cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Ramdan Satra
Contact Email
Ramdan Satra
Phone
-
Journal Mail Official
ramdan@umi.ac.id
Editorial Address
-
Location
Kota makassar,
Sulawesi selatan
INDONESIA
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 574 Documents
 54   55   56   57   58 
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.
Enhanced Multivariate Time Series Analysis Using LSTM: A Comparative Study of Min-Max and Z-Score Normalization Techniques Pranolo, Andri; Setyaputri, Faradini Usha; Paramarta, Andien Khansa’a Iffat; Triono, Alfiansyah Putra Pertama; Fadhilla, Akhmad Fanny; Akbari, Ade Kurnia Ganesh; Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Uriu, Wako
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.v16i2.2333.210-220

Abstract

The primary objective of this study is to analyze multivariate time series data by employing the Long Short-Term Memory (LSTM) model. Deep learning models often face issues when dealing with multivariate time series data, which is defined by several variables that have diverse value ranges. These challenges arise owing to the potential biases present in the data. In order to tackle this issue, it is crucial to employ normalization techniques such as min-max and z-score to guarantee that the qualities are standardized and can be compared effectively. This study assesses the effectiveness of the LSTM model by applying two normalizing techniques in five distinct attribute selection scenarios. The aim of this study is to ascertain the normalization strategy that produces the most precise outcomes when employed in the LSTM model for the analysis of multivariate time series. The evaluation measures employed in this study comprise Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-Squared (R2). The results suggest that the min-max normalization method regularly yields superior outcomes in comparison to the z-score method. Min-max normalization specifically resulted in a decreased mean absolute percentage error (MAPE) and root mean square error (RMSE), as well as an increased R-squared (R2) value. These improvements indicate enhanced accuracy and performance of the model. This paper makes a significant contribution by doing a thorough comparison analysis of normalizing procedures. It offers vital insights for researchers and practitioners in choosing suitable preprocessing strategies to improve the performance of deep learning models. The study's findings underscore the importance of selecting the appropriate normalization strategy to enhance the precision and dependability of multivariate time series predictions using LSTM models. To summarize, the results indicate that min-max normalization is superior to z-score normalization for this particular use case. This provides a useful suggestion for further studies and practical applications in the field. This study emphasizes the significance of normalization in analyzing multivariate time series and contributes to the larger comprehension of data preprocessing in deep learning models
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.
File carving Analyze of Foremost and Autopsy on external SSD mSATA using the Association of Chief Police Officer Method Dahlan, Khoirul Anam; Yudhana, Anton; 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.v16i3.2360.283-295

Abstract

File carving is a method for recovering files using software such as Foremost and Autopsy. The recovery is conducted for deleted files or formatted devices. Popularity Solid State Drive (SSD) has outperformed Hard Disk Drive (HDD) because SSD is faster, more efficient, and shock resistant. However, recovering SSD devices have a lower probability success rate than HDD because the security system often hampers files recovered on SSD. Based on previous research, the success rate of Security Digital High Capacity (SDHC) only achieved 50% more than SSD, whereas SSD can only return 85.7% of its success. Forensics Digital is a part of Forensics Knowledge for deliver valid digital evidence for law investigation. This research aims to increase the success rate of recovery files using two different software: Foremost and Autopsy. The research uses a 512GB Eaget brand SSD with a New Technology File System (NTFS). The file carving is also conducted using the Association of Chief Police Officers (ACPO) method. APCO has several stages: Planning, Capture, Analysis, and Presentation. The experiment results show that Autopsy software with deep recover mode returned 81 out of 88 files (92%), whereas Foremost software run on Debian to make sure no virus on device that could damage computer especially windows system. First attempt recovery can only return 46 out of 88 files (52%). The findings show that the Autopsy software has a higher successful return rate and can be used for evidence in law enforcement and digital forensics investigations.
Expression Detection of Children with Special Needs Using Yolov4-Tiny Sidi, Husri; Rahman, Aviv Yuniar; Marisa, Fitri
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.v16i3.1609.221-227

Abstract

This research addresses the challenge of detecting emotional expressions in children with special needs, who often rely on nonverbal communication due to difficulties in verbal expression. Traditional emotion detection methods struggle to accurately recognize subtle emotions in these children, which can lead to communication barriers in educational and therapeutic settings. This study proposes the use of the Yolov4-Tiny model, a lightweight and efficient object detection architecture, to accurately detect four key facial expressions: Angry, Happy, Smile, and Afraid. The dataset consists of 1500 images, evenly distributed across the four expression classes, captured under controlled conditions. The model was evaluated using various metrics, including Confidence, Precision, Recall, F1-Score, and Mean Average Precision (mAP), across different training-to-testing data splits. The results demonstrated that the Yolov4-Tiny model achieved high accuracy, with a perfect mAP of 100% for balanced and slightly imbalanced splits, and a minimum mAP of 93.1% for more imbalanced splits. This high level of performance highlights the model's robustness and potential for application in educational and therapeutic environments, where understanding emotional expressions is critical for providing tailored support to children with special needs. The proposed system offers a significant improvement over traditional methods, enhancing communication and emotional support for this vulnerable population.

Page 57 of 58 | Total Record : 574

 54   55   56   57   58 

Filter by Year

2016 2025


Reset
Filter By Issues
All Issue Vol 17, No 3 (2025) Vol 16, No 3 (2024) Vol 16, No 2 (2024) Vol 16, No 1 (2024) Vol 15, No 3 (2023) Vol 15, No 2 (2023) Vol 15, No 1 (2023) Vol 14, No 3 (2022) Vol 14, No 2 (2022) Vol 14, No 1 (2022) Vol 13, No 3 (2021) Vol 13, No 2 (2021) Vol 13, No 1 (2021) Vol 12, No 3 (2020) Vol 12, No 2 (2020) Vol 12, No 1 (2020) Vol 11, No 3 (2019) Vol 11, No 2 (2019) Vol 11, No 1 (2019) Vol 11, No 1 (2019) Vol 10, No 3 (2018) Vol 10, No 3 (2018) Vol 10, No 2 (2018) Vol 10, No 2 (2018) Vol 10, No 1 (2018) Vol 10, No 1 (2018) Vol 9, No 3 (2017) Vol 9, No 3 (2017) Vol 9, No 2 (2017) Vol 9, No 2 (2017) Vol 9, No 1 (2017) Vol 9, No 1 (2017) Vol 8, No 3 (2016) Vol 8, No 3 (2016) Vol 8, No 2 (2016) Vol 8, No 2 (2016) Vol 8, No 1 (2016) Vol 8, No 1 (2016) More Issue