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ILKOM Jurnal Ilmiah
Published by Universitas Muslim Indonesia
ISSN : 20871716     EISSN : 25487779     DOI : -
Core Subject : Science,
Computer Science & IT
ILKOM Jurnal Ilmiah is an Indonesian scientific journal published by the Department of Information Technology, Faculty of Computer Science, Universitas Muslim Indonesia. ILKOM Jurnal Ilmiah covers all aspects of the latest outstanding research and developments in the field of Computer science, including Artificial intelligence, Computer architecture and engineering, Computer performance analysis, Computer graphics and visualization, Computer security and cryptography, Computational science, Computer networks, Concurrent, parallel and distributed systems, Databases, Human-computer interaction, Embedded system, and Software engineering.
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Articles 20 Documents
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Search results for , issue "Vol 15, No 2 (2023)" : 20 Documents clear
Classifying BISINDO Alphabet using TensorFlow Object Detection API Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Fahmi, Muhammad
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1692.358-364

Abstract

Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite  SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of  consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well.
Sentiment Analysis for Online Learning using The Lexicon-Based Method and The Support Vector Machine Algorithm M. Khairul Anam; Triyani Arita Fitri; Agustin Agustin; Lusiana Lusiana; Muhammad Bambang Firdaus; Agus Tri Nurhuda
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1590.290-302

Abstract

The pros and cons regarding online learning has been a hot topic in society, both on social media and in the real world. Indonesian netizens still post opinions about online learning on social media such as Twitter. This study aims to analyze public comments to determine whether the trend of the comments is positive, negative, or neutral. The classification of netizen opinions is called sentiment analysis. This study applies 2 ways of carrying out sentiment analysis. The first stage employs the SVM algorithm with data labeling automatically obtained from the Emprit Academy drone portal while the second stage is still using the SVM algorithm but the data labeling with lexicon-based method. The results of this study are comparisons of labels obtained automatically from the Emprit Academy drone portal and labeling using lexicon based. The SVM algorithm obtains an accuracy of 90%, while the use of lexicon-based increases the accuracy value by 5% to 95%. It can be concluded that labeling data using a lexicon-based method can improve the accuracy of the SVM algorithm.
Analisis Performa Metode Support Vector Regression (SVR) dalam Memprediksi Harga Bahan Sembako Nasional Huzain Azis; Purnawansyah Purnawansyah; Nirwana Nirwana; Felix Andika Dwiyanto
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1686.390-397

Abstract

Support Vector Regression (SVR) is a supervised learning algorithm to predict continuous variable values. The basic goal of the SVR algorithm is to find the most suitable decision line. SVR has been successfully applied to several issues in time series prediction. In this research, SVR is used to predict the price of staple commodity, which are constantly changing in price at any time due to several factors making it difficult for the public to get groceries that are easy to reach. National staple commodity data consisting of 17 commodities, including shallots, honan garlic, kating garlic, medium rice, premium rice, red cayenne peppers, curly red chilies, red chili peppers, meat of broiler chicken, beef hamstrings, granulated sugar, imported soybeans, bulk cooking oil, premium packaged cooking oil, simple packaged cooking oil, broiler chicken eggs, and wheat flour. With a data set for the last 3 years, including from January 1, 2020, to December 31, 2022. There are 3 variables in the data set, namely commodity, date, and price. This research divides the entire dataset into 80% training and 20% testing data. The results of this research show that SVR using the RBF kernel produces good forecasting accuracy for all datasets with an average Mean Square Error (MSE) training data of 6,005 while data testing is 6,062, Mean Absolute Deviation (MAD) of training data is 6,730 while data testing is 6.6831, Mean Absolute Percentage Error (MAPE) training data is 0.0148 while data testing is 0.0147, and Root Mean Squared Error (RMSE) training data is 7.772 while data testing is 7.746.
The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia Aris Martono; Henderi Henderi; Giandari Maulani
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1234.240-249

Abstract

This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.
A Soft Voting Ensemble Classifier to Improve Survival Rate Predictions of Cardiovascular Heart Failure Patients Arif Munandar; Wiga Maulana Baihaqi; Ade Nurhopipah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1632.344-352

Abstract

Cardiovascular disease is one of the deadliest diseases, claiming around 17 million lives worldwide each year. According to data from the World Health Organization (WHO), more than four out of five deaths from cardiovascular disease are caused by heart attacks and strokes, and one-third of these deaths occur prematurely in people under the age of 70. Machine learning approaches can be used to detect the disease. This research aims to improve the prediction model of cardiovascular heart failure patient survival using C4.5, KNN, Logistic Regression algorithms, and the ensemble learning method of Voting Classifier. Based on the testing results, each model showed a significant increase in accuracy in the 70:30 ratio. Logistic Regression and C4.5 achieved the same accuracy, 89.47%, KNN obtained 91.23%, and Voting Classifier experienced a considerable improvement, reaching 94.74%. In testing with ratios of 90:10, 80:20, and 70:30, KNN demonstrated high accuracy but had significant overfitting, with a difference of 7-9% between training and testing accuracy scores in the 90:10 and 80:20 ratios. On the other hand, Voting Classifier showed stable performance in the 70:30 ratio, with an accuracy difference between training and testing scores below 1%. The conclusion of this research is that the Voting Classifier can assist the performance improvement of algorithms for classifying the survival expectancy of cardiovascular heart failure patients into 'Survived' or 'Deceased', compared to Logistic Regression, KNN, and C4.5.
Design and Build of IoT Based Flood Prone Monitoring System at Semani’s Pump House Drainage System 'Aisyah 'Aisyah; Aji Ery Burhandenny; Happy Nugroho; Didit Suprihanto
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1581.303-316

Abstract

Floods are a common disaster in watersheds, and flood control is difficult. However, losses can be reduced by quickly disseminating alert status information. This paper proposes a prototype of a monitoring system that can determine the status of flood alerts in real time and quickly disseminating to the community, allowing people to be better prepared for flood disasters. The system was developed using the RD method and consists of hardware and software development. The hardware comprises several sensor modules to read the discharge, temperature, humidity, and water level and to transmit the readings to the software. The software is divided into two applications: a website application and a Telegram application. The public can find the flood alert status history data from the website and obtain flood alert status warning messages and the latest alert status from Telegram. The results of the tests indicated that the sensors were very accurate, with a MAPE value of less than 10%. The software test also showed that the input and output were according to design. The proposed system can potentially reduce flood losses by providing early warning information to the community. The system is also scalable and adaptable to other watersheds.
Comparative Study of Herbal Leaves Classification using Hybrid of GLCM-SVM and GLCM-CNN Purnawansyah Purnawansyah; Aji Prasetya Wibawa; Triyanna Widyaningtyas; Haviluddin Haviluddin; Cholisah Erman Hasihi; Ming Foey Teng; Herdianti Darwis
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1759.382-389

Abstract

Indonesia is a tropical country with a diverse range of plants that ancient people used for traditional medicines. However, the similarity in shape of the leaves became an obstacle to distinguishing them. Therefore, technological advancements are expected to help identify the herbal leaves to use them right on target according to their efficacy. In this research, image classification of katuk (Sauropus Androgynus) and kelor (Moringa Oleifera) leaves is applied using 3 different algorithms i.e hybrid of Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Support Vector Machine (SVM) implementing 4 kernels namely linear, RBF, polynomial, and sigmoid; hybrid of GLCM and Convolutional Neural Network (CNN); and pure CNN. A dataset of 480 images has been collected with 2 different scenarios, including bright and dark intensities. Based on the result, a hybrid of GLCM and SVM showed the highest accuracy of 96% in the dark intensity test using a linear kernel, while sigmoid obtained the lowest accuracy of 35%. On the other hand, it has been discovered that CNN obtained the highest performance in the bright intensity test with an accuracy of 98%. While in the dark intensity test, a hybrid of GLCM and CNN is superior, obtaining 96% accuracy. In conclusion, CNN is more powerful for image classification with bright intensity. For dark intensity images, both the hybrid of GLCM+SVM (linear) and the hybrid of GLCM+CNN are fairly recommended.
Application of General Regression Neural Network Algorithm in Data Mining for Predicting Glass Sales and Inventory Quantity Suryani, Suryani; Intan, Indo; Mochtar Yunus, Farhan; Haris, Adammas; Faizal, Faizal; Nurdiansah, Nurdiansah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1562.229-239

Abstract

FF Jaya Glass is a shop that supplies and installs 3 mm to 12 mm glass. The store obtained glass from suppliers to be processed in shape and size according to customers’ order. After completing the customer's order, the shop worker will install the glass at the requested location. Unfortunately, currently stores do not utilize sales data to predict sales either manually or by utilizing technology. As a result, the store cannot predict when the number of glass orders will increase or decrease. In addition, errors often occur when ordering glass for the next period. As a result, stores often run out of glass supplies due to the large number of glass orders so that the achievement of profits is not optimal. This study aims to identify sales variables in glass sales data and build a general regression neural network model as a data mining method. In addition, this study aims to iterate to find the best value in the sales data training process, design and create applications according to user needs, and conduct system validation tests. The general regression neural network method is used to predict sales. The results of this study indicate that the application of general regression neural networks can be used to predict sales. This will make it easier for the store to provide glass supplies in the coming months with an accuracy of 98.1%.
Combination of YOLOv3 Algorithm and Blob Detection Technique in Calculating Nile Tilapia Seeds Diana Tri Susetianingtias; Eka Patriya; Rini Arianty
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1634.317-325

Abstract

Baby Fish counting must be counted accurately so it will not cause any loss, especially for fish seeds or fingerlings that have a small size. Generally, people still use conventional counting methods that produce low accuracy values. This research will make a Nila Baby Fish fingerlings counter program using the YOLOv3 algorithm and Blobb detection technique. The annotation data process will use LabelImg, and the dataset training will use Google COLABoratory with the Darknet framework in an online environment. Images that will predict in this program will be called and detected with an object detector. The object with a confidence score of more than 0.3 will be converted into a blob. The blob value will be forwarded to the output layer for scaling the bounding box objects. The output of this program is the predicted image, blob value, prediction time, and the number of Nila seeds. The model performance is evaluated using a confusion matrix and got a 98.87% for accuracy score.
Feature Space Augmentation for Negation Handling on Sentiment Analysis Ilmawan, Lutfi Budi; Muladi, Muladi; Prasetya, Didik Dwi
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1695.353-357

Abstract

One crucial issue affecting the performance of sentiment analysis tasks is negation. Handling negation involves determining the negation scope and negation cue. Feature space augmentation is one approach used to address negation. Feature space augmentation has been carried out by some previous researchers using a negation flag with the rule that the negation scope includes all words from the explicit negation cue to the punctuation mark. This study aimed to analyze the classifier's performance when negation handling was applied by adding a new rule for the negation scope. The new rule for determining the negation scope no longer took all words from the negation cue to the punctuation mark, but only considered or ignored words with certain POS tags. The results of this study showed that using the new rule for negation scope contributed to improving the performance of the classifier in sentiment analysis tasks. The proposed approach for negation handling was better than the previous approach in terms of accuracy, precision, recall, and f1-score.

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