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All Journal Jurnal Masyarakat Informatika MATEMATIKA SAINS DAN MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi INTEGER: Journal of Information Technology INOVTEK Polbeng - Seri Informatika Indonesian Journal of Medicine JURTEKSI Aptisi Transactions on Technopreneurship (ATT) J-Kesmas: Jurnal Kesehatan Masyarakat (Indonesian Journal of Public Health) Journal of Soft Computing Exploration Journal of Renewable Energy, Electrical, and Computer Engineering Journal of Indonesian Health Policy and Administration Scientific Journal of Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Integra: Journal of Integrated Mathematics and Computer Science
Aris Puji Widodo
Undip
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Mathematics Medicine & Pharmacology Public Health Social Sciences

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Journal : JURNAL SISTEM INFORMASI BISNIS

 1 
Pengukuran Penerimaan Sistem Informasi EWSKIA Berdasarkan Persepsi Pengguna dengan Menggunakan Technology Acceptance Model Widodo, Aris Puji; Agushybana, Farid; Jati, Sutopo Patria
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 2 (2018): Volume 8 Nomor 2 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (426.184 KB) | DOI: 10.21456/vol8iss2pp166-173

Abstract

This study is a quantitative research that has a goal to measure the level of user acceptance of EWSKIA information system based on perception. EWSKIA is a healthcare application tool used to perform the process of recording and monitoring the health conditions of pregnant and childbirth mothers. The model used in this research is using Technology Acceptance Model (TAM) model with 3 variables, namely Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Behavioral Intention to Use (BITU). This variable consists of independent variables namely PEOU and PU, and the dependent variable is BITU. The respondents were 145 midwives from Grobogan District, Temanggung Regency and Salatiga City. Data analysis to conduct causal relationships between variables using Partial Least Square (PLS). The results of this study statistically show that the 3 hypotheses of H1, H2, and H3 adopted from the TAM model have a positive and significant influence. This is indicated by the value of the regression coefficient is positive and the coefficient of P value is less than 0.005
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.
Benefits and Challenges of ERP Implementation in Higher Education Institutions: A Systematic Literature Review Sholeh, Moch. Badrus; Samodra, Renita Fauziah; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp21-33

Abstract

This study aims to identify the benefits and challenges of Enterprise Resource Planning (ERP) implementation in higher education institutions through a Systematic Literature Review (SLR). Based on the analysis of 37 relevant articles, the findings show that ERP implementation provides significant benefits, such as reducing operational costs, increasing efficiency, and enhancing decision-making. However, challenges such as system complexity, resistance to change, and technological infrastructure limitations remain major obstacles. To address these challenges, increased training, coordination, and management support are required, along with adequate budget allocation. The findings of this study are expected to provide higher education management with insights into the importance of thorough planning in ERP implementation to achieve successful outcomes.
Komparasi COBIT 2019 dan ISO 27001 Terhadap Audit ISO 21001 untuk Akurasi Rekomendasi Audit SI/TI Pendidikan Anggraeni, Retno Setya; Rochim, Adian Fatchur; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp142-151

Abstract

ISO 21001 refers to the standardization of educational organization management and is an international standard, making it mandatory to be implemented in higher education institutions. Along with this, the rapid development of Information Systems and Information Technology (IS/IT) today has changed the paradigm of education and provided a new perspective that IS/IT support not only aids the learning process but also various other areas in higher education. Standardization focusing on this field includes IT Governance (ITG) COBIT 2019 and ISO 27001. Therefore, the researcher conducted a study to analyze which tools are most correlated with the implementation of ISO 21001, so that the findings of this research can provide recommendations that can serve as a basis for policy in selecting the appropriate standardization tools. The research method used is ex post facto. The analysis was conducted using SmartPLS. The researcher designed a questionnaire instrument to be distributed to the population of the Department of Computer Engineering and Informatics Engineering, UNDIP Semarang. The results of the questionnaire will then be tested for validity and reliability, followed by a correlation test while considering the T-Value. Based on the analysis results, it was concluded that both COBIT 2019 and ISO 27001 have an influence on ISO 21001. The influence of COBIT 2019 on ISO 21001 is significant, with a path coefficient and p-value of 0.000, while the influence of ISO 27001 shows a p-value of only 0.091.
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 the Role of Personal Character in Indonesian Peer-to-Peer Lending Purwanto, Iwan; Isnanto, Rizal; Widodo, Aris Puji
Jurnal Sistem Informasi Bisnis Vol 15, No 3 (2025): Volume 15 Number 3 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol15iss3pp297-307

Abstract

This research examines Indonesian P2P lending and bad loan management by analyzing borrower characteristics. The large value of problematic loans is a key signal for this investigation. The Big Five Personality Approach is used to survey P2P lending users. This study solely examines if the Big Five personality traits affect evaluation. On March 5–18, 2023, 17 P2P lending sites gathered data from 197 respondents for two weeks. Data validation continued until 11 respondents were pruned. The remaining 186 respondents' data was evaluated using SPSS. The management process yielded two hypothesis tests, the T-test and the F-test, which showed that the Big Five personality significantly affects present assessment activities. F-Test findings show the Big Five Personalities have a big impact concurrently. These two factors suggest that the Big Five Personalities may evaluate decision support systems for borrower recommendations.
Prediksi Perubahan Hemodinamik Pasien setelah Pemberian Premedikasi menggunakan Machine Learning Neural Network Guna Meningkatkan Kinerja Penanganan Medis Aryasa, Jiyestha Aji Dharma; Widodo, Aris Puji; Widodo, Catur Edi
Jurnal Sistem Informasi Bisnis Vol 14, No 3 (2024): Volume 14 Nomor 3 Tahun 2024
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21456/vol14iss3pp256-266

Abstract

This research presents the development process of a machine learning neural network model for predicting hemodynamic changes in patients after premedication, aiming to enhance the performance of medical interventions. The model was constructed using 3055 patients’ data who underwent premedication processes. The developed neural network model has an architecture consisting of 10 nodes in the input layer, 10 nodes in the hidden layer, and 3 nodes in the output layer. The evaluation results of the model indicate an overall accuracy of 85%. The precision values are high for normal class predictions at 0.85 and for hypertension class predictions at 0.81 with corresponding recalls of 1 (high) and 0.6 (moderate), respectively. However, predictions for the hypotension class still have a low precision of 0.6 and a recall of 0.04 (very low) due to the significantly lower number of samples in the hypotension class compared to the normal and hypertension classes. While testing with new data, the model has successfully predicted whether patients will experience hemodynamic pressure changes. It is expected that this model can contribute to improving the performance of medical interventions, thereby minimizing undesirable hemodynamic pressure changes.
Co-Authors Adi Wibowo Adian Fatchur Rochim Ahmad Zaini Alvianto, Muhammad Nur Hendra Anggraeni, Retno Setya Angin, Maria Perangin Antariksa, Muhammad Deagama Surya Arief Wahyudi Jadmiko arif, erman Aryasa, Jiyestha Aji Dharma Bayu Surarso Catur Edi Widodo Dahur, Arnoldus Janssen Dinar Mutiara Kusumo Nugraheni Dwiantoro, Luky Edi Suharto Eko Adi Sarwoko Eko Sediyono Fardana, Nouvel Izza Farid Agushybana Febriandi, Agistra Firdaus, Zulfia Firhan, Firhan Guruh Aryotejo, Guruh Heri Sutanto Heru Winarno Immanuel, Frans Iwan Purwanto Jazi Eko Istiyanto Kusuma, Teja Kusworo Adi Nabiel Putra Adam, Nabiel Putra Nikken Prima Puspita Nurdin Bahtiar Oktaviana, Aulia Pamungkas, Ardian Pratomo, Annas Setyo R Rizal Isnanto Roziana Roziana, Roziana Samodra, Renita Fauziah Sanjaya, Daniel Robi Sasongko, Cornelius Damar Setyo Purnomo Sholeh, Moch. Badrus Siahaan, Josua Freddy Orlando Sodik, Ilham Akbar Suherman, Suherman Sutikman, Sutikman Sutopo Patria Jati Tanjung, Dio Febrilian Tri Nur Kristina Untung Sujianto Vianita, Etna Vicoriza, Vicoriza Wicaksono, Januar Agung Winarsih Winarsih Woro Ningrum, Eva Liyan Yan Wisnu Prajoko Yuliani Setyaningsih Zahrasita Nur Indira Zukhruf, Afriza Meigi