Journal of Applied Informatics Science
Aim The Journal of Applied Informatics Science is dedicated to advancing the discipline of applied informatics by publishing high-quality, peer-reviewed research that integrates theoretical foundations with practical solutions. The journal seeks to promote scientific excellence, foster technological innovation, and support interdisciplinary collaboration within the global informatics community. Its primary objective is to provide an authoritative platform for researchers, academicians, and industry professionals to disseminate original contributions that address emerging challenges, opportunities, and transformations in intelligent and secure computing. Scope The journal welcomes submissions that explore concepts, models, technologies, and applications across a wide spectrum of applied informatics. Areas of interest include, but are not limited to: Intelligent Systems and Artificial Intelligence: machine learning, deep learning, expert systems, natural language processing, computer vision, robotics, autonomous systems, and intelligent agents. Software Engineering: software development methodologies, agile and DevOps approaches, software testing and quality assurance, software architecture, cloud-native development, and distributed systems. Computing Systems: high-performance computing, embedded and real-time systems, parallel computing, Internet of Things (IoT), sensor networks, edge and fog computing, and cyber-physical system architectures. Cybersecurity and Cryptography: secure communication protocols, network security, intrusion detection and prevention systems, cryptographic techniques, cyber threat modeling, blockchain security, and privacy-preserving technologies. Big Data and Data Analytics: scalable data processing frameworks, data mining, predictive analytics, real-time analytics, data streams, visualization techniques, and analytical dashboards. Business Intelligence and Knowledge Management: decision support systems, enterprise data warehousing, knowledge discovery, digital transformation strategies, and AI-driven business process optimization. The journal accepts original research articles, review papers, technical notes, and case studies that contribute to scientific understanding, technological development, policy insight, or practical implementation of informatics solutions. All submissions undergo a rigorous peer-review process to ensure academic integrity, relevance, and quality. The Journal of Applied Informatics Science encourages interdisciplinary research that connects informatics with domains such as healthcare, education, business, environment, industry, and public services.
Articles
18 Documents
<![CDATA[Analisis Ancaman Perang Siber terhadap Keamanan Nasional Indonesia: Tinjauan Eskalasi dan Mitigasi Tahun 2025 ]]>
<![CDATA[Rizki Dewantara]]>;
<![CDATA[Gina Khayatun Nufus]]>;
<![CDATA[Eko Jhony Pranata]]>;
<![CDATA[Fariz Noor Djati]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 1 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i1.80
<![CDATA[Kemajuan teknologi internet telah menciptakan interkoneksi global yang memicu ancaman perang siber terhadap keamanan nasional. Masalah utama yang dihadapi Indonesia adalah tingginya kerentanan terhadap serangan digital, dengan catatan tiga koma enam puluh empat miliar anomali trafik pada awal dua ribu dua puluh lima. Penelitian ini bertujuan untuk menganalisis berbagai jenis, tingkat bahaya, dan dampak serangan siber dalam mengganggu stabilitas kedaulatan negara. Metode penelitian yang digunakan adalah kualitatif non interaktif melalui pengkajian dokumen sekunder dari jurnal ilmiah dan laporan resmi otoritas siber. Hasil penelitian menunjukkan adanya fluktuasi anomali trafik yang signifikan dengan puncak tertinggi mencapai enam ratus lima belas koma empat juta kejadian pada Juni dua ribu dua puluh lima. Tren serangan mulai bergeser dari eksploitasi teknis menuju rekayasa sosial yang menyasar celah psikologis pengguna. Kesimpulannya, Indonesia masih berada dalam kategori negara rentan sehingga diperlukan penguatan regulasi serta peningkatan kapasitas sumber daya manusia untuk menghadapi evolusi ancaman siber.]]>
<![CDATA[XSentiment-HS: Hierarchical CNN-BiGRU-SVM with Explainable for Indonesian Multi-Level Hate Speech Detection ]]>
<![CDATA[Gina Khayatun Nufus]]>;
<![CDATA[Rizki Dewantara]]>;
<![CDATA[Ardi Susanto]]>;
<![CDATA[Sokid]]>;
<![CDATA[Lia Farhatuaini]]>;
<![CDATA[Jaka Septiadi]]>;
<![CDATA[Mohammad Raihan Akbar]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 1 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i1.81
<![CDATA[Deteksi ujaran kebencian pada media sosial menuntut interpretasi teks yang kompleks karena sifatnya yang spontan dan ambigu, terutama dalam bahasa Indonesia yang kaya akan slang. Tantangan utama saat ini adalah keterbatasan penelitian sebelumnya yang mayoritas hanya melakukan klasifikasi biner tanpa mendeteksi tingkat keparahan konten. Penelitian ini mengusulkan XSentiment-HS, sebuah model deep learning hierarkis dua tahap untuk deteksi multi-tingkat hate speech. Arsitektur model menggabungkan Convolutional Neural Networks (CNN) untuk ekstraksi fitur lokal dan Bidirectional Gated Recurrent Unit (BiGRU) untuk menangkap ketergantungan kontekstual jangka panjang. Model ini juga diperkuat dengan mekanisme Multi-Head Attention dan Support Vector Machine (SVM) sebagai classifier final. Melalui integrasi ini, XSentiment-HS diharapkan mampu mengatasi tantangan ekstraksi fitur dan polisemi secara lebih efektif dibandingkan metode konvensional.]]>
<![CDATA[Integration of Particle Swarm Optimization in Bidirectional Memory Networks for Improved Daily Climate Forecasting Accuracy ]]>
<![CDATA[Ardi Susanto]]>;
<![CDATA[Heru Purnomo Kurniawan]]>;
<![CDATA[Lia Farhatuaini]]>;
<![CDATA[Muhammad Iszul Wilsa]]>;
<![CDATA[Gina Khayatun Nufus]]>;
<![CDATA[Ananda Sathria Maulana Amri]]>
<![CDATA[ Journal of Applied Informatics Science Volume 1 Issue 2 (2025) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v1.i2.85
<![CDATA[Global climate change has caused rainfall patterns to become increasingly fluctuating and difficult to predict using conventional weather forecasting methods. Accurate daily rainfall prediction is crucial for hydrometeorological disaster mitigation and agricultural sector planning. Although Deep Learning models such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) are capable of handling complex time-series data, the manual determination of hyperparameters often results in suboptimal models and entrapment in local optima. This study proposes the integration of the Particle Swarm Optimization (PSO) algorithm to automatically optimize hyperparameters (number of hidden neurons, dropout rate, and learning rate) in LSTM and BiLSTM architectures. The models were evaluated using a multivariate daily climate observation dataset encompassing temperature, humidity, wind speed, and actual rainfall. Experimental results indicate that PSO-based optimization significantly enhances prediction performance compared to baseline models. The PSO-LSTM approach successfully reduced the Root Mean Square Error (RMSE) to 17.59 mm and Mean Absolute Error (MAE) to 9.07 mm, comparable to the performance of PSO-BiLSTM, which achieved an RMSE of 17.59 mm and an MAE of 9.20 mm. These findings prove that automatic parameter tuning using swarm intelligence algorithms can highly optimize sequential neural network architectures in capturing rainfall pattern volatility, making it highly recommended as a foundation for a more accurate early warning system.]]>
<![CDATA[Prototype of an Internet of Things-Based Irrigation System Using Soil Moisture Sensors for Monitoring Rice Field Drought in Lape Village ]]>
<![CDATA[Yudi Mulyanto]]>;
<![CDATA[Eri Sasmita Susanto]]>;
<![CDATA[Syafira Gestiana]]>;
<![CDATA[Yunanri W]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 1 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i1.87
<![CDATA[Water management in rice fields remained a challenge, especially during drought conditions caused by suboptimal manual irrigation systems. Inaccurate timing and water volume often led to insufficient water supply for crops. This study aimed to develop a prototype of an automatic irrigation system based on the Internet of Things using ESP32 and a fuzzy logic method to reduce the risk of drought. The system monitored field conditions and controlled irrigation automatically through a web-based platform by utilizing soil moisture sensors and ultrasonic sensors. The research method employed a quantitative experimental approach through system design and testing. The results showed that the system was capable of automatically controlling water gates and pumps based on fuzzy logic output, enabling precise irrigation. This system was expected to improve water use efficiency in rice fields.]]>
<![CDATA[Design and Build a Website-Based Waste Alms Management Information System Using the Laravel Framework (Case Study of KSM Jameela Brang Biji Sumbawa) ]]>
<![CDATA[Yudi Mulyanto]]>;
<![CDATA[Eri Sasmita Susanto]]>;
<![CDATA[Arzikman]]>;
<![CDATA[I Made Widiarta]]>;
<![CDATA[Fahri Hamdani]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 1 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i1.89
<![CDATA[Waste management remains a crucial issue in various regions, including Sumbawa. KSM Jameela Brang Biji took the initiative to manage waste through a garbage alms program. However, the processes of recording, managing donor data, and reporting are still conducted manually, making them prone to errors, lacking transparency, and highly inefficient. Therefore, this research aims to design and build a website-based Garbage Alms Management Information System using the Laravel framework to facilitate KSM Jameela's operations. The software development applies the Agile Scrum method, which allows for quick iterations and adaptation to user needs. This system was built using the PHP programming language and a MySQL database. Software quality testing was conducted using the Black Box method to ensure all feature functionalities operate according to specifications, and the White Box method to evaluate the internal logical structure of the code. The research results indicate that the developed system successfully digitalizes the garbage alms management process, ranging from donor registration and waste transaction recording to real-time report generation. In conclusion, the implementation of this system is proven to significantly improve operational efficiency, data accuracy, and management transparency, thereby supporting KSM Jameela in optimizing the garbage alms program and encouraging broader community participation.]]>
<![CDATA[Sistem Pendukung Keputusan Penentuan Siswa Berprestasi Menggunakan Metode Weighted Product Berbasis GUI Python pada SMK Plus Al Hilal Tegalgubug ]]>
<![CDATA[Sokid]]>;
<![CDATA[Ananda Sathria Maulana Amri]]>;
<![CDATA[Afifah Zayyin Amatillah]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 1 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i1.92
<![CDATA[Penentuan siswa berprestasi di SMK Plus Al Hilal Tegalgubug selama ini dilakukan secara manual dengan penilaian yang bersifat subjektif dan membutuhkan waktu lama. Penelitian ini bertujuan untuk membangun Sistem Pendukung Keputusan (SPK) penentuan siswa berprestasi menggunakan metode Weighted Product (WP) berbasis aplikasi GUI Python dengan database MySQL. Penelitian menggunakan metode pengembangan sistem Waterfall dan melibatkan 43 siswa kelas XII sebagai alternatif dengan lima kriteria penilaian, yaitu nilai akademik, kehadiran, sikap, prestasi non-akademik, dan pelanggaran. Hasil penelitian menunjukkan sistem yang dibangun mampu menghasilkan peringkat siswa berprestasi secara objektif dan konsisten untuk tiga program keahlian: TJKT, TO, dan AKL. Pengujian sistem menggunakan Black Box Testing menunjukkan seluruh fitur berjalan sesuai kebutuhan. Sistem ini diharapkan dapat membantu pihak sekolah dalam pengambilan keputusan yang lebih efisien dan transparan]]>
<![CDATA[CornLeafNet: Disease-Area-Based Corn Leaf Disease Classification Using Convolutional Neural Networks ]]>
<![CDATA[Herfandi Herfandi]]>;
<![CDATA[Eri Sasmita Susanto]]>;
<![CDATA[Fahri Hamdani]]>;
<![CDATA[Jonathan Afriliansyah]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 2 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i2.96
<![CDATA[Corn leaf diseases can reduce crop productivity by disrupting photosynthesis and plant growth. Manual identification in large-scale fields remains limited due to its dependence on observer expertise, visual similarity among disease symptoms, and variations in field conditions. This study proposes CornLeafNet, a Custom Convolutional Neural Network model for disease-area-based corn leaf disease classification. The dataset consists of XML-annotated corn leaf images, from which disease-affected regions were extracted through annotation parsing and bounding box-based cropping to focus the model on symptomatic leaf areas. CornLeafNet was developed to classify three disease categories: Grey Leaf Spot, Corn Rust, and Leaf Blight. The model achieved a validation accuracy of 97.66% and a testing accuracy of 98.60%, with precision, recall, and F1-score values of 0.9860, respectively. The best-performing model was converted into ONNX format and deployed in a web-based prototype for image- and video-based classification. The testing results showed that all core system functions operated as expected, indicating that CornLeafNet has potential as an automatic and practical support model for early corn leaf disease identification.]]>
<![CDATA[Implementasi Sistem Informasi Supply Chain Management (SCM) Berbasis Web pada Franchise Ayam Super Fried Chicken ]]>
<![CDATA[Raafi Muhammad Raafi Juliyanto]]>;
<![CDATA[M. Teguh Prihandoyo]]>;
<![CDATA[Yerry Febrian Sabanise]]>
<![CDATA[ Journal of Applied Informatics Science Volume 2 Issue 2 (2026) ]]>
Publisher : <![CDATA[GWS Tech Solution ]]>
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DOI: 10.65897/jais.v2.i2.103
<![CDATA[The Ayam Super Fried Chicken franchise previously relied on manual, message-based communication to order raw materials from branch partners to the central warehouse, which frequently caused inaccurate stock records, delayed payment verification, and limited real-time visibility for the business owner. This study aims to design and implement a web-based Supply Chain Management (SCM) information system that automates supply-chain coordination and payment validation for the franchise. The system was built using the Laravel framework, a MySQL database, and Tailwind CSS for the interface, and it integrates the Midtrans payment gateway to automatically verify partner payments. The Waterfall model guided the development process, covering requirement analysis, system design using the Unified Modeling Language (UML), implementation, testing, and maintenance, while Black Box Testing was used to validate system functionality. Testing across four functional modules-authentication, transactions and payment, shipment management, and reporting and security-showed that all eleven tested scenarios ran as expected. The resulting system streamlines raw-material procurement between the central office and branch partners, secures financial transactions through automatic payment verification, and provides real-time operational transparency for franchise management through an analytics dashboard and an activity audit log.]]>
