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International Journal of Artificial Intelligence Research
Published by STMIK Dharma Wacana
ISSN : -     EISSN : 25797298     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal Of Artificial Intelligence Research (IJAIR) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics of Artificial intelligent Research which covers four (4) majors areas of research that includes 1) Machine Learning and Soft Computing, 2) Data Mining & Big Data Analytics, 3) Computer Vision and Pattern Recognition, and 4) Automated reasoning. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : -
Articles 621 Documents
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Selecting the Optimal Location for a New Facility: A PROMETHEE II Analyst Harjanti, Trinugi Wira; Widjaja, Herry Rachmat; Nofirman, N; Sudipa, I Gede Iwan; Pramono, Susatyo Adhi; Rahim, Robbi
International Journal of Artificial Intelligence Research Vol 7, No 1 (2023): June 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.738

Abstract

This paper presents a case study of location selection for a new facility using the multi-criteria decision making (MCDM) method, PROMETHEE II. The PROMETHEE II method is a widely used method for solving problems that involve multiple criteria and alternatives. The method allows for the ranking of alternatives based on their overall net flow, which is calculated by weighting and comparing the criteria values for each pair of alternatives. The case study evaluated five different locations based on criteria such as access to transportation, availability of skilled labor, and cost of living. The results of the analysis indicate that C1 was ranked as the most attractive location, with the highest scores for all criteria and the highest overall net flow among all alternatives. A sensitivity analysis was performed to ensure that the results were robust and not sensitive to small changes in the weight of the criteria. The results of this study demonstrate the utility and effectiveness of the PROMETHEE II method in practice and provide valuable insights for further research and practical action.
Development of E-Commerce Applications to Support Digital-Based Marketing John Friadi; Angelina E. Rumengan; Dodi P. Yani; Muhamad Sigid Safarudin; Suroto Suroto
International Journal of Artificial Intelligence Research Vol 6, No 1.2 (2022)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.2.1010

Abstract

E-commerce is one of the application platforms that support digital-based marketing. The development of e-commerce from year to year is increasing, which is influenced by the development of information technology and the internet. The development of the internet has caused a shift in human activities from offline to online. During the Covid 19 pandemic, the growth of e-commerce has also increased sharply. The Covid 19 pandemic has caused a shift in human habits in purchasing goods from offline to online. This research develops a prototype e-commerce application to support digital-based marketing and conducts usability testing of the developed application using the SUS (System Usability Scale) scale. Based on the analysis and calculation of the SUS scale score, a score of 77.03 is obtained, meaning that the application is functionally good and acceptable to users and shows that using e-commerce applications supports digital-based marketing and affects the increase in marketing and sales
Comparison of Ensemble Learning Methods for Mining the Implementation of the 7 Ps Marketing Mix on TripAdvisor Restaurant Customer Review Data Sunarko, Budi; Hasanah, Uswatun; Hidayat, Syahroni
International Journal of Artificial Intelligence Research Vol 7, No 2 (2023): December 2023
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1096

Abstract

The 7P marketing mix encompasses various business facets, notably the Process element governing internal operations from production to customer service. With the surge in online customer feedback, assessing machine learning efficacy, especially ensemble learning, in classifying 7P-related customer review data has gained prominence. This research aims to fill a gap in existing literature by evaluating ensemble learning’s performance on 7P classification, an area not extensively explored despite prior sentiment analysis studies. Employing a methodology merging Natural Language Processing (NLP) with ensemble learning, the study processes restaurant reviews using NLP techniques and employs ensemble learning for precision and accuracy. Findings demonstrate that DESMI yielded the highest performance metrics with accuracy at 0.697, precision at 0.699, recall at 0.697, and an F1-score of 0.684. These outcomes underscore ensemble learning's potential in handling complex datasets, signifying its relevance for marketers and researchers seeking comprehensive insights from customer reviews within the 7P marketing mix domain. This study sheds light on how ensemble learning outperforms its foundational methods, indicating its prowess in extracting meaningful insights from diverse and intricate customer feedback.
Analysis of Motivation, Leadership Style, and Communication on the Performance of Members of the Sulawesi Truck Driver Association Kotamobagu Branch through Job Satisfaction As an Intervening Variable Dotulong, Lucky; Loindong, Sjendry; Sumarauw, Jacky; Lintong, Debry Ch. A.
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1129

Abstract

The background of this research is that the performance of members of the Sulawesi Truck Driver Association, Kotamobagu City branch, has not been maximized. There are still accidents on the road or when loading and unloading goods and complaints from transportation users who use the services of members of the Sulawesi Truck Driver Association, Kotamobagu City branch personally which makes losses in terms of material service users. Such performance results in the organization's name becoming bad. The purpose of this study was to analyze motivation, leadership style, and communication on the performance of members of the kotamobagu branch of the sulawesi truck driver union through job satisfaction as an intervening variable. The research stages carried out were field observations, then literature studies, data collection both primary and secondary. Followed by research, data analysis and discussion, after that draw conclusions and provide suggestions. The results showed that the variables of motivation, leadership style, and communication had no effect on the job satisfaction of truck drivers who were members of the Sulawesi truck driver union, Kotamobagu city branch. motivation, leadership style, communication, and job satisfaction have no effect on the performance of truck drivers who are members of the Sulawesi truck driver union, Kotamobagu city branch.
Enhancing Electricity Consumption Prediction with Deep Learning through Advanced Data Splitting Techniques Pratiwi, Adinda Putri; Ginardi, Raden Venantius Hari; Saikhu, Ahmad
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1204

Abstract

Energy consumption is increasing due to population growth and industrial activity, making electricity essential in human life. With limited natural resources, effective management of electrical resources is crucial to reduce energy usage amidst rising demand. The current trends on using deep learning as prediction can enhance the performances. To have good performance it needs correct preprocessing data, so it will produce a model with less overfitting. This research proposes a model using time-series cross-validation as the splitting data and correlation to choose the best features set for the prediction of electricity consumption. Experiments will compare time-series cross-validation and holdout methods to see the performances of splitting data and enhancing the multi-horizon data.  The experiment used 8 sets of feature lists, which are paired in combination based on correlation to ensure the best features that are related. The result is splitting data using time-series cross-validation can maintain good perfomances on mode and holdout can maintain a good evaluation performance across the horizon. Feature sets that include temporal features have excellent results, especially when combined with features that have the strongest correlation relationship with electricity consumption, leading to an enhanced R2. Among all the models tested, CNN-GRU had the best model for multistep prediction across various every horizons and feature sets.
Unveiling the Synergy: Exploring the Intersection of Artificial Intelligence, Digital Management Information Systems, and Marketing Management in a Qualitative Research Study Azis, St. Nurhayati; Ahmad, A.; Putra, Aditya Halim Perdana Kusuma
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1147

Abstract

This study investigates the integration of Artificial Intelligence (AI), Digital Marketing Information Systems (DMIS), and Marketing Management to enhance decision-making processes in marketing. The research aims to explore the extent of augmentation in marketing decision-making, identify indications of objectiveness in AI-driven analytics, and propose solutions to ensure transparency and accountability. Methodologically, the study conducts a systematic literature review to synthesize existing research on the topic. Findings suggest that AI technologies offer advanced analytics capabilities, enabling marketers to gain deeper insights into consumer behavior and market trends. However, concerns regarding biases in AI-driven analytics and challenges in data integration and dissemination are identified. The study underscores the importance of interdisciplinary collaboration, transparency, and explainability in AI algorithms to mitigate biases and enhance objectiveness. Moreover, it highlights the need for robust data governance policies and talent development initiatives to foster a culture of data-driven decision-making. The research contributes to theoretical understanding by redefining marketing practices through AI integration and offers practical insights for organizations to leverage AI, DMIS, and Marketing Management effectively.
Improving the Performance of Higher Education Academic Information Systems Using Cloud Computing Technology Nugroho, Handoyo Widi; nurjoko, Nurjoko; Andani, Bethania
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1066

Abstract

Improving the performance of academic information systems using cloud computing technology is very relevant and important. The application of cloud computing technology to higher education academic information systems can also help improve data security and information risk management.  The research results show that cloud computing technology significantly improves the performance of academic information systems in higher education. This technology enables faster and easier access to academic information, and strengthens data management and data analysis capabilities.  
Live Streaming Of Skincare Products: The Role Of Celebrity Endorses On Impulsive Buying Ramdani, Luki; Rufaidah, Popy
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1142

Abstract

The purpose of this study is to identify the influence of live streaming and celebrity endorsers on impulse buying among skincare users. This research was conducted to the users of skincare products with a total sample of 130 respondetns using purposive sampling techniques and applying descriptive quantitative-based research methods with data processing involving the use of statistical analysis methods through the SEM-PLS programme.  The results of this study reveal that the live streaming variable (? = 0.242 p-value <0.01) has a positive and significant effect on impulse buying among users of the skincare products and celebrity endorsers (? = 0.418, p-value <0.01) have a positive and significant effect on impulse buying among users of the skincare products. Thus,H1 and H2 are accepted. From a theoretical point of view, the results provide a new angle that combines live streaming content and celebrity endorsers in increasing customers' impulse buying, where the more often audiences see, hear, and absorb information about skincare products delivered by celebrities, the more likely they are to make impulse buying
Data Mining Using the K-Means Clustering Method Can be Applied to Determine Production Amounts Based on Product Sales Data at PT. Coca Cola Distribution Indonesia Bengkulu Rozzi, Yoli Andi
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1252

Abstract

This research applies data mining using clustering method for determining the amount of production is based on the level of product sales at PT . Coca Cola Distribution Indonesia Bengkulu branch . The algorithm used is the K -Means Clustering , where the data are grouped by similar characteristics will be included in the same group and a set of data entered into non-overlapping groups . Information displayed in the form of a group - a group of product data based on the level of sales , in order to know the amount of production in PT.Coca Cola Distribution Indonesia tailored to cluster products with sales levels high, medium and low. The testing of this research is using RapidMiner 5.3, so it will gain some clusters who really helpful for PT.Coca Cola Distribution of Indonesia in determining the number of next production
INFORMATION ON MOTIVATION, COMPENSATION, WORK ENVIRONMENT AND ITS IMPACT ON THE PERFORMANCE OF SHARIA BANK EMPLOYEES Zahera Mega Utama; Mas Wigrantoro; Watriningsih Watriningsih
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1050

Abstract

Competition of financial institutions is so tight, however the development of Islamic financial institutions the longer shows that the positive trend. The basic problem in islamic bank is how to spur employee performance to always improve. Excellent employees will contribute significantly to achieve the company’s goals. Bank Syariah Indonesia (BSI) have been experiencing a significant growth. This research purposes to know the effect of motivation, compensation and work environment on employee performance at BSI KCP Pancor. The research used the quantitative with correlation method. The sample is collected by using saturated sampling method and multiple linear regression for analyzing the data. The number of samples used in this study amounted to 30 respondents source was distribution of questionnaires to BSI employees. This study found that Motivation (0,032<0,05), Compensation (0,021<0,05) and Work Environment (0,044<0,05) has significant and positive effect on employee performance at BSI KCP Pancor. This study assists to understand the bank management specifically in employee performance. In other hand, the findings can be preferences in making policy for government, helpful to use additional references about comparison of Islamic banks employee performance and its influence.

Page 39 of 63 | Total Record : 621

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