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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 12 Documents
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Search results for , issue "Vol 16, No 2 (2024)" : 12 Documents clear
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
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
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.
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
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.
Telegram bot-based Flood Early Warning System with WSN Integration Wahid, Abdul; Parenreng, Jumadi Mabe; Kusnandar, Welly Chandra Kusumah; Adi, Puput Dani Prasetyo; Mahabror, Dendy; Sariningrum, Ros
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.v16i2.1699.151-160

Abstract

Indonesia experiences frequent flooding, with data from the National Disaster Management Agency (BNPB) revealing that floods account for 41% of all natural disasters (1,441 incidents) recorded in 2021. These floods cause significant property damage and casualties. To address this challenge, we have developed a prototype flood early warning system. This system utilizes ultrasonic sensors for real-time water level detection. Sensor data is transmitted to designated personnel through a website interface. Additionally, the system leverages a Telegram bot to deliver flood early warnings directly to the community residing in flood-prone areas. The sensor data comparison test yielded an error rate of only 0.6175% with an average difference of 1 cm, demonstrating the system's accuracy and functionality. Furthermore, a notification test conducted ten times achieved 100% accuracy. The Telegram bot successfully sent text message alerts (alert 1, alert 2, alert 3) with an average delivery time of 4.07 seconds. This prototype offers a promising solution for flood mitigation. By providing real-time water level data and issuing timely alerts via a user-friendly Telegram bot, the system empowers communities to prepare for potential flooding and minimize associated risks.
Driver Facial Detection Across Diverse Road Conditions Shofiah, Siti; Sediyono, Eko; Hasibuan, Zainal Arifin; Kristianto, Budhi; Setiawan, Santo; Pratindy, Raka; Hakim, M. Iman Nur; Humami, Faris
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.v16i2.1996.108-114

Abstract

This study emphasizes the importance of facial detection for improving road safety through driver behavior analysis. Its employs quantitative methodology to underscore the importance of facial detection in enhancing road safety through driver behavior analysis. The research utilizes the Python programming language and applies the Haar cascade method to investigate how environmental factors such as low light, shadows, and lighting changes influence the reliability of facial detection. Employing the AdaBoost algorithm, the study achieves face detection rates exceeding 95%. Practical testing with an ASUS A416JA laptop and Raspberry Pi under varied lighting conditions and distances demonstrates optimal performance in detecting faces between 30 cm and 70 cm, with reduced efficacy outside this range, particularly in low light conditions and at night. Challenges identified include decreased performance in low light conditions, emphasizing the need for improved algorithmic calibration and enhancement. Future research directions involve refining detection algorithms to effectively handle diverse environmental conditions and integrating advanced machine learning techniques, thereby enhancing the accuracy of driver behavior analysis in real-world scenarios and contributing to advancements in road safety
Optimizing Windowing Techniques to Improve the Accuracy of Artificial Neural Networks in Predicting Outpatient Visits Nurcakhyadi, Fredianto; Hermawan, Arief
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.v16i2.2254.172-183

Abstract

Hospitals are healthcare institutions that play an important role in providing health services to the community. To optimize the service, hospitals need to predict the number of outpatient visits. The objectives of this research are (1) determine the effect of window size on the accuracy of predicting the number of outpatient visits, and (2) identify the best window size and accuracy of neural networks in predicting the daily number of outpatient visits. To achieve the research objectives, the following steps were undertaken: data collection of outpatient visits at RSUD dr. Soedirman Kebumen from 2018 to 2023, preprocessing, applying different window sizes, modeling neural networks, and testing by calculating the RMSE value for each window size. The test results show that the lowest RMSE for 2018 was 1.267 with a window size of 34, for 2019 was 1.262 with a window size of 34, for 2020 was 1.515 with a window size of 17, for 2021 was 1.81 with a window size of 18, for 2022 was 1.282 with a window size of 20, and for 2023 was 1.263 with a window size of 29. These window sizes indicate the cycle of outpatient visits each year. By understanding these visit cycles, the number of outpatient visits can be predicted at any time.
Prediction and Analysis of Rice Production and Yields Using Ensemble Learning Techniques Sulistya, Yudha Islami; Musdholifah, Aina; Sapuletea, Chrissandy; Br Bangun, Elsi Titasari; Hamda, Hizbullah; Anjani, Sarah; Septiadi, Abednego Dwi
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.v16i2.1948.115-124

Abstract

This research focuses on predicting and analyzing rice production and yield throughout the world using ensemble learning techniques. The study applies and compares three methods: linear regression, ARIMA, and ensemble learning, to predict rice harvest yields. The results show that ensemble learning techniques significantly improve prediction performance. For instance, the ensemble model for predicting area harvested, combining Model 6 (linear regression) and Model 10 (ARIMA), achieved  of coefficient of determination outperforming the individual models. Similarly, for predicting yield, the ensemble model combining Model 4 (linear regression) and Model 9 (ARIMA) achieved  of coefficient of determination indicating superior prediction accuracy. For predicting production, the ensemble model combining Model 2 (linear regression) and Model 8 (ARIMA) achieved  of coefficient of determination. These results demonstrate the effectiveness of ensemble learning in enhancing prediction accuracy with lower MSE and RMSE values. By analyzing various factors influencing rice yields, this research provides valuable insights for increasing rice production and yield, supporting efforts to improve the efficiency and effectiveness of rice farming, and contributing to achieving the United Nations Sustainable Development Goals (SDGs).
Enhancing Accuracy by Using Boosting and Stacking Techniques on the Random Forest Algorithm on Data from Social Media X Putra, Teri Ade; Ariandi, Vicky; Defit, Sarjon
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.v16i2.2058.184-189

Abstract

Online loans (commonly referred to as Pinjol) have become a widespread phenomenon in Indonesia, both in legal and illegal forms. It is undeniable that this is in line with the rapid development and innovation of technology. Pinjol cannot be separated from public comments, both positive and negative, on social media X. The study examined the communication patterns of Indonesian people using a sentiment analysis approach. The research utilized the Random Forest algorithm to perform sentient analysis. This algorithm combined the output of several decision trees to achieve a more accurate result. In addition to using a random forest algorithm, this study also made improvements by using stacking and boosting. The results of this study indicated that the highest accuracy of 86% was obtained by the SMOTE+RF+Adaboost (Boosting) model. In contrast, the lowest accuracy  of 60% was obtained in the RF+Adaboost model with a stacking technique.

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