Lab-IS/filkom-research-papers huggingface dataset:
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning
Authors: Alfirsa Damasyifa Fauzulhaq, Wahyu Parwitayasa, Joseph Ananda Sugihdharma, M Fadli Ridhani, Novanto Yudistira
Journal: arXiv e-prints
Year: 2024
Topics: Deep Learning; Natural Language Processing
Abstract: Neuron labeling is an approach to visualize the behaviour and respond of a certain neuron to a certain pattern that activates the neuron. Neuron labeling extract information about the features captured by certain neurons in a deep neural network, one of which uses the encoder-decoder image captioning approach. The encoder used can be a pretrained CNN-based model and the decoder is an RNN-based model for text generation. Previous work, namely MILAN (Mutual Information-guided Linguistic Annotation of Neuron), has tried to visualize the neuron behaviour using modified Show, Attend, and Tell (SAT) model in the encoder, and LSTM added with Bahdanau attention in the decoder. MILAN can show great result on short sequence neuron captioning, but it does not show great result on long sequence neuron captioning, so in this work, we would like to improve the performance of MILAN even more by …
The Development of Insect Pest Classification Mobile Application from Images via MobileNet
Authors: Ahya Zhilalikbar Amin, Fais Al Huda, Novanto Yudistira
Year: 2023
Topics: Computer Vision; Mobile Development
Abstract: Insect pests are insect organisms with a vast diversity which has a considerable amount of impact on agriculture. They are known as Plant Destroying Organism because of their nature to hinder plant's growth on a cultivated land. Knowledge regarding the impact and the nature of insect pests can be an important point to be able to prevent them from having a rapid growth. Utilizing scientific knowledge branch regarding the classification of insect pests through imagery could be an idea for an effort towards insect pests’ control. An algorithm system for classifying insect pests through images has been developed in the previous study using MobileNetV2 as the architecture and it has been tested in a controlled environment with the data of 75.000 insect pests’ images and resulted in a data test accuracy of 71,36%. However, the classification algorithm from the previous study has yet to have a media that could provide …
Hybrid of DiffStride and Spectral Pooling in Convolutional Neural Networks
Authors: Sulthan Rafif, Mohammad Faris Azhar, Mochamad Arfan Ravy Wahyu Pratama, Ahmad Mustafidul Ibad, Novanto Yudistira, Lailil Muflikhah
Year: 2023
Topics: Deep Learning; Computer Vision
Abstract: Stride determines the distance between adjacent filter positions as the filter moves across the input. A fixed stride causes important information contained in the image can not be captured, so that important information is not classified. Therefore, in previous research, the DiffStride Method was applied, namely the Strided Convolution Method with which it can learn its own stride value. Severe Quantization and a constraining lower bound on preserved information are arises with Max Pooling Downsampling Method. Spectral Pooling reduce the constraint lower bound on preserved information by cutting off the representation in the frequency domain. In this research a CNN Model is proposed with the Downsampling Learnable Stride Technique performed by Backpropagation combined with the Spectral Pooling Technique. Diffstride and Spectral Pooling techniques are expected to maintain most of the information …
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning
Authors: Alfirsa Damasyifa Fauzulhaq, Wahyu Parwitayasa, Joseph A Sugihdharma, M Fadli Ridhani, Novanto Yudistira
Year: 2023
Topics: Deep Learning; Natural Language Processing
Abstract: Neuron labelling is an approach to visualize the behaviour and response of a certain neuron to a certain pattern that activates the neuron. Previous work, namely MILAN (Mutual Information-guided Linguistic Annotation of Neuron), has tried to visualize the neuron behaviour using modified Show, Attend, and Tell (SAT) model in the encoder, and LSTM added with Bahdanau attention in the decoder. MILAN can show great results on short-sequence neuron labelling, however, it does not show great results on long-sequence neuron labelling. In this research, we would like to improve the performance of MILAN especially for long sequence captioning by utilizing different kinds of attention mechanisms and additionally adding several attention results into one, to combine all the advantages of several attention mechanisms. The main contribution to this research is we propose a new method for attention mechanism by …
Prediction of COVID-19 by Its Variants using Multivariate Data-driven Deep Learning Models
Authors: Akhmad Dimitri Baihaqi, Novanto Yudistira, Edy Santoso
Journal: arXiv e-prints
Year: 2023
Topics: Deep Learning; Data-driven Models
Abstract: The Coronavirus Disease 2019 or the COVID-19 pandemic has swept almost all parts of the world since the first case was found in Wuhan, China, in December 2019. With the increasing number of COVID-19 cases in the world, SARS-CoV-2 has mutated into various variants. Given the increasingly dangerous conditions of the pandemic, it is crucial to know when the pandemic will stop by predicting confirmed cases of COVID-19. Therefore, many studies have raised COVID-19 as a case study to overcome the ongoing pandemic using the Deep Learning method, namely LSTM, with reasonably accurate results and small error values. LSTM training is used to predict confirmed cases of COVID-19 based on variants that have been identified using ECDC's COVID-19 dataset containing confirmed cases of COVID-19 that have been identified from 30 countries in Europe. Tests were conducted using the LSTM and …
Gated Self-supervised Learning For Improving Supervised Learning
Authors: Erland Hilman Fuadi, Aristo Renaldo Ruslim, Putu Wahyu Kusuma Wardhana, Novanto Yudistira
Journal: arXiv e-prints
Year: 2023
Topics: Machine Learning; Deep Learning
Abstract: In past research on self-supervised learning for image classification, the use of rotation as an augmentation has been common. However, relying solely on rotation as a self-supervised transformation can limit the ability of the model to learn rich features from the data. In this paper, we propose a novel approach to self-supervised learning for image classification using several localizable augmentations with the combination of the gating method. Our approach uses flip and shuffle channel augmentations in addition to the rotation, allowing the model to learn rich features from the data. Furthermore, the gated mixture network is used to weigh the effects of each self-supervised learning on the loss function, allowing the model to focus on the most relevant transformations for classification.
Sentiment and Mobility Analysis on COVID-19 Restrictions with Autoencoder
Authors: Weldy Sujarmanto, Merpati Vortuna Amerta Nalle, Nurul Maulida, Bayu Chandra Putra, Stevania Hildegardis Bere, Novanto Yudistira
Year: 2022
Topics: Sentiment Analysis; Mobility Analysis; Autoencoder
Abstract: In 2020, the world was attacked by a virus known as the COVID-19 virus. Restrictions on people’s activities were conducted in various countries to prevent the spread of the virus. However, since people were vaccinated, restriction levels have been reduced or eliminated, although the new cases of COVID-19 worldwide have not ended. People’s responses to restriction policies vary, including sentiment and human mobility. The possibility of sentiment is either support or resistance, while mobility is staying at home or not. This study analyzes the proportion between the two responses through two types of data: text for sentiment and time series for mobility. Sentiment text data is taken from Twitter and mobility time series data is taken from Google Mobility for February 2020 to April 2022. Twitter and Google Mobility data are collected from several countries using English and implementing restrictions: Australia, Canada …
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction
Authors: Rizki Ramadhan Fitra, Novanto Yudistira, Wayan Firdaus Mahmudy
Journal: arXiv e-prints
Year: 2022
Topics: Deep Learning; Transformer Models
Abstract: Coronavirus disease or COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. The first confirmed case caused by this virus was found at the end of December 2019 in Wuhan City, China. This case then spread throughout the world, including Indonesia. Therefore, the COVID-19 case was designated as a global pandemic by WHO. The growth of COVID-19 cases, especially in Indonesia, can be predicted using several approaches, such as the Deep Neural Network (DNN). One of the DNN models that can be used is Deep Transformer which can predict time series. The model is trained with several test scenarios to get the best model. The evaluation is finding the best hyperparameters. Then, further evaluation was carried out using the best hyperparameters setting of the number of prediction days, the optimizer, the number of features, and comparison with the former models of the Long Short-Term …
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 e-prints
Year: 2022
Topics: Deep Learning; Convolutional Neural Network
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 …
Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images
Authors: Muhammad Hisyam Zayd, Novanto Yudistira, Randy Cahya Wihandika
Journal: arXiv e-prints
Year: 2022
Topics: Deep Learning; Computer Vision
Abstract: We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image patches with global priors of an entire image on each class, forming visually more compelling colorization results. Finally, we validate our approach with a user study evaluation and compare it against state-of-the-art, resulting in improvements.
Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation
Authors: Hensel Donato Jahja, Novanto Yudistira
Journal: arXiv e-prints
Year: 2022
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 …
Klasifikasi Ekspresi Wajah dengan Convolutional Neural Network menggunakan Sobel Filter dan Inception Module
Authors: Rian Nugroho, Fitra Abdurrachman Bachtiar, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2022
Topics: Deep Learning; Convolutional Neural Network
Abstract: Klasifikasi Ekspresi Wajah dengan Convolutional Neural Network menggunakan Sobel Filter dan Inception Module | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Klasifikasi Ekspresi Wajah dengan Convolutional Neural Network menggunakan Sobel Filter dan Inception Module 1.Beranda / 2.Arsip / 3.Vol 6 No 13 (2022): Publikasi Khusus Tahun 2022 / 4.Artikel Klasifikasi Ekspresi Wajah dengan Convolutional Neural Network menggunakan Sobel Filter dan Inception Module Penulis Rian Nugroho Fakultas Ilmu Komputer, Universitas Brawijaya Fitra Abdurrachman Bachtiar Fakultas Ilmu Komputer, Universitas Brawijaya Novanto Yudistira Fakultas Ilmu Komputer, Universitas Brawijaya Kata Kunci: FER, FER2013, Inception Layer, CNN, Sobel Filter Abstrak …
Large-Scale News Classification using BERT Language Model: Spark NLP Approach
Authors: Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Novanto Yudistira
Journal: arXiv e-prints
Year: 2021
Topics: Natural Language Processing; BERT
Abstract: The rise of big data analytics on top of NLP increases the computational burden for text processing at scale. The problems faced in NLP are very high dimensional text, so it takes a high computation resource. The MapReduce allows parallelization of large computations and can improve the efficiency of text processing. This research aims to study the effect of big data processing on NLP tasks based on a deep learning approach. We classify a big text of news topics with fine-tuning BERT used pre-trained models. Five pre-trained models with a different number of parameters were used in this study. To measure the efficiency of this method, we compared the performance of the BERT with the pipelines from Spark NLP. The result shows that BERT without Spark NLP gives higher accuracy compared to BERT with Spark NLP. The accuracy average and training time of all models using BERT is 0.9187 and 35 minutes …
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews
Authors: Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Haftittah Wuswilahaken DW, Fitra Abdurrachman Bachtiar, Novanto Yudistira
Journal: arXiv e-prints
Year: 2021
Topics: Natural Language Processing; BERT
Abstract: User reviews have an essential role in the success of the developed mobile apps. User reviews in the textual form are unstructured data, creating a very high complexity when processed for sentiment analysis. Previous approaches that have been used often ignore the context of reviews. In addition, the relatively small data makes the model overfitting. A new approach, BERT, has been introduced as a transfer learning model with a pre-trained model that has previously been trained to have a better context representation. This study examines the effectiveness of fine-tuning BERT for sentiment analysis using two different pre-trained models. Besides the multilingual pre-trained model, we use the pre-trained model that only has been trained in Indonesian. The dataset used is Indonesian user reviews of the ten best apps in 2020 in Google Play sites. We also perform hyper-parameter tuning to find the optimum trained …
Pengembangan Aplikasi Layanan Informasi Peninggalan Sejarah Peradaban dan Cagar Budaya berbasis Platform Android (Studi Kasus: Dinas Pariwisata, Kepemudaan, Olahraga dan …
Authors: Arifandis Winata, Agi Putra Kharisma, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2021
Topics: Mobile Application Development; Android Development
Abstract: Sejarah merupakan rekontruksi masalalu menurut Prof. DR. Kuntowijoyo, hampir setiap daerah memiliki sejarah, begitu pula dengan Nganjuk sebuah kabupaten yang memiliki sejarah pada masa lampaunya, terbukti dengan adanya peninggalan sejarah seperti candi, alat rumah tangga, alat pertanian, kendaraan, lukisan, dan patung yang diarsipka dalam buku oleh Dinas Pariwisata, Kepemudaan, Olah Raga dan Kebudayaan Nganjuk. Namun, dalam buku tersebut belum tertuang sepenuhnya sejarah Nganjuk karena setiap penemuan peninggalan sejarah baru butuh proses dan waktu yang cukup lama untuk memperbarui informasi yang ada. Sehingga, untuk mempermudah penambahan informasi dan penjelasanya maka digunakan sebuah layanan informasi dengan pengembangan aplikasi perangkat bergerak berbasis android untuk mempermudah akses baik bagi masyarakat umum maupun bagi Dinas Pariwisata, Kepemudaan, Olah Raga dan Kebudayaan Nganjuk. Dengan adanya aplikasi layanan informasi diharapkan dapat mempermudah pencarian dan pembaharuan informasi peninggalan sejarah dengan cepat dan mudah. Aplikasi dikembangakan dengan arsitektur Model-View-Presenter (MVP) dan menggunakan database MySQL sebagai penyimpanannya dengan hasil pengujian semua fungsionalitas menunjukkan bahwa sistem valid dan hasil dari uji usability menunjukkan rata-rata di atas 80% yang berarti aplikasi layak untuk digunakan, serta dapat berjalan pada sistem operasi Android dengan tingkatan SDK 16 sampai 29.
Korelasi Mobilitas Massa, Indeks Ultraviolet, dan Ozon serta Pengaruh Polusi Udara pada Kasus Covid-19 di Indonesia
Authors: Karina Amadea, Novanto Yudistira, Jeowandha Ria Wiyani, Adinda Chilliya Basuki
Journal: RINarxiv
Year: 2020
Topics: Data Analysis; Environmental Science
Rekomendasi Produk UMKM Kabupaten Malang menggunakan Metode Analytical Hierarchy Process (AHP) dan Simple Additive Weighting (SAW)(Studi Kasus: Rumah Kreatif BUMN Telkom …
Authors: Hafshah Durrotun Nasihah, Edy Santoso, Novanto Yudistira
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2020
Topics: Decision Making; Analytical Hierarchy Process
Abstract: Kabupaten Malang merupakan salah satu kabupaten di Jawa Timur yang memiliki populasi terbesar di Pulau Jawa, hal ini menyebabkan pelaku usaha UMKM yang tersebar di Kabupaten Malang sangatlah banyak dan beragam. Penelitian ini bertujuan untuk mendapatkan rekomendasi produk UMKM di Kabupaten Malang yang terdaftar pada RKB Kabupaten Malang. Metode AHP dan SAW merupakan metode yang digunakan untuk melakukan Sistem Pendukung Keputusan yang akan digunakan untuk mendapatkan rekomendasi produk UMKM yang sesuai dengan bobot kriteria yang diinginkan oleh pengguna. Metode AHP digunakan untuk menghitung bobot kriteria produk, sedangkan metode SAW digunakan untuk mendapatkan nilai bobot alternatif produk. Hasil dari perhitungan bobot setiap alternatif produk akan diurutkan dari nilai terbesar hingga nilai terkecil, nilai bobot alternatif produk tertinggi merupakan produk yang direkomendasikan oleh sistem berdasarkan bobot kriteria yang diinginkan pengguna. Berdasarkan hasil dari pengujian dengan menggunakan sebanyak 7 kasus pengujian akurasi yang membandingkan nilai hasil sistem dengan pengguna, dapat dianalisa bahwa metode AHP dan SAW ini cukup efektif digunakan dalam mendapatkan rekomendasi produk dengan rata-rata nilai hasil pengujian sebesar 71, 43%.
Prediksi Persentase Penyelesaian Permohonan Hak Milik Menggunakan Metode Support Vector Regression (SVR)
Authors: Wa Ode May Zhara Averina, Edy SantosoDr Eng Novanto Yudistira, M Kom
Year: 2020
Topics: Machine Learning; Regression Analysis
Abstract: Badan Pertanahan Nasional (BPN) merupakan Lembaga pemerintah di Indonesia yang mempunyai tugas melaksanakan tugas pemerintahan dibidang pertanahan sesuai dengan ketentuan peraturan perundang-undangan. Salah satu produk yang dihasilkan oleh BPN adalah Sertifikat Hak Milik. Sertifikat Hak Milik (SHM) adalah salah satu jenis sertifikat dimana pemiliknya memiliki hak penuh atas kepemilikan tanah pada wilayah dengan luas tertentu yang telah disebutkan dalam sertifikat tersebut. Setiap hari Kantor Badan Pertanahan Nasional Kabupaten Malang melayani pengurusan sertipikasi hak milik oleh masyarakat agar kepemilikan tanah masyarakat tersebut bermanfaat dalam menunjang kesejahteraan masyarakat. Pelayanan sertipikasi tanah masyarakat ternyata membutuhkan waktu yang lama sehingga mengakibatkan jumlah SHM yang dapat diselesaikan tiap bulannya lebih rendah dari jumlah permohonan yang masuk. Hal ini mengakibatkan terjadinya penumpukan pengurusan oleh petugas dari waktu ke waktu. Untuk meminimalisir hal tersebut, maka perlu dilakukan penelitian menggunakan metode prediksi persentase penyelesaian permohonan hak milik yang diimplementasikan ke dalam sistem. Salah satunya dengan menggunakan metode Support Vector Regression. Support Vector Regression merupakan metode yang mampu menyelesaikan masalah regresi serta menghasilkan kinerja yang baik dalam pengambilan solusi. Berdasarkan hasil pengujian yang telah dilakukan untuk metode SVR ini, hasil prediksi dengan nilai evaluasi minimum menggunakan MAPE yakni 0.0308% dengan nilai parameter lambda = 0 …
Prediksi Persentase Penyelesaian Permohonan Hak Milik Menggunakan Metode Extreme Learning Machine (ELM)
Authors: Meilinda Dwi Puspaningrum, Edy Santoso, M Kom, Eng Novanto Yudistira, S Kom
Year: 2020
Topics: Machine Learning; Neural Networks
Abstract: Sertipikat Hak Milik merupakan salah satu jenis sertipikat dimana pemiliknya memiliki hak penuh atas kepemilikan tanah pada wilayah dengan luas tertentu yang telah disebutkan dalam sertifikat tersebut. Pada tahun 2018, Badan Pertanahan Nasional (BPN) terdapat program PTSL yang merupakan proses pendaftaran tanah. Metode PTSL ini merupakan inovasi pemerintah melalui Kementrian ATR/BPN untuk memenuhi kebutuhan dasar masyarakat seperti sandang, pangan dan papan. Proses pelayanan sertifikat tanah masyarakat, Badan Pertanahan Nasional (BPN) Kabupaten Malang memiliki kendala dimana dalam proses tersebut membutuhkan waktu lebih lama dari yang sudah di tentukan sehingga menyebabkan jumlah Sertipikat Hak Milik yang dapat diselesaikan setiap bulannya menjadi lebih rendah daripada jumlah permohonan yang masuk. Pada permasalahan ini menyebabkan terjadinya penumpukan pekerjaan oleh para staf dari waktu ke waktu. Beberapa faktor yang menyebabkan permasalahan ini salah satunya kurangnya SDM yang belum memadai khususnya staf pengukuran dilapangan dan staf pengolah data. Oleh karena itu, untuk memudahkan dalam proses pelayanan sertipikat tanah di butuhkan proses prediksi persentase penyelesaian permohonan hak milik menggunakan metode Extreme Learning Machine (ELM). Berdasarkan hasil pengujian parameter yang telah dilakukan menggunakan metode Extreme Learning Machine (ELM) dengan data permohonan hak milik dari tahun 2014 – 2019 pada pengujian perbandingan data training dan data testing menghasilkan nilai evaluasi paling terkecil dari semua …
Klasifikasi Citra Makanan Model Residual Net Classification of Food Residual Network Model
Authors: Gustavo Thiodorusa, Anugrah Prasetia, Anugrah Prasetiab, Luthfi Afrizal Ardhani, Novanto Yudistira
Year: 2020
Topics: Computer Vision; Deep Learning
Abstract: The activity of people who often upload photos of food before eating i popular social media, such as Facebook, Instagram, Twitter, and Pi image data circulating on social media can be used for business purp analyzing customer behavior patterns. However, not all uploaded im images, so prior to analysis it is necessary to do a classification process images and non-food images. Therefore, the researcher will classify
U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image
Authors: Muthu Subash Kavitha, Novanto Yudistira, Lukman Hakim, Takio Kurita
Journal: (No Title)
Year: 2019
Topics: Computer Vision; Deep Learning; Medical Imaging
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 small vessels and reconnecting the fragmented retinal blood vessels.
HOG feature extraction and KNN classification for detecting vehicle in the highway
Authors: FAI Achyunda Putra, Fitri Utaminingrum, Wayan Firdaus Mahmudy
Journal: IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Year: 2020
Topics: Computer Vision; Machine Learning
Abstract: Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Various countries have created many future systems where computers take over the art of driving. The problem is prior to being attentive in an autonomous car for obtaining high safety. The autonomous car needs an early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.
Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia
Authors: Adyan Nur Alfiyatin, Wayan Firdaus Mahmudy, Candra Fajri Ananda, Yusuf Priyo Anggodo
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Economics
Abstract: Inflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.AbstractInflation is one indicator to measure the development of a nation. If inflation is not …
Hybrid Genetic Algorithms and Simulated Annealing for Multi-trip Vehicle Routing Problem with Time Windows.
Authors: Amalia Kartika Ariyani, Wayan Firdaus Mahmudy, Yusuf Priyo Anggodo
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Optimization; Genetic Algorithms
Abstract: Vehicle routing problem with time windows (VRPTW) is one of NP-hard problem. Multi-trip is approach to solve the VRPTW that looking trip scheduling for gets best result. Even though there are various algorithms for the problem, there is opportunity to improve the existing algorithms in order gaining a better result. In this research, genetic algoritm is hybridized with simulated annealing algoritm to solve the problem. Genetic algoritm is employed to explore global search area and simulated annealing is employed to exploit local search area. Four combination types of genetic algorithm and simulated annealing (GA-SA) are tested to get the best solution. The computational experiment shows that GA-SA1 and GA-SA4 can produced the most optimal fitness average values with each value was 1.0888 and 1.0887. However GA-SA4 can found the best fitness chromosome faster than GA-SA1.
Optimization of Fuzzy Tsukamoto Membership Function using Genetic Algorithm to Determine the River Water.
Authors: Qoirul Kotimah, Wayan Firdaus Mahmudy, Vivi Nur Wijayaningrum
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Fuzzy Logic; Genetic Algorithms
Abstract: Some aquatic ecosystems in rivers depend on the river water, so it needs to be maintained by measuring and analyzing the river water quality. STORET is one of the methods used to measure the river water quality, but it takes a quite high of time and costs. Fuzzy Tsukamoto is an alternative method that works by grouping the river water data, but it is difficult to determine the membership function value. The solution offered in this study is the use of genetic algorithm to determine the membership function value of each criterion. Based on the test results, the optimization of fuzzy membership function using genetic algorithm provides higher accuracy value that is 95%, while the accuracy value without optimization process is 90%. The parameters used in genetic algorithm are as follows: population size is 80, generation number is 175, crossover rate (cr) is 0.6, and mutation rate (mr) is 0.4.
Effective predictive modelling for coronary artery diseases using support vector machine
Authors: Kuncahyo Setyo Nugroho, Anantha Yullian Sukmadewa, Angga Vidianto, Wayan Firdaus Mahmudy
Journal: IAES International Journal of Artificial Intelligence
Year: 2022
Topics: Machine Learning; Healthcare
Abstract: Coronary artery disease (CAD) is a category of cardiovascular disease that causes the highest mortality rate in the world. CAD occurs due to plaque build-up on the walls of the arteries that supply blood to the heart and other organs of the body. To control the mortality rate, a practical model that is capable of predicting CAD is needed. Machine learning approaches have been used in solving various problems in various domains, including biomedicine. However, real-world data often has an unbalanced class distribution that can interfere with classifier performance. In addition, data has many features to process. This study focuses on effective modeling capable of predicting CAD using feature selection to handle high dimensional data and feature resampling to handle unbalanced data. Feature selection is very effective by eliminating irrelevant features from the training data. Hyperparameter tuning is also done to find the best combination of parameters in support vector machines (SVM). Our results show that the SVM cross-validated ten times has a more accurate training result. Furthermore, the grid search on SVM cross-validated ten times had more accurate training model results and achieved 88% accuracy on the test data.
Optimization of multi-product aggregate production planning using hybrid simulated annealing and adaptive genetic algorithm
Authors: Gusti Eka Yuliastuti, Agung Mustika Rizki, Wayan Firdaus Mahmudy, Ishardita Pambudi Tama
Journal: International Journal of Advanced Computer Science and Applications
Year: 2019
Topics: Optimization; Genetic Algorithms
Abstract: In the planning of aggregate production, company stakeholders need a long time due to the many production variables that must be considered so that the production value can meet consumer demand with minimal production costs. The case study is the company that produces more than a type of product so there are several variables must be considered and computational time is required. Genetic Algorithm is applied as they have the advantage of searching in a solution space but are often trapped in locally optimal solutions. In this study, the authors proposed a new mathematical model in the form of a fitness function aimed at assessing the quality of the solution. To overcome this local optimum problem, the authors refined it by combining the Genetic Algorithm and Simulated Annealing so called hybrid approach. The function of Simulated Annealing is to improve every solution produced by Genetic Algorithm. The proposed hybrid method is proven to produce better solutions.
Sistem Rekomendasi Profesi Berdasarkan Dimensi Big Five Personality Menggunakan Fuzzy Inference System Tsukamoto
Authors: Farhanna Mar’i, W Firdaus Mahmudy, Cleoputri Yusainy
Journal: J. Teknol. Inf. dan Ilmu Komput
Year: 2019
Topics: Fuzzy Logic; Psychology
Abstract: Sistem rekomendasi dapat dimanfaatkan sebagai alat bantu untuk pengambilan keputusan. Pada sebuah perusahaan, sistem rekomendasi profesi bisa digunakan untuk menempatkan seorang karyawan pada posisi yang tepat. Pada penelitian ini diusulkan sistem rekomendasi profesi berdasarkan Big Five Personality traits yang meliputi Extraversion, Aggreableness, Conscentiousness, Neuroticm, dan Opennes. Input yang digunakan ialah parameter dimensi Big Five Personality yang dirumuskan oleh John. Metode yang digunakan adalah Fuzzy Inference System (FIS) Tsukamoto. Keakuratan sistem dihitung dengan membandingkan output sistem dengan dengan acuan Top Ranked Personality-Based Work Styles for 22 Job Families yang menghasilkan nilai akurasi sebesar 63%.
Chicken feed optimization using evolution strategies and firefly algorithm
Authors: Andreas Nugroho Sihananto, M Shochibul Burhan, Wayan Firdaus Mahmudy
Journal: International Journal of Electrical and Computer Engineering
Year: 2019
Topics: Optimization; Evolutionary Algorithms
Abstract: Mixing broiler chicken and layer hens feed using various feed ingredients is a difficult task. The feed must fulfill the minimum nutrient requirement and must break the constraint. Some classic approach like Pearson’s Square has been already introduced to solve this problem. However, the approaches cannot guarantee to fulfill nutrient requirements and desirable price. The two metaheuristic algorithms Evolution Strategies (ES) and Firefly Algorithms (FA) are being proposed in this paper to know how well they performed this problems. Result show that ES is perform much better compared to classic Pearson’s Square, but ES itself is outperform by FA on both cases.
Multi-Site Aggregate Production Planning Using Particle Swarm Optimization.
Authors: Aji Prasetya Wibawa, Wayan F Mahmudy, Agung Mustika Rizki, Gusti Eka Yuliastuti, Ishardita Pambudi Tama
Journal: Journal of Engineering, Project, and Production Management
Year: 2022
Topics: Optimization; Particle Swarm Optimization
Abstract: Aggregate planning is a crucial stage in the production process because it supports other processes. Careless production planning may cause production costs to spike sharply that hurts the company financially. This study explores the novel usage of particle swarm optimization (PSO) to discover a set of solutions among the objective of a multi-optimization problem in aggregate production planning. The study uses a small home textile industry with complex production processes of school uniforms as a case study. The results show that the production cost difference between actual data and the proposed method is IDR330, 670,000. Thus, PSO can solve the multi-site aggregate planning by reducing the company production cost.
Extreme learning machine and particle swarm optimization for inflation forecasting
Authors: Adyan Nur Alfiyatin, Agung Mustika Rizki, Wayan Firdaus Mahmudy, Candra Fajri Ananda
Journal: International Journal of Advanced Computer Science and Applications
Year: 2019
Topics: Machine Learning; Economics
Abstract: Inflation is one indicator to measure the development of a nation. If inflation is not controlled, it will have a lot of negative impacts on people in a country. There are many ways to control inflation, one of them is forecasting. Forecasting is an activity to find out future events based on past data. There are various kinds of artificial intelligence methods for forecasting, one of which is the extreme learning machine (ELM). ELM has weaknesses in determining initial weights using trial and error methods. So, the authors propose an optimization method to overcome the problem of determining initial weights. Based on the testing carried out the purposed method gets an error value of 0.020202758 with computation time of 5 seconds.
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; Energy
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 …
Optimization of multi-stage distribution process using improved genetic algorithm
Authors: W Mahmudy, M Sarwani, Asyrofa Rahmi, Agus Wahyu Widodo, UM Pasuruan
Journal: Int J Intell Eng Syst
Year: 2021
Topics: Optimization; Genetic Algorithms
Abstract: A supply chain process in manufacturing industries involves a distribution process that ensures finished goods distribute to their customers properly. The process may involve several parties such as manufacturing plans, distributions centres, and retailers. The companies should optimize the process by minimizing related costs to achieve the maximum profit. The multi-stage distribution problem contains several constrains so obtaining optimum solution using exact methods may require excessive processing time. Genetic algorithm is proposed the solve the complex problem. The genetic algorithm is improved by modifying chromosome representation and related genetic reproduction operators. The new chromosome is designed to address different number of distribution stages in manufacturing industries and could be applied for various distribution problems. Several genetic operators are tested to obtain the most suitable reproduction operator for the multi-stage distribution process. The numerical experiments prove that the improved genetic algorithm is suitable for the optimization of the multi-stage distribution process and produce better result compared to those achieved by original genetic algorithm, simulated annealing, and random search as baseline method. The improved genetic algorithm produces solution with cost of Rp 20,167.8, lower than cost of Rp 21,860.4, Rp 23,354.30 and Rp 34,328.0 obtained by original genetic algorithm, simulated annealing, and random search, respectively. For a bigger size problem, the improved genetic algorithm also produces solution with lower cost. The result proves the robustness of the improved GA …
A hybrid feature selection on AIRS method for identifying breast cancer diseases
Authors: Achmad Ridok, Nashi Widodo, Wayan Firdaus Mahmudy, Muhaimin Rifa’i
Journal: International Journal of Electrical and Computer Engineering
Year: 2021
Topics: Machine Learning; Healthcare
Abstract: Breast cancer may cause a death due to the late diagnosis. A cheap and accurate tool for early detection of this disease is essential to prevent fatal incidence. In general, the cheap and less invasive method to diagnose the disease could be done by biopsy using fine needle aspirates from breast tissue. However, rapid and accurate identification of the cancer cell pattern from the cell biopsy is still challenging task. This diagnostic tool can be developed using machine learning as a classification problem. The performance of the classifier depends on the interrelationship between sample sizes, some features, and classifier complexity. Thus, the removal of some irrelevant features may increase classification accuracy. In this study, a new hybrid feature selection fast correlation based feature (FCBF) and information gain (IG) was used to select features on identifying breast cancer using AIRS algorithm. The results of 10 …
Comparison of regression, support vector regression (SVR), and SVR-particle swarm optimization (PSO) for rainfall forecasting
Authors: Fendy Yulianto, Wayan Firdaus Mahmudy, Arief Andy Soebroto
Journal: Journal of Information Technology and Computer Science
Year: 2020
Topics: Machine Learning; Optimization
Abstract: Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.
Inflation rate prediction in Indonesia using optimized support vector regression model
Authors: Irvi Oktanisa, Wayan Firdaus Mahmudy, Ghozali Maski
Journal: Journal of Information Technology and Computer Science
Year: 2020
Topics: Machine Learning; Optimization
Abstract: Inflation is an indicator that illustrated the economic condition of a country. This moneter phenomenon is signed with the increase of price in the entire case. It can cause an effect on the political sector which impacts economic stability in a nation. The importance of inflation control is very important due to the high and unstable inflation that will harm economic and social in society. One of the solutions to control the inflation rate is determining an appropriate monetary policy based on future prediction of the inflation rate. This research using SVR as machine learning that is being optimized by GA as an evolutionary algorithm as a predicting method. SVR can solve nonlinear regression problems to linear regression using the Kernel function that easies to implement. But, in SVR there is no general rule to set the parameters of SVR. Therefore, this research proposed to use GA to optimize the parameters of SVR. GA can solve the optimization problems in various research on economics prediction problem. Based on the testing that has been conducted, GA-SVR generates the MSE value is 0.03767, lower than SVR basic method is 0.053158. It proves that the GASVR method can be utilized for predicting.
DNA sequence of hepatitis B virus clustering using hierarchical k-means algorithm
Authors: Lailil Muflikhah, Wayan Firdaus Mahmudy
Year: 2019
Topics: Machine Learning; Bioinformatics
Abstract: Hepatitis B virus infection is one of the causes of hepatocellular carcinoma. It is a kind of DNA virus and has highly various genetic. In bioinformatics research, molecular evolution analysis is implemented to extract information from DNA sequence to nucleotide composition. The large volume of DNA sequence and costly complicates calculating of non-numerical nature of data, it is essential in the clustering task for describing data. Therefore, we propose the clustering method for the sequence using the Hierarchical k-Means clustering algorithm. The method is addressed to improve k-Means by defining an initial cluster center through the mean result of the hierarchical clustering method. The experiment result showed that the proposed method obtained higher performance measures than the conventional k-Means cluster algorithm.
Hybrid Genetic Algorithm for Optimization of Food Composition on Hypertensive Patient.
Authors: Aprilia Nur Fauziyah, Wayan Firdaus Mahmudy
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Optimization; Healthcare
Abstract: The healthy food with attention of salt degree is one of the efforts for healthy living of hypertensive patient. The effort is important for reducing the probability of hypertension change to be dangerous disease. In this study, the food composition is build with attention nutrition amount, salt degree, and minimum cost. The proposed method is hybrid method of Genetic Algorithm (GA) and Variable Neighborhood Search (VNS). The three scenarios of hybrid GA-VNS types had been developed in this study. Although hybrid GA and VNS take more time than pure GA or pure VNS but the proposed method give better quality of solution. VNS successfully help GA avoids premature convergence and improves better solution. The shortcomings on GA in local exploitation and premature convergence is solved by VNS, whereas the shortcoming on VNS that less capability in global exploration can be solved by use GA that has advantage in global exploration.
Performance Analysis of Data Mining Methods for Sexually Transmitted Disease Classification.
Authors: Gusti E Yuliastuti, Adyan N Alfiyatin, Agung M Rizki, A Hamdianah, H Taufiq, WF Mahmudy
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Data Mining; Healthcare
Abstract: According to health reports of Malang city, many people are exposed to sexually transmitted diseases and most sufferers are not aware of the symptoms. Malang city being known as a city of education so that every year the population number increases, it is at risk of increasing the spread of sexually transmitted diseases virus. This problem is important to be solved to treat earlier sufferers sexually transmitted diseases virus in order to reduce the burden of patient spending. In this research, authors conduct data mining methods to classifying sexually transmitted diseases. From the experiment result shows that K-NN is the best method for solve this problem with 90% accuracy.