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All Journal Techno.Com: Jurnal Teknologi Informasi EMARA : Indonesian Journal of Architecture JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jiko (Jurnal Informatika dan komputer) Jurnal ULTIMATICS ComTech: Computer, Mathematics and Engineering Applications Jurnal Leverage, Engagement, Empowerment of Community (LeECOM) Jurnal Informatika dan Sistem Informasi Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Buletin Poltanesa Insyst : Journal of Intelligent System and Computation JUSTIN (Jurnal Sistem dan Teknologi Informasi) Eduvest - Journal of Universal Studies EMARA : Indonesian Journal of Architecture
Daniel Martomanggolo Wonohadidjojo
Universitas Ciputra Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Neuroscience Social Sciences

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Journal : Jurnal ULTIMATICS

 1 
Perbandingan Performa Histogram Equalization untuk Peningkatan Kualitas Gambar Minim Cahaya pada Android Claudia Kenyta; Daniel Martomanggolo Wonohadidjojo
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v12i2.1667

Abstract

When the photos are taken in low light condition, the quality of the results will not meet their expectation. Image Enhancement method can be used to enhance the quality of the photos taken in low light condition. One of the algorithms used is called Histogram Equalization (HE), that works using Histogram basis. The superiority of HE algorithm in enhancing the quality of the photos taken in low light condition is the simplicity of the algorithm itself and it does not need a high specification device for the algorithm to run. One variant of HE algorithm is Contrast Limited Adaptive Histogram Equalization (CLAHE). This paper shows the implementation of HE algorithm and its performance in enhancing the quality of photos taken in low light condition on Android based application and the comparison with CLAHE algorithm. The results show that, HE algorithm is better than CLAHE algorithm.
Perbandingan Convolutional Neural Network pada Transfer Learning Method untuk Mengklasifikasikan Sel Darah Putih Daniel Martomanggolo Wonohadidjojo
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i1.2040

Abstract

Analysis of WBC structure from microscopic images and classification of cells into types is challenging. Although white blood cells can be differentiated based on their shape, color and size, one challenging aspect is that they are surrounded by other blood components such as red blood cells and platelets. In this study, transfer learning method using four network architectures that have been trained in advance is applied to classify the white blood cell images. The network architectures used are AlexNet, GoogleNet, ResNet-50 and VGG-16. A comparative analysis of the performance of these architectures was carried out in classifying the images. The evaluation method was undertaken using Confusion Matrix. The performance metrics measured in the evaluation are Accuracy, Precision, Recall and Fmeasure. The results showed that all architectures succeeded in classifying white blood cells using the transfer learning method. ResNet-50 is the network architecture that shows the highest performance in classifying white blood cell images.
Co-Authors Citra, Caecilia Citra Claudia Kenyta Dyah Kusuma Wardhani Dyah Kusuma Wardhani Eric Harianto Hans Richard Alim Natadjaja Hansel Santoso Indra Maryati Jiemesha, Micheila Jonathan Andrei Wibawa Kurniawan, Ardian Laura Mahendratta Tjahjono Lestari, Caecilia Citra Likorawung, Marsha Alexis Mellysa Margarita Susilo Mulijono, Steffany Florence Sugiarto Mychael Maoeretz Engel Natadjaja, Hans Richard Alim Nehemia Sugianto Nusantara, Michelle Swastika Bianglala Rinabi Tanamal Sanjaya, Karyna Budi Santoso, Hansel Sugianto, Nehemia Susan Susilo, Mellysa Margarita Wiradinata, Marcell Jeremy Yosua Setyawan Soekamto Yusuf Ariyanto Yuwono Marta Dinata