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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 14 Documents
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Search results for , issue "Vol. 3 No. 2 (2022)" : 14 Documents clear
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.
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.
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.
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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