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Anggi Zafia
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Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Published by Universitas Telkom
ISSN : -     EISSN : 26228106     DOI : https://doi.org/10.20895/inista
Core Subject : Science,
Computer Science & IT
Journal of Informatics, Information System, Software Engineering and Applications (INISTA) is a scientific journal published by Lembaga Penelitian dan Pengabdian Masyarakat (LPPM) of Institut Teknologi Telkom Purwokerto with ISSN 2622-8106 , Indonesia. Journal of INISTA covers the field of Informatics, Information System, Software Engineering and Applications. First published will be in September 2018 for an electronic version. The aims of Journal of INISTA are to disseminate research results and to improve the productivity of scientific publications. Journal of INISTA is published twice in Mei and November. Publication will be published "Volume 2 number 2" in May 2020.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 126 Documents
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Internet of Thing Implementation in The Library System (A Case Study of STMIK Bina Bangsa Kendari) Aris, Faizal; Kasau, Sukirno
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1812

Abstract

The subject of this article is the Internet of Things (IoT), particularly in the context of libraries. The author explains the definitions and concepts of IoT, the importance of IoT in libraries, and the potential applications of IoT for libraries. This research is located at the STMIK Bina Bangsa Kendari campus. The goal of this research is to design and simulate a prototype of an IoT-based library system utilizing UHF RFID technology. The research method used in this study is the development of a research and development model. The design of the intelligent library system development is based on two components: system hardware architecture and software development. The development approach uses a prototype development model. The library system can monitor the condition of books in real-time, whether they are available on the shelves, loaned out, or not on the shelves. The library system can provide information in the form of shelf monitoring if a book is misplaced on the shelf. The use of UHF RFID technology allows the application to read tag labels up to a maximum distance of 6 meters, while to support optimal QR Code reading in a room measuring 4 x 4 x3 meters (L x W x H), a minimum of one bulb with a power of 18 watts is required.
Extracting Post‑Disaster Health Impact Information from News Reports Using Named Entity Recognition Istiqomah, Nalar; Novika, Fanny
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1814

Abstract

Natural disasters have a significant impact on public health, giving rise to various post-disaster illnesses. This study presents an automated information‑extraction framework based on Named Entity Recognition (NER), leveraging the IndoBERT model to identify disaster types, health impacts, and affected locations from online news reports. Data were gathered via web scraping from multiple reputable news portals and subsequently processed through tokenization, stop‑word removal, and lemmatization. Extracted entities were visualized via bar charts and word clouds to reveal disease patterns associated with each disaster type. Results indicate that floods have a significant public health impact, with skin diseases being the most prevalent, followed by diarrhea, fever, influenza, and Acute Respiratory Infections (ARIs). Volcanic eruptions are linked to health conditions such as ARI, hypertension, diarrhea, and influenza, whereas earthquakes show strong correlations with diarrhea, ARI, skin diseases, and fever. Droughts and landslides are closely associated with diarrheal outbreaks due to compromised sanitation resulting from limited access to clean water. Although less frequently reported, tsunamis also exhibit a notable association with cases of diarrhea. The proposed method achieves 90 % accuracy and an 88 % F1‑score. These findings confirm the effectiveness of our NER-based approach in detecting causal relationships between disasters and health outcomes, providing valuable insights for policymakers and healthcare professionals in designing targeted post-disaster mitigation and response strategies.
Application Management Project Based on Technology Information : Study Case ASANA Evaluation of Courses Interaction Humans and Computers Surabaya State University Suroni, Azis; Sisephaputra, Bonda; Yahya, Saifudin
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1817

Abstract

The development of information technology has had a significant impact on various sectors, including in the world of education, especially in project management. One of the popular information technology-based project management applications is ASANA, which offers various features to facilitate team collaboration and task management. This article aims to evaluate the use of ASANA in the Human Computer Interaction (HCI) course of the Informatics Undergraduate Study Program at Surabaya State University (UNESA) Campus 5, with a focus on ease of use, the effectiveness of team collaboration, and its impact on student work outcomes. This study uses a case study method by collecting data through questionnaires and interviews with students who use ASANA in group projects. The results of the study indicate that ASANA provides convenience in organizing tasks, speeding up communication between team members, and increasing transparency and accountability in project completion. However, several challenges such as difficulties in initial adaptation and limited features in the free version of ASANA were found in the use of this application. Overall, the ASANA application has proven effective in supporting project management in the HCI course, but there are several aspects that need to be improved to maximize its benefits. This study is expected to provide insight into the development of information technology-based project management methods in academic environments.
Application of MobileNetV2-Based Deep Learning in Detecting Diseases in Chili Plants Aji, Nurseno Bayu; Yudantoro, Tri Raharjo; Safitri, Zulfa; Kuntardjo, Samuel Beta; Mardiyono, Mardiyono; Prayitno, Prayitno; Santoso, Kuwat
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1825

Abstract

This study proposes a deep learning model based on MobileNetV2 architecture for the classification of chili leaf diseases using image data. The dataset was compiled from both public and private sources, covering six distinct categories of chili leaf conditions. MobileNetV2 was selected due to its efficiency and accuracy, making it ideal for real-time agricultural applications. The model was enhanced with additional layers to improve feature extraction and classification performance. Stratified 10-fold cross-validation was employed to ensure balanced evaluation across an imbalanced dataset. The experimental results showed an overall accuracy of 91.04% and an average F1-score of 0.906, indicating consistent and reliable classification performance across classes. Confusion matrix analysis highlighted strong predictive capability, particularly in detecting healthy leaves and severe disease symptoms, with minor misclassifications among visually similar categories. The findings confirm the potential of lightweight CNN architectures for practical, mobile-based agricultural diagnostics, contributing to advancements in precision farming and early disease management.
Class Balancing and Parameter Tuning of Machine Learning Models for Enhancing Aphrodisiac Herbal Plant Classification Jayadi, Puguh; Bhagawan, Weka Sidha; Aldida, Jofanza Denis
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1832

Abstract

Herbal plants with aphrodisiac claims are an important part of traditional medicine that continues to evolve within the modern scientific context. However, the classification process for these plant claims is often done manually and subjectively, necessitating a more objective, data-driven approach. Artificial Intelligence (AI) and its various derivatives, such as Machine Learning, present a reliable solution for several related classification studies. The primary challenge in classification lies in data class imbalance and selecting the optimal model parameters. This study proposes an integrated approach that utilizes machine learning algorithms, including Random Forest, Support Vector Machine (SVM), and XGBoost, combined with SMOTE class balancing techniques and hyperparameter tuning through Grid Search, Random Search, and Bayesian Optimization. Experiments were conducted on a dataset of herbal plants with attributes and labels of aphrodisiac claims, and the results were evaluated based on accuracy, precision, recall, and execution time. The findings indicated that the combinatorial approach significantly improved model performance compared to the basic approach. Among the hyperparameter tuning results, the SVM method achieved the best accuracy (0.889) and precision (0.889). This research contributes to the development of an AI-based classification system in the field of ethnopharmacology. It can serve as a reference for creating scientifically validated databases of herbal plants.
Classification of DDoS Attacks based on Network Traffic Patterns Using the k-Nearest Neighbor (k-NN) Algorithm Faiz, Muhammad Nur; Maharrani, Ratih Hafsarah; Sari, Laura; Muhammad, Arif Wirawan; Supriyono, Abdul Rohman
Journal of INISTA Vol 7 No 2 (2025): May 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1834

Abstract

Many server attacks disrupt industrial or business operations. Attacks that flood bandwidth with simultaneous requests can overwhelm a system, leading to significant downtime and financial losses. Additionally, breaches that compromise sensitive data can damage a company's reputation and erode customer trust. DDoS attacks, or Distributed Denial of Service attacks, are among the most common types of server attacks. DDoS has been proven to cause server downtime, and one effective way to mitigate this attack is to detect and classify it using a machine learning approach. The K-Nearest Neighbor (KNN) algorithm, a simple yet effective classification method based on similarity measures, is known for its high accuracy. The current research builds upon two stages: the feature extraction stage and the classification stage, with the ultimate goal of improving the accuracy of DDoS identification using the CICDDoS2019 dataset. Based on this premise, the detection accuracy can be improved by enhancing these two stages. At a value of k equal to 3, this study produces an accuracy of 99.73%.

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