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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 11 Documents
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Search results for , issue "Vol 15, No 3 (2023)" : 11 Documents clear
Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm Sugiono, Bhima Satria Rizki; Hadi, Mokh. Sholihul; Zaeni, Ilham Ari Elbaith; Sujito, Sujito; Irvan, Mhd
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1945.413-426

Abstract

The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions
Analysis of Twitter User Sentiment on Presidential Candidate Anies Baswedan Using Naïve Bayes Algorithm Setiawan, Rudi; Dewi, Fitria
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1775.473-487

Abstract

Indonesian hold presidential election in 2024. One of the most discussed topics by public is the presidential candidates. The discussion about the presidential candidate certainly reaped various kinds of responses from public, ranging from support to statements of disapproval. This research was limited to the candidacy of Anies Baswedan as a presidential candidate before a vice president candidate as his pair was selected. The purpose of this study is to conduct a sentiment analysis of public responses regarding Indonesia 2024 presidential candidate Anies Baswedan using tweets data from October 2022 to January 2023 using the naïve bayes classifier algorithm. This is expected to provide an overview of the public opinions on Twitter. Three test models were carried out with differences in the division of the amount of training data and test data, respectively 60%:40%, 70%:30% and 80%:20%. The test results showed the highest accuracy level was obtained by the 3rd model using training and testing data of 80%:20% with an accuracy value of 76.21%. Further research is recommended to conduct sentiment analysis on the pairs of Presidential and Vice-Presidential candidates who have been officially registered with the General Election Commission using various other classification algorithms.
Sentiment Analysis of Shopee App Reviews Using Random Forest and Support Vector Machine Suswadi, Suswadi; Erkamim, Moh.
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1610.427-435

Abstract

During the COVID-19 outbreak, Indonesian marketplaces were significantly impacted including Shopee app. It is necessary to evaluate the features and services of the Shopee application by looking at the feedback given by the public in Google Play Store reviews. This is what prompted research to be conducted from Kaggle data in the form of Shopee reviews. From this data, sentiment analysis is carried out utilizing the Support Vector Machine (SVM) and Random Forest methods. This method are used to classify reviews based on positive and negative sentiments. The results showed that the level of classification accuracy in the Random Forest model is 82.21%. While the SVM model provides a higher level of accuracy of 84.71%. Data exploration on positive and negative sentiment classes is used to find insight into this problem. In positive sentiment, words that often appear such as “belanja”, “aplikasi”, and “barang” are found. As for the negative sentiments, namely “ongkir”, “kirim”, “aplikasi”. These words can be used to be a quality improvement or evaluation for the Shopee company.
MobileNet Classifier for Detecting Chest X-Ray Images of COVID-19 based on Convolutional Neural Network Ghani, ST. Aminah Dinayati; Intan, Indo; Rizal, Muhammad
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1780.488-497

Abstract

Since the COVID-19 pandemic occurred all over the world, numerous studies were carried out to overcome this problem, including COVID-19 image analysis. An expert analysis based on the Chest X-ray images of COVID-19 determines the progression of the lung condition. Eye visualization and expertise of a radiologist have limitations in handling big cases. This study aims to implement the Convolutional Neural Network (CNN) and MobileNet models as deep learning models to classify chest X-ray images into multiclassification, three categories: COVID-19, normal, and virus. The processes were pre-processing and processing. The pre-processing stage was preparing data, and the processing stage was the implementation model and investigating the best model performance in both convolution and classification in depth-wise convolution and batch normalization. The metrics were accuracy, precision, f1-score, and recall. The CNN results of accuracy, precision, recall, and f1-score respectively were 0.94; 0.99; 0.95; and 0.96. The MobileNet results of the metrics were 0.97; 0.98; 0.99, and 0.99. The MobileNet outperforms the CNN results due to depth-wise convolution and batch normalization. Both models contribute to the faster epoch of the best hyperparameter to achieve loss and accuracy convergence. The models are worth recommending to deployment front-end.
Z-Score and Floyd Warshall Algorithms for Determining Alternative Routes of Mugging-Prone Areas in Medan City, Indonesia Dinata, Rozzi Kesuma; Bustami, Bustami; Fiasari, Fiasari; Retno, Sujacka
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1608.436-444

Abstract

This study analyzes and implements the Floyd Warshall algorithm using Z-Score to track alternative routes to areas in Medan City, Indonesia that are prone to mugging. The data was obtained from Porlestabes (Police station) Medan-Indonesia. This study employed the Z-Score rank method to rank specific values and determine the levels of crime-prone areas. The highest and lowest levels of crime-proneness were identified using the Z-Score method, and the Floyd Warshall Algorithm is used to identify alternative routes to avoid such areas. The language used in this study adheres to objective and formal writing principles, with value-neutral and clear terminology employed throughout.  The results of this analysis showed that out of 99 roads across 18 districts, 4.04% of them were classified as very high prone, 9.09% as high prone, 11.11% as prone, and 75.76% as low prone. The search results from conducting alternative route analysis with the Floyd Warshall algorithm on Perintis Kemerdekaan street indicate the identification of the safest routes.
MobileNet V2 Implementation in Skin Cancer Detection Pradnya Dhuhita, Windha Mega; Ubaid, Muhammad Yahya; Baita, Anna
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1702.498-506

Abstract

Skin cancer is one of the most worrying diseases for humans. In Indonesia alone, skin cancer occupies the third position after cervical cancer and breast cancer. Currently, doctors still use the biopsy method to diagnose skin cancer. It is less effective because this method requires the performance of an experienced doctor, takes a long time, and is a painful process. Because of that, we need a way in which skin cancer can be classified using dermoscopic images to help doctors diagnose skin cancer earlier. Researchers proposed to classify skin cancer into seven classes, namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevus, and vascular lesions. The method used in this study is a convolutional neural network (CNN) with the MobileNet V2 architecture. The dataset used is the HAM10000 dataset, with a total of 10015 images. In this study, a comparison was made between data augmentation, learning rate, epochs, and different amounts of data. Based on the test results, the highest accuracy results were obtained, namely 79%. The best model is implemented into a mobile application.
Realtime Monitoring and Analysis Based on Cloud Computing Internet of Things (CC-IoT) Technology in Detecting Forest and Land Fires in Riau Province Irawan, Yuda; Muzawi, Rometdo; Alamsyah, Agus; Renaldi, Reno; Elisawati, Elisawati; Nurhadi, Nurhadi; Amartha, Mohd Rinaldi; Mitrin, Abdullah; Asnal, Hadi; Hartomi, Zupri Henra
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1636.445-454

Abstract

Forest and land fires in Riau are natural disasters that always repeat every time they enter the dry season. The solution of this research is to apply the leading technology of cloud computing internet of things (CC-IoT) to find out more quickly the existence of forest or land fires. This study uses Particle Argon (Photon) to connect to the internet and several IR Fire Detector sensors, DHT22 MQ2 and GPS Neo 6m. Particle Argon can receive input and perform processing so that it is connected using the CC-IoT concept to a web server so the users can monitor land conditions in real time. Based on the test results, it can be concluded that a fire detector using fire parameters (2000 = Normal and 2000 = Danger) , temperature (≤37 = Normal, 38 – 45 = Alert, and 46 = Danger), humidity (≤50 = Dry, 51 = Humid) , smoke (≤ 1700 = Normal, 1700 = Danger), and soil moisture can work well ( 3500 = Dry Moisture Content, 1500 to 3500 = Medium Moisture Content, and 1500 = High Moisture Content). The fire detection tool developed can detect fires in real time and also has a fire early detection function that is useful for anticipating land conditions to prevent fires. The results obtained from the test are that the sensor can read indications of fire, smoke, soil moisture with a success rate of 93% and send location data and sensor values to the website. The use of sensors has their respective roles so that if there is a problem with one of the sensors, the tool has an alternative sensor and can continue to function.
N-gram and Kernel Performance Using Support Vector Machine Algorithm for Fake News Detection System Jollyta, Deny; Gusrianty, Gusrianty; Prihandoko, Prihandoko; Sukrianto, Darmanta
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1770.398-404

Abstract

The modern technological advancements have made it simpler for fake news to circulate online. The researchers have developed several strategies to overcome this obstacle, including text classification, distribution network analysis, and human-machine hybrid methods. The most common method is text categorization, and many researchers offer deep learning and machine learning models as remedies. An Indonesian language fake news detection system based on news headlines was developed in this work using the Support Vector Machine (SVM) kernel and n-gram. The objective of this research is to identify the model that produces the best performance outcomes. The system deployment on the web will employ the model that produces the greatest outcomes. According to the research findings, the linear kernel SVM algorithm produces the best results, with an accuracy value of 0.974. Furthermore, the bigram feature used in the development of a classification model does not increase the precision of fake news identification in Indonesian. Utilizing the unigram function yields the most accurate results.
Comparative Analysis of Long Short-Term Memory Architecture for Text Classification Fajar Abdillah, Moh; Kusnawi, Kusnawi
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1906.455-464

Abstract

Text classification which is a part of NLP is a grouping of objects in the form of text based on certain characteristics that show similarities between one document and another. One of methods used in text classification is LSTM. The performance of the LSTM method itself is influenced by several things such as datasets, architecture, and tools used to classify text. On this occasion, researchers analyse the effect of the number of layers in the LSTM architecture on the performance generated by the LSTM method. This research uses IMDB movie reviews data with a total of 50,000 data. The data consists of positive, negative data and there is data that does not yet have a label. IMDB Movie Reviews data go through several stages as follows: Data collection, data pre-processing, conversion to numerical format, text embedding using the pre-trained word embedding model: Fastext, train and test classification model using LSTM, finally validate and test the model so that the results are obtained from the stages of this research. The results of this study show that the one-layer LSTM architecture has the best accuracy compared to two-layer and three-layer LSTM with training accuracy and testing accuracy of one-layer LSTM which are 0.856 and 0.867. While the training accuracy and testing accuracy on two-layer LSTM are 0.846 and 0.854, the training accuracy and testing accuracy on three layers are 0.848 and 864.
The Determination of Dawn Time through Image Processing Camera Ramza, Harry; Yudhanto, Tossa Hario; Sugiharto, Dedy; Ulum, As’ad Syaifudin; Mujirudin, Mohammad; Roza, Emilia; Ab-Rahman, Mohammad Syuhaimi; Saksono, Tono; Md Khir, Mohd Haris
ILKOM Jurnal Ilmiah Vol 15, No 3 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i3.1687.405-412

Abstract

Determining the early time prayer is very fundamental for Muslims as it directly relates to the legal requirements of prayer. Prayers are not performed whenever we want, but rather there is a determination of the beginning and end of the prayer time as a guideline for Muslims to carry it out. The Indonesia government sets standards for Muslims to perform the dawn prayer service, by precisely determining the degree of the emergence of the dawn of Sadiq by -200. This study aims to compare the determination of the government's dawn time using different sensors, specifically drone cameras as image sensors. Drones were chosen due to their several advantages. The data generated by the drone is in the form of photos, which are subsequently processed using digital image processing software, called image-J. The data obtained are in the form of mean and standard deviation. All data collected in 1 day is recorded using Excel to form a graph of data which is then carried out by a polynomial approach to find out the cutoff point as the beginning of the dawn of Sadiq which indicates the start of dawn. The method used in this research is using the 4th order polynomial approach and the Sarrus method and the data obtained is the mean value and standard deviation. The conclusions obtained in the image analysis research are that the government's dawn time is 15 minutes too fast, the standard obtained in this study is -14.98° and unlike 2D SQM data, 3D drone data results in more accurate data analysis.

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