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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics E-Dimas: Jurnal Pengabdian kepada Masyarakat Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Jurnal Aisyah : Jurnal Ilmu Kesehatan Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Biomedical Science and Bioengineering Jurnal Informatika
Adi Wibowo
Departement Of Computer Science, Informatics, Universitas Diponegoro
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Journal : International Journal of Advances in Intelligent Informatics

 1 
Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning Retno Kusumaningrum; Iffa Zainan Nisa; Rizka Putri Nawangsari; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.737

Abstract

Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.
Android skin cancer detection and classification based on MobileNet v2 model Adi Wibowo; Cahyo Adhi Hartanto; Panji Wisnu Wirawan
International Journal of Advances in Intelligent Informatics Vol 6, No 2 (2020): July 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i2.492

Abstract

The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy.
Fragile watermarking for image authentication using dyadic walsh ordering Prajanto Wahyu Adi; Adi Wibowo; Guruh Aryotejo; Ferda Ernawan
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1017

Abstract

A digital image is subjected to the most manipulation. This is driven by the easy manipulating process through image editing software which is growing rapidly. These problems can be solved through the watermarking model as an active authentication system for the image. One of the most popular methods is Singular Value Decomposition (SVD) which has good imperceptibility and detection capabilities. Nevertheless, SVD has high complexity and can only utilize one singular matrix S, and ignore two orthogonal matrices. This paper proposes the use of the Walsh matrix with dyadic ordering to generate a new S matrix without the orthogonal matrices. The experimental results showed that the proposed method was able to reduce computational time by 22% and 13% compared to the SVD-based method and similar methods based on the Hadamard matrix respectively. This research can be used as a reference to speed up the computing time of the watermarking methods without compromising the level of imperceptibility and authentication.
Co-Authors Abraham Timotius Asmoro Putro Adiyono, Soni Alfania Sarah Handayani Asep Insani Bayu Surarso Beta Noranita Beta Noranita Boko Nurdiyanto S. Budi Warsito Budi Warsito Cahyo Adhi Hartanto Dedy Kurniadi Djalal Er Riyanto Eka Rahmawati Eko Adi Sarwoko Ferda Ernawan Fra Siskus Dian Arianto Guruh Aryotejo Iffa Zainan Nisa Indra Jaya Indra Waspada Kabul Kurniawan Kiswanto Kiswanto mohamad jamil Muh. Abdur Rohman Muh. Abdur Rohman Noel Segura Meraz Nurdin Bahtiar Prajanto Wahyu Adi Rahmat Gernowo Retno Kusumaningrum Rini Nuraini Rizka Putri Nawangsari Sukmawati Nur Endah Sutikno Sutikno Wahyul Amien Syafei Wirawan, Panji Wisnu