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INDONESIA
Jurnal Informatika Ekonomi Bisnis
Published by Asia Pacific Network for Sustainable Agriculture, Food and Energy
ISSN : 27148491     EISSN : 27148491     DOI : https://doi.org/10.37034/infeb
Core Subject : Economy,
Economics, Econometrics & Finance
The Jurnal Informatika Ekonomi Bisnis (INFEB) is an interdisciplinary journal. It publishes scientific papers describing original research work or novel product/process development. The objectives are to promote an exchange of information and knowledge in research work, and new inventions/developments on the use of Informatics in Economics and Business. This journal is useful to researchers, engineers, scientists, teachers, managers, and students who are interested in keeping a track of original research and development work being carried out in the broad area of informatics in economics and business through a scholarly publication.
Arjuna Subject : Ilmu Ekonomi, Ekonometrika dan Keuangan (semua kategori) - Ilmu Ekonomi, Ekonometrika dan Keuangan (Lain-Lain)
Articles 45 Documents
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Search results for , issue "Vol. 6, No. 4 (December 2024)" : 45 Documents clear
Determinan Kebijakan Dividen dengan Pandemi Covid-19 Sebagai Variabel Moderasi Pada Perusahaan Manufaktur Periode 2017-2022 Qaedi, Razan; Darmansyah; Darminto, Dwi Prastowo
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.995

Abstract

This research aimed to examine the effect of managerial ownership, institutional ownership, profitability, covid-19 pandemic which moderates of managerial ownership and profitability on the dividend policy with company size as a control variable. The research sample used manufacturing companies which are listed Indonesia Stock Exchange (IDX) in 2017-2022 periods. The sample collection technique has been done by using purposive sampling method with several criteria determined by the researcher. Based on those criteria, it took 55 companies with 330 reseacrh data. The analysis method of this research used panel data regression analysis with the best model is common effect model (CEM) with Eviews version 12 software. The result of the research showed that managerial ownership have no effect on the dividend policy, institutional ownership had effect on the dividend policy, profitability had effect on the dividen policy, covid-19 is unable to modetare the relationship of managerial ownership on the dividend policy and covid-19 is unable to modetare the relationship of profitability (ROE) on the dividend policy.
Transformation of Maslahah Principles in E-Commerce in Al - Ghazali's View Daredmi, Salsabilla; Rozalinda; Zulvianti, Nora
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1003

Abstract

This study explores the application of the principle of maslahah as formulated by Al-Ghazali in the context of e-commerce in Indonesia, particularly regarding Sharia-compliant online loans (pinjol) that grew significantly during 2022-2023. The study analyzes this phenomenon through the lens of maslahah, focusing on the protection of wealth (hifz al-mal) and honor (hifz al-‘ird). The results indicate that although Sharia-compliant fintech provides broader financial access, unethical practices such as high returns (0.1% per day or 36% annually) and aggressive debt collection methods contradict consumer protection principles. A lack of transparency in contracts and data breaches further threaten core maslahah values. The study recommends stricter regulations from the OJK and DSN-MUI and improving Sharia financial literacy to ensure a balance between technological innovation, Sharia compliance, and consumer protection. In conclusion, Al-Ghazali's maslahah principles remain relevant and must be adapted to address e-commerce challenges in the digital era.
Pengaruh Interpersonal Interactions terhadap Purchase Intention pada TikTok Shop Live Streaming Margaretta, Melissa; Andajani, Erna; Novika, Fitri
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1013

Abstract

This study aims to analyze the influence of perceived expertise, perceived similarity, perceived familiarity, perceived likability, perceived informativeness, perceived responsiveness, and swift guanxi that can affect purchase intention in shopping on TikTok Shop through live streaming. The research uses a quantitative method. A survey was conducted by distributing a questionnaire via Google Forms to 327 respondents. The data were analyzed using Smart PLS 4.0. The study found that swift guanxi occurring during live streaming influences purchase intention. The results also showed that perceived expertise, perceived similarity, perceived likeability, perceived informativeness, and perceived responsiveness influence swift guanxi, thereby increasing purchase intention. Meanwhile, perceived familiarity does not affect swift guanxi.
Comparative Analysis of Multicriteria Inventory Classification and Forecasing: A Case Study in PT XYZ Purwandaru, Dhanang; Ruldeviyani, Yova; Nugraheni, Sani; Prisillia, Galuh
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1014

Abstract

One crucial aspect of supply chain management is inventory management. Inefficient inventory management can lead to various issues, such as product expiration, where a high number of items in the warehouse either have expired or are approaching expiration. This issue is experienced by a distribution SME in Indonesia, PT XYZ. Without such classifications, it becomes challenging to predict demand and manage stock levels efficiently. Therefore, the aim of this study is to classify inventory to identify the most important items to business and make a forecasting model of sales quantity to predict inventory replenishment using machine learning algorithms. To advance our research, we adopted the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. For inventory classification, we conducted a hybrid approach that combined TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ABC analysis (A: high-value items, B: medium-value items, and C: low-value items). The data employed in this study comprised secondary data, including purchase orders, sales orders, and stock movement records. The result reveals that 11 of the total 383 items under class A are important items for business. After obtaining labels from the ABC Analysis, we proceed to train models using KNN, SVC, and Random Forest for predicting inventory classification. Notably, the Random Forest model showcased remarkable performance and outperformed the rest of the models, achieving an accuracy of 99.21%. For inventory forecasting ARIMA displays a competitive performance with RMSE value 5.305 and MAE value 3.476, indicating a relatively accurate prediction with lower forecasting errors than two other models
Achieving ISO 9001 Compliance in Agile Software Development Projects at Indonesian Research Institute Hermawati, Anisa; Raharjo, Teguh; Wayan Trisnawaty, Ni
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1021

Abstract

In the modern era, e-government provides comprehensive online services. Currently, there is no standardized quality management regulation in development projects. Therefore, it is crucial for Indonesia, especially in one of the Indonesian Institute of Sciences, to consider implementing ISO 9001:2015 in project development. By doing so, it can demonstrate its dedication to efficient project management and quality assurance to improve quality and customer satisfaction, as it can significantly enhance the reputation and credibility of the Institute in the eyes of the public. The main objective of this study was to examine the integration of ISO 9001:2015 standard with Agile methodology. This study evaluated the critical documentation required to fulfill ISO 9001:2015 in Agile-based projects. This research utilizes triangulation methods, which include interviews, observations, and field notes in qualitative research. Aligning Agile practices with the rigorous quality management standards of ISO 9001 is essential when incorporating ISO 9001:2015 principles into Agile project development. This research recommends standard operating procedures for implementing the documentation prerequisites of ISO 9001:2015 in advancing Agile projects in the public sector, particularly at the Indonesian Institute of Sciences, to realize the benefits of integrating Scrum and ISO 9001 in improving the quality of public services.
Perancangan Sistem Point of Sale CCTV Berbasis Desktop Menggunakan Metode Waterfall (CV. Kadai Komputer) Sari, Atalya Kurnia
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1023

Abstract

CV. Kadai Komputer merupakan sebuah toko yang bergerak dalam bidang teknologi, antara lain closed-circuit television (CCTV), penjualan laptop serta instalasi laptop maupun komputer. Usaha ini melayani pembelian dan pemasangan serta perbaikan dalam hal CCTV. Pada saat ini Kadai Komputer masih menggunakan kertas sebagai media untuk merekam alur jejak transaksi. Sistem seperti ini akan menghabiskan banyak waktu dalam kegiatan rekapitulasi data mulai dari transaksi penjualan, pemasukan serta stok barang dan juga laporan. Tujuan dari penelitian ini adalah untuk membuat sebuah perancangan aplikasi berbasis desktop yang dapat melakukan manajerial pertokoan sederhana sehingga memudahkan pengisian dalam transaksi pembelian dan penjualan, serta membuat laporan dari hasil penjualan tersebut. Aplikasi ini dirancang dengan metode Waterfall. Metode ini mengikuti pendekatan linier dan terstruktur dalam pengembangan sistem, dimulai dari analisis kebutuhan hingga pemeliharaan sistem. Dengan demikian sistem Point of Sale yang dikembangkan dengan menggunakan metode Waterfall akan menjadi solusi yang tepat untuk menyederhanakan proses penjualan pada CV. Kadai Komputer.
Pengaruh Iklim Organisasi dan Motivasi Terhadap Komitmen Organisasi Generasi Z di GKI Kota Wisata Roestandi, David; Winarto, Jacinta
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1034

Abstract

Penelitian ini mengemban tujuan secara spesifik guna melangsungkan penganalisisan terkait pengaruh iklim organisasi dan motivasi yang merujuk pada komitmen organisasi generasi Z di GKI Kota Wisata. Penelitian ini menggunakan pendekatan kuantitatif. Dalam skema riset ini melibatkan metode regresi linier secara berganda dan menghimpun sejumlah data dengan kuantitas 72 partisipan yang terdiri dari remaja dan pemuda GKI Kota Wisata. Temuan dalam riset ini menegaskan jika motivasi mendatangkan pengaruhnya dengan derajat signifikan yang menyasar langsung ke komitmen organisasi generasi Z, sementara iklim organisasi tidak berpengaruh signifikan. Dengan kontribusi keseluruhan secara simultan sebesar 46,6% terhadap variabilitas komitmen organisasi disarankan agar gereja fokus pada peningkatan motivasi melalui penghargaan, pengakuan, dan kesempatan pengembangan diri bagi generasi Z.
Pengaruh Motivasi dan Lingkungan Kerja terhadap Turnover Intention Pada Generasi Milenial Stefanya, Yemima; Winarto, Jacinta
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1044

Abstract

This research aims to analyze the influence of work motivation and work environment on turnover intention at Property Company X in Bandung City using a quantitative approach. Data collection was carried out by distributing questionnaires to 40 employees. The results of this research are that both motivation and the work environment influence turnover intention. Motivation and the work environment also simultaneously influence turnover intention by 89.7%. There is an influence of work motivation on turnover intention in a negative direction. There is an influence between the work environment on turnover intention in a negative direction. Motivation and the work environment both simultaneously have an influence on the turnover intention of Property Company X employees. Property Company With increased motivation, employees will feel more engaged and satisfied, thereby reducing their likelihood of leaving the company. Property Company X can ensure that the work environment provided to employees is conducive and supportive. The company must have a pleasant working atmosphere, as well as support from the company. Comfortable working area conditions not only increase employee satisfaction but also encourage them to stay in the long term. Property Company X also needs to set clear and measurable goals that are in line with employees' personal aspirations. By setting specific and relevant goals, employees will feel more committed and motivated to achieve them. Apart from that, companies can also implement a team assessment system to increase accuracy and objectivity in performance appraisals.
Comparative Analysis of Deep Learning Architectures for Predicting Software Quality Metrics in Behavior-Driven and Test-Driven Development Approaches Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1045

Abstract

The impact of software development methodologies on quality metrics is a crucial area of study in empirical software engineering. This research evaluates the performance of three deep learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in predicting key software quality indicators, including maintainability index, test coverage, and code complexity, for projects developed using Behavior-Driven Development (BDD) and Test-Driven Development (TDD) approaches. Using a static tabular dataset containing software quality metrics, the models are evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the R^2 coefficient. The MLP achieves the best performance, with the lowest RMSE (6.41) and MAE (6.34) and the highest R^2 value (−4.21), demonstrating its suitability for tabular data. The CNN performs moderately, while the LSTM underperforms due to its reliance on temporal dependencies absent from the dataset. These results emphasize the need for careful architectural alignment with dataset characteristics. The findings contribute to understanding the predictive power of deep learning models in software quality analysis and highlight the potential of MLP as a robust tool for such predictions. Future work can explore hybrid models and domain-specific feature engineering to enhance prediction accuracy.
Advancing Alzheimer’s Diagnosis: A Comparative Analysis of Deep Learning Architectures on Multidimensional Health Data Airlangga, Gregorius
Jurnal Informatika Ekonomi Bisnis Vol. 6, No. 4 (December 2024)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v6i4.1046

Abstract

Alzheimer’s Disease (AD) is a leading cause of disability among the elderly, with its prevalence projected to triple by 2050. Early detection remains critical for effective disease management, yet traditional diagnostic methods are often time-intensive and subjective. This study investigates the effectiveness of three machine learning architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) in detecting Alzheimer’s Disease using a multidimensional dataset comprising demographic, lifestyle, medical, cognitive, and functional data from 2,149 patients. Each model was evaluated using 10-fold cross-validation, with performance metrics including accuracy, precision, recall, and F1-score. The CNN model demonstrated superior performance, achieving an average accuracy of 88.65%, surpassing both the MLP (84.41%) and LSTM (75.57%) models. These results highlight CNNs’ capability to effectively extract spatial patterns in health data, making them a promising tool for Alzheimer’s diagnosis. In contrast, LSTM underperformed due to the lack of temporal relationships in the dataset. This study underscores the importance of aligning model architecture with dataset characteristics and provides a foundation for integrating machine learning into clinical workflows. Future work will focus on hybrid architectures and real-world validation to enhance diagnostic accuracy and scalability.

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