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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 45 Documents
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Search results for , issue "Vol 6 No 2 (2024): June" : 45 Documents clear
Optimizing Business Intelligence System Using Big Data and Machine Learning James, Gabriel Gregory; P, Oise G; G, Chukwu E; A, Michael N; F, Ekpo W; E, Okafor P
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.v6i2.631

Abstract

The Business Intelligence (BI) and Data Warehouse (DW) system deployed in the Nigerian National Petroleum Corporation should provide cooperate decision makers with real-time information to help them identify and understand key business factors to make the best decisions for the situation at any given time. The relentless collection of data from user interactions have introduced both a high level of complexity, as well as a great opportunity for businesses. In addition to connecting not just people, but also machines to the internet, and then collecting data from these machines via sensors would result in an unimaginable repository of data. This ever-increasing collection of data is known as Big Data. Integrating this with existing Business intelligence systems and deep analysis using Machine Learning algorithms, Big Data can give useful insights into business problems and perhaps even to make suggestions as to when and where future problems will occur (Predictive Analysis) so that problems can be avoided or at least mitigated. This paper targets at developing a system capable of optimizing a business intelligence using big data and machine learning approach. The design of a system to optimize the Business Intelligence System using Machine Learning and Big Data at NNPC was successfully carried out. The System was able to automatically analyze the sample report under NNPC permission to use and it generated expected predictive outputs which serves as a better guide to managers. When applying Deep Learning, one seeks to stack several independent neural network layers that, working together, produce better results than the already existing shallow structures.
An Artificial Neural Network Model for Predicting Children at Risk of Defaulting from Routine Immunization in Nigeria Evwiekpaefe, Abraham Eseoghene; Lawi, Valerie Plangnan
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.v6i2.689

Abstract

It has been widely recognized that immunization remains one of the most successful for decreasing child mortality rates and preventing several serious childhood diseases globally. This study proposed a prediction model for accurate identification of routine immunization defaulters in Nigeria. The proposed framework classified defaulters at five different risk stages: insignificant risk, minor risk, moderate risk, major risk and severe risk to reinforce targeted interventions by accurately predicting children at risk of defaulting from the immunization schedule. Data from Nigerian Demographic and Health Survey 2018 was obtained for this study and thirty-four (34) demographic and socio-economic factors were used to predict children at risk of defaulting from routine immunization in Nigeria by using Artificial Neural Network (ANN) to train the dataset. The results indicated that ANN model produced an accuracy of 99.16% for correctly identifying children who are likely to default from immunization series at different risk stages. Other performance measures include Precision of 99%, Recall of 99% and F1 Score of 99%. The model was further validated using one thousand (1000) dataset, out of which nine hundred and seventy four (974) were correctly predicted.
Ethical Provision of Online Learning in South African High Schools Chipangura, Baldreck
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.v6i2.691

Abstract

Drawing from Kantianism, utilitarianism, information systems ethical models, and South Africa Department of Education policies, this study investigated how high schools can ethically provide online learning. The study was prompted by two unethical concerns highlighted in the literature: firstly, the potential discrimination to online learning against learners who do not have access to information technology resources, and secondly, the cyber risks faced by learners from prolonged exposure to Internet connected devices. To gather data for the study, interviews were conducted with 15 schoolteachers, who were conveniently sampled from five schools in Centurion, Pretoria city, South Africa. The data was thematically analysed, and the results of the study found constructs that inform ethical provision of online learning, which are: equal access to online learning, teacher competence, teacher empathy, and cyber security of learners. The findings of this study inform the policy on providing ethical online learning in South Africa and any other country.
Shielding Social Media: BERT and SVM Unite for Cyberbullying Detection and Classification Aggarwal, Parth; Mahajan, Rhea
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.v6i2.692

Abstract

This paper presents a novel approach for cyberbullying detection and classification in social media text using an ensemble model that combines BERT (Bidirectional Encoder Representations from Transformers) and Support Vector Machine (SVM) with grid search for multiclass classification. We have also compared the performance of our proposed with various machine and deep learning models and the results show that our proposed model outperforms other models achieving an accuracy of 90% on testing data. Further, we have used to used SHapley Additive exPlanations (SHAP) an Explainable (XAI) technique to interpret the predictions of the BERT-SVM ensemble model.
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.
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.

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