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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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Utilize Extreme Programming Method for Developing Financial Report Standards Apps Al Amin, Budi; Sutanto, Yusuf; Susanti, Nani Irma
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.693

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in providing employment opportunities, especially when job competition in the formal sector is intense. Eliza Catering, an MSME located in Surakarta city, operates in the culinary sector and traditionally maintains simple financial records. This practice hampers the ability to accurately measure the company's performance and determine its profitability. This research aims to document the daily transactions of Eliza Catering using the BukuKas application and to generate financial reports in accordance with Financial Accounting Standards (FAS) EMKM. The data analysis process involved three stages: data reduction, data presentation, and conclusion drawing. The findings reveal that Eliza Catering previously only recorded income, lacking comprehensive financial documentation. By utilizing the BukuKas application, daily transactions were systematically recorded. The Extreme Programming method was employed to develop this research system, resulting in the preparation of financial reports based on FAS EMKM, which include profit and loss statements, financial position reports, and notes to the financial statements.
Digitalization of Archipelago Cultural Insight Education Using Extreme Programming Method Putra, Ade; Syakti, Firamon; Ananda, Azhara; Armelia, Verinditha Oktiza
Journal of Information System and Informatics Vol 6 No 1 (2024): March
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i1.695

Abstract

This paper presents the development of the Archipelago Cultural Insight Education Application, an Android-based platform designed to enhance the learning of Nusantara’s rich cultural heritage. Utilizing Extreme Programming (XP), an agile software development methodology, the application was developed to accommodate the dynamic requirements of educational content and interface design. The XP approach facilitated rapid iterations and continuous feedback, ensuring the application remained aligned with educational goals and user needs. The application features a user-friendly interface with dedicated sections for Traditional Houses, Local Attractions, Regional Foods, Folk Songs, and other cultural elements. Each section provides comprehensive data and detailed descriptions that aim to educate and engage users. The design prioritizes intuitive navigation and ease of content management, which is critical for the educational effectiveness and sustainability of the app. Moreover, the integration of multisensory learning elements, such as auditory content in the Folk Songs section, enhances the educational experience by providing a more immersive understanding of the cultural context. The application's development process and its features illustrate the benefits of applying agile methodologies in educational technology, highlighting how they can be used to produce a robust, engaging, and informative educational platform. This study contributes to the field by demonstrating the practical application of agile principles in the design and implementation of educational technology that effectively bridges cultural education and digital innovation.
Efficient Thesis Management: A Study of Universitas Multimedia Nusantara's Application Development Using Extreme Programming Principles Cagananta, Cagananta; Istiono, Wirawan
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.701

Abstract

The objective of this study is to develop and construct a thesis application utilizing the extreme programming approach, and to assess user contentment with the application using the End User Computing Satisfaction measurement technique. The Informatics study program at the Multimedia Nusantara University campus is encountering issues pertaining to the thesis procedure. The problems were identified through interviews with numerous lecturers, students, and the head of the Informatics department's study program at Universitas Multimedia Nusantara. The challenges include the decentralized distribution of information pertaining to theses, obstacles in obtaining thesis proposals, difficulties in obtaining details regarding the research specializations of lecturers, recapitulation of supervisors, and an array of additional issues. Based on these problems, a thesis application was designed and built using the extreme programming development method. The research findings indicate that the application has been effectively developed. The test results reveal that 87.267% of users strongly agreed that the application was highly beneficial in the thesis process.
Air Quality Prediction Using the Support Vector Machine Algorithm Widyarini, Liza; Purnomo, Hindriyanto Dwi
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.705

Abstract

Air quality is an important factor in maintaining the health and well-being of humans and the environment. To anticipate and manage air pollution, air quality prediction has become an important research topic. In this research, researchers propose using the Support Vector Machine (SVM) algorithm to predict air quality. SVM has proven to be an effective method in classification and regression, especially in the context of complex and non-linear data such as air quality data. Researchers utilized historical air quality datasets that include various parameters such as particulates, ozone, nitrogen dioxide and carbon monoxide. Experiments were conducted to compare the performance of SVM with other prediction methods, and the results show that SVM provides accurate and reliable predictions in modeling air quality.
Sentiment Analysis of Unemployment in Indonesia During and Post COVID-19 on X (Twitter) Using Naïve Bayes and Support Vector Machine Setiawati, Putu Ayulia; Suarjaya, I Made Agus Dwi; Trisna, I Nyoman Prayana
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.713

Abstract

The COVID-19 pandemic has impacted health, economy, and society. Social distancing measures and quarantine policies have restricted economic activities, leading to downturns in COVID-19-affected regions and a subsequent rise in unemployment rates, particularly in urban areas. Concurrently, there has been a remarkable surge in the utilization of the X (Twitter) platform, with Indonesia ranking 6th globally in X (Twitter) users. This study aims to understand the diverse perspectives of society on unemployment and the factors influencing society's views on unemployment through sentiment analysis of X (Twitter) data. By analyzing 576,764 tweets from April 2020 to October 2023, tweets are categorized into positive, neutral, and negative classes. Classification model was built to classify tweet data by implementing TF-IDF for word weighting, and a pair of machine learning algorithms, Naïve Bayes and Support Vector Machine (SVM). Model evaluation yielded the highest accuracy of 81.5% using Naïve Bayes. The classification outcomes highlight prevalent negative perceptions of unemployment among Indonesians, totaling 50.03%. This research contributes to the literature by providing a large-scale analysis of social media data to uncover public sentiment trends and offering insights for policymakers to address unemployment and improve welfare.
Assessing the Accuracy Level of University-Based Website-Based Search Engines Using F-Measure and Hellinger Santiko, Irfan; Andriana, Gerry
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.716

Abstract

Websites are an information medium that is becoming something that is needed in this era. Including the media within the campus environment. The problem is that the campus as a forum or place for student learning is considered less than optimal in presenting information on student learning activities. For example, library reference information, administration, important announcements, and other similar information. The current solution is that universities use social media platform communication media which are considered accurate, which actually adds to problems when the media is used not in accordance with its function, such as promotions, hoax information and irrelevant information. This causes the information to become too massive so that the level of accuracy and relevance is reduced. The author's solution is to optimize the search engine on the campus website platform to be used as an absolute information medium. So the information obtained will be more targeted and accurate. Starting from measuring the level of accuracy to the impact of the results will be discussed in this article. The technique used to measure accuracy is a quantitative technique consisting of the F-Measure and the Hellinger Method. As a result, the campus will know that to distribute related news, the campus can find out keywords that are considered strategic in every report on the media website.
Comparison Study of NIST SP 800-86 and ISO/IEC 27037 Standards as A Framework for Digital Forensic Evidence Analysis fFaizal, Arif; Luthfi, Ahmad
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.717

Abstract

To ensure a comprehensive and scientifically rigorous analysis, adhering to standardized procedures serves as the foundation of any investigation. In the realm of digital forensics, the establishment of well-defined protocols for generating exhaustive reports to analyze digital evidence holds paramount importance. These reports not only carry significance in legal contexts but are also increasingly valuable across various industries for internal purposes. Esteemed organizations like the International Organization for Standardization (ISO) and the National Institute of Standards and Technology (NIST) have played a pivotal role in shaping recognized standards in this domain. The primary goal of this report is to conduct an in-depth comparison between two prominent digital forensics standards: ISO/IEC 27037, widely embraced in industries, and NIST SP 800-86, predominantly prevalent in academic circles. Through this comprehensive analysis, the report aims to provide valuable insights to Digital Evidence First Responders (DEFR), including law enforcement, academia, and industry professionals. By elucidating the discrepancies, scopes, and limitations inherent in each standard, DEFRs can bolster their understanding, thus empowering them to make well-informed decisions during digital investigations. Future works in this field should focus on the continual evolution of digital forensic practices, adapting to new technologies and challenges, and ensuring that standards remain up to date with the dynamic digital landscape.
Role of Local Government in Localizing SDG in Bangladesh Islam, Saiful
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.718

Abstract

This study is unearthing the localizing process of SDG in Bangladesh in meeting the Sustainable Development Goals (SDGs) targets. Though nations bear the main responsibility for the SDGs, these goals specifically call for involvement from local authorities. Around 12 of the 17 SDGs require comprehensive strategies at the grassroots level to tackle interconnected issues like poverty, poor health, social challenges, and environmental degradation, with exceptions being Goals 9, 12, 13, 14, and 17. Bangladesh's constitution wisely allocates significant responsibilities for social and economic development, including the formulation and execution of plans concerning public services and economic progress, to the local government bodies, particularly the union parishad (UP), which serves as the primary interface with the community [Article 59(2)(c)(Constitution, 2004)]. Local Government Institutions in Bangladesh are positioned to have a significant influence on the localization of the SDGs, given their proximity to the most marginalized and frequently vulnerable rural communities, allowing for direct impact. Despite this potential, they have yet to make a substantial contribution to SDG localization, and with the deadline approaching, there is a pressing need to address this issue. This paper seeks to examine the obstacles encountered by local government in Bangladesh in their efforts to localize the SDGs, aiming to gain insight into the challenges hindering effective policy implementation.
Analyzing the Relationship Between Meteorological Parameters and Electric Energy Consumption Using Support Vector Machine and Cooling Degree Days Algorithm Azizah, Nabila Wafiqotul; Puspaningrum, Eva Yulia; Mas Diyasa, I Gede Susrama Susrama
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.719

Abstract

Nowadays, electricity is increasing rapidly. This increase is caused by several factors, one of which is meteorological factors. Meteorological parameters have various types, but this research uses three types in the form of temperature, humidity, and wind speed. The selection of these three types is due to the fact that they have a very close relationship with human life. In line with that, this research uses datasets obtained from the official websites of BMKG (Meteorology, Climatology and Geophysics Agency) and PLN (State Electricity Company). On this occasion, researchers used several methods, namely Cross-Industry Standard Process for Data Mining (CRISP-DM), Cooling Degree Days (CDD), and Support Vector Machine (SVM). The CRISP-DM method is useful for describing the data mining cycle so that the process can be more organized. The SVM algorithm is useful for predicting electricity consumption based on meteorological parameters in January to April 2024, while the CDD method is useful for knowing the correlation of meteorological parameters to electricity consumption in winter. In line with this, this research produces predictions of electricity consumption based on meteorological parameters in January 2024 to April 2024 with an average range of 20.9 Watts per day. In addition, trends and predictions during model evaluation obtained a precision value of 0.796, recall of 0.793, F1 score of 0.793, MAPE of 17.2%, RMSE of 0.41, MAE of 0.167 and accurate of 0.98. These values indicate that the performance of the accuracy model is very high.
Predictive Analytics on Shopee for Optimizing Product Demand Prediction through K-Means Clustering and KNN Algorithm Fusion Febima, Mesi; Magdalena, Lena
Journal of Information System and Informatics Vol 6 No 2 (2024): June
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i2.720

Abstract

This study focuses on predictive analysis in the context of the Shopee market, aiming to optimize product demand forecasting through the combination of K-Means clustering and KNN algorithms. With the exponential growth of e-commerce platforms like Shopee, accurately predicting product demand is becoming increasingly important for inventory management and marketing strategies. In this research, we propose a novel approach that combines the strengths of K-Means clustering and the KNN algorithm to improve demand prediction accuracy. By leveraging K-Means clustering to group similar products into two clusters, namely “Low Interest” with 64 data points and “High Interest” with 25 data points, we then apply the KNN algorithm to predict demand within each cluster. The KNN algorithm produces two classifications: Low Sales and High Sales. Based on tests using the KNN algorithm with k values of 3, 5, and 7, it was demonstrated that the product “Soraya Bedsheet Cotton Gold Motif Dallas Ask Grey Tua” can be predicted to fall under “High Sales.” The sales prediction accuracy rate for Shopee marketplace products is 96%. The implications of these findings indicate that the combination of K-Means and KNN algorithms can improve the accuracy of product demand predictions and optimize inventory and marketing strategies.

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