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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 574 Documents
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Smart Egg Incubator Based on IoT and AI Technology for Modern Poultry Farming Wahyuni, Refni; Irawan, Yuda; Febriani, Anita; Nurhadi, Nurhadi; Tri Saputra, Haris; Andrianto, Richi
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.v16i2.1957.134-144

Abstract

The productivity of egg hatching in the poultry industry is often hindered by conventional methods, resulting in low hatch rates and slow production. This study introduces the UHTP (Universitas Hang Tuah Pekanbaru) Smart Egg Incubator, which incorporates Internet of Things (IoT) and Artificial Intelligence (AI) technologies, specifically the Mamdani Fuzzy Logic Algorithm, to enhance egg hatchability. The incubator features a 100-egg capacity, automatic temperature and humidity control, cooling systems, and real-time monitoring via mobile devices. It also includes a camera for movement detection, capturing images of hatching eggs, and sending notifications to users. The automatic egg-turning mechanism ensures even temperature distribution. Experimental results show that the incubator maintains optimal temperatures between 37.7°C and 38.8°C, with successful hatching observed on the 19th day. The fuzzy logic AI system effectively manages environmental changes, ensuring a stable hatching process by dynamically adjusting the conditions within the incubator. The user-friendly interface and remote monitoring capabilities provide convenience and efficiency for poultry farmers. This innovative design significantly improves hatch rates and supports the economic productivity of chicken farming, offering practical solutions for modern poultry farming. The integration of this AI technology can lead to higher profitability and sustainability in poultry farming, addressing common challenges such as inconsistent environmental conditions and labor-intensive processes, thus contributing to the advancement of agricultural practices
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.
Multiclass Classification of Rupiah Banknotes Based on Image Processing Azis, Huzain; Purnawansyah, Purnawansyah; Alfiyyah, Nurul
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.v16i1.1784.87-99

Abstract

This research aims to classify the nominal value of Rupiah banknotes using image processing and classification methods. The research design was conducted by collecting a dataset of Rupiah banknotes consisting of 30 classes, each with 100 images. This research uses image preprocessing using Canny Segmentation to create object edges and clarify image details. The Hu Moments method, which describes pixel distribution and object shape, is used to extract special features from the image. Classification modeling is then performed using Decision Tree and Random Forest to classify banknotes based on the extracted characteristics. Model evaluation is performed by measuring accuracy, precision, recall, and f1socre performance and using cross-validation with k-fold=5. The results show that the Decision Tree method is able to classify Rupiah banknotes well. In the performance evaluation, the Decision Tree method achieved the highest accuracy of 86.83% and good precision, recall, and f1-score for several banknote classes. The Random Forest method also achieved good results, with the highest accuracy of 78.67%. The classification evaluation results show that the Decision Tree method is better than the Random forest in classifying Rupiah banknotes.
Ensemble Techniques Based Risk Classification for Maternal Health During Pregnancy Mustamin, Nurul Fathanah; Buang, Ariyani; Aziz, Firman; Nur, Nur Hamdani
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.v16i2.2005.190-197

Abstract

This research focuses on the critical aspect of maternal health during pregnancy, emphasizing the need for early detection and intervention to address potential risks to both mothers and infants. Leveraging various classification methods, including Naïve Bayes, decision trees, and ensemble learning techniques, the study investigates the prediction of childbirth potential and pregnancy risks. The research begins with data collection, followed by preprocessing to clean and prepare the data, including handling missing values and normalization. Next, cross-validation is performed to ensure model robustness. Five ensemble techniques are used for risk classification: Ensemble Boosted Trees, which enhances the performance of decision trees; Ensemble Bagged Trees, which combines predictions from decision trees trained on different subsets of data; Ensemble Subspace Discriminant, which applies discriminant analysis on random subspaces; Ensemble Subspace KNN, which uses K-Nearest Neighbors (KNN) within random subspaces; and Ensemble RUS Boosted Trees. Key variables such as maternal age, height, Hb levels, blood pressure, and previous pregnancy history are considered in these analyses. Additionally, the study introduces Ensemble Learning based on Classification Trees, revealing significant improvements in accuracy compared to cost-sensitive learning approaches. The comparison of methods, including Naïve Bayes and K-Nearest Neighbor, provides insights into their respective performances, with ensemble techniques demonstrating their potential. The proposed ensemble learning techniques, namely Ensemble Boosted Trees, Ensemble Bagging Trees, Ensemble Subspace Discriminant, Ensemble Subspace KNN, and Ensemble RUS Boosted Trees, are systematically evaluated in classifying pregnancy risks based on a comprehensive dataset of 1014 records. The results showcase Ensemble Bagging Trees as a standout performer, with an accuracy of 85.6%, indicating robust generalization and effectiveness in clinical risk assessment compared to traditional methods such as Decision Tree (61.54% accuracy), K-Nearest Neighbor (74.48%), Ensemble Learning based on Cost-Sensitive Learning (73%), Ensemble Learning based on Classification Tree (76%), Gaussian Naïve Bayes (82.6%), Multinomial Naïve Bayes (84.8%), and Bernoulli Naïve Bayes (84.8%). Ensemble Bagging Trees achieved the highest accuracy proving to be more effective than the other methods. However, the study emphasizes the need for continuous refinement and adaptation of ensemble methods, considering both accuracy and interpretability, for successful deployment in healthcare decision-making. These findings contribute valuable insights into optimizing pregnancy risk classification models, paving the way for improved maternal and infant healthcare outcomes.
Machine Learning and Internet of Things (IoT): A Bibliometric Analysis of Publications Between 2012 and 2022 Gani, Hamdan; Damayanti, Annisa Dwi; Nurani, Nurani; Zuhriyah, Sitti; Jabir, St. Nurhayati; Gani, Helmy; Zhipeng, Feng; Rejeki, Aisyah Sri
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.v16i1.1700.27-37

Abstract

The implementation between machine learning and the Internet of Things (IoT) has been scientifically investigated in many studies. However, not many bibliometric studies categorize the output in this area. By keeping an eye on the publications posted on the Web of Science (WoS) platform, this study aims to give a bibliometric analysis of research on Machine Learning and IoT, identifying the state of the art, trends, and other indicators. 6.170 different articles made up the sample. The VOS viewer software was used to process the data and graphically display the results. The study examined the concurrent occurrence of publications by year, keyword trends, co-citations, bibliographic coupling, and analysis of co-authorship, countries, and institutions. several prolific authors are discovered. However, the body of literature on machine learning and IoT issues is expanding quickly; only five papers accounted for more than 2193 citations. Then, 40.34 percent of the articles from the 694 sources reviewed were published as the most important paper. At the same time, the USA is the top nation for research on this subject area. In addition to identifying gaps and promising areas for future research, this study offers insight into the current state of the art and the field of machine learning and IoT.
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.
Improving Source Code Quality by Minimizing Refactoring Effort Oumarou, Hayatou; Tizi, Kabirrou Hamadou
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.v16i2.1908.145-150

Abstract

Software maintenance is a time-consuming and costly endeavor. As a part of maintenance, refactoring is aimed at enhancing quality. Due to project deadlines and limited resources, developers need to prioritize refactoring activities. In this paper, we present a livestock management-inspired approach for identifying and prioritizing classes to refactor within an object-oriented program. This approach empowers developers to enhance the time/quality ratio. The novelty of our approach lies in utilizing established metrics for detecting code defects to prioritize each class. To validate its effectiveness, the approach was tested on four distinct Pharo-based open source programs. The results demonstrate the approach's efficacy in improving software quality, reducing development time, and enhancing team productivity
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.
Classification of Correlation Patterns Based on electrocardiogram Data of Heart Defects Using the Pearson Correlation Coefficient Method Sumiati, Sumiati; Fernando, Donny; Hasoloan, Hamonangan Iman; Purnamasari, Marlia
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.v16i1.1927.100-107

Abstract

This study was conducted to map the relationship between a symptom and the type of heart disease, based on the results of the electrocardiogram medical record data. The purpose of this study was to apply a symptom correlation pattern based on electrocardiogram data of heart abnormalities. Where the results of this study produce values that determine symptoms that have a very close relationship with the type of heart disorder, and make an analysis to diagnose normal and abnormal heart disorders using the Pearson Correlation Coefficient (PCC) approach. The results show that the relationship between symptoms has a very strong relationship. dominant with normal heart defects is the relationship between AV conduction duration and other symptoms because the relationship between AV conduction duration and other symptoms has a very strong average level of association. symptoms also have a strong average level of association, while the relationship between other symptoms appears to have a moderate relationship and does not even have any relationship with someone who is identified as having a heart abnormality diagnosis (abnormal) and normal heart
Optimizing THD in Modified Multilevel Inverters with IoT-Integrated MPPT Systems for Enhanced Efficiency Syarifuddin, Andi; Pakka, Hariani Ma'tang; Eren, Halit; AlGhamdi, Ahmed Saeed; Baso, Nur Fadliah
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.v16i2.2092.198-209

Abstract

This work proposes a new Modified Multilevel Inverter (MMLI) and provides a comprehensive comparison with Conventional Cascaded H-bridge Inverters. The MMLI features fewer switching devices compared to the conventional H-Bridge Inverter for 9-level voltages and higher. Maximum Power Point Tracking (MPPT) incorporated with a Boost converter ensures a constant output from Photovoltaic (PV) arrays, which is then fed to the inverter to achieve the desired number of voltage levels. To enhance the performance and efficiency of the system, IoT technologies were integrated for real-time monitoring and control. Smart sensors and cloud-based platforms were utilized for data collection and analysis, enabling precise control of the MPPT and inverter systems. The integration of IoT resulted in significant improvements in the system's dynamic response, energy conversion efficiency, and overall reliability. The results were validated through simulations in Simulink, with outcomes presented and compared for voltage waveform and harmonic spectrum. The integration of IoT technologies provided substantial benefits, showcasing the interdisciplinary approach of this research in reducing Total Harmonic Distortion (THD) while optimizing inverter operations.

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