Lontar Komputer : Jurnal Ilmiah Teknologi Informasi

Journal Information
ISSN / EISSN: 20881541 / 25415832
Published by: Universitas Udayana
Total articles ≅ 133

Latest articles in this journal

Ditha Nurcahya Avianty, I Gede Pasek Suta Wijaya, Fitri Bimantoro
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 128-136; https://doi.org/10.24843/lkjiti.2022.v13.i02.p06

Coronavirus 2 (SARS-CoV-2) is the cause of an acute respiratory infectious disease that can cause death, popularly known as Covid-19. Several methods have been used to detect COVID-19-positive patients, such as rapid antigen and PCR. Another method as an alternative to confirming a positive patient for COVID-19 is through a lung examination using a chest X-ray image. Our previous research used the ANN method to distinguish COVID-19 suspect, pneumonia, or expected by using a Haar filter on Discrete Wavelet Transform (DWT) combined with seven Hu Moment Invariants. This work adopted the ANN method's feature sets for the Support Vector Machine (SVM), which aim to find the best SVM model appropriate for DWT and Hu moment-based features. Both approaches demonstrate promising results, but the SVM approach has slightly better results. The SVM's performances improve accuracy to 87.84% compared to the ANN approach with 86% accuracy.  
Oka Sudana, I.W. Wahyu Ivan M.J, Desy Purnami S.P
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 117-127; https://doi.org/10.24843/lkjiti.2022.v13.i02.p05

Hindu Mantram is chants of speech with supernatural powers, which should not be done carelessly. The Balinese Hindu Mantram is a modified form of the Hindu Mantram that adapts to the local wisdom of the Balinese Hindu Community. The problem is that there is no digital education platform regarding the Balinese Hindu Mantram. Based on these problems, a mobile-based information system was built that integrates the Balinese Hindu Mantram and Yadnya Ceremony with its ceremonial procession. This information system applied Model Tree and UAT with PSSUQ Method. This research aimed to develop an application that can be a platform to provide education about the Balinese Hindu Mantram and its relationships. The results obtained from this research were the E-Mantram Android mobile application that implemented the Tree Model and UAT results with a System Usefulness value of 1.94, Information Quality of 2.06, Interface Quality of 2.06, and Overall of 2.01.
Imam Riadi, Abdul Fadlil, Fahmi Auliya Tsani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 84-95; https://doi.org/10.24843/lkjiti.2022.v13.i02.p02

One of the popular cryptographic algorithms is the Vigenère Cipher. This algorithm is included in classical cryptographic algorithms, so its capabilities are limited to text-type data. Through this research, this research try to modify the Vigenère Cipher so that it can be used on digital image media. The improvement is performed using ASCII code as a Vigenère table and the key generated by the SHA512 hash technique with salt. The encryption and decryption process was carried out on ten jpg and ten png files and showed a 100% success rate. Speed and memory consumption tests on the encryption process by comparing it with the AES algorithm show that AES excels in speed with 409,467 Mb/swhile Vigenère wins in memory consumption by utilizing only 5,0007 Kb for every Kilobytes of the processed digital image file. 
Linda Perdana Wanti, Nur Wachid Adi Prasetya, Laura Sari, Lina Puspitasari, Annisa Romadloni
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 105-116; https://doi.org/10.24843/lkjiti.2022.v13.i02.p04

Preeclampsia is a disease often suffered by pregnant women caused by several factors such as a history of heredity, blood pressure, urine protein, and diabetes. The data sample used in this study is data on pregnant women in the 2020 time period recorded at health services in the former Cilacap Regency. This study was conducted to compare the final results of the Naive Bayes method and the certainty factor method in providing the results of a diagnosis of preeclampsia seen from the symptoms experienced by these pregnant women. The naïve Bayes approach provides decisions by managing statistical data and probabilities taken from the prediction of the likelihood of a pregnant woman showing symptoms of preeclampsia. Symptoms of preeclampsia, while the certainty factor method determines the certainty value of the diagnosis of preeclampsia in pregnant women based on the calculation of the CF value. The research output compares the two methods, showing that the certainty factor method provides more accurate diagnostic results than the Naive Bayes method. It happens because the CF method requires a minimum value of 0.2 and a maximum of 1 for each rule on the factors/symptoms involved, while the Naive Bayes method only requires values of 0 and 1 for each factor causing preeclampsia in pregnant women. 
Syamsul Bahri, Muhammad Rijal Alfian, Nurul Fitriyani
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 96-104; https://doi.org/10.24843/lkjiti.2022.v13.i02.p03

Sunlight is an energy source that is a gift from God and is a source of life for living things, including humans as caliphs on earth. Judging from its impact, solar radiation is an environmental parameter that has positive and negative effects on human life. The pattern of distribution of solar radiation is important information for human life to be the attention of many people, both policymakers and researchers in the field of environment. This study objects to modeling the radiation of solar using a dynamic neural network (DNN) model. The data used in this research is the meteorological data of Mataram City for the period January 2018 to May 2019, which was obtained from the Department of Environment and Forestry of West Nusa Tenggara Province. In the development of this model, solar radiation was seen as a function of a combination of several variables related to meteorological (wind speed, wind direction, humidity, air pressure, and air temperature) and solar radiation data at some previous time. Considering the advantages and effectiveness of the activation function in the proposed DNN model learning process, this study's network learning in the hidden layer employed two activation functions: hyperbolic tangent (Type I) and hyperbolic tangent sigmoid functions (Type II). The output aggregation used two aggregates for each type: the weighted aggregation function (Type a) and the maximum function (Type b). The results of computer simulations based on the root of mean square error (RMSE) measure indicate that the model for modeling solar radiation in these two cases is quite accurate. Furthermore, it could be seen that the model's performance using the hyperbolic tangent activation function (Type b) is relatively better than the hyperbolic tangent sigmoid type of the activation function (Type a), with the RMSE values are 18.3924 and 18.4005, respectively.
Anita Desiani, Azhar Kholiq Affandi, Shania Putri Andhini, Sugandi Yahdin, Yuli Andirani, Muhammad Arhami
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 72-83; https://doi.org/10.24843/lkjiti.2022.v13.i02.p01

 The purpose of this study was to determine how the effect of using Bootstrapping Samples for resampling the Harlev dataset in improving the performance of single-cell pap smear classification by dealing with the data imbalance problem. The Harlev dataset used in this study consists of 917 data with 20 attributes. The number of classes on the label had data imbalance in the dataset that affected single-cell pap smear classification performance. The data imbalance in the classification causes machine learning algorithms to produce poor performance in the minority class because they were overwhelmed by the majority class. To overcome it, The resampling data could be used with Sample Bootstrapping. The results of the Sample Bootstrapping were evaluated using the Artificial Neural Network and K-Nearest Neighbors classification methods. The classification used was seven classes and two classes. The classification results using these two methods showed an increase in accuracy, precision, and recall values. The performance improvement reached 10.82% for the two classes classification and 35% for the seven classes classification. It was concluded that Sample Boostrapping was good and robust in improving the classification method. 
Antonius Rachmat Chrismanto, Afiahayati Afiahayati, Yunita Sari, Anny Kartika Sari, Yohanes Suyanto
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 46-59; https://doi.org/10.24843/lkjiti.2022.v13.i01.p05

The more popular a public figure on Instagram (IG), the number of followers also increase. When a public figure posts something, there are many comments from other users. In fact, from all the comments, not all of them are relevant to the post, such as advertising, links, or clickbait comments. The type of comments that are irrelevant to the post is usually called spam comments.  Spam comments will interfere with information flow and may lead to misleading information.  This research compares machine learning (ML) and deep learning (DL) classification methods based on our collected Indonesian IG spam comment dataset. This research was conducted in the following steps: dataset preparation, pre-processing, simple normalization, features generation using TF-IDF and word embedding, application of ML and DL classification methods, performance evaluation, and comparison. The authors compare accuracy, F-1, precision, and recall from ML and DL results. This research shows that ML and DL methods do not significantly differ.The Linear SVM, Extreme Tree (ET), Regression, and Stochastics Gradient Descent algorithms can reach the accuracy of 0.93. At the same time, the DL method has the highest accuracy of 0.94 using the SimpleTransformer BERT architecture.  The difference between ML and DL methods is not significantly different.
Parmonangan R. Togatorop, Megawati Sianturi, David Simamora, Desriyani Silaen
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 60-71; https://doi.org/10.24843/lkjiti.2022.v13.i01.p06

Heart disease is a leading cause of death worldwide, and the need for effective predictive systems is a major source of the need to treat affected patients. This study aimed to determine how to improve the accuracy of Random Forest in predicting and classifying heart disease. The experiments performed in this study were designed to select the most optimal parameters using an RF optimization technique using GA. The Genetic Algorithm (GA) is used to optimize RF parameters to predict and classify heart disease. Optimization of the Random Forest parameter using a genetic algorithm is carried out by using the Random Forest parameter as input for the initial population in the Genetic Algorithm. The Random Forest parameter undergoes a series of processes from the Genetic Algorithm: Selection, Crossover Rate, and Mutation Rate. The chromosome that has survived the evolution of the Genetic Algorithm is the best population or best parameter Random Forest. The best parameters are stored in the hall of fame module in the DEAP library and used for the classification process in Random Forest. The optimized RF parameters are max_depth, max_features, n_estimator, min_sample_leaf, and min_sample_leaf. The experimental process performed in RF uses the default parameters, random search, and grid search. Overall, the accuracy obtained for each experiment is the default parameter 82.5%, random search 82%, and grid search 83%. The RF+GA performance is 85.83%; this result is affected by the GA parameters are generations, population, crossover, and mutation. This shows that the Genetic Algorithm can be used to optimize the parameters of Random Forest. 
Furqon Hensan Muttaqien, Inna Syafarina, Intan Nuni Wahyuni, Arnida Lailatul Latifah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 35-45; https://doi.org/10.24843/lkjiti.2022.v13.i01.p04

Areas covered by tropical forests, such as Borneo, are vulnerable to fires. Previous studies have shown that climate data is one of the critical factors affecting forest fire. This study aims to predict the forest fire over Borneo by considering the temporal aspects of the climate data. A time seriesbased model, Long Short-Term Memory (LSTM), is used. Three LSTM models are applied: Basic LSTM, Bidirectional LSTM, and Stacked LSTM. Three different experiments from January 1998 to December 2015 are conducted by examining climate data, Oceanic Nino Index (ONI), and Indian Ocean Dipole (IOD) index. The proposed model is evaluated by Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and correlation number. As a result, all models can capture the spatial and temporal pattern of the forest fires for all three experiments, in which the best prediction occurs in September with a spatial correlation of more than 0.75. Based on the evaluation metrics, Stacked LSTM in Experiment 1 is slightly superior, with the highest annual pattern correlation (0.89) and lowest error (MAE= 0.71 and RMSE=1.32). This finding reveals that an additional ONI and IOD index as the prediction features would not improve the model performance generally, but it specifically improves the extreme event value.
I Putu Kerta Yasa, Ni Kadek Dwi Rusjayanthi, Wan Siti Maisarah Binti Mohd Luthfi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, Volume 13, pp 23-34; https://doi.org/10.24843/lkjiti.2022.v13.i01.p03

Stroke is a disease caused by blockage or rupture of blood vessels in the brain due to disruption of blood flow, where the blood supply to an area of the brain is suddenly interrupted. This study discusses stroke classification using the K-Means and Deep Learning methods. This study aims to segment patient data to produce patient class labels and classify the results of grouping the data to test the performance of the classification algorithm used. The 4,906 patient data used in this study were grouped using the K-Means method into multiple clusters, including 2 clusters, 3 clusters, 4 clusters, and 5 clusters, and the data grouping findings will be classified. The cluster validation method is the Davies Bouldin Index and the Silhouette Index, while the algorithm used in the classification process is the Deep Learning Algorithm. The classification results produce the most excellent accuracy value in the number of clusters tested, namely 2 clusters of 99.71%. 
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