Lab-IS/filkom-research-papers huggingface dataset:
Classification of Alzheimer's disease using the Convolutional Neural Network (CNN) with transfer learning and weighted loss
Authors: Muhammad Wildan Oktavian, Novanto Yudistira, Achmad Ridok
Journal: arXiv preprint arXiv:2207.01584
Year: 2022
Topics: Deep Learning; Computer Vision
Abstract: Alzheimer's disease is a progressive neurodegenerative disorder that gradually deprives the patient of cognitive function and can end in death. With the advancement of technology today, it is possible to detect Alzheimer's disease through Magnetic Resonance Imaging (MRI) scans. So that MRI is the technique most often used for the diagnosis and analysis of the progress of Alzheimer's disease. With this technology, image recognition in the early diagnosis of Alzheimer's disease can be achieved automatically using machine learning. Although machine learning has many advantages, currently the use of deep learning is more widely applied because it has stronger learning capabilities and is more suitable for solving image recognition problems. However, there are still several challenges that must be faced to implement deep learning, such as the need for large datasets, requiring large computing resources, and requiring careful parameter setting to prevent overfitting or underfitting. In responding to the challenge of classifying Alzheimer's disease using deep learning, this study propose the Convolutional Neural Network (CNN) method with the Residual Network 18 Layer (ResNet-18) architecture. To overcome the need for a large and balanced dataset, transfer learning from ImageNet is used and weighting the loss function values so that each class has the same weight. And also in this study conducted an experiment by changing the network activation function to a mish activation function to increase accuracy. From the results of the tests that have been carried out, the accuracy of the model is 88.3 % using transfer learning, weighted loss and …
Multi instance learning via deep CNN for multi-class recognition of Alzheimer's disease
Authors: Muthusubash Kavitha, Novanto Yudistira, Takio Kurita
Year: 2019
Topics: Deep Learning; Computer Vision
Abstract: In recent years, number of classification techniques for Alzheimer's disease (AD) have been developed that produced methods based on the use of hand-crafted machine learning and obscure deep learning models. This study proposed a new classification framework based on the combination of Unet-like 2D convolutional neural networks (CNN) and multinomial logistic regression classifier, which learns the intra-slice for multi-class classification after the selection of the 3D positron emission tomography (PET) image into a sequence of 2D slices. The CNNs are performed to generate the attention features of the brain while the logistic regression incorporated to learn those specifically localized features of various classes for AD classification. At the end of the network, we used a average pooling layer before the softmax for four-class classification problem. It can efficiently generate a flexible class of transformations …
Batik classification using convolutional neural network with data improvements
Authors: Dewa Gede Trika Meranggi, Novanto Yudistira, Yuita Arum Sari
Journal: JOIV: International Journal on Informatics Visualization
Year: 2022
Topics: Deep Learning; Computer Vision
Abstract: Batik is one of the Indonesian cultures that UNESCO has recognized. Â Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have aÂ'core motif'printedÂ repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. Â However, withÂ so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assisted with artificial intelligence. Machine learning and deep learning have producedÂ muchÂ good performance in image recognition. In this study, we use deep learning based onÂ a ConvolutionalÂ Neural Network (CNN) to automate the classification of batik motifs. There are two datasets used in this study. The old dataset comes from a public repositoryÂ with 598Â data withÂ fiveÂ types of motifs. Meanwhile, the newÂ datasetÂ updatesÂ the old dataset by replacing the anomalous data in the old datasetÂ with 621 dataÂ with five types of motifs. TheÂ lereng motif is changed to pisanbali due to the difficulty of obtainingÂ the lerengÂ motif. Each dataset was divided intoÂ threeÂ ways: original, balance patch, and patch. We used ResNet-18 architecture, whichÂ used a pre-trained model to shorten the training time. The best test results were obtained in the new dataset with the patch way of 88.88% Â±0.88, andÂ in the old dataset, the best accuracy was found in the patch way on the test data of 66.14% Â±3.7. The dataÂ augmentationÂ in this study did not significantly …
Peran big data dan deep learning untuk menyelesaikan permasalahan secara komprehensif
Authors: Novanto Yudistira
Journal: Expert
Year: 2021
Topics: Big Data; Deep Learning
Abstract: Peran sain data besar (Big Data) dan pembelajaran mesin dewasa ini tidak dapat terelakkan terutama untuk menganalisis data dan memberikan kecerdasan pada komputer agar bekerja secara otonom untuk menyelesaikan suatu pekerjaan tertentu. Perkembangan teknologi sensor dan internet membuat ketersediaan data tersebut melimpah yang selanjutnya dapat dilakukan analisis data dalam jumlah yang besar. Hal tersebut mempengaruhi bagaimana cara pandang komputasi dalam berbagai macam bidang baik ilmu alam maupun sosial. Data yang terkumpul dapat berupa beragam format dengan laju pertambahan yang cepat dan dinamis. Kita perlu algoritma atau model yang mumpuni untuk memahami dan menggali pengetahuan pada set data yang besar tersebut beserta rancangan modelnya yang secara otomatis mempunyai kemampuan memprediksi atau mendeteksi. Deep Learning dengan kapasitasnya yang besar serta hubungan korelasi antar neuron yang sangat banyak diharapkan mampu menjawab tantangan tersebut didukung oleh beberapa penelitian terkini pada penerapannnya di berbagai bidang keilmuan. Dalam paper ini akan dipaparkan contoh pemanfaatan Deep Learning pada Big Data yang telah kita lakukan pada pengenalan video aksi manusia pada Youtube, Segmentasi pada sel berskala besar, citra dada x-ray dan data time-series multi variabel hubungannya dengan pandemi COVID-19.
Correlation net: Spatiotemporal multimodal deep learning for action recognition
Authors: Novanto Yudistira, Takio Kurita
Journal: Signal Processing: Image Communication
Year: 2020
Topics: Deep Learning; Computer Vision
Abstract: This paper describes a network that captures multimodal correlations over arbitrary timestamps. The proposed scheme operates as a complementary, extended network over a multimodal convolutional neural network (CNN). Spatial and temporal streams are required for action recognition by a deep CNN, but overfitting reduction and fusing these two streams remain open problems. The existing fusion approach averages the two streams. Here we propose a correlation network with a Shannon fusion for learning a pre-trained CNN. A Long-range video may consist of spatiotemporal correlations over arbitrary times, which can be captured by forming the correlation network from simple fully connected layers. This approach was found to complement the existing network fusion methods. The importance of multimodal correlation is validated in comparison experiments on the UCF-101 and HMDB-51 datasets. The …
Learning where to look for COVID-19 growth: Multivariate analysis of COVID-19 cases over time using explainable convolution–LSTM
Authors: Novanto Yudistira, Sutiman Bambang Sumitro, Alberth Nahas, Nelly Florida Riama
Journal: Applied Soft Computing
Year: 2021
Topics: Deep Learning; Time Series Analysis
Abstract: Determinant factors which contribute to the prediction should take into account multivariate analysis for capturing coarse-to-fine contextual information. From the preliminary descriptive analysis, it shows that environmental factor such as UV (ultraviolet) is one of the essential factors that should be considered to observe the COVID-19 epidemic drivers. Moreover, there are education, government, morphological, health, economic, and behavioral factors contributing to the growth of COVID-19. Besides descriptive analysis, in this research, multivariate analysis is considered to provide comprehensive explanations about factors contributing to pandemic dynamics. To achieve rich explanations, visual attribution of explainable Convolution-LSTM is utilized to see high contributing factors responsible for the growth of daily COVID-19 cases. Our model consists of 1 D CNN in the first layer to capture local relationships among …
Perbandingan Model AlexNet dan ResNet dalam Klasifikasi Citra Bunga Memanfaatkan Transfer Learning
Authors: Bana Falakhi, Elmira Faustina Achmal, Muhammad Rizaldi, Renata Rizki Rafi'Athallah, Novanto Yudistira
Journal: Jurnal Ilmu Komputer dan Agri-Informatika
Year: 2022
Topics: Deep Learning; Computer Vision
Abstract: Klasifikasi spesies bunga otomatis berbasis citra merupakan masalah penting bagi para ahli biologi yang membuat katalog bunga digital. Banyak penelitian tentang pengenalan spesies bunga telah diusulkan sejauh ini berdasarkan rutinitas pemrosesan gambar tradisional. Saat ini, para peneliti menerapkan pembelajaran mendalam pada berbagai tugas pengenalan objek berbasis gambar. Dalam makalah ini, deep learning berbasis transfer learning diterapkan pada klasifikasi spesies bunga. Metode yang diusulkan menggunakan model transfer learning AlexNet dan ResNet. Dataset Flower102 yang memiliki banyak kategori digunakan dalam karya eksperimental. Berbagai hasil eksperimen menunjukkan bahwa masing-masing model telah mencapai kinerja akurasi 87% dan 96% untuk AlexNet dan ResNet. Hasil yang diperoleh menunjukkan efektivitas model berbasis ResNet lebih tinggi bila dibandingkan dengan model berbasis AlexNet.
Indonesia’s fake news detection using transformer network
Authors: Jibran Fawaid, Aisyah Awalina, Rifky Yunus Krisnabayu, Novanto Yudistira
Year: 2021
Topics: Natural Language Processing; Deep Learning
Abstract: Fake news is a problem faced by society in this era. It is not rare for fake news to cause provocation and problems for the people. Indonesia, as a country with the 4th largest population, has a problem in dealing with fake news. More than 30% of the rural and urban population are deceived by this fake news problem. As we have been studying, there is only a little literature on preventing the spread of fake news in Bahasa Indonesia. So, this research is conducted to prevent these problems. The dataset used in this research was obtained from a news portal that identifies fake news, turnbackhoax.id. Using Web Scrapping on this page, we got 1116 data consisting of valid news and fake news. This dataset will be combined with other available datasets. The dataset is then processed by eliminating irrelevant words and dividing the data into training and testing data with a ratio of 80:20. All neural network methods use …
Prediction of sequential organelles localization under imbalance using a balanced deep U-Net
Authors: Novanto Yudistira, Muthusubash Kavitha, Takeshi Itabashi, Atsuko H Iwane, Takio Kurita
Journal: Scientific reports
Year: 2020
Topics: Deep Learning; Computer Vision
Abstract: Assessing the structure and function of organelles in living organisms of the primitive unicellular red algae Cyanidioschyzon merolae on three-dimensional sequential images demands a reliable automated technique in the class imbalance among various cellular structures during mitosis. Existing classification networks with commonly used loss functions were focused on larger numbers of cellular structures that lead to the unreliability of the system. Hence, we proposed a balanced deep regularized weighted compound dice loss (RWCDL) network for better localization of cell organelles. Specifically, we introduced two new loss functions, namely compound dice (CD) and RWCD by implementing multi-class variant dice and weighting mechanism, respectively for maximizing weights of peroxisome and nucleus among five classes as the main contribution of this study. We extended the Unet-like convolution neural …
Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum
Authors: Timothy Bastian Sianturi, Imam Cholissodin, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2023
Topics: Deep Learning; Time Series Analysis
Abstract: Salah satu bentuk dari inovasi perkembangan teknologi adalah cryptocurrency yang secara luas telah diakui sebagai alternatif dari pertukaran mata uang. Salah satu cryptocurrency yang cukup populer saat ini adalah Ethereum yang mulai diperdagangkan pertama sekali pada 7 Agustus 2015 dengan harga US $2, 83 dan mencapai harga tertinggi pada tanggal 8 November 2021 dengan harga US $4822, 97. Ethereum memiliki fluktuasi harga yang tinggi dan memiliki banyak faktor yang mempengaruhi harga ethereum seperti politik atau masalah ekonomi pada tingkatan global sehingga menyebabkan risiko investasi yang cukup besar. Penelitian ini melakukan beberapa tahapan dalam memprediksi pergerakan harga Ethereum, yaitu pre-processing, normalisasi data, training algoritma Long Short-Term Memory (LSTM), dan melakukan evaluasi dengan Mean Square Error (MSE). Berdasarkan hasil penelitian ini peneliti berhasil melakukan prediksi harga Ethereum menggunakan algoritma LSTM berbasis multi fungsi aktivasi dengan parameter pengujian proporsi data training dan data testing sebesar 70%: 30%, jumlah sequence sebesar 14 yang menggambarkan data selama 14 hari, nilai hidden unit sebesar 64, jumlah epoch sebesar 150, dan sigmoid sebagai fungsi aktivasi yang dibuktikan dengan nilai MSE sebesar 0, 0121.
SparseSwin: Swin transformer with sparse transformer block
Authors: Krisna Pinasthika, Blessius Sheldo Putra Laksono, Riyandi Banovbi Putera Irsal, Syifa’Hukma Shabiyya, Novanto Yudistira
Journal: Neurocomputing
Year: 2024
Topics: Deep Learning; Computer Vision
Abstract: Advancements in computer vision research have put transformer architecture as the state-of-the-art in computer vision tasks. One of the known drawbacks of the transformer architecture is the high number of parameters, this can lead to a more complex and inefficient algorithm. This paper aims to reduce the number of parameters and in turn, made the transformer more efficient. We present Sparse Transformer (SparTa) Block, a modified transformer block with an addition of a sparse token converter that reduces the dimension of high-level features to the number of latent tokens. We implemented the SparTa Block within the Swin-T architecture (SparseSwin) to leverage Swin's proficiency in extracting low-level features and enhance its capability to extract information from high-level features while reducing the number of parameters. The proposed SparseSwin model outperforms other state-of-the-art models in image …
UV light influences covid-19 activity through big data: trade offs between northern subtropical, tropical, and southern subtropical countries
Authors: Novanto Yudistira, Sutiman B Sumitro, Alberth Nahas, Nelly Florida Riama
Journal: medRxiv
Year: 2020
Topics: Big Data; Epidemiology
Abstract: UV (ultraviolet) light is an important factor should be considered to predict coronavirus epidemic growth pace. UV is different from weather temperature since UV is electromagnetic wavelength from 10 nm to 400 nm in size, shorter than of visible lights. For some people, UV light can lead to cancer from unprotected sun exposure, however, for tropical people, which have been used to live in such condition, have resisted from negative effect high UV index. Moreover, UV has the capability to inactivate virus. This conclusion has been discussed deeply with biological experts. Although UV light has the ability to inactivate viruses, it may be meaningless in areas with high air pollution where UV light turns into heat. The data visualization code is available here https://github.com/cbasemaster/uvcorona
Regularizer based on Euler characteristic for retinal blood vessel segmentation
Authors: Lukman Hakim, Muthu Subash Kavitha, Novanto Yudistira, Takio Kurita
Journal: Pattern Recognition Letters
Year: 2021
Topics: Medical Imaging; Computer Vision
Abstract: Segmentation of retinal blood vessels is important for the analysis of diabetic retinopathy (DR). Existing methods do not prioritize the small and disconnected vessels for DR. With the aim of paying attention to the small and disconnected vessel regions, this study introduced Euler characteristics (EC) from topology to calculate the number of isolated objects on segmented vessel regions, which is the key contribution of this study. In addition, we utilized the number of isolated objects in a U-Net-like deep convolutional neural network (CNN) architecture as a regularizer to train the network for improving the connectivity between the pixels of the vessel regions. The proposed network performance of the regularizer based on EC in reconstructing vessel regions is compared over the network without our regularizer. Furthermore, the capacity of the proposed regularizer approach in enhancing the smoothness and pixel …
Klasifikasi citra makanan/non makanan menggunakan metode Transfer Learning dengan model Residual Network
Authors: Gustavo Thiodorus, Anugrah Prasetia, Luthfi Afrizal Ardhani, Novanto Yudistira
Journal: Teknologi: Jurnal Ilmiah Sistem Informasi
Year: 2021
Topics: Deep Learning; Transfer Learning
Abstract: People's activities that often upload photos of food before eating are common in popular social media, such as Facebook, Instagram, Twitter, and Pinterest. The food image data circulating on social media can be used for business purposes, such as analyzing customer behavior patterns. However, not all of the uploaded images are food images, so that before the analysis is carried out, it is necessary to do a classification task between food images and non-food images. Therefore, the researcher proposes automatic food image classification using transfer learning method using the Residual Network model version of 18 (ResNet-18). Residual Network model is used because it has a residual connection mechanism to solve the vanishing gradient problem. In addition, transfer learning was chosen because this method leverages the features and weights that have been generated in the previous training process on large and more general data (Imagenet) and thus reduce computation time and increase accuracy. The test was carried out by comparing the capabilities of the ResNet18 model with AlexNet. In addition, the fine tuning and freeze layer methods used to improve the quality of the model were also carried out in this study. In the experiment, the data set was divided into 3,000 images for training data and 1,000 images for test data, while the evaluation used was correctness accuracy. The results obtained in the ResNet18 model, namely the fine tuning training method, produced an accuracy value of 0.981 while the freeze layer resulted in the best accuracy value of 0.988. The AlexNet model that uses the fine tuning training method produces …
Mask usage recognition using vision transformer with transfer learning and data augmentation
Authors: Hensel D Jahja, Novanto Yudistira
Journal: Intelligent Systems with Applications
Year: 2023
Topics: Computer Vision; Deep Learning
Abstract: The COVID-19 pandemic has disrupted various levels of society. The use of masks is essential in preventing the spread of COVID-19 by identifying an image of a person using a mask. Although only 23.1% of people use masks correctly, Artificial Neural Networks (ANN) can help classify the use of good masks to help slow the spread of the Covid-19 virus. However, it requires a large dataset to train an ANN that can classify the use of masks correctly. MaskedFace-Net is a suitable dataset consisting of 137016 digital images with 4 class labels, namely Mask, Mask Chin, Mask Mouth Chin, and Mask Nose Mouth. Mask classification training utilizes Vision Transformers (ViT) architecture with transfer learning method using pre-trained weights on ImageNet-21k, with random augmentation. In addition, the hyper-parameters of training of 20 epochs, an Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0 …
Weakly-supervised action localization, and action recognition using global–local attention of 3D CNN
Authors: Novanto Yudistira, Muthu Subash Kavitha, Takio Kurita
Journal: International Journal of Computer Vision
Year: 2022
Topics: Computer Vision; Deep Learning
Abstract: 3D convolutional neural network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the information loss that occurs seems unavoidable. To improve the visual explanations and classification in 3D CNN, we propose two approaches; (i) aggregate layer-wise global to local (global–local) discrete gradient using trained 3DResNext network, and (ii) implement attention gating network to improve the accuracy of the action recognition. The proposed approach intends to show the usefulness of every layer termed as global–local attention in 3D CNN via visual attribution, weakly-supervised action localization, and action recognition. Firstly, the 3DResNext is trained and applied for action classification using backpropagation concerning the maximum predicted class. The gradient and activation of every layer are then up …
State-of-health prediction of lithium-ion batteries using exponential smoothing transformer with seasonal and growth embedding
Authors: Muhammad Rifqi Fauzi, Novanto Yudistira, Wayan Firdaus Mahmudy
Journal: IEEE Access
Year: 2024
Topics: Machine Learning; Time Series Analysis
Abstract: In the world of modern energy, Lithium-Ion batteries reign supreme, offering rechargeability, sustainability, and long-term energy storage. However, their lifespan is not infinite, calling for accurate prediction of remaining life under various conditions. Deep learning shines in this domain, with the Transformer architecture blossoming as a powerful tool for time series forecasting. This research dives into data collection, processing, model design, training, and evaluation, making key methodological contributions to battery life prediction. Notably, the SGEformer model, a Transformer enhanced with growth and seasonal embedding, emerges as a groundbreaking innovation. Comparing SGEformer to ETSformer, Informer, Reformer, Transformer, and LSTM reveals its unique strengths. With an impressive MSE score of 0.000117, SGEformer establishes itself as a highly effective tool for battery life prediction, highlighting the …
Prediksi Harga Cabai menggunakan Metode Long-Short Term Memory (Case Study: Kota Malang)
Authors: Michael David, Imam Cholissodin, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2023
Topics: Deep Learning; Time Series Analysis
Abstract: Cabai adalah buah atau tanaman dari genus Capsicum. Buah-buahan dapat diklasifikasikan sebagai rempah-rempah atau sayuran. Sebagai bumbu, cabai rawit sangat populer di Asia Tenggara sebagai penambah cita rasa makanan. Cabai memiliki beberapa kandungan gizi didalam 100 gram cabai, diantaranya: Air, energi, protein, lemak, karbohidrat, serat, kalsium, dll. Cabai juga memiliki beberapa manfaat juga, diantara lain: Meringankan rasa sakit, memelihara kesehatan pencernaan, menjaga kadar gula darah, dll. Di Kota Malang sendiri harga cabai mengalami fluktuatif, sehingga harga cabai itu sendiri sulit diprediksi. Hal inilah yang membuat pemerintah khawatir dalam menjaga stabilisasi harga cabai agar tetap terjangkau sehingga inflasi harga cabai di kota Malang akan baik. Pada penelitian ini, dilakukan beberapa proses prediksi, diantaranya pre-processing, normalisasi data, training dan prediksi menggunakan metode Long Short-Term Memory, dan hasil error menggunakan Mean Square Error (MSE). Berdasarkan hasil pengujian yang sudah dilakukan menggunakan data harian harga cabai rawit dari tanggal 1 Januari 2021 sampai 31 Juli 2022 di kota Malang menggunakan metode Long Short-Term Memory diperoleh hasil MSE terkecil sebesar 0, 0155 dengan proporsi data training dan data testing sebesar 70%; 30%, dengan sequence data sebanyak 21, jumlah hidden unit 128 dan jumlah epoch sebanyak 150.
Facial expression recognition using residual convnet with image augmentations
Authors: Fadhil Yusuf Rahadika, Novanto Yudistira, Yuita Arum Sari
Journal: Jurnal Ilmu Komputer dan Informasi
Year: 2021
Topics: Computer Vision; Deep Learning
Abstract: During the COVID-19 pandemic, many offline activities are turned into online activities via video meetings to prevent the spread of the COVID 19 virus. In the online video meeting, some micro-interactions are missing when compared to direct social interactions. The use of machines to assist facial expression recognition in online video meetings is expected to increase understanding of the interactions among users. Many studies have shown that CNN-based neural networks are quite effective and accurate in image classification. In this study, some open facial expression datasets were used to train CNN-based neural networks with a total number of training data of 342,497 images. This study gets the best results using ResNet-50 architecture with Mish activation function and Accuracy Booster Plus block. This architecture is trained using the Ranger and Gradient Centralization optimization method for 60000 steps with a batch size of 256. The best results from the training result in accuracy of AffectNet validation data of 0.5972, FERPlus validation data of 0.8636, FERPlus test data of 0.8488, and RAF-DB test data of 0.8879. From this study, the proposed method outperformed plain ResNet in all test scenarios without transfer learning, and there is a potential for better performance with the pre-training model. The code is available at https://github. com/yusufrahadika-facial-expressions-essay.
Synthesis of batik motifs using a diffusion-generative adversarial network
Authors: One Octadion, Novanto Yudistira, Diva Kurnianingtyas
Journal: Multimedia Tools and Applications
Year: 2025
Topics: Generative Models; Deep Learning
Abstract: This paper presents a novel approach for generating intricate Batik motifs using a modified Diffusion-Generative Adversarial Network (Diffusion-GAN) augmented with StyleGAN2-Ada. Motivated by the rich cultural heritage of Indonesian Batik, our research addresses the challenge of synthesizing high-quality, diverse patterns that capture the artistry and complexity of traditional designs. Traditional generative models often struggle with stability and fidelity in artistic synthesis. We integrate StyleGAN2-Ada and Diffusion techniques to overcome these limitations, optimizing model architecture and employing a curated Batik dataset. Evaluation metrics including Frechet Inception Distance (FID), Kernel Inception Distance (KID), precision, recall, and non-redundancy assess the quality and diversity of generated motifs. Our results demonstrate significant advancements in the realism and authenticity of synthesized Batik …
Perbandingan Metode Naïve Bayes, Support Vector Machine Dan Decision Tree Dalam Klasifikasi Konsumsi Obat
Authors: Chindy Aulia Sari, Annisa Sukmawati, Rishani Putri Aprilli, Prais Sarah Kayaningtias, Novanto Yudistira
Journal: Jurnal Litbang Edusaintech
Year: 2022
Topics: Machine Learning; Classification Algorithms
Abstract: Obat adalah suatu bahan yang berisi zat-zat yang berguna untuk mencegah atau menyembuhkan suatu penyakit pada makhluk hidup sehingga terjadi perubahan secara fisiologis atau psikologis selain itu obat psikoaktif merupakan obat yang mempengaruhi mental tetapi kebanyakan obat psikoaktif disalahgunakan untuk hal-hal yang tidak penting bahkan membahayakan. Terdapat banyak jenis obat psikoaktif, diantaranya ada 18 jenis obat-obatan legal maupun ilegal yaitu alcohol, amphetamines, amyl nitrite, benzodiazepine, cannabis, chocolate, cocaine, caffeine, crack, ecstasy, heroin, ketamine, legal highs, LSD, methadone, mushrooms, nicotine and volatile substance abuse consumption. Metode penelitian yang digunakan adalah metode Algoritma Naive Bayes, SVM, dan Decision Tree yaitu untuk memprediksi kecocokan data tersebut dilihat dari hasil akurasi yang didapatkan. Semakin besar akurasi yang dihasilkan maka semakin cocok data tersebut, dan semakin kecil akurasi yang dihasilkan maka semakin tidak cocok data tersebut. Peneliti menggunakan dataset Drug Consumption dalam bentuk format file. csv. Dataset ini diambil dari UCI Data Repository. Dataset Drug Consumption mengelompokkan tipe konsumen narkoba menurut pengukuran kepribadian. Berdasarkan penelitian yang telah dilakukan, maka hasil yang didapatkan dari membandingkan algoritma naive bayes, SVM (Support Vector Machine) dan Decision Tree adalah SVM (Support Vector Machine) merupakan algoritma yang paling baik. Sehingga kesimpulan yang didapat dari penelitian ini adalah sebelum memilih algoritma sebaiknya peneliti menganalisis …
Deteksi Covid-19 dari Citra X-ray menggunakan Vision Transformer
Authors: Javier Ardra Figo, Novanto Yudistira, Agus Wahyu Widodo
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2023
Topics: Medical Imaging; Deep Learning
Abstract: Virus Corona adalah virus beruntai tunggal RNA yang dapat menginfeksi manusia dan beberapa hewan. Pemanfaatan citra x-ray dada dapat menjadi salah satu sarana untuk pemeriksaan dan memonitor keadaan paru-paru seperti pada terduga penyakit tuberculosis, pneumonia, dan hernia. Pemanfaatan citra x-ray dengen deep learning menjadi salah satu solusi untuk pendeteksian virus covid-19. Arsitektur Vision transformer (ViT) merupakan arsitektur yang terinspirasi dari arsitektur Transformer yang merupakan state-of-the-art (SOTA) dalam pemrosesan bahasa alami. Salah satu dataset citra x-ray yang dapat digunakan oleh public adalah CovidX. Dataset CovidX terbagi menjadi 3 kelas yaitu pneumonia, covid-19, dan normal dengan jumlah total sebanyak 30.530 citra x-ray. Dataset akan menggunakan teknik augmentasi data berupa gaussian blur dan colorjitter. Percobaan arsitektur vision transformer akan dibagi menjadi 3 ukuran yaitu base, large, dan huge. Arsitektur ini akan dikombinasikan dengan transfer learning dan augmentasi data. Proses pelatihan akan berjalan sebanyak 40 Epoch, menggunakan Optimizer Stochastic Gradient Descent, Scheduler WarmupCosine, dan fungsi Cross Entropy Loss. Pengujian akan dilakukan dengan pengujian parameter ukuran arsitektur, pengujian pengaruh transfer learning, pengujian pengaruh augmentasi data, dan pengujian dengan arsitektur lain. Hasil pengujian terbaik dengan parameter akurasi adalah ResNet50 yang dipretrained yang mencapai akurasi sebesar 0, 9617006 pada data validasi dan 0, 9548872 pada data tes. Berdasarkan nilai akurasi tersebut dapat disimpulkan bahwa …
Convolutional neural network with transfer learning for classification of food types in tray box images
Authors: Gustavo Thiodorus, Yuita Arum Sari, Novanto Yudistira
Year: 2021
Topics: Deep Learning; Transfer Learning
Abstract: ∗ Food is one of the primary needs that cannot be separated from people’s lives. The various food types tend to increase every day, directly proportional to the human need for food increases as well. However, the lack of knowledge of the diversity of types of food is still common. It will cause nutrition and food problems for the community. Therefore, artificial intelligence with image processing techniques and computer vision that can recognize types of food can help people recognize various types of food. So, in this study, an image processing technique based on an artificial neural network, namely a Convolutional Neural Network (CNN) with transfer learning methods is used to classify the types of food in the tray box images. The use of the transfer learning method aims to overcome the problem of lack of training data for food images by utilizing the weights from the training model using the ImageNet dataset so that …
Klasifikasi jenis tanaman tembakau di Indonesia menggunakan Naïve Bayes dengan seleksi fitur information Gain
Authors: Fahmi Achmad Fauzi, Muhammad Tanzil Furqon, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Machine Learning; Naïve Bayes
Abstract: Tanaman tembakau merupakan hasil perkebunan tetapi bukan tanaman pangan, daunnya biasa digunakan untuk bahan utama pembuatan rokok keretek maupun cerutu. Penanaman tembakau sudah dikenal sejak lama di Indonesia, penanaman tembakau dari generasi ke generasi membuat munculnya banyak varietas-varietas baru di berbagai daerah di Indonesia. Banyaknya varietas tembakau dapat dikelompokkan berdasarkan budidaya dan tipe. Banyaknya varietas tembakau membuat petani mengalami kesulitan untuk membedakan tipe tanaman tembakau karena morfologi dan biologi antara tanaman tembakau ciri-cirinya hampir serupa, maka untuk memudahkan menentukan tipe tanaman tembakau dibutuhkan sebuah sistem dengan metode klasifikasi. Salah satu metode klasifikasi yaitu dengan algoritme Naive Bayes. Pada penelitian ini kelas yang digunakan sebanyak 11 kelas dan fitur yang digunakan sebanyak 19 fitur. Selain klasifikasi digunakan juga metode seleksi fitur untuk mendapatkan kombinasi fitur dan nilai akurasi yang baik, metode seleksi fitur yang digunakan adalah Information Gain. Pada evaluasi digunakan metode K-fold cross validation untuk menghilangkan bias pada data dengan k= 10. Dari seluruh pengujian yang dilakukan akurasi rata-rata tertinggi untuk seluruh pengujian kelas seimbang yaitu 52, 72% dengan menggunakan 17 fitur. Sedangkan untuk akurasi tertinggi dari seluruh pengujian kelas tidak seimbang adalah 64, 06% ketika menggunakan 15 fitur.
U-Net with graph based smoothing regularizer for small vessel segmentation on fundus image
Authors: Lukman Hakim, Novanto Yudistira, Muthusubash Kavitha, Takio Kurita
Year: 2019
Topics: Deep Learning; Image Processing
Abstract: The detection of retinal blood vessels, especially the changes of small vessel condition is the most important indicator to identify the vascular network of the human body. Existing techniques focused mainly on shape of the large vessels, which is not appropriate for the disconnected small and isolated vessels. Paying attention to the low contrast small blood vessel in fundus region, first time we proposed to combine graph based smoothing regularizer with the loss function in the U-net framework. The proposed regularizer treated the image as two graphs by calculating the graph laplacians on vessel regions and the background regions on the image. The potential of the proposed graph based smoothing regularizer in reconstructing small vessel is compared over the classical U-net with or without regularizer. Numerical and visual results shows that our developed regularizer proved its effectiveness in segmenting the …
IndoHerb: Indonesia medicinal plants recognition using transfer learning and deep learning
Authors: Muhammad Salman Ikrar Musyaffa, Novanto Yudistira, Muhammad Arif Rahman, Ahmad Hoirul Basori, Andi Besse Firdausiah Mansur, Jati Batoro
Journal: Heliyon
Year: 2024
Topics: Deep Learning; Transfer Learning
Abstract: The rich diversity of herbal plants in Indonesia holds immense potential as alternative resources for traditional healing and ethnobotanical practices. However, the dwindling recog-nition of herbal plants due to modernization poses a significant challenge in preserving this valuable heritage. The accurate identification of these plants is crucial for the continuity of traditional practices and the utilization of their nutritional benefits. Nevertheless, the manual identification of herbal plants remains a time-consuming task, demanding expert knowledge and meticulous examination of plant characteristics. In response, the application of computer vision emerges as a promising solution to facilitate the efficient identification of herbal plants. This research addresses the task of classifying Indonesian herbal plants through the implementation of transfer learning of Convolutional Neural Networks (CNN). To support our study, we …
Analisis Sentimen terhadap Perundungan Siber pada Twitter menggunakan Algoritma Bidirectional Encoder Representations from Transformer (BERT)
Authors: Niluh Putu Vania Dyah Saraswati, Novanto Yudistira, Putra Pandu Adikara
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2023
Topics: Natural Language Processing; BERT
Abstract: Perundungan adalah tindakan yang sengaja dilakukan untuk menyakiti secara fisik, verbal, dan psikologis seseorang yang merasa tidak berdaya. Perundungan adalah masalah penting yang telah/diidentifikasi sebagai masalah sosial yang sangat serius di Amerika Serikat dan banyak bagian dunia lainnya. Perundungan siber adalah penggunaan teknologi informasi dan komunikasi untuk pelecehan yang disengaja/terencana dan aktivitas permusuhan, yang dilakukan berulang dan terstruktur dengan cara yang berpotensi membahayakan. Jenis perundungan siber termasuk intimidasi seseorang melalui media sosial, pelecehan, sexting, penipuan, meniru, dan mengirim pesan jahat melalui ruang obrolan dan pesan instan. Selama beberapa tahun terakhir, Bidirectional Encoder Representation from Transformers (BERT) telah menjadi model representasi yang digunakan secara luas dan efisien yang mencapai kinerja mutakhir pada tugas-tugas tingkat kalimat dan token-level, mengungguli banyak arsitektur tugas khusus. Tingkat akurasi pada klasifikasi perundungan siber dengan menggunakan data uji dan data validasi dalam Bahasa Indonesia yang dihasilkan dengan menggunakan algoritma BERT sebesar 0, 81 yang dimana dalam persentase akurasinya adalah 81%. Algoritma BERT dapat membantu mengidentifikasikan masukan yang mengarah pada perundungan siber dengan memberikan keluaran berupa kelas dengan kategori yang telah ditentukan, yakni netral, bahasa kasar maupun mengandung ujaran kebencian. Sistem juga memberikan persentase terhadap kategori yang didapatkan melalui program tersebut.
Multicommodity prices prediction using multivariate data-driven modeling: Indonesia case
Authors: Marsellino Prawiro Halim, Novanto Yudistira, Candra Dewi
Journal: IEEE Transactions on Computational Social Systems
Year: 2022
Topics: Data Science; Multivariate Analysis
Abstract: One of the problems experienced by micro, small, and medium enterprises (MSMEs) during this pandemic is that most MSME actors do not understand plan-making during a crisis. This situation was exacerbated by erratic commodity prices, which resulted in several MSME players choosing to temporarily close because their turnover got a drastic decline. To help MSME actors maintain their business by knowing commodity price predictions, we propose a deep learning model using the long short-term memory (LSTM) method to predict commodity prices in Indonesia. LSTM is a type of recurrent neural network (RNN) with a memory cell to store information and solve the vanishing gradient problem in RNN. Furthermore, multivariate LSTM leverages the model to predict datasets with more than one feature. This study used a dataset collected from the Pusat Informasi Harga Pangan Strategis Nasional (PIHPS Nasional …
Supervised virtual-to-real domain adaptation for object detection task using yolo
Authors: Akbar Satya Nugraha, Novanto Yudistira, Bayu Rahayudi
Year: 2024
Topics: Computer Vision; Domain Adaptation
Abstract: Deep neural network shows excellent use in a lot of real-world tasks. One of the deep learning tasks is object detection. Well-annotated datasets will affect deep neural network accuracy. More data learned by deep neural networks will make the model more accurate. However, a well-annotated dataset is hard to find, especially in a specific domain. To overcome this, computer-generated data or virtual datasets are used. Researchers could generate many images with specific use cases also with its annotation. Research studies showed that virtual datasets could be used for object detection tasks. Nevertheless, with the usage of the virtual dataset, the model must adapt to real datasets, or the model must have domain adaptability features. We explored the domain adaptation inside the object detection model using a virtual dataset to overcome a few well-annotated datasets. We use VW-PPE dataset, using 5000 and …
Prediksi Deret Waktu Menggunakan Deep Learning
Authors: Novanto Yudistira, Lies Mardiana Dwi Alfiansih, Nina Isti Andriyani, Wafa Essayem, Irvan Wahyu Nurdian, Nisrina Arintia Maghfiroh, Nurul Maulida
Year: 2023
Topics: Deep Learning
Abstract: Buku ajar" Prediksi Data Deret Waktu Menggunakan Deep Learning" membahas penggunaan teknik deep learning untuk memprediksi data deret waktu, yang merupakan topik yang penting dalam bidang sains data dan pembelajaran mesin. Prediksi data deret waktu sangat berguna untuk diaplikasikan pada berbagai bidang seperti prediksi nilai saham, harga komoditas, laju perubahan iklim, curah hujan, laju pandemi seperti COVID-19, dan lain sebagainya. Buku ini membahas teori dasar deep learning untuk memprediksi deret waktu, langkah-langkah yang diperlukan untuk mempersiapkan data deret waktu, pemrograman secara sederhana model deep learning menggunakan library seperti Pytorch dan TensorFlow, dan mengevaluasi performa model. Pembaca juga akan mempelajari bagaimana menggunakan teknik seperti Recurrent Neural Networks (RNNs), Long Short-term Memory (LSTM), dan Transformer untuk memprediksi data deret waktu beserta perhitungan manualnya yang penting untuk skripsi, tesis, dan disertasi. Dengan menggunakan bahasa yang mudah dipahami dan dilengkapi dengan contoh dan studi kasus, buku ini cocok untuk para pembaca yang ingin mempelajari teknik deep learning untuk memprediksi data deret waktu. Selain mahasiswa dan akademisi, buku ini juga cocok untuk para profesional di bidang sains data dan pembelajaran mesin yang ingin memperdalam pengetahuan mereka tentang teknik deep learning.
Prediksi Pergerakan Harga Cryptocurrency Bitcoin terhadap Mata Uang Rupiah menggunakan Algoritme LSTM
Authors: Maulana Ahmad Maliki, Imam Cholissodin, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2022
Topics: Deep Learning; Time Series Analysis
Abstract: Investasi bukanlah hal yang asing lagi bagi sebagian besar orang terutama bitcoin yang banyak digemari belakangan tahun ini. Pola pergerakan harga dalam berinvestasi merupakan wawasan yang penting bagi investor untuk meminimalkan risiko investasi, namun memprediksi perubahan tren merupakan sebuah tantangan yang sangat sulit dikarenakan memiliki selisih nilai yang fluktuatif. Nilai kenaikan serta penurunan harga bitcoin sendiri dipengaruhi oleh faktor ketidakpastian seperti masalah politik ataupun masalah ekonomi pada tingkat global. Maka diperlukan sebuah algoritme untuk melakukan prediksi harga dimasa yang akan datang yang merupakan salah satu strategi untuk memaksimalkan keuntungan dalam berinvestasi. Penelitian ini melakukan beberapa proses untuk melakukan prediksi pergerakan harga bitcoin di antaranya, pre-processing, normalisasi, pelatihan algoritme Long-Short Term Memory (LSTM), evaluasi matriks regresi menggunakan Mean Square Error (MSE). Berdasarkan hasil pengujian yang telah dilakukan pada penelitian ini algoritme LSTM dapat melakukan prediksi pergerakan harga bitcoin dibuktikan dengan nilai matriks evaluasi MSE sebesar 0, 00374 dengan parameter pengujian antara lain hidden_size sebanyak 64, sequence data sebanyak 18, optimizer menggunakan ADAM, learning_rate sebesar 0,005, dan epoch sebanyak 200. Penelitian ini juga melibatkan beberapa algoritme pembaruan bobot yaitu Stochastic Gradient Descent (SGD), Stochastic Gradient Descent with Momentum (SGDM), dan Adaptive Moment Estimation (ADAM) untuk mencari hasil prediksi yang optimal.
Algoritma Decision Tree Dan Smote Untuk Klasifikasi Serangan Jantung Miokarditis Yang Imbalance
Authors: Novanto Yudistira, Aldi Fianda Putra
Journal: Jurnal Litbang Edusaintech
Year: 2021
Topics: Machine Learning; Data Imbalance
Abstract: Serangan jantung atau dalam medis bernama Myocardial Infarction atau infark miokard adalah gangguan jantung yang sangat serius. Dalam pendeteksian ini menggunakan komplikasi-komplikasi yang diderita oleh pasien. Algoritma yang akan dievaluasi yaitu Naive Bayes, Decision Tree, dan Support Vector Machine. Namun tidak serta merta dapat dilakukan evaluasi. Sebelum mengevaluasi ketiga algoritma ini dilakukan perbaikan dataset, karena pada dataset ini sendiri terdapat data yang kosong. Perbaikan dilakukan dengan cara mengimputasikan data dimana nilai diperkirakan berdasarkan rata-rata dari anggota klaster pada kelas yang sama. Setelah melakukan imputasi data, maka dapat dilakukan normalisasi dengan metode MinMax dengan tujuan agar rentang fitur terutama data numerik kontinu tidak terlalu besar. Setelah pemrosesan data awal dilakukan maka barulah kita dapat melakukan evaluasi dengan menggunakan metode K-fold Cross Validation. Namun lagi-lagi ditemukan kesalahan yakni data latih yang digunakan ternyata tidak seimbang. Oleh sebab itu dilakukan oversampling pada data agar data menjadi seimbang. Setelah seimbang maka kita dapat melakukan evaluasi kembali dan diperolehlah algoritma yang cocok untuk mengklasifikasikan data seperti dataset Myocardial Infarction Complications adalah algoritma Decision Tree dengan akurasi 98%, diikuti algoritma Support Vector Machine dengan akurasi 91% dan Naïve Bayes dengan akurasi paling rendah yakni 49%.
Autocyclic: Deep learning optimizer for time series data prediction
Authors: Christian Arthur, Novanto Yudistira, Candra Dewi
Journal: IEEE Access
Year: 2024
Topics: Deep Learning; Optimization
Abstract: Time series prediction poses a formidable challenge, marked by the inherent difficulty in capturing long-term dependencies and adapting to intricate data patterns. Existing methods, spanning statistical models and neural networks, often grapple with issues such as underfitting and overfitting. This study addresses these challenges by introducing Autocyclic Learning Rate (AutoCyclic), an innovative approach that seamlessly integrates cosine cyclic learning rates with considerations for autocorrelation and variance. AutoCyclic dynamically adjusts learning rates based on the characteristics of time series data, effectively mitigating challenges related to local minima and demonstrating robust adaptability to outliers. In evaluation across diverse datasets, including ETTm2, M4, and WindTurbine, AutoCyclic consistently outperforms traditional optimizers such as Adams Optimizer and Cosine Cyclic Learning Rate. The …
Analisis Kinerja Algoritma Klasifikasi Teks Bert dalam Mendeteksi Berita Hoaks
Authors: Assyfa Rasida Hanum, Ivykaeyla Adriana Zetha, Salwa Cahyani Putri, Rafifah Ayud Wulandari, Sherla Puspa Andina, Julia Nur Fajrina, Novanto Yudistira
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2024
Topics: Natural Language Processing; Machine Learning
Abstract: Metode BERT dapat digunakan untuk menghasilkan hasil yang akurat dalam klasifikasi berita palsu dan berita benar. Hasil evaluasi menunjukkan bahwa model klasifikasi BERT memiliki akurasi sebesar 76% pada data validasi dalam mengklasifikasikan berita hoaks, yang menunjukkan performa atau kinerja model Machine Learning dalam melakukan klasifikasi berita hoaks. Sedangkan pada model klasifikasi BERT Multilingual memiliki akurasi lebih rendah, yakni 63%. Potensi metode ini dapat membantu dalam memerangi penyebaran berita palsu. Penelitian ini berpotensi memberikan kontribusi penting dalam memerangi penyebaran berita palsu di dunia digital yang semakin kompleks. Dengan menggunakan BERT sebagai pendekatan, model ini memungkinkan pengidentifikasian berita palsu yang lebih akurat, serta membantu masyarakat dalam menghindari konsumsi informasi yang salah. Dengan hasil yang positif ini, penelitian ini menunjukkan bagaimana teknologi machine learning dapat digunakan untuk melawan disinformasi dan menjadikan dunia maya menjadi tempat yang lebih terpercaya.