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Implementing a non-local means method to CTA data of aortic dissection 主动脉夹层CTA数据的非局部均值方法实现
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-06-17 DOI: 10.14710/JTSISKOM.2021.14125
M. Fitria, Cosmin Adrian Morariu, J. Pauli, Ramzi Adriman
{"title":"Implementing a non-local means method to CTA data of aortic dissection","authors":"M. Fitria, Cosmin Adrian Morariu, J. Pauli, Ramzi Adriman","doi":"10.14710/JTSISKOM.2021.14125","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.14125","url":null,"abstract":"It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46087494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimasi proses penjadwalan mata kuliah menggunakan algoritme genetika dan pencarian tabu 使用遗传算法和禁忌搜索优化学校眼图绘制过程
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-06-17 DOI: 10.14710/JTSISKOM.2021.14137
Arif Amrulloh, Enny itje Sela
{"title":"Optimasi proses penjadwalan mata kuliah menggunakan algoritme genetika dan pencarian tabu","authors":"Arif Amrulloh, Enny itje Sela","doi":"10.14710/JTSISKOM.2021.14137","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.14137","url":null,"abstract":"Penjadwalan mata kuliah merupakan permasalahan yang sering terjadi pada perguruan tinggi, di antaranya adalah bentrok waktu mengajar dosen, ruangan dan kelas mahasiswa. Kajian ini mengusulkan optimasi penjadwalan mata kuliah menggunakan algoritme genetika dan pencarian tabu. Algoritme genetika berfungsi untuk menghasilkan generasi terbaik kromosom yang tersusun atas gen dosen, hari, dan jam. Pencarian tabu digunakan untuk pembagian ruang perkuliahan. Penjadwalan dilakukan di fakultas Informatika yang mempunyai empat program studi dengan 65 dosen, 93 mata kuliah, 265 penugasan dosen, dan 65 kelas. Proses pembangkitan 265 jadwal membutuhkan waktu selama 561 detik dan tidak ada bentrok yang terjadi. Kombinasi algoritme genetika dan pencarian tabu dapat memproses jadwal mata kuliah yang cukup banyak dengan lebih cepat daripada cara manual.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"9 1","pages":"157-166"},"PeriodicalIF":0.0,"publicationDate":"2021-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45784861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM 基于支持向量机的定向梯度直方图、GLCM和形状特征提取方法在癌症分类中的比较
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-05-18 DOI: 10.14710/JTSISKOM.2021.14104
Hanimatim Mu'jizah, D. C. R. Novitasari
{"title":"Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM","authors":"Hanimatim Mu'jizah, D. C. R. Novitasari","doi":"10.14710/JTSISKOM.2021.14104","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.14104","url":null,"abstract":"Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45216208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Traffic sign recognition using convolutional neural networks 基于卷积神经网络的交通标志识别
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-04-30 DOI: 10.14710/JTSISKOM.2021.13959
M. Akbar
{"title":"Traffic sign recognition using convolutional neural networks","authors":"M. Akbar","doi":"10.14710/JTSISKOM.2021.13959","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.13959","url":null,"abstract":"Traffic sign recognition (TSR) can be used to recognize traffic signs by utilizing image processing. This paper presents traffic sign recognition in Indonesia using convolutional neural networks (CNN). The overall image dataset used is 2050 images of traffic signs, consisting of 10 kinds of signs. The CNN layer used in this study consists of one convolution layer, one pooling layer using maxpool operation, and one fully connected layer. The training algorithm used is stochastic gradient descent (SGD). At the training stage, using 1750 training images, 48 filters, and a learning rate of 0.005, the recognition results in 0.005 of loss and 100 % of accuracy. At the testing stage using 300 test images, the system recognizes the signs with 0.107 of loss and 97.33 % of accuracy.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67021163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Implementasi Reccurent Neural Network Untuk Memprediksi Harga Saham 神经分析网络的实施以预测股票价格
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-04-30 DOI: 10.14710/JTSISKOM.2021.13898
Didi Harlianto, Andris Rachardi, Deandra Aulia Rusdah, E. Safitri, Ely Sudarsono, Alhadi Bustamam
{"title":"Implementasi Reccurent Neural Network Untuk Memprediksi Harga Saham","authors":"Didi Harlianto, Andris Rachardi, Deandra Aulia Rusdah, E. Safitri, Ely Sudarsono, Alhadi Bustamam","doi":"10.14710/JTSISKOM.2021.13898","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.13898","url":null,"abstract":"Saham adalah instrumen investasi dengan harga yang sangat fluktuatif. Harga saham dalam kurun waktu tertentu membentuk suatu data runtun waktu. Saat ini, salah satu metode yang cukup populer untuk menangani data runtun adalah Recurrent Neural Network (RNN). Tulisan ini membahas penerapan RNN di masa yang akan datang dalam memprediksi harga saham berdasarkan data harga saham beberapa tahun ke belakang. Tetapi RNN standar memiliki kelemahan yaitu terjadinya kondisi vanishing gradient. Oleh karena itu, arsitektur Long Short Term Memory (LSTM) digunakan pada RNN untuk mengatasi masalah tersebut. Sebagai pembanding, ditampilkan pula hasil prediksi dengan menggunakan model RNN standar. Hasilnya, RNN dengan arsitektur LSTM dapat dengan baik memprediksi harga saham dibandingkan RNN standar yang direfleksikan oleh nilai Mean Absolute Error (MAE) antar kedua model.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42863055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise 分布村落成熟度的识别:基于密度的含噪声空间聚类的村落分类应用
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-04-26 DOI: 10.14710/JTSISKOM.2021.13998
O. Okfalisa, Angraini Angraini, Sh Novi, Hidayati Rusnedy, Lestari Handayani, M. Mustakim
{"title":"Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise","authors":"O. Okfalisa, Angraini Angraini, Sh Novi, Hidayati Rusnedy, Lestari Handayani, M. Mustakim","doi":"10.14710/JTSISKOM.2021.13998","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.13998","url":null,"abstract":"The rural development measurement is undoubtedly not easy due to its particular needs and conditions. This study classifies village performance from social, economic, and ecological indices. One thousand five hundred ninety-one villages from the Community and Village Empowerment Office at Riau Province, Indonesia, are grouped into five village maturation classes: very under-developed village, under-developed village, developing village, developed village, and independent village. To date, Density-based spatial clustering of applications with noise (DBSCAN) is utilized in mining 13 of the villages’ attributes. Python programming is applied to analyze and evaluate the DBSCAN activities. The study reveals the grouping’s silhouette coefficient values at 0.8231, thus indicating the well-being clustering performance. The epsilon and minimum points values are considered in DBSCAN evaluation with percentage splits simulation. This grouping can be used as guidelines for governments in analyzing the distribution of rural development subsidies more optimal.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46819518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province 支持向量机与反向传播模型在巴厘岛外国游客数量预测中的比较
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-02-26 DOI: 10.14710/JTSISKOM.2021.13847
Imelda Alvionita Tarigan, I. P. Bayupati, G. A. A. Putri
{"title":"Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province","authors":"Imelda Alvionita Tarigan, I. P. Bayupati, G. A. A. Putri","doi":"10.14710/JTSISKOM.2021.13847","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2021.13847","url":null,"abstract":"Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41721833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Three combination value of extraction features on GLCM for detecting pothole and asphalt road GLCM上用于检测坑洞和沥青路面的三种提取特征组合值
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-01-31 DOI: 10.14710/JTSISKOM.2020.13828
Yoke Kusuma Arbawa, Fitri Utaminingrum, Eko Setiawan
{"title":"Three combination value of extraction features on GLCM for detecting pothole and asphalt road","authors":"Yoke Kusuma Arbawa, Fitri Utaminingrum, Eko Setiawan","doi":"10.14710/JTSISKOM.2020.13828","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2020.13828","url":null,"abstract":"The rate of vehicle accidents in various regions is still high—accidents caused by many factors, such as driver negligence, vehicle damage, road damage, etc. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use humans as drivers but uses machines automatically. However, vehicle accidents are still possible because automatic machines do not have intelligence like humans to see all the obstacles in front of the vehicle. Obstacles can take many forms, one of them is road potholes. We propose a method for detecting road potholes using the Gray-Level Cooccurrence Matrix with three features and using the Support Vector Machine as a classification method. We analyze the combination of GLCM Contrast, Correlation, and Dissimilarity features. The results showed that the combination of Contrast and Dissimilarity features had the best accuracy of 92.033% with a computing time of 0.0704 seconds per frame.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44547107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Sampled and discretized of short-time Fourier transform and non-negative matrix factorization: the single-channel source separation case 短时傅立叶变换和非负矩阵分解的采样和离散化：单通道源分离情况
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-01-31 DOI: 10.14710/JTSISKOM.2020.13858
J. Hendry, Isnan Nur Rifai, Yoga Mileniandi
{"title":"Sampled and discretized of short-time Fourier transform and non-negative matrix factorization: the single-channel source separation case","authors":"J. Hendry, Isnan Nur Rifai, Yoga Mileniandi","doi":"10.14710/JTSISKOM.2020.13858","DOIUrl":"https://doi.org/10.14710/JTSISKOM.2020.13858","url":null,"abstract":"The Short-time Fourier transform (STFT) is a popular time-frequency representation in many source separation problems. In this work, the sampled and discretized version of Discrete Gabor Transform (DGT) is proposed to replace STFT within the single-channel source separation problem of the Non-negative Matrix Factorization (NMF) framework. The result shows that NMF-DGT is better than NMF-STFT according to Signal-to-Interference Ratio (SIR), Signal-to-Artifact Ratio (SAR), and Signal-to-Distortion Ratio (SDR). In the supervised scheme, NMF-DGT has a SIR of 18.60 dB compared to 16.24 dB in NMF-STFT, SAR of 13.77 dB to 13.69 dB, and SDR of 12.45 dB to 11.16 dB. In the unsupervised scheme, NMF-DGT has a SIR of 0.40 dB compared to 0.27 dB by NMF-STFT, SAR of -10.21 dB to -10.36 dB, and SDR of -15.01 dB to -15.23 dB.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46697816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Comparison of various epidemic models on the COVID-19 outbreak in Indonesia 新冠肺炎在印度尼西亚爆发的各种流行病模型的比较
Jurnal Teknologi dan Sistem Komputer Pub Date : 2021-01-23 DOI: 10.14710/jtsiskom.2021.14222
I. Wahyuni, Ayu Shabrina, Inna Syafarina, A. Latifah
{"title":"Comparison of various epidemic models on the COVID-19 outbreak in Indonesia","authors":"I. Wahyuni, Ayu Shabrina, Inna Syafarina, A. Latifah","doi":"10.14710/jtsiskom.2021.14222","DOIUrl":"https://doi.org/10.14710/jtsiskom.2021.14222","url":null,"abstract":"This paper compares four mathematical models to describe Indonesia's current coronavirus disease 2019 (COVID-19) pandemic. The daily confirmed case data are used to develop the four models: Logistic, Richards, SIR, and SEIR. A least-square fitting computes each parameter to the available confirmed cases data. We conducted parameterization and sensitivity experiments by varying the length of the data from 60 until 300 days of transmission. All models are susceptible to the epidemic data. Though the correlations between the models and the data are pretty good (>90%), all models still show a poor performance (RMSE>18%). In this study case, Richards model is superior to other models from the highest projection of the positive cases of COVID-19 in Indonesia. At the same time, others underestimate the outbreak and estimate too early decreasing phase. Richards model predicts that the pandemic remains high for a long time, while others project the pandemic will finish much earlier.","PeriodicalId":56231,"journal":{"name":"Jurnal Teknologi dan Sistem Komputer","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47414138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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