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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.
Arjuna Subject : -
Articles 580 Documents
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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.
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
Deep Learning Based Technical Classification of Badminton Pose with Convolutional Neural Networks Tukino, Tukino; Pratiwi, Mutiana; Defit, Sarjon
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1951.76-86

Abstract

This research aims to identify and categorize badminton strategies using a Convolutional Neural Network (CNN) model combined with BlazePose architecture and Mediapipe Pose Solution tools, yielding understandable and practical results. The challenge of finding the best mobility strategy for badminton serves as the primary motivation for this study. The research employs an image recognition and supervised learning approach to classify poses in badminton training videos. The training data comprises various photos and images representing different badminton techniques, such as Service Technique and Smash Technique. After data processing, the CNN model is trained using the training data to identify and classify poses in badminton training videos. Testing is conducted using test data, and classification accuracy is evaluated using the CNN method. The results show that the CNN model implemented alongside BlazePose and Mediapipe Pose Solution achieves significant classification accuracy, ranging from 80% to 90%. Thus, this research presents an effective and practical method for classifying badminton strategies based on poses in training videos.
Vulnerability Assessment and Penetration Testing on Student Service Center System Isnaini, Khairunnisak; Asyari, Muhammad Hasyim; Amrillah, Sigit Fathu; Suhartono, Didit
ILKOM Jurnal Ilmiah Vol 16, No 2 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i2.1969.161-171

Abstract

The number of system breaches has recently increased across various sectors, including the education sector. These breaches are carried out through various methods such as SQL Injection, XSS Attack, web defacement, malware, and others. Security vulnerabilities in the system also pose a potential threat to the Student Service Center owned by XYZ University, which stores a significant amount of confidential and sensitive data. The worst impact of all is the system is paralyzed, damaging the ongoing performance and reputation of institutions. The purpose of this research is to identify security vulnerabilities in the system using the Vulnerability Assessment and Penetration Testing (VAPT) method. The results showed that the system identified file upload functionality that poses a risk of being exploited for security attacks. Additionally, file path traversal can allow unauthorized access to directories, potentially enabling the injection of malicious code. Future research could explore the application of machine learning to enhance security measures and streamline the penetration testing process
Quantifying of runC, Kata and gVisor in Kubernates Purwoko, Rahmat; Priambodo, Dimas Febriyan; Prasetyo, Arbain Nur
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1679.12-26

Abstract

The advent of container technology has emerged as a pivotal solution for application developers, addressing concerns regarding the seamless execution of developed applications during the deployment process. Various low-level container runtimes, including runC, Kata Container, and gVisor, present themselves as viable options for implementation. The judicious selection of an appropriate low-level container runtime significantly contributes to enhancing the efficiency of Kubernetes cluster utilization. To ascertain the optimal choice, comprehensive testing was conducted, encompassing both performance and security evaluations of the low-level container runtimes. This empirical analysis aids developers in making informed decisions regarding the selection of low-level container runtimes for integration into a Kubernetes cluster. The performance assessments span five key parameters: CPU performance, memory utilization, disk I/O efficiency, network capabilities, and the overall performance when executing an nginx web server. Three distinct tools—sysbench, iperf3, and Apache Benchmark—were employed to conduct these performance tests.  The findings of the tests reveal that runC exhibits superior performance across all five parameters evaluated. However, a nuanced consideration of security aspects is imperative. Both Kata Container and gVisor demonstrate commendable host isolation, presenting limited vulnerability to exploitation. In contrast, runC exposes potential vulnerabilities, allowing for exploits against the host (worker node), such as unauthorized directory creation and system reboots. This comprehensive analysis contributes valuable insights for developers, facilitating an informed decision-making process when selecting low-level container runtimes within a Kubernetes environment.
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.

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