja Support Vector Machine (SVM) Dalam Mengenali Wajah Menggunakan SURF DAN GLCM Bahri, Syamsul; Saddami, Khairun; Arnia, Fitri; Muchtar, Kahlil
JURNAL NASIONAL TEKNIK ELEKTRO Vol 8, No 2: July 2019
Publisher : Jurusan Teknik Elektro Universitas Andalas

Face recognition is one part of the biometrics research. Face recognition is widely used in identification and recognition process. Speed-up Robust Feature (SURF) is one of feature extraction method used in face recognition system. This research aims to compare face recognition performance between SURF and Gray Level Co-occurence Matrix (GLCM) methods for perspective rotation. In this study, the image features were extracted using SURF and GLCM. Each feature was used on classification stage using Support Vector Machine (SVM). The dataset was obtained from National Cheng Kung University (NCKU). The NCKU dataset has more variation of rotation angle. The dataset used in this study consists of 10 classes that showed 10 of the subject. The results show that SURF method obtained 85% of accuracy and GLCM method reached 50% of accuracy. Therefore, we concluded that SURF method has better performance on implementing on face recognition system.Keywords : SURF, GLCM, Face Recognition, SVMÂ AbstrakÂ Pengenalan wajah merupakan salah satu bagian dari penelitian biometrika. Pengenalan wajah banyak digunakan dalam proses identifikasi manusia. Metode ekstraksi fitur Speed-Up Robust Feature (SURF) merupakan salah satu metode yang digunakan untuk mengenali wajah. Penelitian ini bertujuan untuk membandingkan kinerja sistem pengenalan wajah dengan menggunakan metode ekstraksi fitur SURF dan Gray Level Co-occurence Matrix (GLCM). Pada penelitian ini, data input wajah akan diekstraksi fiturnya menggunakan SURF dan GLCM. Setiap fitur digunakan pada tahapan klasifikasi menggunakan Support Vector Machine (SVM). Data yang digunakan merupakan data yang didapatkan dari National Cheng Kung University (NCKU). Data wajah NCKU mempunyai sudut rotasi yang lebih banyak. Dataset yang digunakan pada penelitian ini terdiri dari 10 kelas yang menunjukkan 10 subjek penelitian. Pengenalan wajah menggunakan metode SURF dan SVM mempunyai akurasi 85%, sedangkan menggunakan metode GLCM mempunyai akurasi 50%. Hasil menunjukkan bahwa metode SURF mempunyai kinerja yang lebih baik dari metode GLCM.Kata Kunci : SURF, GLCM, pengenalan wajah, SVM

Pengenalan Aksara Jawi Tulisan Tangan Menggunakan Freemen Chain Code (FCC), Support Vector Machine (SVM) dan Aturan Pengambilan Keputusan ., Safrizal; Arnia, Fitri; Muharar, Rusdha
JURNAL NASIONAL TEKNIK ELEKTRO Vol 5, No 1: Maret 2016
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v5n1.185.2016

Jawi is one variant of Arabic script consists of 35 characters. Some of Jawi characters have the same main shape, but different number of dots in different location. Thus, recognition process of Jawi characters can be done by performing a classification based on the main shape. In recognition process, feature extraction plays an important role. In this research, Freeman Chain Code (FCC) was used as feature extraction and Support Vector Machine (SVM) as classifier. Then we apply the decision rules to classifySVMresult into Jawi characters. FCC is used to represent the boundary of Jawi characters into a chain code. Then the chain code is used bySVMto classify the characters into 19 groups. Feature of location and the number of dots are used by decision rules to classify the groups into Jawi characters. The Jawi characters are handwritten and generated by 10 writers from different backgrounds and ages. The recognition rate of this research was 80.00%.Keywords : Jawi script, handwriting, FCC, SVM, decision rules.Abstrakâ€”Aksara Jawi merupakan salah satu varian dari aksara Arab yang terdiri dari 35 aksara. Dari 35 aksara Jawi Â tersebut terdapat beberapa aksara dengan bentuk bagian utama yang sama namun memiliki letak dan jumlah titik yang berbeda. Karena perbedaan tersebut maka proses pengenalan aksara Jawi dapat dilakukan dengan melakukan klasifikasi berdasarkan perbedaan bentuk bagian utama. Pada penelitian ini Freeman Chain Code (FCC) digunakan sebagai ekstraksi fitur dan Support Vector Machine (SVM). FCC digunakan untuk merepresentasikan garis batas (boundary) aksara Jawi kedalam kode rantai. Kode rantai tersebut diklasifikasi dengan menggunakan SVM kedalam 19 kelompok. Fitur letak titik dan jumlah titik digunakan sebagai aturan pengambilan keputusan terhadap 19 kelompok hasil klasifikasi SVM kedalam aksara Jawi. Aksara Jawi yang digunakan merupakan tulisan tangan dari 10 orang penulis dari berbagai latar belakang dan umur. Tingkat keberhasilan klasifikasi penelitian ini mencapai 80,00%.Kata Kunci : aksara Jawi, tulisan tangan, FCC, SVM, aturan pengambilan keputusan

Co-Authors . Melinda . Roslidar ., Safrizal ., Zulfan Abbas Adam AzZuhri Akhyar Bintang Andika Saputra Arsy Febrina Dewi Aulia Syarif Aziz Bahri, Syamsul Cut Mutia Cut Mutia Devi Sara, Ira Dwipayana, Mahendar Elizar Elizar Fardian Fardian Fardian Fardian Faridah Faridah Fathurrahman Fathurrahman Fery Irianda Fikri, Rizal Hardian Saputra Hayatun Maghfirah Hendra Hidayat Hendri Syahputra Hendrik Leo Hubbul Walidainy Ilal Mahdi Iqbal, TWK Muhammad Kahlil Muchtar Khairul Fajri Khairul Munadi Khairul Munadi Khairul Munadi Khairul Munadi Khairul Munadi Khairul Munadi Khairun Saddami Khairun Saddami Khairun Saddami Khusnul Azima Laila Nujmi Burhan Lina Marlina Listia Sukma Putri Maghfirah, Hayatun Maulisa Oktiana Maya Fitria Maya Muthia Muchtar, Kahlil Muhammad Haries Muhammad Irhmasyah Muhammad Irwandi Muhammad Rizky Syahputra Muharar, Rusdha Muharar, Rusdha Munadi, Khairul Nailul Mustaqim Abdi Nargaza, Juanda Nasaruddin Nasaruddin Novandri, Andri Nur Amalia Hasma Nuriza Pramita Nuriza Pramita Nuriza Pramita Oktiana, Maulisa Oktiana, Maulisa Putri Rizkiah Rahmatika, Nisa Adilla Raihan Islamadina Raihan Islamadina Raihan Islamadina Ramadhani Ramadhani Ramiady, Luthfiar Ramzi Adriman Risnaty Utami Marsal Rizal Fikri Rizka Ramadhana Rizki Faulianur Roslidar Roslidar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar Rusdha Muharar, Rusdha Rusdha Muharrar Saddami, Khairun Safrizal Razali Saputra, Andika Siti Aisyah Siti Aisyah Syahputra, Hendri Syahrul Wahyudi Syamsul Bahri Tata Arsatria Taufik Fuadi Abidin Taufik Fuadi Abidin Taufik Fuadi Abidin Tia Ernita TWK Muhammad Iqbal Yunida, Yunida Yunidar Yusni, Y Yustina Dhyanti Yuwaldi Away Zakiah Zakiah Zharifah Muthiah Zuhar Musliyana Zuhar Musliyana, Zuhar Zul Syukri Zulfan .

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