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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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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).
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.
Evaluation of Machine Learning Models for Predicting Cardiovascular Disease Based on Framingham Heart Study Data Suhatril, Ruddy J; Syah, Rama Dian; Hermita, Matrissya; Gunawan, Bhakti; Silfianti, Widya
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.v16i1.1952.68-75

Abstract

The Framingham Heart Study Community is a research community that produces data related to Cardiovascular Disease (CVD). This research applies technology to predict CVD using machine learning based on data from the Framingham Heart Study. The eight machine learning algorithms are deployed in this study, they are decision tree, naïve bayes, k-nearest neighbors, support vector machine, random forest, logistic regression, neural network, and gradient boosting.This research uses several stages of research such as load dataset, preprocessing data, data modeling, evaluation of various data modelling, and input new data.  The best performance was produced by the random forest model with an accuracy value of 0.84, a precision value of 0.84, a recall value of 0.85, an f1-score value of 0.79 and an AUC value of 0.72. The prediction generated by the proposed machine learning model is high risk or low risk of CVD.
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.
Fall Rate Detection, Identification and Analysis Object Oriented for Elderly Safety Sudirman, Sudirman; Suyuti, Ansar; Zainuddin, Zahir; Fauzan, Arief
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.v16i1.1654.1-11

Abstract

The aged population in Indonesia in 2021 is 30. Sixteen million people. The aged populace elderly 60 years and over reached 11.01% of the complete populace of Indonesia, which amounted to 273.88 million humans. There are ages who live on their own because of busy households with work. if there's an incident of falling elderly, a motion detection gadget is needed for monitoring the situation of the elderly at domestic. This takes a look at designing a visual synthetic intelligence hobby recognition gadget with entry from the digital camera to come across aged sports from video. take video records with the photograph Acquisition technique, Foreground Detection for changing photographs into binary, masks R-CNN to come to aware of detection items and discover the location of the incident, movement history photo, and C_motion to represent the placement of the detected object's body, SVM magnificence to categorize aged statistics falls or sports of every day residing. The experimental outcomes display that this device can come across the condensed-space version with an accuracy of ninety-seven, 50.
The Application of Weighted Ranking Method Using Combination of ROC and CPI to Select Eligible Family for Keluarga Harapan Program Aids Laswi, Aishiyah Saputri; Ulvah, Ulvah; Dasril, Dasril
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.v15i3.1614.465-472

Abstract

The Keluarga Harapan Program (KHP), a financial assistance, is a program launched by the government to deal with poverty in various regions of Indonesia by conducting direct surveys and collecting data on disadvantaged families in each region. However, the issue is that many recipients do not meet the appropriate criteria or are not categorized as recipients. The Composite Performance Index and Rank Centeroid algorithms are a solution in the selection process for the recipients for the KHP by carrying out the analysis and comparison stages of whether they are categorized as KM (Disadvantage Families) through several stages. The results obtained based on analysis for recipient selection with a minimum performance index coverage value of 70% can be categorized as eligible to receiv assistance. In this study, 50 KM data samples were taken with the highest assessment value 128.41. In the top tenth ranking of the highest score from the 50 data held indicated that they were truly entitled to receive PKH KM financial assistance. Before using this method, only around 40% was eligible recipients.
Enhancing RESTful API Authentication with Cryptography in Student Information Systems Sucipto, Sucipto; Muzaki, Muhammad Najibulloh; Karaman, Jamilah; Zakur, Yahya
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.v16i2.2255.125-133

Abstract

Data integration in this era is necessary for building a valid information system. Data in an information system must have a concept that interacts with other systems. With the development of information systems, data storage will increase. Big data must be channeled with a supporting information system connected to the data center information system. This research develops an API-integrated system with increased security in Basic Authentication with Cryptography. This research uses the Linear Sequential Model method with increased API security in Basic Authentication with Cryptographic hashes. test results using the CURL Library obtained appropriate data, and response time testing obtained an average result of 0.0611 per second. Acceptance testing obtained a percentage of results of 78%, which was included in the excellent functioning category. The research found that the Rest API can integrate and validate data between information systems
Crack Detection of Concrete Surfaces with A Combination of Feature Extraction and Image-Based Backpropagation Artificial Neural Networks Wahyudi, Erfan; Imran, Bahtiar; Subki, Ahmad; Zaeniah, Zaeniah; Samsumar, Lalu Delsi; Salman, Salman
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2249.228-235

Abstract

Concrete surface imperfections can signify a structure undergoing severe degradation. It deteriorates when concrete is exposed to elemental reactions such as fire, chemicals, physical damage, and calcium leaching. Due to its structural degradation, concrete deterioration poses a risk to the surrounding environment. Structural buildings can collapse due to severe concrete decline. To prevent concrete cracks early, it is imperative to identify the concrete surface. This requires the development of a technique for detecting the image-based concrete surface. One way to detect concrete surfaces is to create artificial neural networks. The purpose of this study is to combine feature extraction and artificial neural networks to detect cracks in concrete surfaces. The data used is concrete surface image data divided into two classes, namely cracked class and uncracked class. The total data is 600 data points, 300 data points, and 300 data points. The technique used is feature extraction from GLCM and Backpropagation Artificial Neural Network. Test results using epoch five show 95% accuracy, epoch 10 shows 95% results, epoch 100 shows 83% accuracy, and epoch 250 shows 73% results. The test results that have been carried out show a decrease in lower accuracy results when the epoch is determined to be higher. The results of this study epoch that shows the highest accuracy results are epoch 5 with 95% accuracy and epoch 10 with 95% accuracy.
A Comparative Analysis of Forensic Similarity and Scale Invariant Feature Transform (SIFT) for Forensic Image Identification Al Jum'ah, Muhammad Na'im; Wijaya, Hamid; Pomalingo, Suwito
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2357.371-381

Abstract

The image manipulation process has contributed to the widespread dissemination of false information. image forensics can help law enforcement agencies in addressing the spread of false news or information issues through visual media. Forensic image identification can be conducted using various methods, including Scale Invariant Feature Transform (SIFT) and Forensic Similarity. This study compared two methods, SIFT and Forensic Similarity, for forensic image identification. The test results showed the SIFT method identified image forensics by detecting image similarity through calculation of the key point values of each image. The process of searching the key point values was performed to extract information from the image. A high key point value indicated a large amount of information obtained from the image extraction results. On the other hand, the Forensic Similarity method also performed image forensic detection by examining whether image patches shared the same forensic traces. The advantage of the Forensic Similarity method over the SIFT method was that Forensic Similarity was more detailed because it involved many processes. Thus, Forensic Similarity was able to find similarities between two image patch objects. Additionally, the results obtained from the Forensic Similarity method were more detailed in detecting image similarity by considering the key point matching value and Cosine Similarity. Several previous studies have already implemented the SIFT and Forensic Similarity methods for image forensics, but there was no research that directly compared these two methods. This is the strength of this research. However, this study only used three data samples from three different devices for data collection. Future research can use a larger sample size to observe the comparison results
Weather Prediction for Strawberry Cultivation Using Double Exponential Smoothing and Golden Section Optimization Methods Herlinah, Herlinah; Asrul, Billy Eden William; HS, Hafsah; Faisal, Muhammad; Lee, Swa Lee; Gani, Hamdan; Feng, Zhipeng
ILKOM Jurnal Ilmiah Vol 16, No 3 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i3.2290.305-317

Abstract

Strawberry is one of the fruit commodities that has a high demand so that it is widely cultivated by most people in Bantaeng Regency to meet with the market needs. The high intensity of weather changes is the main challenge in the strawberry production, which is influenced by climate dynamics and the start season time changes. Climate change does not only affect the amount of rainfall, but also causes a shift in the rainy season and dry season start. As a result, in the cultivation of plants such as strawberries, there are often difficulties in adjusting or slow anticipation in the extreme changes of rainfall. This research began with the data collection stage through field observations, interviews, and literature studies. The design tool used a systematically organized UML, which included a use case diagram, then an activity diagram, as well as an elaboration into sequence diagrams, and class diagrams. The system was developed by implementing the PHP programming language on the interface design as well as MySQL as a database processing. The algorithm used to predict the air temperature feature, wind speed feature, and rainfall feature was Double Exponential Smoothing, followed by the optimization of the Golden Section method to select the right smoothing value. Referring to the results of this study, the system can provide planting time recommendations based on prediction of rainfall, air temperature, and wind speed parameters through a web-based platform. Based on the calculation of the accuracy value of the prediction results using the Mean Absolute Percentage Error (MAPE), the obtained forecast error value was of 5.89% for wind speed, 0.63% for air temperature, and 0.69% for rainfall. The Golden Section Optimization in Double Exponential Smoothing provided the best smoothing for prediction.

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