Jurnal Teknologi dan Sistem Komputer

Journal Information
ISSN / EISSN : 2620-4002 / 2338-0403
Total articles ≅ 328
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Latest articles in this journal

Mohammad Romano Diansyah, Wisnu Ananta Kusuma, Annisa Annisa
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 126-132; doi:10.14710/jtsiskom.2021.13985

Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.
Damar Wicaksono, Tatag Lindu Bhakti, Restiadi Bayu Taruno, Melvin Rahma Sayuga Subroto, Anita Mustikasari
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 83-89; doi:10.14710/jtsiskom.2021.13996

This study aims to develop low-cost and environmentally friendly material galvanic-based dissolved oxygen sensors. A Dissolved oxygen (DO) sensor has been designed and fabricated on an 85 x 205 mm galvanic-based. The sensor structure device consists of Al-Zn reference layer electrode, Ag/AgCl active electrode, 120ml KCl electrolyte solvent 0,1 M, and closed by TiO2 membrane (PTFE). The Al-Zn formation reference electrode was done by Ag layer chlorination using FeCl3, and the TiO2 membrane was formed by TiO2 paste screen printing. The test was done to measure the sensor’s performance based on the current-voltage characteristics between 1.0 and 1.8 V. The results showed that a stable diffusion current was obtained when the input voltage was 1.5 V, resulting in the best sensor performance with a sensitivity of 0.7866 μA L/mg and a stable step response time of 3 mins. This prototype sensor showed high potential for prototyping for a low-cost water quality monitoring system.
Mutaqin Akbar
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 120-125; doi:10.14710/jtsiskom.2021.13959

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.
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 113-119; doi:10.14710/jtsiskom.2021.13943

A Pap smear is used to early detection cervical cancer. This study proposes the segmentation and analysis method of Pap smear cells images using the K-means algorithm so that cytoplasmic cells, nuclear cells, and inflammatory cells can be segmented automatically. The results of the feature analysis from the cytoplasmic, nuclear, and inflammatory cell images were classified using the J48 algorithm with 37 training data. The training resulted in an accuracy of 94.594 %, precision of 95 %, and sensitivity of 94.6 %. The classification of 24 testing images resulted in an accuracy of 91.6%, a precision of 92.5 %, and a sensitivity of 91.7 %.
Irsandy Maulana Satya Viddin, , Diksy Media Firmansyah
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 106-112; doi:10.14710/jtsiskom.2021.13931

This study aims to develop an authentication alternative by applying the Histogram shifting steganography method. The media used for authentication is image media. Histogram shifting utilizes the histogram of an image to insert a secret message. The developed authentication has implemented the Histogram shifting to insert user credentials into the carrier image. Users can use the steganographic image to log into their accounts. The method extracts the credentials from the image during the login. PSNR test of the steganographic images produces an average value of 52.52 dB. The extraction capability test shows that the method can extract all test images correctly. In addition, this authentication method is also more resistant to attacks common to password authentication.
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 96-105; doi:10.14710/jtsiskom.2021.13902

The selection of students' interests based on the 2013 curriculum (K-13) is carried out before students start learning in class X. Accuracy in its determination is required to ensure that students learn according to their interests and talents. This study applies three DSS methods, namely profile matching, SAW, and a combination of both, to provide accurate recommendations for determining these students' interests. The three methods are compared using the same alternatives and criteria to find the most dominant method. The results of this study indicate that the application of SPK can assist PPDB activities with an accuracy of 79.2 %. In determining interest for students, the combination method is the most dominant, with an accuracy of 78 %. The application of DSS not only helps the specialization process to be faster but also accurate. This is indicated by only 6 out of 122 students who chose specialization based on the DSS recommendation getting a score below the KKM.
Imelda Alvionita Tarigan, I Putu Agung Bayupati, Gusti Agung Ayu Putri
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 90-95; doi:10.14710/jtsiskom.2021.13847

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.
Agus Subhan Akbar, R. Hadapiningradja Kusumodestoni
Jurnal Teknologi dan Sistem Komputer, Volume 9; doi:10.14710/jtsiskom.2021.14007

This correct the article "Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel (Optimization of k value and lag parameter of k-nearest neighbor algorithm on the prediction of hotel occupancy rates)" in vol. 8, no. 3, pp. 246-254, Jul. 2020; https://doi.org/10.14710/jtsiskom.2020.13648In the original published article, the placement of Figure 8 and Figure 9 less appropriate, which causes the manuscript hard to read. In addition, Table 2 through Table 6 need to be repositioned. These placing errors have been corrected online.The publisher apologizes for these errors.
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 77-82; doi:10.14710/jtsiskom.2021.13748

Imbalanced data results in errors in the classification, such as WMMOTE, and can decrease its performance and accuracy. Clustering in MWMOTE can be optimized to improve synthetic data generation and improve MWMOTE performance. This study aims to optimize the MWMOTE algorithm's performance in the clustering process in making synthetic data with complete linkage (CL). The dataset used a variety of data ratios to handle imbalanced data. The decision tree is used to determine the performance of MWMOTE and CL-MWMOTE oversampling. CL-MWMOTE evaluation results provide good, optimal performance and increase precision 0.53 %, 0.66 % recall, 0.67 % accuracy, and f-measure 0.65 %.
, Ali Rizal Chaidir, Fahrul Isman
Jurnal Teknologi dan Sistem Komputer, Volume 9, pp 70-76; doi:10.14710/jtsiskom.2021.13844

Stroke or Cerebrovascular accident (CVA) can cause weakness in one side of the body, including the upper limbs such as the hand. Rehabilitation is needed to restore the function of the hand. Rehabilitation should also measure the strength of the movements carried out. This article aims to forecast the strength of movement based on Electromyography (EMG) signals using the Extreme Learning Machine (ELM). This study collected EMG signal data and movement strength, carried out data pre-processing and data extraction using various extraction features, applied ELM for forecasting strength based on EMG signals, and applied created models in stroke therapy robots. The forecasting model is evaluated by measuring the Mean Squared Error (MSE). The average value of the best MSE in offline testing is 1.77, while the real-time testing is 0.79. A small MSE value indicates that the model is good enough. The resulted value of strength can be applied to make the stroke therapy robots actuating properly.
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