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Journal of Information System Exploration and Research
Published by shm publisher
ISSN : 29641160     EISSN : 29636361     DOI : https://doi.org/10.52465/joiser
Core Subject : Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Engineering
Journal of Information System Exploration and Research (JOISER) (e-ISSN: 2963-6361, p-ISSN: 2964-1160) is a journal that publishes and disseminates scientific research papers on information systems to a wide audience, particularly within the information system society. Articles devoted to discussing any and all aspects of the most recent and noteworthy advancements in the fields of Decision Science, Computer Science, and Computer Science Applications will be considered for publication. Submit your paper now through Online submission ONLY. The JOISER publication period is carried out every six months, namely in January and July. But, authors can submit their work to JOISER at any time throughout the year, as the submission process is continuous. The JOISER has been indexed by Google Scholar, Crossref, Copernicus, and BASE. The Journal of Information Systems Exploration and Research aim publishes articles concerning the design and implementation information system, data models, process models, algorithms, and software for information systems. Subject areas include data management, data mining, machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. We welcome system papers that focus on decision science and machine learning, computer science application, pplication domains, Internet of Things, which present innovative, high-performance, and scalable solutions to data management problems for those domains.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 5 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2025): January 2025" : 5 Documents clear
The Asthma Classification Using an Adaptive Boosting Model with SVM-SMOTE Sampling Dullah, Ahmad Ubai; Utami, Putri; Unjung, Jumanto
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.486

Abstract

Asthma is a disease that affects the human respiratory tract, characterized by inflammation and narrowing of the respiratory tract such as wheezing, coughing, and shortness of breath. The causes of asthma can come from genetics, lifestyle, and a bad environment. Diagnosis made to asthma patients is very influential on the severity and treatment carried out. However, the diagnosis process may not be able to precisely determine asthma patients because the diagnosis is influenced by the classification of asthma based on the symptoms that appear. Therefore, this study proposes an asthma disease classification model that is optimized using a sampling method to balance the data. The proposed classification model uses the Adaptive Boosting algorithm with a sampling technique using SVM-SMOTE to help balance the data. The results obtained from the experiment achieved an accuracy of 98.60%. This result shows that the proposed model is more accurate and optimal in performing classification when compared to previous research.
Prototyping Disaster Preparedness Information System: A Case of Pandeglang District, Indonesia Juanara, Elmo; Hakim, Ade Anggian; Maeda, Yasunobu
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.495

Abstract

In December 2018, a tsunami triggered by the eruption of Anak Krakatau Volcano (AKV) devastated the coastal area of Pandeglang, Indonesia, claiming hundreds of lives and leaving thousands missing. This tragedy underscores the critical importance of enhancing tsunami awareness through disaster preparedness and education. However, the lack of disaster preparedness in vulnerable areas, such as Pandeglang, remains a significant challenge. This is evident from the absence of early warning systems and evacuation initiatives at the time of the tsunami, highlighting the urgent need for improved disaster resilience in at-risk communities. This research aims to develop the disaster preparedness information system to equip society with sufficient knowledge and skill in case of the next disaster. The method this research uses is Soft Systems Methodology (SSM) to obtaining system requirements to the development of prototype. The prototype of a disaster preparedness information system was developed as a result. The system can be accessed using a smartphone or computer. This study introduces a novel approach by proposing a new prototype of disaster preparedness information specifically tailored for vulnerable areas in developing countries.
Machine Learning Techniques for Classifying Indonesian Foods and Drinks by Nutritional Profiles Al Qohar, Bagus; Tanga, Yulizchia Malica Pinkan; Darmawan, Aditya Yoga
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.528

Abstract

Local ingredients and Indonesia's diverse culinary traditions play an important role in shaping people's health and eating habits. Understanding the nutritional profile of Indonesian food is crucial to promoting healthier food choices. This study aims to classify Indonesian food and beverages based on their nutritional content, with a focus on calories, protein, fat, and carbohydrates. To achieve this, a dataset of 1,346 food items was preprocessed using normalization techniques to improve model performance. Each food item was categorized as High Protein, High Fat, or High Carbohydrate based on its dominant macronutrient content. Five machine learning models which are K-Nearest Neighbors, Decision Trees, Support Vector Machines, Random Forest, and Multilayer Perceptron-were used and compared. Among these models, the Support Vector Machine achieved the highest classification accuracy of 99.1%. These findings demonstrate the potential of machine learning in nutrition research, providing a basis for developing data-driven dietary recommendations tailored to individual nutritional needs. This research bridges traditional dietary research with modern computational approaches, offering insights for public health initiatives and personalized nutrition planning.
Classification of Student Grading Using Naïve Bayes Method with Under-sampling Approach to Handle Imbalance Aziz, Alif Abdul; Prasetiyo, Budi
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.537

Abstract

This study explores the application of the Naive Bayes classification method to predict student grades based on important attributes such as timeliness of assignment submission, attendance rate, and quality of work. This research uses a dataset that includes three attributes, namely timeliness of submission, attendance level in learning, and evaluation of the quality of assignments collected by students. The pre-processing is performed to clean the data, followed by an under-sampling stage to balance the class distribution. Then, the classification model is evaluated and tested using specific data samples to measure prediction accuracy. The results showed a significant improvement in model accuracy after applying under-sampling, highlighting the importance of handling data imbalance in predictive analysis. The implications of these findings are not only relevant in the context of higher education, but also offer opportunities for further development in data-driven decision-making in various fields.
An Implementation of Loyalty Program Theory Based on Recency Frequency Monetary Score in Information Systems to Increase Customer Loyalty Rajagukguk, Ricky
Journal of Information System Exploration and Research Vol. 3 No. 1 (2025): January 2025
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v3i1.538

Abstract

This study aims to help online retail stores find the right strategy for treating customers through customer segmentation based on Recency, Frequency, and Monetary (RFM) Score. With a quantitative approach, this study uses the K-Means Clustering algorithm to group customers based on their RFM values ​​and applies it within the Loyalty Program Theory framework. The results show that the Best Customers segment has the highest percentage at 26.3%, which emphasizes the importance of retaining high-value customers through exclusive loyalty programs such as VIP access and premium offers. In contrast, the Lost Customers segment at 24.8% requires attention through retargeting and discount programs to attract them back. This study proves that data-based customer segmentation and the implementation of relevant strategies can strengthen long-term relationships with customers, increase loyalty, and ultimately help the development of online retail businesses.

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