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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : -
Articles 1,172 Documents
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Effectiveness of Using Virtual Reality Media for Students' Knowledge and Practice Skills in Practical Learning Refdinal, Refdinal; Adri, Junil; Prasetya, Febri; Tasrif, Elfi; Anwar, Muhammad
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2060

Abstract

Virtual Reality (VR) has become an option to be used as a learning medium in engineering. A study of the effectiveness of VR is needed to determine which fields and types of learning are suitable to employ it. This work aims to reveal the effectiveness of virtual reality media on students' cognitive and practice skills. The role of the media as a tool to make learning more efficient and effective. VR media brings learning in the virtual world that seems to be done in real terms. The research method used was a quasi-experiment with a posttest-only control group design research approach. The research subject consisted of two homogeneous classes. Learning outcomes are evaluated by testing students' cognitive and practice skills. The novelty of this research is the creation of learning media that are identical to the welding process simulator. Visual practice places and equipment in virtual form through VR are made to resemble practice places and equipment used in real situations. This similarity aims to provide concrete information about the welding process. The study revealed that the use of VR media significantly affected their knowledge. However, it did not significantly affect their practice skills. VR has not been able to provide an experience closer to real-life conditions during welding, such as heat, sparks, and sounds that appear when the electrode touches the workpiece. The distance between the electrode and the workpiece significantly affects the welding result in the welding process.
A Web-based Group Decision Support System for Retail Product Sales a Case Study on Padang, Indonesia Azmi, Meri; Satria, Deni; Mulya, Farhan Rinsky; Sonatha, Yance; Putra, Dwi Sudarno
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1331

Abstract

The industrial sector's growth has led to an increase in the number of industrial products available in the market. However, this has made it more challenging for retail merchants to choose which items to sell due to the overwhelming number of options. The seller must carefully consider various factors such as the type, quality, and probability of selling the goods to turn a profit. This research proposes a group decision support system to assist retail sellers in selecting the products to sell. The system is designed to process various information on comparing retail products against specific criteria, enabling sellers to make quick and accurate decisions. To achieve optimal results, this study combines three methods in the decision-making calculation process: Fuzzy Logic, EDAS, and Borda methods. The Fuzzy Logic method is used to assign a value to an unclear criterion, followed by the EDAS method ranking process, and ending with the combination of the decision-making results using the Borda method. The group decision support system is web-based and has been proven to provide effective solutions for retail business actors to increase sales and reduce losses. By using this system, retail sellers can make informed decisions about their products, enabling them to optimize their profits and reduce their risks. In conclusion, the increase in the number of industrial products has created challenges for retail merchants, but this research proposes a solution in the form of a group decision support system. Combining Fuzzy Logic, EDAS, and Borda methods results in an effective decision-making process that allows retail sellers to make informed decisions and achieve their business goals.
GoEkopz: An E-Koperasi and Marketplace Synergy of Koperasi MSMEs Model Platform - Case Study: Koperasi Giat, eKopz Startup, PPKM Community Hendriyanto, Robbi; Agung, Anak Agung Gde; Mandasari, Rizza Indah Mega; Widaningsih, Sri; Setyorini, Retno
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2048

Abstract

With a population of 273.5 million in 2020, the people's economy is very important for Indonesia. The populist economic model has existed in Indonesia for a long time, locally known as “Koperasi”. However, Koperasi could not keep up with information and technology in the industrial 4.0 era. When other industrial models adopt information technology massively, Koperasi seems to struggle to shift from the conventional model. Slowly but surely, Koperasi become less popular, especially among Generation Z, who considers information technology part of their daily needs and lifestyle. The scope of Koperasi’sbusiness shrinks and becomes limited, along with their business capital. On the other hand, information technology provides opportunities for small and medium-sized entrepreneurs. The data shows that this sector contributes to 60.5% of the national GDP, absorbs 96.9% of the workforce, and provides 99.9% of total employment. Unfortunately, many Micro, Small, and Medium Enterprises (MSME) have limited capital and tend to prefer fintech services that offer easier accessibility than Koperasi. This paper aims to propose an E-Koperasi and the synergy of Koperasi and MSMEs model platform, specifically the digital marketplace platform. We design the platform requirements using the Software Requirement Specification with the User Acceptance Test. As a result, an application is developed as a model platform available for Koperasi and MSMEs. The platform is proposed to support the Government’s program to digitalize the Koperasi and the MSMEs and increase their competitiveness in Industrial 4.0.
Text Summarization on Verdicts of Industrial Relations Disputes Using the Cross-Latent Semantic Analysis and Long Short-Term Memory Wicaksono, Galih Wasis; Hakim, Muhammad Nafi Maula; Hayatin, Nur; Hidayah, Nur Putri; Sari, Tiara Intana
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2052

Abstract

The information presented in the documents regarding industrial relations disputes constitutes four legal disputes. However, too much information leads to difficulty for readers to find essential points highlighted in industrial relations dispute documents. This research aims to summarize automated documents of court decisions over industrial relations disputes with permanent legal force. This research involved 35 documents of court decisions obtained from Indonesia’s official Supreme Court website and employed an extractive summarization approach to summarize the documents by utilizing Cross Latent Semantic Analysis (CLSA) and Long Short-Term Memory (LSTM) methods. The two methods are compared to obtain the best results CLSA was employed to analyze the connection between phrases, requiring the ordering of related words before they were converted into a complete summary. Then, the use of LSTM is combined with the Attention module to decoder and encoder the information entered so that it becomes a form that can be understood by the system and provides a variety of splitting of documents to be trained and tested to see the highest performance that the system can generate. The research has found out that the CLSA method gave a precision of 79.1%, recall score of 39.7%, and ROUGE-1 score of 50.9%, and the use of LSTM was able to improve the performance of the CLSA method with the results obtained 93.6%, recall score of 94.5 %, and ROUGE-1 score of 93.9% on the variation of splitting 95% training and 5% testing.
Development of IoT Control System Prototype for Flood Prevention in Bandung Area Permatasari, Yessy; Firdaus, M Ridwan; Zuhdi, Hafidh; Fakhrurroja, Hanif; Musnansyah, Ahmad
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2083

Abstract

Bandung is one of the areas with high rainfall that can increase the volume of river water, which, if not handled properly, has the potential for significant floods that can cause material damage and loss of life. With this problem, the authors' rationale for designing a control system for flood prevention. This system develops prototypes using Internet of Things technology and fuzzy logic. For Internet of Things technology, the author uses Arduino, which controls sensors and actuators, while Raspberry Pi is used to process data. In addition, the author uses ultrasonic sensors to measure the water level and a water pump to control the water level. So, if the water level exceeds the specified limit, the pump will move the water to another place, in this prototype, using an aquarium. For fuzzy logic, the criteria used are dry, filled, and full. In addition, this system is equipped with a website-based dashboard used to monitor real-time data from the sensor. The results of this study indicate the system is running well, with an average error of 32.2%. This indicates that the system has been well designed because the errors obtained are feasible to be minor, although there are several influencing factors, such as prototype construction and sensor readings. Thus, this prototype can be applied as a reference for making a real system for flood control.
Indonesian Hate Speech Detection Using IndoBERTweet and BiLSTM on Twitter Kusuma, Juanietto Forry; Chowanda, Andry
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1035

Abstract

Hate speech is an act of speech to spread hate to other people. In this digital era where everyone connects with social media, hate speech is growing rapidly and uncontrollably. Many people do not realize they are giving hate speech when critics something on social media due to a lack of awareness of the difference between hate speech and free speech. The results make victims feel alienated from society, and the people who spread it would often face the law. Detection in the sentences to identify whether it contains hate speech is essential to counter people's ignorance. For detecting such sentences, a machine learning algorithm is widely used to help identify each sentence. In this paper, we used a subset from machine learning named deep learning with the latest IndoBERT model named IndoBERTweet and combined it with RNN layer named BiLSTM. The appearance of IndoBERTweet opened more chances to further improve text classification performance with the addition of BiLSTM layer. The model first made a token representative from the sentence, then calculated it to analyze and made the classification based on the calculation. For this model to be effective, we trained our model with the labeled public dataset retrieved from Twitter. These datasets are classified into hate speech and non-hate speech, and these labels are applied to the models. We evaluated our model and achieved an accuracy of 93.7%, an improvement for classifying hate speech sentences from previous research.
A New Face Region Recovery Algorithm based on Bicubic Interpolation Al-Hadaad, Muntadher H.; Thabit, Rasha; Zidan, Khamis A.
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1671

Abstract

Recently, researchers focused on face image manipulation detection and localization techniques because of their importance in image security applications. The previous research has not highlighted the recovery of the face region after manipulation detection. This paper presents a new face region recovery algorithm (FRRA) to be included in the face image manipulation detection algorithms (FIMD). The proposed FRRA consists of two main algorithms: face data generation algorithm and face region restoration algorithm. Both algorithms start by detecting the face region using Multi-task Cascaded Neural Network followed by a face window selection process. In the face data generation algorithm, the recovery information is generated from the shirked face window using bicubic interpolation technique. In the face region restoration algorithm, the face region zoomed using bicubic interpolation technique. The proposed FRRA has been tested and compared with previous recovery methods for different color face images, and the results proved that the FRRA could recover the face region with better visual quality at the same data length compared to previous methods. The main contributions of this research are a) the suggestion of including a face region recovery algorithm to FIMD, b) the study of previous recovery data generation algorithms for color face images, and c) introducing a new algorithm for generating the recovery data based on bicubic interpolation. In the future, the proposed algorithm can be included in the recent FIMD algorithms to recover the face region, which can be very useful in practical applications, especially those used in data forensics systems.
Voice-Authentication Model Based on Deep Learning for Cloud Environment Hachim, Ethar Abdul Wahhab; Gaata, Methaq Talib; Abbas, Thekra
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1303

Abstract

Cloud computing is becoming an essential technology for many organizations that are dynamically scalable and employ virtualized resources as a service done over the Internet. The security and privacy of the data stored in the cloud is cloud providers' main target. Every person wants to keep his data safe and store it in a secure place. The user considers cloud storage the best option to keep his data confidential without losing it. Authentication in the trusted cloud environment allows making knowledgeable authorization decisions for access to the protected individual's data. Voice authentication, also known as voice biometrics, depends on an individual's unique voice patterns for identification to access personal and sensitive data. The essential principle for voice authentication is that every person's voice differs in tone, pitch, and volume, which is adequate to make it uniquely distinguishable. This paper uses voice metric as an identifier to determine the authorized customers that can access the data in a cloud environment without risk. The Convolution Neural Network (CNN) architecture is proposed for identifying and classifying authorized and unauthorized people based on voice features. In addition, the 3DES algorithm is used to protect the voice features during the transfer between the client and cloud sides. In the testing, the experimental results of the proposed model achieve a high level of accuracy, reaching about 98%, and encryption efficiency metrics prove the proposed model's robustness against intended attacks to obtain the data.
433Mhz based Robot using PID (Proportional Integral Derivative) for Precise Facing Direction Hariyadi, Mokhamad Amin; Fadila, Juniardi Nur; Sifaulloh, Hafizzudin
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1841

Abstract

This research endeavor aims to evaluate the effectiveness of the robot's direction control system by employing PID (Proportional Integral Derivative) output and utilizing wireless communication LoRa E32 433MHz. The experimental robot used in this study was a tank model robot equipped with 4 channels of control. LoRa was implemented in the robot control system, in conjunction with an Android control application, through serial data communication. The LoRa E32 module system was selected based on its established reliability in long-range communication applications. However, encountered challenges included the sluggishness of data transmission when using LoRa for transferring control data and the decreased performance of the robot under Non-Line of Sight conditions. To overcome these challenges, the PID method was employed to generate control data for the robot, thereby minimizing the error associated with controlling its movements. The PID system utilized feedback from a compass sensor (HMC5883L) to evaluate the setpoint data transmitted by the user, employing Kp, Ki, and Kd in calculations to enable smooth movements toward the setpoint. The findings of this study regarding the direct control of the robot using wireless LoRa E32 communication demonstrated an error range of 0.6% to 13.34%. A trial-and-error approach for control variables determined the optimal values for Kp, Ki, and Kd as 10, 0.1, and 1.5, respectively. Future investigations can integrate additional methodologies to precisely and accurately determine the PID constants (Kp, Ki, and Kd) mathematically.
Classification of EEG Signal using Independent Component Analysis and Discrete Wavelet Transform based on Linear Discriminant Analysis Melinda, Melinda; Maulisa, Oktiana; Nabila, Nissa Hasna; Yunidar, Yunidar; Enriko, I Ketut Agung
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1219

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopment syndrome decreasing sufferers' social interaction, communication skills, and emotional expression. Autism syndrome can be detected using an electroencephalogram (EEG). This study utilized the EEG of autistic people to support the classification study of machine learning schemes to produce the best accuracy. One of the best approaches to classify the EEG signal is The Linear Discriminant Analysis (LDA), a machine learning technique to classify autism and normal EEG signals. LDA was chosen because it can maximize the distance between classes and minimize the number of scatters by utilizing between and within-class functions. This method was combined with other methods: Independent Components Analysis (ICA) and Discrete Wavelet Transform (DWT), to improve the accuracy system. ICA removes artifacts or signals other than brain signals that can cause noise in the EEG signal, so the analyzed signal was a complete EEG signal without other factors. DWT can help increase noise suppression in the EEG signal and provide signal information through frequency and time representation. The EEG dataset was collated from 16 children (eight autistic and eight normal). The signals from the dataset were filtered by artifacts using ICA, decomposed by three levels through DWT, and classified using the Linear Discriminant Analysis (LDA) technique. Using the Confusion Matrix, the results reveal the best accuracy of 99%.

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