cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
JURNAL SISTEM INFORMASI BISNIS
Published by Universitas Diponegoro
ISSN : 20883587     EISSN : 25022377     DOI : -
Core Subject : Economy, Science,
Computer Science & IT Economics, Econometrics & Finance
JSINBIS merupakan jurnal ilmiah dalam bidang Sistem Informasi bisnis fokus pada Business Intelligence. Sistem informasi bisnis didefinisikan sebagai suatu sistem yang mengintegrasikan teknologi informasi, orang dan bisnis. SINBIS membawa fungsi bisnis bersama informasi untuk membangun saluran komunikasi yang efektif dan berguna untuk membuat keputusan yang tepat waktu dan akurat. Business intelligence sebagai dasar pengembangan dan aplikasi SINBIS menjadi kerangka kerja teknologi informasi yang sangat penting untuk membuat agar organisasi dapat mengelola, mengembangkan dan mengkomunikasikan aset dalam bentuk informasi dan pengetahuan. Dengan demikian SINBIS merupakan kerangka dasar dalam pengembangan perekonomian berbasis pengetahuan.
Arjuna Subject : -
Articles 410 Documents
 37   38   39   40   41 
Increasing the Accuracy of Random Forest Algorithm Using Bagging Techniques in Cases of Stunting Toddlers Ali, Amir; Purwanto, Purwanto; Mundakir, Mundakir
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp167-172

Abstract

Increasing the accuracy value can be increased by using other algorithms. Increasing the accuracy value of a classification algorithm, the level of success of the algorithm's prediction is more precise and appropriate in providing its label. The purpose of the research is look performance of accuracy value for prediction with bagging algorithm. This research use random forest algorithm and bagging algorithm used for optimization. 12 data whose position is far from other data. 12 data deviate from the data pattern and are outliers. With z-score process, it will be processed to eliminate outlier data. After removing the outlier data, the data clean is 137 toddler data. After removing outliers and standardizing the data, the accuracy value obtained was 71% up to 100th accuracy with random forest algorithm. Optimization of a bagging algorithm to predict stunting in a dataset of toddlers that has been acquired and assessed its performance. This can be seen from the optimization of prediction results up to the 100th iteration, where the prediction accuracy results were 80.67%. Using the Random Forest algorithm and bagging techniques, the prediction of stunting in toddlers works well. Optimization of prediction results up to the 100th iteration, where the prediction accuracy results were 80.67%.
Improving Fake News Detection Accuracy with Lexicon-based Approach and LSTM through Text Preprocessing Mashuri, Chamdan; Prastyo, Edwin Hari Agus; Hariri, Fajar Rohman
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp285-292

Abstract

Fake news detection has become a critical issue in the digital era, especially with the rapid growth of social media and online platforms. This research aims to enhance the accuracy of detecting fake news in Indonesian by developing a model using lexicon-based and Long Short-Term Memory (LSTM) approaches. The study integrates sentiment analysis with lexicon-based scoring to identify key features in news articles, while LSTM is employed to analyze sequential patterns in the data. The methods were tested on a dataset consisting of both hoax and non-hoax news collected from reliable sources. The results indicate that the hybrid model significantly improves the detection accuracy, achieving an impressive accuracy rate of 99%. This research demonstrates the potential of combining lexicon-based and LSTM approaches to overcome challenges in detecting fake news, especially in low-resource languages like Indonesian. The findings contribute to advancing the development of reliable and efficient systems for combating misinformation in the digital age.
Review of Systematic Literature about Sentiment Analysis Techniques Sasongko, Cornelius Damar; Isnanto, Rizal; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp227-236

Abstract

Sentiment analysis, also known as opinion mining, is an important task in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment or opinion expressed by the author. With the advancement of technology and the widespread use of social media and online review platforms, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. The purpose of this research is to present a comprehensive literature review on sentiment analysis techniques. This research utilizes the systematic literature review method. This method involves systematic steps in searching, evaluating, and analyzing relevant literature in the field of sentiment analysis. The literature search was conducted through scientific databases and other reliable sources. Relevant articles were then selected based on pre-determined inclusion and exclusion criteria. The data from the selected articles were then comprehensively analyzed to identify the sentiment analysis techniques used and the key findings in the research. The results show that there are various techniques and approaches that have been developed and tested in sentiment analysis, some of the commonly used techniques include rule-based methods, classification-based methods, and machine learning-based methods.
Resolving Data Imbalance using SMOTE for the Analysis and Prediction of Hate Speech Sentences Sutikman, Sutikman; Sutanto, Heri; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp198-203

Abstract

Hate speech is characterized as a form of communication that expresses hostility or discontent towards particular individuals, groups, or ethnicities, with the intent to belittle one party. This research aims to examine hate speech expressions on Twitter, assessing their categorization as hate speech through the application of machine learning methodologies. The study incorporates feature engineering techniques, such as Term Frequency-Inverse Document Frequency (TF-IDF) and the Synthetic Minority Over-sampling Technique (SMOTE), to mitigate challenges related to data imbalance. The machine learning models utilized include Logistic Regression (LR), Decision Tree (DT), Gradient Boosting (GB), and Random Forest (RF). Among these models, Logistic Regression (LR) demonstrated the highest efficacy, achieving an accuracy of 91.43%, precision of 88.83%, recall of 93.99%, and an F1 score of 97.10%.
Implementation of the Ensemble Machine Learning Algorithm for Student Dropout Prediction Analysis Winarsih, Winarsih; Sutanto, Heri; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp159-166

Abstract

Educational Data Mining provides an effective approach to tackle numerous issues within the education sector, including the capacity to perform predictive analyses regarding student attrition based on academic information. In this research, data from the Open University Learning Analytics dataset (OULAD), which is publicly accessible, has been employed, which encompasses student information collected during online learning. We apply various Machine Learning models, including Decision Trees, Naïve Bayes, Logistic Regression, and ensemble approaches like Random Forest and AdaBoost. Among the models tested, Random Forest (RF) achieved the highest accuracy of 89.37%, along with a precision of 89.57% and a recall of 93.86%, using the data splitting approach. When employing an alternative evaluation model, specifically K-Fold Cross Validation, the maximum F1 score achieved was 9.45%. In summary, the ensemble machine learning algorithm, specifically Random Forest (RF), exhibited strong performance in predicting student academic achievement quality.
Examining Social Support and Trust Transfer Theory in Online Health Community Adoption Saputra, Ragil; Dharmawan, Bagus Dwiky; Adhy, Satriyo; Mutiara, Dinar; Yasin, Hasbi
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp277-284

Abstract

Online Health Communities (OHCs) have become a key source of social support for individuals with health concerns. OHC members engage in communication and information exchange, with trust among members playing a crucial role in the acceptance of these platforms. This research aims to examine the determinants affecting OHC acceptance by employing trust transfer theory, social support, and self-efficacy as core variables. The proposed model was empirically tested using data from 100 members of the Indonesian Diabetes Forum on Facebook. This quantitative study employed a 5-point Likert scale to evaluate user perceptions. The findings indicate that OHC acceptance is significantly supported by both information support and emotional support, which foster trust among community members. Trust in members subsequently leads to trust in the broader community, culminating in the sustained use of the OHC. Furthermore, emotional support positively influences self-efficacy, encouraging users to join and actively participate in OHCs. However, information support does not have a significant effect on self-efficacy. This research offers significant understanding of the relationships among social support, self-efficacy, and trust in promoting the continued use of OHCs. The research model offers a framework that can be applied in other contexts with similar technological and community-based perspectives. 
The 7D BIM Modeling for Building Asset Data Management Using Revit, COBIe Extension, and QR code Hatmoko, Jati Utomo Dwi; Taqy, Muhammad Ravy Arkan; Utama, Rahmana Pria; Hermawan, Ferry
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp271-276

Abstract

The management of building asset data has experienced many problems related to accessibility and storage issues. This causes difficulties for asset managers in managing and developing buildings after the construction phase. This research aims to develop a 7D Building Information Modeling (BIM) model of buildings to facilitate access to asset data in operational and maintenance activities. Autodesk Revit was used to perform BIM modeling and was integrated with COBIe and the cloud via QR code. The Faculty of Engineering Building Diponegoro University was used as a case study. Data was collected through observation, project documents, as-built drawings, technical specifications, etc. This 7D BIM model with COBIe plug-in is expected to address gaps in asset management, improve operational efficiency, reduce the risk of damage, improve the ability to classify assets in a systematic and integrated manner, and improve collaboration between stakeholders to increase the effectiveness of asset management significantly.
Implementation of Project Management in the Development of an Android-Based Household Waste Monitoring System using JIRA Software Bunga, Munengsih Sari; Gernowo, Rahmat; Ishlakhuddin, Fauzan; Mulyani, Esti; Fikri, Moh Ali; Rosyalia, Syofi
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp204-210

Abstract

The increasing amount of household waste presents a major environmental challenge, worsened by inefficient and outdated waste management practices. Traditional systems lack real-time monitoring and responsiveness, creating a gap in timely waste management. This research introduces a creative solution through the development of an Android-based Household Waste Monitoring System, integrating Internet of Things (IoT) technology to provide real-time data on waste bin capacities and immediate notifications. Unlike conventional approaches, this system creatively bridges the gap by enabling proactive waste management through instant alerts and real-time tracking, allowing users to act before issues escalate. The system development follows an Agile/Scrum framework, fostering rapid iteration and user-driven enhancements. Through the innovative application of IoT and Agile methodologies with JIRA Software, this solution effectively addresses the inefficiencies of current waste management systems, as evidenced by an 80% success rate across five testing activities. This creative approach not only improves development efficiency but also accelerates adaptability in response to evolving waste management needs.
Designing a Model for Bi-criteria Objective Scheduling at Flexible Flowshop Production in Finishing Furniture Industries Bahaudin, Achmad Fawwaz; Susanty, Aries; Saptadi, Singgih
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp152-158

Abstract

The bi-criteria objective scheduling is essential in the Jepara furniture industry due to its competitiveness. Scheduling that not only considers the company's profits but also takes into account the customer's perspective can add significant value to the company. Based on that, this paper proposed a mathematical model for the furniture finishing industry. Then it transformed into a Microsoft Excel Solver model. Cost calculation is also considered to choose the best model. The system's characteristic is flexible flow shop production, not identical at the last stage,  and sequence-dependent set up time. The objective of scheduling is to minimize total maximum completion time and total weighted tardiness. There are 3 scenarios in this paper, company focused, customer, and bi-criteria objective. After running the model, scenario 3 is the best choice for completing priority orders on time, while scenario 1 is ideal when seeking efficiency in production with delays being less of a concern.
Front matter JSINBIS_15 (2) 2025 JSinbis, Editorial
Jurnal Sistem Informasi Bisnis Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss2pp%p

Abstract

-

Page 39 of 41 | Total Record : 410

 37   38   39   40   41 

Filter by Year

2011 2025


Reset
Filter By Issues
All Issue Vol 15, No 3 (2025): Volume 15 Number 3 Year 2025 (Publication in Progress) Vol 15, No 2 (2025): Volume 15 Number 2 Year 2025 Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025 Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024 Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024 Vol 14, No 2 (2024): Volume 14 Nomor 2 Tahun 2024 Vol 14, No 1 (2024): Volume 14 Nomor 1 Tahun 2024 Vol 13, No 2 (2023): Volume 13 Nomor 2 Tahun 2023 Vol 13, No 1 (2023): Volume 13 Nomor 1 Tahun 2023 Vol 12, No 2 (2022): Volume 12 Nomor 2 Tahun 2022 Vol 12, No 1 (2022): Volume 12 Nomor 1 Tahun 2022 Vol 11, No 2 (2021): Volume 11 Nomor 2 Tahun 2021 Vol 11, No 1 (2021): Volume 11 Nomor 1 Tahun 2021 Vol 10, No 2 (2020): Volume 10 Nomor 2 Tahun 2020 Vol 10, No 1 (2020): Volume 10 Nomor 1 Tahun 2020 Vol 9, No 2 (2019): Volume 9 Nomor 2 Tahun 2019 Vol 9, No 1 (2019): Volume 9 Nomor 1 Tahun 2019 Vol 8, No 2 (2018): Volume 8 Nomor 2 Tahun 2018 Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018 Vol 7, No 2 (2017): Volume 7 Nomor 2 Tahun 2017 Vol 7, No 1 (2017): Volume 7 Nomor 1 Tahun 2017 Vol 6, No 2 (2016): Volume 6 Nomor 2 Tahun 2016 Vol 6, No 1 (2016): Volume 6 Nomor 1 Tahun 2016 Vol 5, No 2 (2015): Volume 5 Nomor 2 Tahun 2015 Vol 5, No 1 (2015): Volume 5 Nomor 1 Tahun 2015 Vol 4, No 3 (2014): Volume 4 Nomor 3 Tahun 2014 Vol 4, No 2 (2014): Volume 4 Nomor 2 Tahun 2014 Vol 4, No 1 (2014): Volume 4 Nomor 1 Tahun 2014 Vol 3, No 3 (2013): Volume 3 Nomor 3 Tahun 2013 Vol 3, No 2 (2013): Volume 3 Nomor 2 Tahun 2013 Vol 3, No 1 (2013): Volume 3 Nomor 1 Tahun 2013 Vol 2, No 3 (2012): Volume 2 Nomor 3 Tahun 2012 Vol 2, No 2 (2012): Volume 2 Nomor 2 Tahun 2012 Vol 2, No 1 (2012): Volume 2 Nomor 1 Tahun 2012 Vol 1, No 3 (2011): Volume 1 Nomor 3 Tahun 2011 Vol 1, No 2 (2011): Volume 1 Nomor 2 Tahun 2011 Vol 1, No 1 (2011): Volume 1 Nomor 1 Tahun 2011 More Issue