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Transaction on Informatics and Data Science
Published by Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Purwokerto
ISSN : -     EISSN : 30641772     DOI : https://doi.org/10.24090/tids
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Transactions on Informatics and Data Science (TIDS), with ISSN: 3064-1772 (online), is a scientific journal that publishes the latest research in the fields of informatics and data science, focusing on both theoretical advances and practical applications. Published by the Department of Informatics, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto, Purwokerto, this journal serves as a platform for researchers, academics, and practitioners to share new ideas and innovations in data science, artificial intelligence, natural language processing, cloud computing, and information technology applications across various domains. It promotes collaboration and deep knowledge exchange within the scientific community, bridging the gap between theory and practice in the rapidly evolving fields of informatics and data science. Aims Transaction on Informatics and Data Science aims to advance the frontiers of informatics and data science knowledge by publishing high-quality research that encompasses theoretical advancements and practical applications. The journal seeks to contribute significantly to the understanding and developing of innovative approaches, methodologies, and technologies in these domains. Scopes The scope of "Transaction on Informatics and Data Science" covers a wide range of topics related to informatics and data science, including but not limited to: - Data analysis and mining - Artificial intelligence and machine learning - Natural language processing and understanding - Cloud computing and big data technologies - Information retrieval and knowledge management - Data-driven decision-making and predictive modelling - Internet of Things (IoT) and data analytics - Cybersecurity and privacy in data science - Informatics and data science applications in various healthcare, finance, education, and other domains. The journal welcomes original research articles, reviews, case studies, and technical notes that contribute significantly to advancing knowledge and practice in informatics and data science. Submissions should demonstrate novelty, tightness, and relevance to the rapidly evolving landscape of information technology and data-driven decision-making processes.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2024)" : 5 Documents clear
Classification of Cavendish Banana Quality using Convolutional Neural Network Suryani, Ajeng Ayu; Athiyah, Ummi; Nur, Yohani Setiya Rafika; Warto
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12191

Abstract

Indonesia's agricultural production is divided into two main categories: vegetables and fruits. The vegetable category includes shallots, garlic, chilies, mushrooms, spinach, cabbage, and potatoes. One of the fruit commodities from the fruit horticulture subsector is bananas, which are divided into several types, including ambon, plantains, Cavendish, pipit, and horn bananas. One of the bananas that has a good selling value in Indonesia is the Cavendish banana, but the selling value of the Cavendish banana is determined by the quality of the banana fruit. A classification process is necessary to find out the quality of bananas. We perform classification using one of the deep learning algorithms, namely Convolutional Neural Network. The experiment uses 1047 images, divided into 65% training data, 15% validation data, and 20% testing data by using epochs 20 times with 16 batch sizes, the accurate results obtained are 99%. The results indicate the effectiveness of the confusion matrix in identifying training data and detecting images. It can be concluded that using more training data leads to higher accuracy, as fewer image reading errors occur when fewer images are processed. This classification is expected to be able to classify bananas with good quality like the real condition.
Naive Bayes Classification for Software Defect Prediction Prastyo, Edwin Hari Agus; Yaqin, Muhammad Ainul; Suhartono; Faisal, M.; Firdaus, Reza Augusta Jannatul
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12192

Abstract

Software defects are an inevitable aspect of software development, exerting substantial influence on the reliability and performance of software applications. This research addresses the imperative need to enhance the prediction and monitoring of software defects within the software development domain. With a focus on system stability and the prevention of software malfunctions, this study underscores the significance of proactive measures, including robust software testing, routine maintenance, and continuous system monitoring. The central challenge addressed in this research pertains to the insufficient efficiency of predicting software defects during the development phase. To address this challenge, the study employs the Naive Bayes classification method. Test results conducted on the complete dataset reveal that the Naive Bayes method yields classifications with an exceptionally high accuracy rate, reaching 98%. These findings suggest that the method holds great potential as an effective tool for predicting and preventing software defects throughout the software development process. Additionally, through linear regression analysis, the model exhibits an intercept value of -0.09359968 and a coef coefficient of 0.00761893. The outcomes of this research bear significant implications for the implementation of the Naive Bayes method in software bug prediction analysis, particularly in the utilization of the Python programming language with the assistance of Google Colab. The adoption of this method can play a pivotal role in mitigating risks and elevating the overall quality of software during the developmental stages.
K-Means Clustering in Relevance Grouping of Undergraduate Informatics Jobs: Case Study at the Informatics Engineering Department, Universitas Muhammadiyah Malang, Malang, Indonesia Hariyanto, Dikky Cahyo; Harini, Sri; Chamidy, Totok
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12231

Abstract

Higher education is one of the levels of study expected to produce graduates competent in the field of knowledge taken. The large number of graduates from tertiary institutions with many job opportunities causes many graduates to work in ways that do not match their majors, so there is a need to evaluate the level of success of graduates learning achievements in tertiary institutions. This research aims to analyze data on the relevance of the work of undergraduate graduates in Informatics Engineering to what they have learned by the learning outcomes in the Informatics Engineering study program at the University of Muhammadiyah Malang using K-Means clustering. Using data from questionnaires measuring graduate learning outcomes and measuring job suitability for 137 respondents who had been tested for validity, reliability, and multicollinearity, the results of this research showed that the data was formed into three clusters with the analysis that 29.92% of UMM Informatics Engineering graduates were able to meet graduate learning outcomes and obtain jobs that are relevant to what they studied, 49.63% of other graduates also got jobs that were relevant to their major even though they lacked mastery of specific skills as measured by graduate learning outcomes, and 20.45% of other graduates got jobs that were less relevant to the field of Informatics engineering.
Comparison of the Accuracy Between Naive Bayes Classifier and Support Vector Machine Algorithms for Sentiment Analysis in Mobile JKN Application Reviews Septiani, Erni; Akhriza, Tubagus M.; Husni, Mochamad
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12232

Abstract

The Mobile JKN (National Health Insurance) application is a form of BPJS Health's commitment to implementing health insurance programs since 2014. The large number of reviews of the Mobile JKN application on the Google Play Store requires sentiment analysis with an algorithm that produces the best accuracy. This research compares the accuracy obtained from the Naive Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. This algorithm is implemented directly in sentiment analysis and combined with the Synthetic Minority Over-Sampling Technique (SMOTE) technique to overcome data imbalance. The data in this research was obtained from reviews of the Mobile JKN application on the Google Play Store using the data scraping method. We use data scraping and labeling processes before performing sentiment analysis. The sentiment analysis process includes text preprocessing and processing (modeling) by dividing the data into 30%, 40%, and 50% test data, with the rest becoming training data. The results of this research showed that the algorithm with the best accuracy was the NBC algorithm using the SMOTE technique with 50% test data and the SVM algorithm without the SMOTE technique with 50% test data. Both give the same accurate results, namely 0.90 or 90%. Experiments show that the amount of test data and the application of SMOTE affect the accuracy of the two compared algorithms.
Analysis of Academic Information System Using Information System Success Model and System Quality Model Case Study of Institut Teknologi Nasional Malang setyowati, Kurnia Dwi; Chamidy, Totok; Faisal, Muhammad
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12234

Abstract

Advancements in information technology are progressing rapidly, providing numerous conveniences across various domains, ranging from personal needs to general activities. Academic Information System (SIAKAD) facilitates academic information management in educational institutions. The utilization of website-based SIAKAD is gaining popularity due to its ability to streamline access to information and minimize administrative errors. This Research aims to identify key factors contributing to the success of website-based SIAKAD at the National Institute of Technology Malang. Additionally, the Research seeks to assess the level of success of SIAKAD and evaluate the key factors that influence its success. This Research employed the Information System Success Model (ISSM) method and the System Quality Model (SQM). This study follows a seven-step workflow, from problem identification to conclusion, assessing SIAKAD ITN Malang's success using ISSM and SQM models. A survey of 100 respondents evaluated system quality, functionality, and user satisfaction. Related research highlights the importance of system quality in achieving positive user outcomes and overall success. Based on the research results, all hypotheses are accepted, indicating that Information Quality (IQ), System Quality (SQ), and Service Quality (SeQ) significantly influence Intention to Use (IU), User Satisfaction (US), and Net Benefit (NB) in the Use of SIAKAD at the National Institute of Technology Malang.

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