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JINAV: Journal of Information and Visualization
Published by PT. Mattawang Mediatama Solution
ISSN : -     EISSN : 27461440     DOI : https://doi.org/10.35877/jinav
Core Subject : Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Mathematics
JINAV: Journal of Information and Visualization is an international peer-reviewed open-access journal dedicated to interchange for the results of high-quality research in all aspects of information science and technology, data, knowledge, communication, and their 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 JINAV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 139 Documents
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Convolutional Neural Network (CNN) Method for Classification of Images by Age Nurtiwi Nurtiwi; Ruliana Ruliana; Zulkifli Rais
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1481

Abstract

Image classification is one of the studies that is currently being developed. The details of the characteristics that must be captured make researchers compete to find the most suitable method for classifying. The Convolutional Neural Network (CNN) algorithm is one of the most superior algorithms in the field of object classification and identification today. With the help of several packages contained in Google Colab for classification, this algorithm is easier to use. In this study, the target case is the age of a person who will be classified using photos or images taken from the internet which are then stored in the form of Google Drive. The research data used is divided into 2 parts, namely for training data as many as 23.440 images, and 10,046 for testing data. Then to facilitate the extraction of features from the features to be identified, the researchers carried out the preprocessing stage, namely grayscale images, and data augmentation. The purpose of this study is to implement the concept of Deep Learning with Convolutional Neural Networks (CNN) in image classification and to determine the level of accuracy of the CNN model in classifying images. After the algorithm is run and the model has been formed, an accuracy of 78.5% is obtained. It can be concluded that the Convolutional Neural Network (CNN) method is good at classifying images
Financial Technology and Human Resource Competency in Financial Management for UMKM at Palopo City A. Wulan Lestari; Antong Antong; Halim Usman
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1483

Abstract

Abstract This study aint to determine the effect of fintech and human resource competencies in the financial management of umkm in palopo city. The data source in this study is primary data. The primary data used in this study were obtained from distributing questionnaires. The population selected for this study were UMKM in Palopo City. Respondents were given a number of statements and then asked to respond with their own statements. Furthermore, respondents were asked to fill out the questionnaire using an anonymous system. This approach aims to avoid potential bias during the data collection process. The research method used is quantitative method and analyzed using Structural Equation Model (SEM) approach using AMOS software. The questionnaire in this study was prepared based on the variables to be tested, namely Financial Technology, Resources, Financial Management of UMKM. All variables are measured using a Liket scale with 5 (five) alternative answers. The following are the specifications: 1) Strongly Disagree; 2) Disagree; 3) Neutral; 4) Agree; 5) Strongly Agree. The results showed that (1) The results of data analysis show that human resource competencies affect financial technology. (2) The results showed that simultaneously the umkm financial management variable had a significant influence on financial technology. (3) While the results of umkm financial management variables have an effect on human resource competencies. Keywords: Fintech, Finance, Human Resources, Management, UMKM
Leveraging the Decision Support System and Simple Additive Weighting Method for Optimal Retail Location Identification Muhammad Ade Kurnia Harahap; Hardisal Hardisal; Ahyuna Ahyuna; Robbi Rahim
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1485

Abstract

The decision-making process is a crucial part of any business or organization, and the Decision Support System (DSS) using the Simple Additive Weighting (SAW) method is a widely used tool for evaluating multiple options or alternatives. This study aimed to determine the best location for a new retail store using the DSS and SAW method. Four criteria were considered in the evaluation process: population density, proximity to competitors, average income, and rent cost. Data was collected and analyzed, and the weighted scores were calculated using the SAW method. The results of this study showed that Location A had the highest weighted score, and thus was determined to be the best option for the new retail store. This study provides a reliable method for evaluating multiple options and determining the best one. It also highlights the advantages of using the DSS and SAW method, such as the objective decision-making process based on data and criteria, and the high reliability of the results. Additionally, this study also points out the limitations of not using the DSS and SAW method, such as the subjective decision-making process based on personal bias and low reliability of the results. The DSS SAW method can be applied in various decision-making scenarios, and this study can serve as a guide for future research and decision-making processes.
Forecasting Analysis of Fishermen’s Productivity Data Using Single Exponential Smoothing Taufiq Dwi Cahyono; Heri Purwanto; Iwan Adhicandra; Kraugusteeliana Kraugusteeliana; Edy Winarno
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1487

Abstract

One of the reasons why it is vital to forecast fisher production data in coastal regions is to increase fish resource management efficiency. By calculating the number of fishing boats, the amount of fish that must be caught, and the amount of raw materials required for fish processing based on the anticipated amount of fishermen's production in the following period, decision-makers can determine the amount of fish that must be caught and the amount of raw materials required for fish processing. So that the objective of the research is to forecast fishermen's production data using the Single Exponential Smoothing method, this method is effectively used to perform forecasting of time series data with short period data intervals to produce forecasts for the next period, and it can measure the rate of change of fishermen's production data each period. The results of forecasting data on fishermen's production utilizing time series data intervals from October 2022 to January 2023 to make forecasts for February 2023, namely a MAPE error rate of 2.85%, indicate that the forecasting results are within the "good" category.
A Study on the Effectiveness of k-NN Algorithm for Career Guidance in Education Indriyani Indriyani; Laros Tuhuteru; Gentur Wahyu Nyipto Wibowo; Alex Wenda; I Nengah Sandi
JINAV: Journal of Information and Visualization Vol. 3 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1491

Abstract

This study aims to evaluate the performance of k-NN algorithm in recommending career paths for students based on their interests, past courses, and career goals. The k-NN algorithm was applied to a dataset of student information and its performance was evaluated using quantitative or qualitative measures such as accuracy or user satisfaction. The results indicated that the algorithm provided accurate recommendations and that the choice of k and the use of Euclidean distance measure were crucial for the performance of the algorithm. However, the study also highlighted the limitations of the research, such as the size and diversity of the dataset used, which could have affected the generalizability of the results. This study emphasizes the potential of data-driven approaches in career guidance in education and the k-NN algorithm as a valuable tool in this field. Future research could include incorporating additional factors such as student demographics or academic performance into the algorithm and using more diverse and larger datasets
Depression Detection on Twitter Social Media Platform using Bidirectional Long-Short Term Memory Andre Agasi Simanungkalit; Warih Maharani; Prati Hutari Gani
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1503

Abstract

Depression is one of the mental disorders that are often experienced by a person in daily life. Social media platforms is a new thing as an alternative to tell stories and express current feelings by people today. Twitter is one of the social media that is often used to express feelings and opinions through tweets posts, including tweets that contain hate speech which indirectly shows symptoms of depressive disorder through statements uploaded. It also requires modeling that can recognize users with the potential to experience depression so that they can get initial treatment. This can be implemented using the BiLSTM (Bidirectional Long Short-Term Memory) method and the Word2Vec feature. It can be concluded that the dimensional size of the large feature word2vec, LSTM, and Conv1d layers influenced the model in detecting depression which can be seen in the testing accuracy and F-1 score according to the split data used.
Sentiment Analysis on Twitter Social Media towards Shopee E-Commerce through Support Vector Machine (SVM) Method Putri Samapa Hutapea; Warih Maharani
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1504

Abstract

Shopee is e-commerce widely accessed and used in this era. Many people use Shopee because the products offered are cheaper and more affordable. Despite the fact that Shopee is a well-known e-commerce, it still requires responses and suggestions from the public to maintain or improve the features required. In this study, public sentiment analysis was carried out on Twitter social media related to the Shopee marketplace. This study collected data that contained tweets from predetermined keywords and used Word2Vec and Support Vector Machine classification methods. The use of Word2Vec influenced the level of accuracy so that it increased for each SVM kernel. Meanwhile, the best hyperparameter tuning was found in the polynomial kernel, with an accuracy rate of 93.20%.
ELECTRE III for Human Resource Management: A Study of Recruitment and Retention Strategies Meithiana Indrasari; Delipiter Lase; Indriyani Indriyani; Jacomina Vonny Litamahuputty; Iwan Adhicandra; Robbi Rahim
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1505

Abstract

This paper presents the application of the ELECTRE III multi-criteria decision making method for human resource management. The case study conducted involves the evaluation of different recruitment and retention strategies using multiple criteria such as cost, time to fill a position, quality of candidates, retention rate, diversity and inclusion, employee satisfaction, and compliance. The study demonstrates the step-by-step process of how to conduct an ELECTRE III analysis, including the identification of criteria and alternatives, the calculation of concordance and discordance indices, and the determination of final rankings using the global outranking relation. The results of this analysis can be used by organizations to make informed decisions about recruitment and retention strategies that best align with their goals and objectives. The study highlights the importance of data quality and the need for sensitivity analysis to check the robustness of the results. Additionally, it is suggested that future research could be conducted on how to effectively communicate the results of the analysis to stakeholders and decision-makers, and on comparing the results of this analysis with other methods.
Retweet Predictions Regarding COVID-19 Vaccination Tweets through The Method of Multi Level Stacking Vena Erla Candrika; Jondri Jondri; Indwiarti Indwiarti
JINAV: Journal of Information and Visualization Vol. 4 No. 1 (2023)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1518

Abstract

The rapid development of technology from day to day indirectly influences increasing social media use. This can be seen from spreading information that is very easily found on social media, one of which is Twitter. It is one of the most popular platforms for expressing people’s feelings by tweeting and interacting with other users at the same time. Various opinions about the COVID-19 vaccination began to be discussed on the Twitter platform. Moreover, most people take advantage of the feature available on Twitter, namely retweets. Users do retweet because there are many influencing factors. It can be caused by a reason that they have the same opinions and thoughts as the tweet owner, and so on. A retweet feature is also a form of information diffusion on the Twitter platform. The diffusion of information on Twitter has several factors, such as the most influential users, using hashtags or URLs, and others. In this conclusion, retweet predictions have been carried out regarding COVID-19 vaccination tweets using the features user-based and time-based through the Multi-Level Stacking classification method. This method indicates the best results when oversampling with an F1-Score of 96.23%.
Assessing the Relative Importance of Price, Safety, Energy Efficiency, Brand Reputation, and Warranty in Car Selection using SMART Method as Decision Support System Mochammad Anshori; Jimmy Moedjahedy; Samuel PD Anantadjaya; Ardimansyah Ardimansyah; Susi Indriyani
JINAV: Journal of Information and Visualization Vol. 3 No. 2 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav1523

Abstract

The Simple Multi Attribute Rating Technique (SMART) is a decision support system that is widely used for evaluating and comparing alternatives based on multiple criteria. In this study, we applied the SMART method to assess the relative importance of price, safety, energy efficiency, brand reputation, and warranty in car selection. We assigned scores to each car model for each criterion, weighted the criteria based on their relative importance, and calculated an overall score for each car model. The results of this study show that the SMART method is a simple and easy-to-use tool that can help in making informed decisions in car selection. The method allows for the inclusion of both quantitative and qualitative criteria, making it versatile and applicable to a wide range of decision-making problems. Future research could focus on developing methods to address the limitations of the SMART method. Another area of research could be to integrate the SMART method with other decision-making tools, such as multi-criteria decision analysis or artificial intelligence, to improve its performance and applicability. Additionally, more research could be done on how to effectively use SMART method in real-life scenarios where the data is uncertain, incomplete and inconsistent

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