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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 14 Documents
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Search results for , issue "Vol 16, No 3 (2024)" : 14 Documents clear
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.
LoRaWAN-Based Communication for Autonomous Vehicles: Performance and Development Saharuna, Saharuna; Adiprabowo, Tjahjo; Yassir, Muhammad; Nurdiana, Dian; Adi, Puput Dani Prasetyo; Kitagawa, Akio; Satyawan, Arief Suryadi
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.v16i3.2311.236-254

Abstract

Automotive technology in the future continues to develop with a variety of sophistication, especially in vehicles that can move on their own, this research is new from previous developments, intelligent vehicles can be seen from various system developments ranging from the ability to find parking positions, have the right navigation system, and are equipped with various artificial senses such as LiDAR, Smart Camera, Artificial Intelligence, and various components for telecommunications. A small part that will be discussed in this research is in terms of data communication. The development of intelligent vehicles in a broader scope can be included in one of the categories to build a Smart City. In the analysis system, this research develops in terms of analyzing the possibility of data collisions or how to avoid them, with various methods that can be developed and approached comprehensively using LoRaWAN, so that a method can be determined using LoRaWAN Communication and LoRa Modules that can have an important impact in the development of intelligent vehicles or autonomous vehicles for Smart City. In this paper, the LoRa data transmission approach is to use the GPS Module, the GPS Module data is sent from each car to the nearest LoRaWAN Gateway, the car can automatically select the nearest Gateway for data optimization, reducing Packet Loss and Signal Attenuation due to LoRa data communication in the NLOS area, This article still uses data transmission simulation using MATLAB and is planned to be applied to Smart vehicles directly, the contribution of this research is the discovery of a new method in terms of LoRaWAN-based multi-point data transmission that can avoid data collisions from the position of intelligent vehicles in Mobile or moving, in building Smart City technology in the future.
An AI-integrated IoT-based Self-Service Laundry Kiosk with Mobile Application Kusrini, Kusrini; Muhammad, Alva Hendi; Fauzi, Moch Farid; Kuswanto, Jeki; Bernadhed, Bernadhed; Widayani, Wiwi; Pramono, Eko; Muktafin, Elik Hari; Ariyanto, Yossy
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.v16i3.2050.382-393

Abstract

This paper proposes KILAO, an IoT-based self-service laundry kiosk connected with a mobile application that aims to improve the laundry experience by improving user convenience and operational efficiency. This study aims to streamline the washing process using autonomous payment systems, real-time monitoring, and AI-based queue management, resulting in better resource utilization and higher user satisfaction. The development technique comprises identification and requirement gathering, development of both software and hardware prototypes, and evaluation of the prototype. In the requirement-gathering phase, the design of a kiosk machine that consists of hardware and software is defined by combining regular washing machines with IoT technologies for remote control and monitoring. We also developed a mobile application to engage with the kiosk machine. The kiosk simplifies the choice of laundry bundles and accepts various payment options, including cash, cashless transactions, and card-based purchases. The evaluation procedure of the prototype was conducted by using expert evaluations. They are from academics and industry professionals who verified the system’s effectiveness and market potential. The results have shown several unique selling features for KILAO. Extensive payment options and self-service operations were highlighted from the customer’s perspective as key benefits. From the seller’s perspective, its interoperability with traditional washing machines enables a low-cost shift to intelligent, self-service operations, eliminating the need for pricey coin-operated machines. Also, the automatic monitoring system that detects cycle completion can reduce waiting times and improve energy efficiency. In summary, KILAO presents a significant advancement in laundry automation by integrating IoT and AI. Moreover, the Gradient boosting algorithm forecasts waiting times and gives real-time information on machine availability, removing the need for physical queueing. The research demonstrates that KILAO’s capability to provide self-service laundry by providing a user-friendly mobile application can enhance user experience, operational efficiency, and energy utilization.
Techniques for Video Authenticity Analysis Using the Localization Tampering Method to Support Forensic CCTV Investigations Anggraini, Ririn; Prayudi, Yudi
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.v16i3.2378.318-329

Abstract

Closed Circuit Television (CCTV) is frequently utilized as legal evidence in judical proceedings. However, the authenticity of CCTV footage is often contested, requiring forensic analysis to verify its reliability as digital evidence. This study aimed to assess the authenticity of video footage using the Localization Tampering method. To simulate manipulation, various manipulation techniques, such as zooming, cropping, converting to grayscale, deleting frames, and rotating video sections, were applied. The Localization Tampering method was then used to detect manipulated areas by analyzing individual frames, calculating their histograms, and interpreting the histogram graph result. The findings demonstrated the method's ability to accurately identify the location and duration of manipulated frames. This offered a valuable tool to support forensic investigations of CCTV footage. Furthermore, this study highlights the challenges in detecting manipulation in low-quality videos, which required more sophisticated remediation techniques. Despite these challenges, the Localization Tampering method demonstrated consistent reliability in preserving the integrity of video footage, making it a practical solution for verifying digital evidence in a legal context. Overall, this study provides an effective approach to ensure that manipulated videos can be identified and corrected, contributing to a more robust CCTV forensics process and maintaining the credibility as evidence in a crime case.
Performance Analysis of LoRaWAN Communication Utilizing the RFM96 Module Yassir, Muhammad; Soepandi, Harry; Hanani, Ajib; Prakasa, Johan Ericka Wahyu; Puspitadewi, Ganis Chandra; Wibowo, Sastya Hendri; Adi, Puput Dani Prasetyo; Kitagawa, Akio
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.v16i3.2326.255-270

Abstract

This research discusses the utilization of LoRa and LoRaWAN or Low Power Wide Area (LPWA) and Low Power Wide Area Network (LPWAN). In this study, the application server is utilized using Telkom IoT. In its utilization, Telkom IoT can provide comprehensive results regarding LoRa quality of service capabilities such as bit rate, latency, and longitude and latitude data. Terrestrial measurements conduct tests in different areas with different conditions that cause different data obstruction, with several LoRa end-node points transmitting data with low bit-rate. For example, heart rate data. Some other parameters are the spreading factor (SF) and power consumption. Some parameters that determine the quality of transmitting data include the Spreading Factor and the Bandwidth used. From the analysis dan Experiment results, the Delay (ms) generated from measurements using RFM96 LoRa for IoT is around 0.02 seconds or 20 ms to around 0.05 seconds or 50 ms, and sometimes it can reach 0.07 ms to 0.09 ms. RSSI and SNR show the quality of the signal obtained which will provide a Quality of Service (QoS) value. From the measurement results using Telkom IoT in several times of data collection and testing, the average RSSI (-dBm) is at -110 dBm to -115 dBm. While SNR is at -10 dB to -16 dB.
Evaluation of K-Means Clustering Using Silhouette Score Method on Customer Segmentation Yulisasih, Baiq Nikum; Herman, Herman; Sunardi, Sunardi; Yuliansyah, Herman
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.v16i3.2325.330-342

Abstract

Customer segmentation is a critical process in businesses to understand and meet the diverse needs of customer. This study focused on the challenges of managing large and complex volumes of customer data and identifying the right segments to personalize marketing strategieshow about if I . K-Means Clustering has been widely utilized for its ability to group multidimensional data, but this method often generated broad clusters that lack detailed insights. Therefore, cluster evaluation with the Silhouette Score method became essential to ensure the optimality and validity of the generated groupings. The purpose of this study was to evaluate the quality of K-Means Clustering using the Silhouette Score method on customer segmentation. This research began with the acquisition of a dataset comprising 2,000 data points characterized with 7 attributes: sex, marital status, age, education, income, occupation, and settlement size. The data then underwent pre-processing by checking missing values and normalizing data. K-Means Clustering was then applied to group data into several clusters based on their proximity to the cluster center (centroid). The results of the clusters were assessed using the Silhouette Score method to determine the most optimal number of clusters. The results of this study consisted of manual calculations using Microsoft Excel on 27 data points to facilitate understanding of the logic, steps, methods and practical foundations before implementation on the complete dataset. Furthermore, the results of the Python calculation in 2000 data points showed that the optimal number of clusters (close to the value of 1) between k = 2 to k = 7 was the k = 4 cluster with a Silhouette Score value of 0.43, categorized as a weak structure. Although this value indicated a weak cluster structure, it was the highest value in the test, indicating that the division of data into four clusters (k = 4) was better than the number of other clusters. However, the quality of this cluster indicates the need for futher improvement. Future work should review the used attributes, data normalization methods, or consider other clustering algorithms to achieve a more robust structure and more meaningful interpretation.
Refining the Performance of Indonesian-Javanese Bilingual Neural Machine Translation Using Adam Optimizer Putri, Fadia Irsania; Wibawa, Aji Prasetya; Collante, Leonel Hernandez
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.v16i3.2467.271-282

Abstract

This study focuses on creating a Neural Machine Translation (NMT) model for Indonesian and Javanese languages using Long Short-Term Memory (LSTM) architecture. The dataset was sourced from online platforms, containing pairs of parallel sentences in both languages. Training was performed with the Adam optimizer, and its effectiveness was compared to machine translation (MT) conducted without an optimizer. The Adam optimizer was utilized to enhance the convergence speed and stabilize the model by dynamically adjusting the learning rate. Model performance was assessed using BLEU (Bilingual Evaluation Understudy) scores to evaluate translation accuracy across different training epochs. The findings reveal that employing the Adam optimizer led to a significant enhancement in model performance. At epoch 2000, the model using the Adam optimizer achieved the highest BLEU score of 0.989957, reflecting very accurate translations, whereas the model without the optimizer showed lower results. Furthermore, translations from Indonesian to Javanese were found to be more precise than those from Javanese to Indonesian, largely due to the intricate structure and varying speech levels of the Javanese language. In summary, the implementation of the LSTM method with the Adam optimizer significantly improved the accuracy of bidirectional translations between Indonesian and Javanese. This research contributes notably to the advancement of local language translation technologies, supporting language preservation in the digital age and holding promise for applications in other regional languages.
Comparison of Accuracy Level of Certainty Factor Method and Bayes Theorem on Cattle Disease Setyawan, Muhammad Rizki; Bahari Putra, Fajar Rahardika; Fakhri, La Jupriadi
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.v16i3.1943.343-355

Abstract

This study aims to address the challenges of livestock disease diagnosis in Okaba district, Meraoke, Papua. A total of 2 paramedics or veterinarians and 1 assistant is not sufficient because of the long distances that the medics have to travel, traveling from all areas of Okaba District to its interior. Keepers can only utilize their basic skills for temporary care. The researcher's process included interviews with experts covering the disease, its symptoms and prevention, then analyzed with the provision of utilizing certainty factors and Bayes' theorem to increase the accuracy and veracity of the findings. In this scenario, the data is used as a reference point for analysis in the web-based expert system. The results obtained when processing the problem estimation are disease information, symptom information, and treatment. The reference in the application and analysis shows that the Certainty Factor method is superior in providing consistent accuracy, with a percentage reaching 98.79% in the case of worms, while the Bayes Theorem method shows lower accuracy, around 73%. The comparison indicates that Certainty Factor is more suitable in high uncertainty environments, while Bayes' Theorem is more effective when sufficient probabilistic data is available. Future suggestions can expand the scope by testing other methods such as Machine Learning or Artificial Neural Networks to increase the accuracy of the diagnosis percentage. In addition, more extensive trials on different types of livestock and different environmental conditions will help in developing a more flexible and robust system.

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