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All Journal Jurnal Ilmiah Sinus Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara)
Akhmad Fadjeri
Universitas Maarif Nahdlatul Ulama Kebumen
Computer Science & IT

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Karakteristik Morfologi Tanaman Selada Menggunakan Pengolahan Citra Digital Akhmad Fadjeri; Bayu Aji Saputra; Dicki Kusuma Adri Ariyanto; Lisna Kurniatin
Jurnal Ilmiah SINUS Vol 20, No 2 (2022): Vol. 20 No. 2 Juli 2022
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v20i2.601

Abstract

The aim of this study is to find out characteristics of Green Rapid lettuce with digital image processing using morphological technique on image and first order statistical feature extraction. In digital image processing, the original images were processed to be RGB image and then processed again to be grayscale one. The grayscale images were processed to be biner image so that the image can be changed into biner form (0 and 1). In morphological process, this study used edge detection, dilation, and erosion. The final stage of morphological process used feature extraction process to find out image morphological characteristics which can be described mathematically. The result of extraction showed its samples within range or average area about 9.593.622.205.915.820 pixels, perimeter about 8.427.724.410.694.360 pixels, length about 829.877.174.308.893 pixels, and width about 1.654.002.663.010.820 pixels
Analisis Teks Bahasa Indonesia Dan Inggris Dari Sebuah Citra Menggunakan Pengolahan Citra Digital Akhmad Fadjeri; Atik Muhimatun Asroriyah; Atiq Rahmawati
Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) Vol 10, No 2 (2022): Jurnal TIKomSiN, Vol. 10, N0. 2, Oktober 2022
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/tikomsin.v10i2.650

Abstract

The purpose of this research is to analyze Indonesian text and English text from images using digital image processing. The method used in this research is an experimental method such as: literature review , identification problems, hypotheses, analyzing materials, designing programs, conducting tests and drawing conclusions. The result of this research is to detect the text contained in the image. This research result a digital image processing program in text that is containing from an image that has text in it, using input or input in form of a character image. The image that will be recognized into text with Tesseract OCR and output the text detected by the program. This program can show the level of accuracy to recognize characters and words. It conducted kind of testing on 8 images use several of text characters on it. In this case, the system can recognize characters on the text form, with an average accuracy 96% of the test data taken on images sourced from the internet. According to the testing results, this level of accuracy indicates that this application is quite accurate and efficient in identification the image of certain word or text character.
Analisis Perbandingan Hasil Pengolahan Citra Asli Dan Cropping Untuk Mengidentifikasi Karakteristik Tanaman Selada Menggunakan Metode Morfologi Dan Ekstrasi Ciri akhmad fadjeri; Lisna Kurniatin; Dicki Kusuma Adri Ariyanto; Bayu Aji Saputra
Jurnal Ilmiah SINUS Vol 21, No 1 (2023): Vol. 21 No. 1 Januari 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v21i1.664

Abstract

Penelitian ini bertujuan untuk mengetahui hasil perbandingan  tanaman selada menggunakan metode morfologi dan ekstrasi ciri dari proses kroping pada citra dan citra asli. Teknik kroping merupakan teknik memperkecil ukuran citra. Sedangkan citra asli merupakan citra hasil dari tangkapan alat citra digital. Pada pengolahan citra digital terdapat teknik atau metode morfologi pada citra. Sebelum tahap morfologi, citra akan di proses menggunakan 2 metode yaitu metode kroping pada citra dan citra asi atau tanpa kroping. Setelah proses kroping atau tanpa kroping citra di olah menjadi citra abu-abu (grayscale), selanjutnya citra akan di proses menggunakan deteksi tepi untuk memperoleh hasil bagian tepi pada citra. Kemudian citra diubah menjadi bentuk biner (0 dan 1) atau sering disebut citra biner. Setelah pengolahan citra digital selesai kemudian tahap morfologi, dimana terdapat proses dilasi dan erosi. Proses ini bertujuan untuk mempertebal dan menipiskan hasil citra atau untuk meningkatkan hasil pendeteksian pada citra atau objek. Tahap selanjutnya yaitu proses ekstrasi ciri, dimana proses ini bertujuan untuk memperoleh nilai matematis pada sebuah citra.Proses ekstrasi ciri pada citra dilakukan sebanyak dua kali yaitu ekstrasi ciri citra asli dan ekstrasi ciri citra kroping. Dari hasil ekstrasi dapat diperoleh nilai matematis citra dengan ketentuan luas, keliling, kebundaran, kerampingan, panjang dan lebar. Proses ekstrasi ciri yang pertama atau citra asli didapatkan nilai matematis dengan rentang atau rata-rata luas 51.667.147.953.650.600, keliling 58.594.529.654.404.700, kebundaran 1.000.149.515.265.920, kerampingan 30.741.557.873.547.900, panjang 3.496.560.371.093.740 dan lebar 8.489.952.148.437.490. Sedangkan  proses  ekstrasi ciri kroping pada citra didapat nilai matematis citra dengan rentang atau rata-rata luas 83.364.228.960.567.300, keliling 26.081.033.779.659.700, kebundara 10.002.171.123.342.300, kerampingan 41.652.085.165.818.800, panjang 27.072.842.968.749.900 dan lebar 7.598.551.957.031.240.
Klasifikasi Biji Kopi Berdasarkan Bentuk Menggunakan Image Processing dan K-NN Akhmad Fadjeri
Jurnal Ilmiah SINUS Vol 21, No 2 (2023): Volume 21 No. 2 Juli 2023
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v21i2.726

Abstract

Temanggung Regency is the largest coffee producing area in Central Java. Robusta and Arabica are two types of coffee grown in this area. The manual method of sorting coffee beans is still used so the results are more subjective. Therefore, we require a system for categorizing coffee beans so that the results are more objective and reliable. This study uses K-NN classification and morphological features to recognize coffee beans based on the type and shape of the coffee bean defects. This aim of this study discovers which characteristics are better at classifying coffee beans into four categories (whole Robusta, whole Arabica, Robusta broken, and broken Arabica). A total of 110 coffee bean photos were used, with 80 images as training data, 40 images as test data, and a total of five morphological features.Our findings reveal that morphological traits may classify coffee beans into four categories with an accuracy of 62.5%, which is very good for detecting 100% whole Robusta and 90% Arabica but remains weak for recognizing broken coffee beans by type. Lean performs better in distinguishing coffee beans based on four classes, with a 70% accuracy. Morphological features outperform color features in distinguishing coffee beans based on shape defects, with an accuracy score of 83%. 
Co-Authors Atik Muhimatun A Atiq Rahmawati Bayu Aji Saputra Bayu Aji Saputra Dicki Kusuma Adri Ariyanto Dicki Kusuma Adri Ariyanto Lisna Kurniatin Lisna Kurniatin