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Journal of Informatics Development
Published by Institut Teknologi dan Bisnis Widya Gama
ISSN : 2963055X     EISSN : 29630568     DOI : https://doi.org/10.30741/jid
Core Subject : Science,
Computer Science & IT
Focus and Scope Journal of Informatics Development cover all topics under the fields of Informatics, Information System, Information Technology, Computer Science, and Computer Engineering. Informatics and Information system IT Audit Software Engineering Big Data and Data Mining Internet Of Thing (IoT) Game Development IT Management Computer Network and Security Mobile Computing Security For Mobile Decision Support System Web and Cloud Computing Accounting Information system Electrical and Computer Engineering Sensors and Trandusers Signal, Image, Audio and Video processing Communication and Networking Robotic, Control and Automation Fuzzy and Neural System Artificial Intelligent
Arjuna Subject : Umum - Umum
Articles 34 Documents
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Sentiment Analysis of Ijen Crater Reviews using Decision Tree Classification and Oversampling Optimization Hizham, Fadhel Akhmad; Asyari, Hasyim; Urrochman, Maysas Yafi
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1399

Abstract

Sentiment analysis is a text mining technique that classifies content as positive, negative, or neutral polarity in each sentence or document. These lines or papers may be user reviews assessing the quality of a product or material supplied to them. The purpose of this study is to better understand the function of sentiment analysis in assessing evaluations of the Ijen Crater tourist destination based on Google Maps user comments. This study is conducted in four steps, beginning with data gathering in the form of Google Maps evaluations obtained by data scraping. Following data collection, text preparation includes case folding, tokenization, stopword elimination, and stemming. Following text preprocessing, the next stage is imbalaced data optimization, which involves modifying the minority class samples to be nearly equal to the majority class by randomly duplicating minority class samples. Then, each review is categorized according to sentiment using the Decision Tree (DT) method. Testing has done by comparing DT without optimization and DT with SMOTE-ENN and ADASYN optimization. The result shown DT with SMOTE-ENN optimization has the best accuracy improvement with 1.62%, from 96.94% to 98.56%.
Detection of Diabetes in Pregnant Women Using Machine Learning as an Effort Towards Golden Indonesia 2045 Muliawan, Agung; Rohim, Muhamat Abdul; Fauziah, Difari Afreyna; Yusuf, Hamzah Fansuri
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1418

Abstract

One of the goals of the Golden Indonesia 2045 program is to utilize health technology to enhance public health, with diabetes being a prominent concern. This research aims to employ ensemble classifier optimization techniques in machine learning for the early detection of diabetes among pregnant women. The study uses physiological data, including variables such as glucose levels, number of pregnancies, skin thickness, blood pressure, insulin levels, body weight, family history, and age. By combining multiple models, ensemble classifiers can enhance prediction accuracy, stability, and overall model performance. This research utilizes an open Kaggle dataset on pregnant women to train and test machine learning models, specifically Support Vector Machine (SVM) and Deep Learning, incorporating ensemble techniques such as bagging and boosting. Experimental results indicate that the ensemble classifier approach significantly enhances diabetes classification, with SVM using bagging achieving the highest accuracy at 76.95%. These findings suggest that ensemble classifier methods could be a valuable tool for early diabetes detection, providing timely intervention and improved risk management during pregnancy, which supports the objectives of improving public health under the Golden Indonesia 2045 initiative.
Implementation Of Arima Model In The Analysis Of City Temperature Averag Rohim, Muhamat Abdul; Muliawan, Agung; Wiranto, Ferry
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1419

Abstract

This study analyzes the daily average temperature data of Delhi city from 2013 to 2017 using the Autoregressive Integrated Moving Average (ARIMA) model to model and predict temperature trends. The temperature data processed in this study is non-stationary, so differentiation is applied to achieve stationarity. Two ARIMA models were evaluated: ARIMA (1,1,1) and ARIMA (1,1,1)(1,0,1). The ARIMA (1,1,1) model is effective in capturing short-term patterns, while the ARIMA (1,1,1)(1,0,1) model performs better in handling seasonal components. The findings show that the ARIMA (1,1,1)(1,0,1) model provides more accurate prediction results by accounting for seasonal fluctuations in temperature data. This research is expected to serve as a reference for preventive measures related to temperature changes, as temperature variations can affect public health, infrastructure, and quality of life in rapidly growing cities like Delhi. Understanding temperature trends and making accurate predictions helps in city planning, resource management, and developing adaptation strategies for climate change, which is crucial for mitigating negative impacts and planning for a more sustainable future.
Clustering of Lecturer Performance Using K-Means Rouf, Abdur; Qoritunnadyah, Marita; Asyari, Hasyim; Urrohman, Maysas Yafi
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1430

Abstract

Lecturers serve as professional educators and scientists whose primary roles are knowledge transformation, development, and dissemination in fields such as science, technology, and the arts through education, research, and community service. They play a critical role in fostering an educated generation, and as such, must maintain high levels of integrity in their work. The academic position of a lecturer often reflects their involvement in research, community service, and scientific publications, indicating a broad scope of expertise. This study aims to cluster lecturers based on their academic positions, research activities, community service, and number of publications, using secondary data from the Community Service Research Institute, UPT Academic Positions and Lecturer Certification, and UPT Publications. The clustering was conducted using a non-hierarchical k-means method, which resulted in three clusters: Cluster 1 with 26 members showing minimal productivity in the tridharma tasks, Cluster 2 with 6 members demonstrating high engagement, and Cluster 3 with 20 members with moderate involvement. These findings suggest that universities need to monitor and support lecturers in Cluster 1 to improve their contributions to education, research, and community service. This clustering provides insights that can guide universities in promoting a balanced and active academic environment.
Design and Development of an Exhibition Management System for Final Project Products of Practicum Using the Prototyping Method Ariyadi, David Juli; Etikasari, Bety
Journal of Informatics Development Vol. 3 No. 1 (2024): Oktober 2024
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i1.1498

Abstract

This study focuses on the design and development of the Exhibition Management System (EMS) through the Prototyping Method exemplifies a workable solution to the challenges of organizing exhibitions aimed at showcasing products developed from Project Based Learning (PBL) in practicum activities. By integrating features such as participant registration, product submission, feedback collection, and archive management, this system presents an organized platform that boosts user interaction and streamlines operations. Requirement prototype technique of prototyping is utilized because the developer builds the system by outlining its functions and processes, especially when the user or owner of the system is unable to accurately. The system is built using the Laravel framework and MySQL as data storage. This system is not only a digital platform in exhibition management, but also serves as a reference that can be implemented for other educational institutions that want to increase public engagement in the institution's activities to improve the experience through digital solutions. The successful implementation of this system as a benchmark shows its potential to increase innovation, teamwork, and effective results in project-based learning.
Analysis and Visualization of Data on the Impacts of Covid-19 Globally and Locally Iqbal, Muhammad; Yudha, Julius Chaezar Bernard Buana; Umimah, Reza Nazilatul; Hizham, Fadhel Akhmad
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1513

Abstract

The COVID-19 pandemic has had a profound impact on multiple aspects of human life, including food supply, mental health, and healthcare service management. This study aims to examine these impacts by applying a combination of data analysis methods such as data preprocessing, exploratory data analysis (EDA), predictive algorithms, and data visualization. The datasets utilized include information related to mental health conditions, food security, and COVID-19-related health statistics. The findings indicate a significant increase in mental health issues, such as anxiety and depression, as well as disruptions in food supply chains that have adversely affected global food security. Moreover, data visualization has proven to be a valuable tool in supporting decision-making processes in healthcare management. However, most implementations remain limited in scope and are often confined to internal agency use. Therefore, this study recommends further development in integrating data sources, enhancing the application of predictive algorithms, and optimizing data visualization for more effective decision-making in managing global health crises.
Integration of AHP and TOPSIS Methods in Decision Making Models to Identify High Achieving Students Hermansyah, Masud; Mujiono, Mujiono; Fatimatuzzahra, Fatimatuzzahra; Dedes, Khen; Firdausi, M Faiz
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1547

Abstract

Selection of outstanding students is an essential process in education to ensure that students with high achievements receive appropriate recognition and guidance. However, this process often suffers from subjectivity and the absence of a structured decision-making system. This study aims to develop an objective and accountable decision support model by integrating the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The AHP method is used to determine the weight of each selection criterion, while the TOPSIS method is used to rank students based on their proximity to the ideal solution. The study involved 10 student candidates and assessed them based on 6 criteria, including academic performance, discipline, extracurricular activities, and religious values. The results show that the integrated AHP-TOPSIS model successfully identifies students with the highest preference values as the most outstanding, while those with lower values are recommended for further coaching. The model demonstrates its effectiveness in supporting accurate, data-driven student selection at MIMA 37 Sunan Kalijogo Ambulu.
Product Demand Forecasting in E-Commerce with Big Data Analytics: Personalization, Decision Making and Optimization Murni, Cahyasari Kartika; Choiri, Achmad Firman; Rahmawati, Febriane Devi
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1548

Abstract

This study explores the role of Big Data in forecasting product demand in the e-commerce sector through the application of machine learning and time series methods. A quantitative descriptive approach is used, involving data collection, preprocessing, analysis, and model evaluation. Forecasting techniques applied include ARIMA for time series prediction and XGBoost for supervised learning to identify key demand factors. Model performance is evaluated using accuracy metrics such as RMSE, MAE, and MAPE. The results indicate that the XGBoost model provides the highest forecasting accuracy at 89%, while the ARIMA model achieves 78%. These findings demonstrate that Big Data significantly supports strategic decision-making in e-commerce by enhancing personalization, optimizing inventory, and enabling data-driven marketing strategies.
Application of Three-Parameter Logistic (3PL) Item Response Theory in Learning Management System (LMS) for Post-Test Analysis Ariyadi, David Juli
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1554

Abstract

In the edu-digital era, Learning Management Systems (LMS) have become pivotal in delivering and managing education. However, many LMS platforms lack sophisticated analytical tools to evaluate the quality of post-test assessments. This research explores the application of Item Response Theory (IRT) as a psychometric model integrated into an LMS to enhance post-test analysis. By leveraging IRT, the system can evaluate item difficulty, discrimination, and guessing parameters, providing more accurate insights into both test quality and student ability levels. The study implements a three-parameter logistic (3PL) IRT model and integrates it into an LMS prototype. Empirical data from real student post-tests are analyzed to validate the model's effectiveness. The results demonstrate that IRT-based analysis significantly improves the assessment feedback mechanism, allowing educators to identify poorly performing items, adapt instructional strategies, and personalize learning paths. This research contributes to the development of intelligent assessment systems in educational technology, promoting more effective, fair, and data-driven evaluation processes.
AnalysisSentimentAlun-Alun LumajangReviewusingSupportVector Machine Urrochman, Maysas Yafi
Journal of Informatics Development Vol. 3 No. 2 (2025): April 2025
Publisher : Institut Teknologi dan Bisnis Widya Gama Lumajang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30741/jid.v3i2.1555

Abstract

Alun-Alun Lumajangis one of room the public that becomes center activity community and tourists . Perception public to place Thiscan measured through analysis sentiment to reviews available on digital platforms such as Google Maps. Research This aiming For classifysentiment review the use Support Vector Machine (SVM) method , one of the effective machine learning algorithms Fortask classification text . Data used in the form of review collected text fromGoogle Maps, then through pre-processing data such as cleaning text , tokenization , and deletion stopword . Sentiment label determined manually to be three categories : positive, negative , and neutral . Next , the data is extracted use TF-IDF technique before classified using SVM. Research results showthat SVM algorithm is capable of classify sentiment with level high accuracy , making it proper method For analysis opinion public based on text . Findings This expected can give input for government area in increase quality services and management room public in Lumajang.

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