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TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer
Published by Universitas Wiralodra
ISSN : 26218070     EISSN : 26863219     DOI : https://doi.org/10.31943/teknokom
Core Subject : Science,
Computer Science & IT
Jurnal Teknologi dan Rekayasa Sistem Komputer (TEKNOKOM) with frequency 2 (two) times a year, ie in March and September. The editors receive scientific writings from lecturers, teachers and educational observers about the results of research, scientific studies and analysis and problem solving closely related to the field of ICT and System Computer.
Arjuna Subject : -
Articles 139 Documents
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PRIORITIZING TASKS IN INFORMATION SYSTEM PROJECTS: A NOVEL APPROACH USING VIKOR METHOD Soetam Rizky Wicaksono
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.149

Abstract

Information Systems (IS) projects often face significant challenges, with many failing to meet their objectives. One of the key factors contributing to these failures is the improper prioritization of tasks. This article addresses this issue by proposing a comprehensive approach to task prioritization in IS projects using the VIKOR method. The approach takes into account four key criteria - cost, time, risk, and complexity - and uses the VIKOR method to rank tasks based on these criteria. The VIKOR method provides a systematic and objective way of prioritizing tasks, ensuring effective resource allocation, appropriate risk management, and the achievement of strategic objectives. The application of the VIKOR method in the context of IS projects is a novel contribution of this article. However, it is also recognized that the proposed approach has its limitations and that further research is needed to consider additional criteria, explore other decision-making methods, and examine the dynamic nature of IS projects. Overall, this article contributes to the growing body of knowledge on project management in IS and provides a practical tool for project managers to improve their decision-making processes.
IMPLEMENTASI DIGITAL IMAGE PROCESSING SEBAGAI PENDETEKSI POSISI MATAHARI PADA PERANCANGAN DUAL AXIS SOLAR TRACKER Devith Christian; Dian Budhi Santoso; Reni Rahmadewi
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.150

Abstract

One of the uses of new renewable energy (EBT) in Indonesia which has great potential is solar panels. Solar panels are electrical devices that are used to convert solar energy into electrical energy. The limited surface area of the panels requires users to be able to pay attention to the work efficiency of solar panels. The aspect that has the most influence on optimizing the efficiency of solar panel performance is the placement of the surface of the solar panel against incoming solar radiation. Therefore, it is necessary to have a method to detect the position of the sun to provide input to the actuator so that the surface of the solar panel is positioned in the direction of the sun's rays. This study describes the accuracy of detecting the position of the sun using digital image processing methods. This process needs to be done as a reference in designing a dual axis solar tracker. Detection of the sun's position using this method gets 100% accurate results in cloudless sky conditions and gets 80% success results in cloudy sky conditions.
PERBANDINGAN BERBAGAI MODEL MACHINE LEARNING UNTUK MENDETEKSI DIABETES Ahmad Maulid Ridwan; Gilang Dwi Setyawan
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.152

Abstract

  Diabetes mellitus, often known as diabetes, is a significant metabolic illness that has a negative impact on living organisms. It causes high blood sugar levels by either creating inadequate insulin or using it inefficiently. Diabetes that is not effectively treated raises the risk of heart attacks, retinopathy, vision loss, skin disorders, and other ailments. Early detection is critical for guiding essential actions. In this setting, machine learning (ML) has emerged as a potent tool. We used Python data manipulation tools to develop ML techniques for discovering patterns and risk factors in the Pima Indian diabetes dataset in our study. We correctly identified patients as diabetes or non-diabetic using K-Nearest Neighbors (KNN), AdaBoost, Logistic Regression (LR), Light Gradient Boosting, Random Forest (RF),  dan Support Vector Machine (SVM). Notably, we used the Synthetic Minority Over-sampling Technique (SMOTE) to solve class imbalance, which enhanced model performance. By efficiently utilizing ML and SMOTE in diabetes categorization, our work greatly adds to the scientific area. We suggest studying cutting-edge technology and undertaking external validation and clinical studies to assure trustworthy and generalized models for diabetic patient care in the future. With diabetes's increasing prevalence, such improvements have enormous promise for improving early identification and management, eventually leading to better health outcomes.
IMPLEMENTASI METODE ADASYN DALAM DETEKSI URL BERBAHAYA MENGGUNAKAN MACHINE LEARNING: DEMI MENINGKATKAN KEAMANAN SIBER DI ERA DIGITAL Gilang Dwi Setyawan; Andrie Yuswanto; Ahmad Maulid Ridwan; Budi Wibowo; Maman Firmansyah
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.153

Abstract

Cybercriminals exploit malicious URLs as a distribution channel to spread harmful software across the internet. They take advantage of vulnerabilities in browsers to install malicious software with the aim of gaining remote access to the victims' computers. Typically, this malicious software aims to gain access to networks, steal sensitive information, and silently monitor targeted computer systems. In this research, a data mining approach known as Classification Based on Association (CBA) is employed to detect malicious URLs using both the URL itself and the features of the presented web pages. The CBA algorithm utilizes a training dataset of URLs as historical data to discover association rules that can be used to create an accurate classifier. By detecting dangerous URLs and malicious software, this contribution can assist organizations and individual users in enhancing the security of their computer systems and networks, thereby protecting sensitive data and reducing the risk of security incidents. The experimental results demonstrate that CBA achieves performance on par with tested classification algorithms, achieving an accuracy of 99% and low rates of false positives and false negatives. Future research could expand its focus to detect malicious URLs and software on mobile devices and embedded systems, as they have become significant targets for cybercriminals.
ANALISIS SENTIMEN PRODUCT TOOLS & HOME MENGGUNAKAN METODE CNN DAN LSTM Safrizal Ahmad; Ahmad Maulid Ridwan; Gilang Dwi Setyawan
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.154

Abstract

Sentiment analysis is gaining popularity as the number of internet users increases. Internet users often express their opinions through reviews on websites. The customer opinions expressed have a huge impact on sellers and customer numbers, as many consumers rely on online reviews as a reference when purchasing products. In order to quickly understand sentimental views and tendencies towards a product or event, a text sentiment analysis is performed on the opinions expressed by users. Sentiment analysis focuses on understanding the sentiments contained in the text. One common approach in sentiment analysis is to use Deep Learning (DL) models. This study aims to analyze product sentiment in the Tools & Home category from Amazon using models such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The CNN model is used to extract features from words that reflect short-term sentiment dependencies, while LSTM is used to establish long-term sentiment relationships between words. CNN and LSTM are sophisticated DL models, capable of efficiently processing text data and recognizing relationships and patterns that exist at various levels of abstraction. The purpose of this study is to understand the differences in the performance of the DL model in conducting sentiment analysis, it is hoped that it can also be a reference for those who plan to apply other DL models.
CLOUD-BASED BIGBLUEBUTTON IMPLEMENTATION SUPPORTS VIRTUAL MEETINGS AT RRI MEDAN Muhammad Rifqi Sakti; Akhyar Lubis
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.158

Abstract

Virtual meetings have become an important necessity in today's digital era, especially in the midst of the COVID-19 pandemic which requires limiting physical contact. Various platforms exist to provide online meeting solutions. One platform that can be used for virtual meetings is the cloud-based BigBlueButton. This platform has been actively used in internal and external meetings, saving travel and time costs, as well as increasing work productivity and effectiveness. This research aims to implement BigBlueButton as a solution to support virtual meetings at RRI Medan. In this study, BigBlueButton is implemented using cloud infrastructure to increase system scalability and availability. Data collection methods used are direct observation, interviews, and secondary data collection. The results of the study show that the implementation of cloud-based BigBlueButton has succeeded in increasing the efficiency and flexibility of virtual meetings at RRI Medan. This study contributes to expanding understanding of the use of cloud-based BigBlueButton to support virtual meetings in the RRI Medan environment.
ANALYSIS OF SOLAR ENERGY POTENTIAL IN THE PT. SUMEDANG TELEVISI UTAMA BUILDING AS AN ALTERNATIVE ENERGY SOURCE Tendy Ferdyansyah; Arnisa Stefanie
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.159

Abstract

Indonesia is a tropical region with significant solar energy potential, where the average daily sunlight reaches 4.5 - 4.8 kWh/m²/day. This makes solar energy one of the promising forms of renewable energy worthy of development. The instability of electricity supply from the national grid (PLN) is a prevalent issue across various regions. PT. Sumedang Televisi Utama (SMTV), a television broadcasting company, frequently encounters disruptions in TV broadcasts due to power supply issues originating from PLN. Hence, this research holds the urgency to explore alternatives that can provide electricity supply during interruptions in the PLN power distribution. The contribution of this study lies in the development of a practical and applicable Photovoltaic Solar Power Plant (PLTS) module. With the implementation of this module, it is anticipated that PT. Sumedang Televisi Utama (SMTV) and other entities can have a backup electricity supply source, mitigating the impact of power supply disturbances from PLN. Suggesions for further research include delving deeper into the efficiency and performance assessment of the designed PLTS module under various environmental conditions and weather patterns that are typical in Indonesia. Furthermore, subsequent research could focus on integrating a more effective energy storage system with the PLTS module, thereby maximizing solar energy utilization even during periods of limited sunlight. This endeavor would enhance the resilience and availability of alternative power supply during instances of disruptions in the national grid (PLN) distribution.
DETEKSI OTOMATIS NOMINAL UANG KERTAS RUPIAH UNTUK TUNANETRA MENGGUNAKAN ALGORITMA ARSITEKTUR SSD MOBIILENETV3 Ario Prima; Dian Budhi Santoso; Lela Nurpulaela
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.166

Abstract

Money is a legal tender in the buying and selling transactions of society. However, visually impaired people face visual limitations that affect their ability to recognize the value of Rupiah banknotes. Although Rupiah banknotes already have a blind code feature, this is often ineffective due to the condition of the banknotes. In this study, we use Artificial Intelligence technology, especially deep learning, to help visually impaired people recognize the value of Rupiah banknotes more easily. Our system is built using Convolutional Neural Network (CNN) technique with MobileNetV3 architecture and Single Shot Multibox Detector (SSD) algorithm. The results show that our system is able to operate well under various lighting conditions, including daytime and nighttime. Under sufficient lighting conditions, the system achieves an accuracy of between 80% to 95%. However, we acknowledge that in low-light or nighttime conditions, our system has problems detecting banknotes. Thus, this research contributes to the effort of improving the accessibility of visually impaired people in recognizing Rupiah banknotes, although we also recognize the need for more attention to address low lighting conditions in future development.
DIGITAL HERITAGE PORTAL BASED ON PROGRESSIVE WEB APP: EFFORTS FOR THE DEVELOPMENT OF CULTURAL HERITAGE AND TOURISM IN LAMPUNG Brilliant, Muhamad; Nurhasanah, Iis Ariska; Oktaria, Herlini; Handoko, Dwi
TEKNOKOM Vol. 7 No. 1 (2024): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v7i1.169

Abstract

According to Undang-undang No. 11 Th. 2010 regarding Cultural Heritage, preservation involves dynamic efforts to safeguard the existence and values of Cultural Heritage through protection, development, and utilization. Despite rapid technological progress, there is currently no dedicated digital platform designed to promote and preserve cultural heritage in Lampung Province. The diverse cultural legacies could be better harnessed and developed, as they hold significant potential for attracting tourists to the region. This research aims to design a Progressive Web App-based Digital Heritage Portal to foster cultural heritage and tourism in Lampung Province. Digital Heritage employs technology to understand and conserve cultural legacies. As an innovative approach, the information system is crafted using Progressive Web App technology for easy user access online or offline, without requiring prior app installation. The system is anticipated to aid in the promotion, preservation, and advancement of cultural heritage and tourism in Lampung. The research follows the Waterfall method with these stages: 1) Problem analysis, (2) Data collection, (3) System requirement analysis, (4) Coding, (5) Deployment, and (6) System testing. The research yields an application design functioning as an informative platform for historical and cultural heritage tourism in Lampung Province. The application operates smoothly across platforms and offline. Test results affirm proper menu functionality aligned with stipulated system requirements.
Kombinasi Algoritma Priority Scheduling dan Earliest Due Date untuk Sistem Penjadwalan Slitting Produk Berbasis Web Afrianto, Muhammad Iqbal; Fauziah, Fauziah; Wijaya, Yunan Fauzi
TEKNOKOM Vol. 7 No. 1 (2024): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v7i1.176

Abstract

Production scheduling is one of the most critical components of production management that can impact efficiency and resource utilization. This research focuses on the development of a web-based production scheduling system that integrates two algorithms, Earliest Due Date (EDD) and Priority Scheduling. This research includes the analysis, design, implementation and testing of a those two algorithms in a web-based application for production management. The result of this research will be analyzed through the results of how both algorithms affects lateness and tardiness. The research findings indicate that the application can effectively manage Work Orders. The application can generate an optimal schedule when conditions arise where some tasks have higher priority than others by combining both algorithms. The system can identify potentially late tasks by calculating the tardiness of the given jobs. And from the analysis results, the application can generate an efficient production schedule with an on-time accuracy rate of 74%, and the average delay for each job is 0.95 hours.

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