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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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Application of Object Mask Detection Using the Convolution Neural Network (CNN) Yuhandri; Musli Yanto; Eka Naufaldi Novri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5059

Abstract

The spread of Coronavirus Disease (Covid-19) is still a serious problem that we are currently facing. Spread occurred very quickly through the face-to-face interaction process. The face-to-face interaction process that occurs both in public spaces and in closed spaces has a great risk of transmitting the Covid-19 virus. One of the efforts to deal with the spread of the Covid-19 virus is to increase the use of masks in both public and closed spaces. On the basis of this, this study aims to develop an object detection process in image processing techniques. Object detection development using the convolution neural network (CNN) method to provide optimal output. CNN can process the input image, which is converted into a pixel matrix and then sent to the convolution layer. The research data set consists of 2000 images of masks and not masks. The images were obtained from open sources, github.com and kaggle.com. The results of the study present a system capable of detecting masks in real time. CNN provides very good performance with an accuracy rate of 99.05%. With these results, the contribution of this research can be used in the monitoring of public services for the community to increase the use of masks.
The Joint Channel Coding and Pre-Distortion Technique on the USRP-Based MIMO-OFDM System Melki Mario Gulo; I Gede Puja Astawa; Arifin; Yoedy Moegiharto; Hendy Briantoro
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5093

Abstract

Modern wireless communication systems use orthogonal frequency division multiplexing (OFDM), a multi-carrier modulation method that resists multipath channels and provides bandwidth efficiency. OFDM is generally used with a Multiple-Input Multiple-Output (MIMO) system to boost diversity gain and channel capacity. MIMO-OFDM has several advantages, but its high PAPR value is a drawback. A non-linear high-power amplifier (HPA) can distort signals with high PAPR values. This issue can be resolved by employing predistortion, which compensates for nonlinear HPA. In addition to PD, channel coding can be used to improve the quality of systems with high PAPR values by adding redundant bits to the bits to be sent. In this paper, we report the experimental evaluations of the joint channel coding and pre-distortion (PD) technique on a 2x2 MIMO OFDM system using USRP hardware. The experiments are conducted in two scenarios: line-of-sight (LOS) and nonline-of-sight (NLOS) scenarios. The channel coding used in this scenario is convolutional code with code rates of 1/2, 2/3, and 3/4. From the results of the experiment, it can be seen that the system that uses PD combined with the convolution code produces better performance in the LOS and NLOS scenarios compared to the system without PD. In the LOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by approximately 58.74%, 75.97%, and 96.20%. In the NLOS scenario, the use of PD can improve the SNR value of code rates 1/2, 2/3, and 3/4 by about 60.71%, 73.59%, and 71.84%. The measurement of the LOS scenario gives a better SNR value than the NLOS scenario, with a maximum SNR value of 30.86 dB, while the maximum SNR value of the NLOS scenario is 30.23 dB. This happened because the LOS scenario suffered minimal multipath fading compared to the NLOS scenario
Exploring Research Trends and Impact: A Bibliometric Analysis of RESTI Journal from 2018 to 2022 Ronal Watrianthos; Yuhefizar Yuhefizar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5101

Abstract

This study provides a comprehensive analysis of the RESTI Journal, a prominent publication in the field of systems engineering and information technology. The analysis aims to evaluate the journal's publication output, citation impact, and overall contribution to the field. The study utilizes data from the Dimensions database, focusing on articles published between 2018 and 2022, resulting in a dataset of 594 articles. To analyze the collected data, the study employs bibliometric and network visualization tools such as Bibliometrix and VOSviewer. The analysis reveals a notable increase in the number of publications over time, indicating a growing interest and research activity in the field. Furthermore, the distribution of author productivity deviates from Lotka's law, highlighting variations in author patterns and productivity levels. An examination of institutional affiliations reveals Telkom University as the dominant institution, making a substantial contribution to the journal. Visualizations based on author-provided titles, abstracts, and keywords highlight research trends in image recognition and classification, with a particular emphasis on utilizing Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). Overall, this study provides valuable insights into the performance and trends of the RESTI Journal. The findings contribute to a deeper understanding of the journal's impact and its role in advancing knowledge in systems engineering and information technology. These insights can inform researchers, practitioners, and stakeholders in the field, guiding future research directions and enhancing the scholarly impact of the RESTI Journal.
Assessing User Experience and Usability in the OVO Application: Utilizing the User Experience Questionnaire and System Usability Scale for Evaluation Ali Ibrahim; Onkky Alexander; Ken Ditha Tania; Pacu Putra; Allsela Meiriza
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5137

Abstract

The OVO application, despite having a large user base in Indonesia, has received low ratings compared to other digital wallet apps on the Google Play Store and App Store. Users frequently complain about the user experience, which greatly affects their overall satisfaction. This study evaluates the user experience and usability of the OVO application using the User Experience Questionnaire (UEQ) and System Usability Scale (SUS). The UEQ results show that efficiency is excellent (1.55), while attractiveness, perspicuity, dependability, and stimulation are above average (1.56, 1.67, 1.33, and 1.16, respectively). However, the novelty aspect falls below average (0.64), indicating a need for improvement. The SUS score is 77.53, classifying the application as "Acceptable" with a "C" grade and an overall "Good" rating. Addressing identified shortcomings can enhance user experience and usability, ultimately improving user satisfaction. This study contributes valuable empirical data to the field, offering insights for researchers and practitioners in assessing the user experience and usability of mobile applications.
Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model Rizki Firdaus Mulya; Ema Utami; Dhani Ariatmanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.5182

Abstract

Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.
Precision Marketing Model using Decision Tree on e-Commerce Case Study Orebae.com Fadil Indra Sanjaya; Anna Dina Kalifia
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.4531

Abstract

The development of the industrial world towards industry 4.0 has resulted in changes in the lifestyle of the wider community in carrying out their activities through digital media, one of which is shopping. This has an impact on the emergence of many business actors in the e-Commerce field, which brings its own challenges to stay alive and face the competition. The demands for innovation in competitive competition are also increasingly diverse with various approaches ranging from technology, social science, management science, and even artificial intelligence. One form of innovation that is widely carried out by e-Commerce today is looking for an ideal and effective form of marketing, where the form of marketing itself is considered less able to accommodate e-Commerce needs. One form of real innovation in finding the ideal and effective marketing is precision marketing. Precision marketing itself is marketing that is carried out by utilizing data where consumers are the center of preference for data collection. In fact, many of the e-commerce companies that were launched were unable to keep up with the competition because they were unable to develop marketing strategies and eventually went bankrupt. Therefore, we need a special way to bridge these problems so that e-Commerce can stay alive, especially for e-Commerce classified as Small and Medium Enterprises (SMEs). This research will focus on developing a precision marketing model in e-Commerce for small businesses, namely orebae.com which can be used as a tool in the development of marketing strategies. This research was carried out using a machine learning approach by adopting a decision tree algorithm. The results of this study showed that the precision marketing model for orebae.com based on customer preferences can be used to increase the number of sales of orebae.com and to reduce marketing costs.
Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns Grandhys Setyo Utomo; Ema Rachmawati; Febryanti Sthevanie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.4798

Abstract

The general population is concerned with traffic accidents. Driver fatigue is one of the leading causes of car accidents. Several factors, including nighttime driving, sleep deprivation, alcohol consumption, driving on monotonous roads, and drowsy and fatigue-inducing drugs, can contribute to fatigue. This study proposes a facial appearance-based driver fatigue detection system. This is based on the assumption that facial features can be used to identify driver fatigue. We categorize driver conditions into three groups: normal, talking, and yawning. In this study, we used Adaboost to propose Boosting Local Binary Patterns (LBP) to improve the image features of fatigue drivers in the Support Vector Machine (SVM) model. The experimental results indicate that the system's optimal performance achieves an accuracy value of 93.68%, a recall value of 94%, and a precision value of 94%.
Design Factors in Evaluating and Formulating IT Governance Systems in Public Organizations Muhammad Rifai Katili; Lanto Ningrayati Amali; Siiti Suhada
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.4939

Abstract

The application of information technology (IT) by central and regional governments to public services is intended to efficiently and effectively improve performance and public services. However, many studies have shown the ineffective application of IT. In Gorontalo province, this can be seen in the e-readiness value of Gorontalo province as a prerequisite for successful IT implementation, which is still at 58.15 points, which means that it is at a moderate level of readiness. This shows that implementing IT governance in the local government of Gorontalo province is still not optimal in terms of performance or public services. This study aims to identify the design factors that need to be considered when implementing IT Governance to achieve better public service performance. This study uses a quantitative approach based on a survey method. The results showed six models at Level 3: BAI06, BAI07, DSS01, DSS03, DSS04, and DSS05. In addition, four models were at level 4: APO12, APO13, BAI10, and DSS02. Levels 3 and 4 show that the IT governance capability of the Gorontalo provincial government in each model is not yet optimal. This study recommends that the Gorontalo provincial government evaluate and formulate an effective IT Governance system by focusing on each model's IT Governance design factors to improve public service performance.
Knowledge Management System Adoption Approach and the Critical Success Factors in Small Medium Enterprise: A Systematic Literature Review Agnes Agnes; Ajie Tri Hutama; Dana Indra Sensuse; Sofian Lusa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.4954

Abstract

Knowledge is a substantial factor in an organization; therefore, the successful implementation of Knowledge Management (KM) or Knowledge Management System (KMS) is important for many organizations. This applies both for large companies and for companies categorized as Small Medium Enterprise (SME). How each company finds a solution to deal with KM problems, how to adopt KMS in its company structure, and what critical success factors (CSF) must be highlighted to implement those KMS often vary depending on the size of the organization. Regarding this issue, this study aims to find out how the adoption approach and CSF are used in the implementation of KM / KMS in SME. However, this study can also improve the state-of-the-art for KM / KMS implementation in SME and the important CSF in implementing it. In this review of the literature, a systematic review was performed with the steps as follows: (1) structure the research question, (2) define inclusion-exclusion criteria, (3) evaluation of paper quality, and (4) data extraction. The study found that in the last 5 years from the time when this research is conducted, TABLE 1 which is from 2016 to 2021, SME has been using many methods like training, meeting, sharing session, repository, and research as part of their KM / KMS adoption approach. We found also in the last 5 years that the CSF for implementing KM / KMS in SME is as follows: organization structure and flexibility, organization culture towards KM adoption, the quality of the knowledge, and communication within and across areas in the organization. communication within and across areas of the organization, and the team works within and across areas of the organization. SMEs can use this research as a guide to implement KM / KMS in their organization.
Employee Education and Training Recommendations using the Apriori Algorithm Arief Wibowo; Vasthu Imaniar Ivanoti; Megananda Hervita Permata Sari
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 5 (2023): October 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i5.4973

Abstract

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data on the implementation of education and training in 2022 in the MoF Central ICT Department, only 27.35% of the employees participated in education and training according to the proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or substitute participants who are not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the a priori algorithm to analyze historical data on employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resources Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance.

Page 74 of 105 | Total Record : 1046

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