Jurnal LPPM iSTTS
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189 research outputs found
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Comparative Analysis of Neural Network Architecture Optimization: A Study on Genetic Algorithm, Random Search, Grid Search, and Adaptive Search Methods for Digit Classification
Full text linkThis research presents a comprehensive comparative analysis of four neural network architecture optimization methods: Genetic Algorithm (GA), Random Search, Grid Search, and Adaptive Search. Using the MNIST digits dataset, a systematic evaluation was performed based on accuracy, computational efficiency, and architectural complexity. The experimental results demonstrate that the Genetic Algorithm achieved the highest accuracy at 98.33%, while Grid Search demonstrated computational efficiency with the fastest execution time at just 31.06 seconds. Random Search and Adaptive Search showed competitive performance with accuracies of 97.78% and 97.22% respectively, with varying computational requirements. The study revealed that simpler architectures with one or two layers often performed comparably to more complex structures, challenging the common assumption that deeper networks necessarily yield better results. The Genetic Algorithm converged to an optimal single-layer architecture with 119 neurons and ReLU activation, while Adaptive Search explored a more complex three-layer solution. The research identified a non-linear relationship between accuracy gains and computational costs, indicating that substantial increases in computational investment may yield diminishing returns in performance improvement. The convergence patterns of each method provided additional insights, with GA showing steady improvement across generations while Random Search achieved early discovery of good solutions. These findings contribute to both theoretical understanding and practical applications of neural network optimization, offering valuable insights into the trade-offs between methods and practical guidelines for selecting appropriate architecture optimization strategies based on specific requirements for accuracy and computational constraints
Pengembangan Sistem Informasi Estimasi Biaya Proyek Perangkat Lunak Berbasis Function Point
Full text linkPenelitian ini bertujuan untuk mengembangkan sistem informasi yang dapat mengotomatiskan proses estimasi biaya proyek perangkat lunak secara akurat dan efisien. Sistem ini mengadopsi metode Function point Analysis untuk menentukan ukuran fungsional perangkat lunak, kemudian menghitung estimasi biaya berdasarkan kompleksitas setiap komponen yang dinilai oleh manajer proyek. Pengembangan sistem menggunakan bahasa pemrograman VB.NET dengan arsitektur client-server dan database Microsoft SQL Server. Metode waterfall digunakan dalam tahapan pengembangan, memastikan setiap fase, mulai dari analisis kebutuhan hingga pengujian, dilaksanakan secara sistematis. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu menghasilkan estimasi biaya yang lebih akurat dan cepat dibandingkan metode konvensional. Sistem ini juga menyediakan fitur pelaporan komprehensif yang memudahkan manajemen proyek dalam mengambil keputusan yang tepat. Fitur ini meliputi laporan rinci mengenai komponen proyek, estimasi waktu, dan sumber daya yang dibutuhkan. Selain itu, sistem ini mampu menghasilkan harga proyek yang dapat digunakan sebagai dasar penentuan harga penawaran kepada klien, meningkatkan transparansi dan kepercayaan dalam negosiasi proyek. Sistem informasi yang dikembangkan tidak hanya membantu dalam mengelola biaya proyek secara lebih efisien, tetapi juga meningkatkan efisiensi operasional perusahaan secara keseluruhan. Dengan menyediakan estimasi yang akurat dan laporan yang komprehensif, manajemen proyek dapat merencanakan dan mengalokasikan sumber daya dengan lebih efektif. Kesimpulannya, sistem informasi ini dapat menjadi alat bantu yang sangat berguna bagi perusahaan dalam mengelola proyek perangkat lunak, meningkatkan akurasi estimasi biaya, dan mempercepat proses pengambilan keputusan, sehingga perusahaan dapat bersaing lebih baik di pasar yang kompetitif
A Hybrid Machine Learning and Deep Learning Approach for In-Game Assistance
Full text linkThe rapid development of artificial intelligence (AI) has opened new possibilities for enhancing user interaction within video games. This study presents the design and implementation of a button-based assistant system for the simulation game Story of Seasons: Friends of Mineral Town, aimed at simplifying repetitive player tasks and improving the overall gameplay experience. The proposed system leverages a hybrid approach that combines Machine Learning and Deep Learning techniques, specifically Optical Character Recognition (OCR) with Tesseract, object detection using a custom-trained YOLOv7 model, the A* pathfinding algorithm for navigation, and automated input control through scripting. The assistant is capable of reading in-game time, weather, and events directly from screen captures, recognizing non-player characters (NPCs), and automatically directing the player’s character to desired locations or NPCs based on contextual data such as day, time, and weather conditions. A database-driven module stores key information such as NPC schedules, favorite gifts, and daily events to enable informed decision-making and interaction automation. Comprehensive testing was conducted, including comparisons of pathfinding algorithms, model accuracy assessments, and user experience evaluations involving volunteers. Results showed high detection accuracy with YOLOv7 and positive user feedback on the assistant's interface and usability. Users reported a more streamlined and enjoyable gaming experience, especially in managing daily tasks and character interactions. This research demonstrates how a hybrid AI-based approach can be effectively applied to traditional video games, offering a foundation for future development in intelligent game assistance systems. The proposed methodology not only improves convenience but also provides insights into the practical integration of AI in user-centric game design
The Utilization of Virtual Reality Technology for First Aid Learning in Vocational High School Nursing Students: English
No full textThis VR application can be used to train nurses for future professional challenges they may face. The continuous development of VR applications enables trainees to effectively confront real-life simulations and experience increasingly concrete situations, which can be highly important in nursing education. The use of 3D visualization allows for study understanding of various activities and can help prevent potential errors in the future. Nursing education has always utilized simulation-based learning methods, which are widely recognized in nurse education. Skills laboratories have been established in recent years as a form of learning that provides skills and knowledge through repeated practice without requiring placement. This study discusses the utilization of virtual reality technology in first aid training. First aid training using VR technology is expected to increase the effectiveness of learning by at least 20%. The VR application for first aid learning consists of four first aid training modules, including treatment for burns, fractures, bleeding wounds, and breathing difficulties. This VR application was tested on students to determine whether it could improve the effectiveness of first aid learning. Based on the application testing using a respondent questionnaire, it was found that the assessment of knowledge enhancement in learning reached 72.08. Furthermore, this virtual reality application is more effective when used in first aid training, as evidenced by the superiority of the post-test scores of students who learned first aid using virtual reality compared to those who learned first aid without VR, which was 23.4%
Identifying Types of Peanuts Diseases with Naive Bayes Method
Full text linkPeanut plants are susceptible to various constraints that significantly hinder their productivity, with eight prevalent diseases posing serious threats to their health. The peanut plant is one of the important commodities in Indonesia; peanuts play a strategic role in supporting the country's economy and food, where peanuts are a source of protein and a source of vegetable oil. Many farmers, especially those new to peanut cultivation, often lack the necessary knowledge to identify and manage these diseases effectively. To address this gap, this study developed an expert system that employs the Naive Bayes method to facilitate the identification of peanut plant diseases. This system aims to provide farmers with accessible and accurate information regarding symptoms, disease types, and management strategies. The knowledge base for the expert system was constructed from data gathered from peanut farming experts, ensuring the reliability of the information provided. Testing of the system revealed consistent results with manual calculations, particularly in identifying Sclerotium stem rot disease with a probability value of 0.44507. Additionally, the system successfully recognized leaf rust disease during its evaluation. By equipping farmers with a user-friendly tool for disease identification and management, this expert system seeks to enhance their understanding and response to peanut plant diseases, ultimately improving productivity and sustainability in peanut farming. The findings underscore the potential of integrating technology into agriculture to support farmers in overcoming challenges related to crop health
Faktor Pengaruh Peralihan ke Online Learning pada Pegawai Negeri Sipil Berbasis Teori Push Pull Mooring
Full text linkPegawai Negeri Sipil (PNS) diwajibkan untuk terus mengembangkan kompetensi minimal 20 jam per tahun, yang umumnya dilakukan melalui pembelajaran tradisional Pandemi COVID-19 tahun 2020 mengubah paradigma ini dan menjadikan Online Learning sebagai solusi utama akibat pembatasan sosial dan lockdown untuk mengendalikan penyebaran virus. Bahkan, dalam perkembangannya, banyak instansi pemerintah mengadopsi konsep Corporate University untuk mendukung Online Learning, meskipun banyak menghadapi tantangan, seperti wilayah yang luas dan tersebar pada 38 provinsi dan 514 Kabupaten/Kota dan literasi digital yang rendah. Namun, seiring dengan meredanya pandemi, Online Learning menjadi suatu pilihan, bukan lagi keharusan dan pembelajaran dengan Traditional Learning kembali dibuka. Penelitian ini berusaha mengungkap faktor-faktor Pendorong (push) yang memengaruhi PNS untuk meninggalkan Traditional Learning, faktor-faktor yang menjadi Penarik (Pull) yang memengaruhi PNS untuk beralih pada Online Learning, dan faktor Penambat (Mooring) yang memengaruhi PNS untuk tetap menggunakan Traditional Learning atau beralih pada Online Learning. Berdasarkan data dari 463 responden PNS yang pernah menggunakan Traditional Learning maupun Online Learning yang diolah dengan metode analisa Structural Equation Model (SEM) dengan bantuan aplikasi SPSS dan AMOS, dapat diketahui bahwa seluruh variabel dalam Pull Factor secara keseluruhan memengaruhi keinginan berpindah, dan tidak seluruh variabel dalam Push Factor dan Mooring Factor yang memengaruhi keinginan berpindah
Classification of Skin Diseases Using Transfer Learning with ResNet-50 Architecture and Data Preprocessing Using Real-ESRGAN and Wiener Filter
Full text linkThe skin is a vital organ which serves as a barrier against external factors, yet it’s highly susceptible to diseases. These diseases are often presented as lesions with similar appearances, making it difficult to be diagnosed and prone to human errors. To address this challenge, this study uses Deep Learning, particularly the ResNet-50 architecture using Transfer Learning, to classify skin diseases from lesion. In this study, data augmentation is implemented to increase dataset size, thus improving model performance and preventing overfitting. Data is then preprocessed using Real-ESRGAN to enhance resolution and the Wiener Filter to sharpen the features. Adam optimizer is used to further enhance the model’s performance. Hyperparameter tuning is also implemented to optimize the model parameters, and dropout regularization is applied to enhance the model's ability to be able to accurately classify unseen data. The model managed to achieve a high accuracy of 99.09%, with 0.96 precision, 0.95 recall, and 0.95 F1-score. These results demonstrate the effectiveness of combining Real-ESRGAN and Wiener Filter with the ResNet-50 architecture and the Adam optimizer in developing a robust model for skin disease classification. This approach offers a promising tool for healthcare professionals which may help reduce human error in dermatological diagnosis
Pengembangan Sistem Informasi LSP Indohusada Berbasis Web Menggunakan Design Science Research (DSR)
Full text linkLembaga Sertifikasi Profesi Tenaga Jasa Pelayanan Kesehatan Indohusada (LSP Indohusada) menghadapi beberapa kendala dalam mengelola proses uji kompetensi, yang berdampak pada efisiensi dan keakuratan operasional. Kendala-kendala tersebut meliputi: (1) proses pendaftaran yang hanya dapat dilakukan dengan datang langsung ke kantor LSP Indohusada atau melalui Google Forms, hal tersebut kurang praktis bagi calon peserta; (2) penyebaran informasi jadwal uji kompetensi melalui aplikasi WhatsApp, di mana pengiriman pesan harus dilakukan secara manual kepada penerima satu per satu; (3) proses rekapitulasi laporan hasil uji kompetensi yang dilakukan secara manual, sehingga rentan terhadap kesalahan manusia; dan (4) pencatatan logbook asesor yang juga dilakukan secara manual, menyebabkan kurangnya efisiensi dan keterlambatan dalam pengelolaan data. Untuk mengatasi masalah tersebut, penelitian ini menggunakan metode Design Science Research (DSR) dan model pengembangan sistem waterfall. Sistem informasi yang dikembangkan berbasis website menggunakan bahasa pemrograman PHP 8.2.4 dengan framework Laravel 10.23.0, serta Bootstrap 5.3.2 untuk antarmuka pengguna. Sistem ini menggunakan MySQL 10.4.28 sebagai DBMS dan Unified Modeling Language (UML) untuk pemodelan sistem. Hasil dari penelitian ini adalah sistem informasi LSP Indohusada berbasis website yang dirancang untuk 4 aktor utama (guest, admin, asesor, asesi) dengan total 79 fitur yang terstruktur dalam use case diagram, use case description, class diagram, dan sequence diagram. Fungsionalitas sistem diuji menggunakan teknik black box testing dengan hasil 100% pass. Selain itu, pengujian non-fungsional compatibility testing menggunakan alat Testingbot menunjukkan bahwa sistem bekerja dengan baik di berbagai sistem operasi, browser, dan resolusi layar dengan hasil 100% pass. Pengujian keamanan (security testing) menggunakan OWASP ZAP 2.12.0 juga menunjukkan bahwa sistem aman dari potensi serangan cyber. Dengan demikian, sistem informasi ini diharapkan mampu meningkatkan efisiensi dan akurasi dalam proses uji kompetensi yang dikelola oleh LSP Indohusada