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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
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Preface JSINBIS 14 (2) 2024 Prof Mustafid
Jurnal Sistem Informasi Bisnis Vol 14, No 2 (2024): Volume 14 Nomor 2 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Preface JSINBIS 14 (2) 2024
Pengukuran Tingkat Risiko dan Keamanan Informasi Menggunakan Metode FMEA Berbasis ISO/IEC 27001 pada Instansi XYZ untuk Keamanan Sistem Informasi Kusnandar, Aris; Rochim, Adian Fatchur; Gunawan, Vincensius
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp375-384

Abstract

The more information stored in an organization, the higher the risks that may arise, such as damage, loss, or the exposure of personal information to irresponsible parties. XYZ Institution faces information security threats from various sources, including data theft, damage, and computer hacking. It is essential for the organization to understand the level of information security risk to ensure information remains secure. Therefore, this study proposes measuring information security risk using the FMEA method and analyzing information security risks based on ISO/IEC 27001:2013. The aim of this study is to identify and assess the level of information security risk at XYZ Institution to provide recommendations for information security. The study's results revealed 4 high-risk information security threats, 9 medium-risk threats, and 16 low-risk threats. The findings demonstrate that the organization needs to pay more attention to information security to ensure its smooth operation in the future.
A Novel Fusion of Machine Learning Methods for Enhancing Named Entity Recognition in Indonesian Language Text Widyawan, Widyawan; Utomo, Bayu Prasetiyo; Rizala, Muhammad Nur
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp311-320

Abstract

One of the important implementations in machine learning is Named Entity Recognition (NER), which is used to process text and extract entities such as people, organizations, laws, religions, and locations. NER for the Indonesian language still faces significant challenges due to the lack of high-quality labelled datasets, which limits the development of more advanced models. To address this issue, we utilized several pre-trained BERT models (bert-base-uncased, indobenchmark/indobert-base-p1, indolem/indobert-base-uncased) and datasets (NERGRIT-IndoNLU, NERGRIT-Corpus, NERUGM, and NERUI). This study proposes a novel fusion approach by integrating deep learning architectures such as CNN, Bi-LSTM, Bi-GRU, and CRF to detect 19 entities. This approach enhances BERT’s sequence modelling and feature extraction capabilities, while CRF improves entity prediction by enforcing global word-sequence constraints. Experimental results demonstrate that the fusion approach outperforms previous methods. On the bert-base-uncased dataset, accuracy reached 94.75%, while indobenchmark/indobert-base-p1 achieved 95.75%, and indolem/indobert-base-uncased achieved 95.85%. This study emphasizes the effectiveness of combining deep learning architectures with pre-trained transformers to improve NER performance in the Indonesian language. The proposed methodology offers significant advancements in entity extraction for languages with limited datasets, such as Indonesian.
Evaluasi Pengalaman Pengguna Pada Learning Management System Menggunakan Metode User Experience Questionnaire Wijanarko, Bambang Dwi; Leandros, Riyan; Murad, Dina Ftiria
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp385-391

Abstract

The purpose of this study was to conduct a survey to assess the performance of the Learning Management System (LMS). This is done related to the need to evaluate its use by users. This study used the User Experience Questionnaire (UEQ) method regarding (1) attractiveness, (2) clarity, (3) efficiency, (4) reliability, (5) stimulation, and (6) novelty. The survey was conducted online via a Google form and had 164 participants. The benchmark results show that all criteria are above the average, but the UEQ scale for novelty is the lowest at 0.87, while attractiveness is 1.44, pragmatic quality is 1.37, and hedonic quality is 1.05. Therefore, it is suggested that the learning management system be improved through interactive learning styles, such as live discussions in LMS, integration with other technologies, updating artificial intelligence and big data-based technologies, and adapting to the metaverse, while maintaining the attractiveness and identity of the platform through design. rework color schemes, content, including engaging videos and gamification, and implement live chat with lecturers to overcome time constraints during video conferencing.
Impact Of Sarcasm Detection on Sentiment Analysis Using Bi-LSTM and FastText Amalia, Junita; Matondang, Dian Filia; Hutajulu, Gibert E.M.; Hasibuan, Agustina
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp353-362

Abstract

Sentiment analysis categorizes a collection of texts in a document as either positive or negative. However, sometimes it cannot give accurate results due to sarcastic sentences. Sarcasm involves the use of positive language to convey negative meanings, So sarcasm detection is needed for sentiment classification to provide better results. One method that can be used to perform Sentiment classification is Bidirectional Long Short-Term Memory (Bi-LSTM). However, text data cannot be processed by Bi-LSTM, so it requires word embedding to convert text data into vectors. In this study, the word embedding used is FastText because it can learn the form of words by considering subword information. The results showed that sentiment classification with sarcasm detection could improve evaluation results by 0.08 for precision, 0.07 for recall, 0.07 for F1-score, and 0.07 for accuracy. A paired sample t-test was conducted on precision, recall, F1-score, and accuracy to examine whether there is a difference between sentiment classification with and without sarcasm detection. The obtained p-values are 2.84.10-9, 4.63.10-7, and 2.40.10-8, 6.22.10-8, respectively. This indicates a difference between sentiment classification with and without sarcasm detection. Therefore, with a 95% confidence level, it can be concluded that sarcasm detection impacts sentiment classification.
Analisis Sentimen Ulasan Wisatawan Terhadap Alun-Alun Kota Batam: Perbandingan Kinerja Metode Naive Bayes dan Support Vector Machine Friadi, John; Kurniawan, Dwi Ely
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp403-407

Abstract

Batam City, as a rapidly developing tourism destination in Indonesia, continues to strive to enhance the potential of its tourist attractions to attract more visitors. The assessment of reviews from tourists is crucial in identifying necessary development measures to improve the quality of tourist attractions. This research aims to analyze the sentiment of reviews for the Alun-Alun Kota Batam tourist destination by leveraging data from Google Maps. Two classification methods, Naive Bayes and Support Vector Machine, are employed for sentiment analysis, and their performances are compared. From 1140 collected reviews, the data is categorized into three labels: positive, negative, and neutral. The research results indicate that the Support Vector Machine method achieves higher accuracy (94%) compared to Naive Bayes (83%). This study contributes insights into visitor sentiments towards Alun-Alun Kota Batam, with implications for policy development and more effective actions in enhancing local tourism appeal.
Strategic Evaluation of Whistleblower Software Security in Government: ISO/IEC 25010 and AHP Method Purbaratri, Winny; Sembiring, Irwan; Setiawan, Adi; Setyawan, Iwan
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp321-328

Abstract

To assess the effectiveness of software security measures in government whistleblower systems, we will utilize the ISO/IEC 25010 standard and the Analytic Hierarchy Process (AHP) methodology. Through the integration of various frameworks, our objective is to build a complete evaluation model that effectively identifies and enhances any vulnerabilities in these crucial systems. The strategy we employ combines the qualitative and quantitative evaluation capabilities of ISO/IEC 25010 and AHP, respectively, to offer a comprehensive perspective on software security performance. The results indicate substantial improvements in the security and reliability of whistleblower software, underscoring the effectiveness of our suggested evaluation technique in identifying crucial areas for refinement. Moreover, the utilization of AHP permitted the ranking of security qualities, guaranteeing focused and efficient improvements. Ultimately, the study emphasizes the significance of thorough security assessments for government whistleblower systems and verifies the effectiveness of utilizing ISO/IEC 25010 and AHP as a methodical approach to improve software security. This research enhances the ongoing endeavor to protect confidential data, fostering a more secure and reliable atmosphere for individuals who expose wrongdoing.
Sistem Pendukung Keputusan Berbasis K-Means untuk Evaluasi Keberhasilan Bisnis dan Nilai Perusahaan Sarmini, Sarmini; Ma'arifah, Windiya; Tahyudin, Imam
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp363-374

Abstract

Business development is in line with the development of increasingly sophisticated technology. This requires every company to compete and be motivated to increase its value as an indicator of success in managing the company so that investors are interested in investing. This study aims to design a K-means-based Decision Support System with a clustering approach to classify the growth rate of company value. Investment Opportunity Set (IOS) and profitability variables are the leading indicators of increasing company value. The problem formulation is how the design of this K-means-based decision support system can assist in classifying the growth rate of the company's value based on the IOS and profitability variables. This research aims to produce a decision support system that can organize the growth rate of company value using the K-means method. System testing is conducted to evaluate the effectiveness of the applied clustering method, focusing on the accuracy of the results. The weighting of IOS and profitability variables is based on the percentage of positive relationship to firm value, and the ultimate goal is to group companies with different growth rates. As a result, the K-means-based Decision Support System, or "Business Growth Prediction Decision Support System," successfully clustered the growth rate of firm value. With reasonable accuracy, measured using the silhouette coefficient, the calculation results show an overall mean silhouette coefficient of 0.684, close to the maximum value of 1. This result confirms that this decision support system can group companies in the L (Low), M (Medium), and H (High) categories based on the level of value growth, using the IOS and profitability variables as the leading indicators. Thus, this research supports decisions related to company growth strategies using K-means-based decision support systems.
Developing Data Mining Prediction System for Health Center Medicine Inventory using Naïve Bayes Classifier Algorithm Roziana, Roziana; Widodo, Aris Puji; Wibowo, Adi
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp329-336

Abstract

Public health centers mostly use conventional methods in managing drug supply, usage, and demand data, without a system that can predict the number of drug requests. This research aims to develop a data mining solution by implementing a prediction system using the Naïve Bayes Classifier algorithm to predict drug supplies from the Koni Health Center, Jambi, to the Health Office Pharmacy Installation. The method applied in this research is a quantitative approach through the experimental method. The research data includes inventory, usage, and remaining stock of various types of drugs from 2017 to 2021 which are divided into four quarters. The results of this study show that the classification system using the Naïve Bayes Classifier method is able to classify data quickly and efficiently according to drug supply. The system test results show an accuracy of 73.91%, recall of 85.71%, and precision of 54.54%. These findings can help Puskesmas in optimizing drug inventory management, reducing errors in inventory estimates, and increasing accuracy in meeting patient drug requests.
Penerapan Metode Case-Based Reasoning pada Website SORTING (Sorong Atasi Stunting) Sebagai Implementasi Smart City (Studi Kasus: Distrik Sorong Timur) Manuhutu, Melda; Tindage, Jalmijn; Bobii, Permenas
Jurnal Sistem Informasi Bisnis Vol 14, No 4 (2024): Volume 14 Nomor 4 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss4pp392-402

Abstract

Stunting is one of the issues designated as a national priority issue at this time. Determining this priority issue is to accelerate the level of achievement of national development goals. East Sorong District, Sorong City, Southwest Papua Province is one of the focus location with a high prevalence rate. This study aims to develop an information technology called SORTING (Sorong Atasi Stunting) using the case-based reasoning method. This research produces a system that can be accessed from anywhere and at any time, which can be used by residents, especially mothers, to consult regarding their baby's growth problems. SORTING was developed using the expertise of nutritionists who usually deal with stunting problems. SORTING resulted in several conclusions that the application expert system can diagnose stunting by using the case-based reasoning method as an engineering approach to the knowledge base; The application of the SORTING expert system can provide an overview of stunting including the results of the percentage of diagnoses, factors and recommendations for solutions that must be taken; The application of the SORTING expert system can be used as a second alternative to consult with experts in diagnosing stunting. And it can also be an alarm to continue consulting with experts directly; The application of the SORTING expert system can increase knowledge about stunting. The researchers hope that SORTING can assist the government in the National Action Plan for Stunting Management which emphasizes reducing stunting rates and can help the community recognize stunting diseases early and prevent them.

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