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This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

This paper explores the epistemological dimensions of the digital transformation occurring in traditional game development through the integration of machine learning systems. By examining how knowledge creation, validation, and application have evolved in this domain, we identify fundamental shifts in the epistemological frameworks governing game development practices. The research investigates how machine learning has redefined creative processes, technical implementation, and experiential design while challenging traditional notions of authorship, expertise, and knowledge transmission. Through analysis of industry case studies, technological capabilities, and theoretical frameworks, this paper contributes to understanding how machine learning systems are not merely tools but epistemological agents that fundamentally transform how knowledge is generated, validated, and utilized in game development ecosystems.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

The advancement of information technology has had a significant impact across various sectors, including healthcare. The digitalization of healthcare services has become a solution to improve efficiency, effectiveness, and accessibility for the public. Puskesmas Selindung still uses a manual patient registration system, which leads to several issues such as long queues, extended waiting times, and the risk of lost or damaged patient records. Based on visit data, the number of patients coming to Puskesmas Selindung has increased each year. Therefore, a digital queue system is needed to optimize the patient registration process. This research aims to analyze and design an Android-based patient registration queue application to improve service efficiency at Puskesmas Selindung. The research methods include system requirements analysis, user interface design, and the development of core features to support the online patient registration process. The implementation of this application is expected to reduce long queues, speed up administrative processes, and make it easier for patients to access healthcare services more effectively and accurately. With this Android-based system, the quality of healthcare services at Puskesmas Selindung is expected to improve significantly.

PAMTIRTA Tempino is an institution that provides clean water services in the Tempino area. The process of recording water use and monitoring water turbidity is still done manually, making it prone to recording errors and making it difficult to monitor the water quality distributed to the community. This study aims to design a website-based water turbidity recording and monitoring system by focusing on User Interface (UI) and User Experience (UX) aspects using the Design Thinking method. The research follows five stages of Design Thinking: empathize, define, ideate, prototype, and test. Data collection involves observation and in-depth interviews with PAMTIRTA officers. The results include a design with key features such as digital water meter recording, turbidity monitoring dashboards, and complaint services. The prototype was tested using Maze and the System Usability Scale (SUS), achieving a score of 80.1 and falling into the "Good" category (grade B). These results demonstrate that the UI/UX design effectively provides an easy-to-understand, operationally suitable, and efficient solution for PAMTIRTA Tempino's water recording and turbidity monitoring needs. This design offers a ready-to-implement solution to improve the efficiency, accuracy, and quality of clean water services in the Tempino area.

Barru Regency tourism has a variety of tourist attractions, but information related to the location and potential of tourism has not been digitally integrated, making it difficult for tourists and the government to access data. This study aims to design and build a web-based Geographic Information System (GIS) that can map the location of tourist attractions in Barru Regency interactively, easily accessible, and equipped with supporting information in the form of descriptions, types of tourism, photos, and travel routes. The research method used is Research and Development (R&D) with the stages of tourist attraction surveys, interface design, feature development, and system testing. The results of the study are in the form of a website "Web-Based Geographic Information System for Mapping Tourist Attractions in Barru Regency" which is able to present tourist information systematically and easily understood. The conclusion of this study shows that the developed system can be a supporting medium in disseminating tourism information, helping tourists find tourist locations, and supporting the local government in managing and developing the tourism sector based on spatial data. This application also has the potential to be an educational and promotional tool to increase tourist visits to Barru Regency

Knowledge must be managed effectively to facilitate transfer between individuals, groups, and organizations. The Informatics Engineering study program currently lacks a system for knowledge management. Currently, the study program facilitates offline discussion forums for the sharing of knowledge gained by lecturers and students. These offline discussion forums require significant costs, time, and space, often resulting in delays in knowledge sharing. This research focused on the analysis and design of a Knowledge Management System to meet the needs of Informatics Engineering students at Universitas PGRI Madiun. The system development method used was the Knowledge Management System Lifecycle (KMSL). In this study, the TIF KMS system using the KMSL method has been successfully built. The results of testing using the Blackbox Testing method showed that 5 scenarios and 18 cases were successfully executed as expected with a 100% success rate. Based on the system test results, the TIF KMS is ready to proceed to the implementation stage. Future implementation can be done by developing additional features such as a digital library

Penelitian ini bertujuan merancang sistem informasi penjualan berbasis web untuk meningkatkan efektivitas pencatatan transaksi pada Toko Bangunan Subur Kaliwungu yang sebelumnya masih dilakukan secara manual. Permasalahan utama yang dihadapi meliputi pencatatan yang tidak efisien, kesulitan pencarian data, serta kurangnya ketepatan dalam laporan keuangan. Metode penelitian yang digunakan adalah kualitatif dengan pendekatan Research and Development (R&D) menggunakan model Prototype. Sistem dikembangkan dengan menerapkan metode akuntansi berbasis Akrual (Accrual Basis) agar laporan keuangan dapat disusun lebih akurat dan real time. Hasil penelitian menunjukkan bahwa sistem ini mampu mencatat transaksi penjualan dan pembelian, mengelola data barang dan pelanggan, serta menyajikan laporan penjualan dan laporan keuangan secara digital. Fitur tambahan seperti histori transaksi, informasi stok barang, serta perhitungan laba rugi juga membantu pemilik toko dalam pengambilan keputusan. Implementasi sistem memberikan dampak positif, di antaranya peningkatan efisiensi, keakuratan data, dan kemudahan monitoring aktivitas penjualan.

Infeksi Saluran Pernapasan Akut (ISPA) merupakan masalah kesehatan utama di Kabupaten Tegal, terutama menyerang anak-anak dan lansia, dengan 25.273 kasus tercatat pada Januari–Juni 2025. Faktor seperti kepadatan penduduk, polusi udara, dan sanitasi buruk mempersulit analisis konvensional dalam mengidentifikasi wilayah berisiko tinggi. Penelitian ini bertujuan mengembangkan Sistem Informasi Geografis (SIG) berbasis web untuk memetakan dan memantau kasus ISPA secara akurat menggunakan pendekatan spasial. Metode Waterfall digunakan dalam pengembangan sistem, meliputi analisis kebutuhan, perancangan, implementasi, pengujian, dan pemeliharaan. Data spasial (koordinat lokasi) dan non-spasial (usia, jenis kelamin, jumlah kasus) dikumpulkan dari Dinas Kesehatan dan dianalisis menggunakan teknik kepadatan titik dan klaster untuk mengidentifikasi pola sebaran. Sistem ini dibangun dengan framework Laravel sebagai backend dan Leaflet.js untuk visualisasi peta interaktif. Hasilnya, SIG ini mampu memvisualisasikan sebaran kasus ISPA per kecamatan, mendukung Dinas Kesehatan dalam mengalokasikan sumber daya medis secara efisien dan mengidentifikasi wilayah prioritas seperti Dukuhwaru dan Tarub. Bagi masyarakat, sistem ini meningkatkan akses informasi digital, mendorong kesadaran pencegahan, dan mendukung edukasi kesehatan. Sistem ini terbukti efektif sebagai alat strategis untuk pengambilan keputusan berbasis data, mempercepat respons terhadap ISPA, dan menjadi fondasi untuk pengembangan SIG dalam memantau penyakit lain di masa depan.

Digital transformation in government bureaucracy has become a strategic step to improve efficiency, accountability, and transparency, including in the management of regional legal products. In the Regional Secretariat of Tegal Regency, the process of submitting legal products is still done manually, which causes inefficiencies, delays, and a lack of document traceability. To address these issues, a web-based legal product submission application has been developed with document tracking features. This research uses the Waterfall system development method, and implements a FIFO (First In First Out) queuing system in the submission process, along with Role-Based Access Control (RBAC) for managing user access rights. The goal of this system is to create a faster, more transparent, and digitally documented application process. The implementation results show that the application is able to systematically and integratively manage the flow of submissions, corrections, verification, and the ratification of legal products. Features such as a Login page with CAPTCHA, analysis dashboards, tracking, and monthly reports enhance the monitoring and security functions of the system. This application can be an effective solution in supporting the digitization of regional legal bureaucracy, as well as providing ease and efficiency for regional officials in preparing and submitting drafts of legal products digitally.

Terrestrial digital television transmitter stations are the main facilities in the transmission of digital television broadcasts to the public. The quality of the transmitted signal is expected to reach the Central Java-1 service area well so as to provide optimal and reliable quality of digital television broadcast performance according to the needs of the community, but currently, complaints about signal problems such as service coverage and reception quality still occur a lot, coverage and signal quality received by community-owned television transmitters cannot be separated from the influence of the quality performance of digital television transmission stations. The purpose of this research is to analyse the coverage performance of digital television services of digital television transmitter stations using the K-Means Clustering Method, identify areas with the best signal coverage and group areas based on the level of signal performance. The data used includes field strength parameters collected through field measurements at 25 service area location points, topography factors and transmitter distance were found to be the main causes of signal quality differences. Data analysis shows that the K-Means Clustering method effectively clusters areas with signal reception quality categories of very good cluster 3 areas, good cluster 8 areas, fair cluster 5 areas and poor cluster 9 areas. The results of this study can be recommended in the evaluation and optimisation of tele-transmitting station networks.

The coffee farming sector in Gunungmanik Village, Indonesia, plays a significant role in the local economy. However, the monitoring and management of coffee crops remain largely manual and conventional, making it difficult for farmers to respond quickly to environmental threats such as drought, pests, or sudden temperature shifts. This research presents the development of iotgm.id, a web-based monitoring system integrated with Internet of Things (IoT) devices designed to provide real-time environmental data for coffee plantations. The system measures key parameters including temperature, soil moisture, and motion detection (as a proxy for pest activity), and delivers this data via a user-friendly web interface. It also features digital farm record management, real-time alerts for abnormal conditions, and data visualization through interactive dashboards. Field testing with local farmers showed that the system improves decision-making, speeds up responses to environmental changes, and reduces the need for direct field visits. Unlike earlier systems that often required technical expertise or focused on single parameters, this system offers multi-parameter monitoring and is accessible to farmers without advanced digital literacy. The system bridges the gap between sophisticated agricultural technologies and practical field-level application. It contributes to the adoption of precision agriculture in rural areas, offering a scalable model for broader implementation in similar contexts

The digital revolution has transformed retail industry into a new era full of opportunities through e-commerce but also presenting challenges in online furniture sales. Due to their inability to precisely measure and see furniture in their spaces, many customers are hesitant to make purchases. Augmented Reality (AR) offers solutions through realistic visualizations, but it’s costly and has a lot of accessibility problems. WebAR solves these issues by delivering AR features directly on mobile browsers without requiring additional apps. This study investigates the impact of WebAR innovation on e-commerce furniture by examining the roles of interactivity (IN), vividness (VI), augmentation (AU), customization (CT), and ease of access (EA) on purchase intention (PI), mediated by spatial presence (SP), decision comfort (DC), and satisfaction (SF). Using the Stimulus-Organism-Response (SOR) model, data from 100 respondents were analyzed with the PLS-SEM algorithm using SmartPLS 4.0. The findings tell us that WebAR significantly enhances consumers’s shopping experience and ultimately boosts purchase intention.

The insurance business in an insurance company offers insurance products owned by insurance companies. There are many transactions such as the purchase of insurance products and the application of disbursement of insurance benefits to customers, so that disputes occur in the reports generated every month and this does not become effective and time-efficient as needed. This study aims to evaluate the effectiveness of a website-based real-time insurance transaction reporting monitoring system. This research method involves the development of a web-based system designed to monitor and report insurance transactions directly, as well as the evaluation of system performance using quantitative and qualitative approaches. The research stages include needs analysis, system design and development, implementation, and system testing and evaluation in insurance companies. The results of the study show that a website-based system can facilitate evaluation Monitoring the results of reports on ongoing transactions, so that reports every month can be formed digitally through the system that has been created.  

State Senior High School 7 Surakarta is one of the public schools located in Surakarta City. The library activities, including member data management, book processing, and book borrowing and returning, are still conducted manually using physical logbooks. This manual process is considered inefficient and prone to errors. The purpose of this study is to develop a book borrowing system at State Senior High School 7 Surakarta that serves as a tool to assist officers in recording, loans, and returning books. This system is designed using the CodeIgniter framework to support WEB displays, programming in PHP, and using MySQL for database management. This system is created using the System Development Life Cycle (SDLC) method with a waterfall model that includes the stages of analysis, design, implementation, testing, and maintenance. System testing was conducted using Black-Box Testing and the System Usability Scale (SUS). The Black-Box Testing results showed that all features and functions operated correctly. The SUS evaluation produced a score of 75.68%, indicating that users generally agreed with the implementation of the system, which falls under the "acceptable" classification.

The digital transformation in education has encouraged the adoption of computer-based tests (CBT) using video content, which demands stable and efficient network performance. This study aims to evaluate the performance of two queue management algorithms, namely Random Early Detection (RED) and Per Connection Queue (PCQ), in maintaining the quality of service (QoS) of school networks during online video-based examinations. A case study approach was applied using a real network topology in a school environment, and QoS parameters such as throughput, delay, packet loss, and jitter were measured. The implementation was conducted using a MikroTik RB450Gx4 router configured with simple queue settings for each algorithm. The results show that PCQ provides more consistent performance under high user loads, achieving an average throughput of 56,482 bps and lower delay compared to RED. Conversely, RED performs better in scenarios with a small number of users. The study recommends using PCQ for networks with dynamic and dense user environments, while RED is more suitable for low-traffic conditions where latency stability is crucial. These findings offer practical guidance for managing bandwidth and improving the quality of CBT delivery in educational settings.

Penerapan konsep kota cerdas menjadi arah strategis dalam mengelola kompleksitas urbanisasi sekaligus meningkatkan kualitas sumber daya manusia agar lebih adaptif dan kompetitif. Namun, dalam praktiknya, masih ditemukan berbagai permasalahan seperti keterbatasan infrastruktur digital, rendahnya literasi teknologi masyarakat, serta belum optimalnya integrasi teknologi dalam tata kelola kota. Kesenjangan ini dapat menghambat upaya membangun SDM yang berdaya saing tinggi. Studi ini bertujuan untuk menganalisis bagaimana teknologi kecerdasan buatan dapat mendukung transformasi kota cerdas dalam kerangka peningkatan daya saing manusia. Metode yang digunakan adalah pendekatan deskriptif berbasis studi literatur, dengan fokus pada pemetaan peran kecerdasan buatan dalam mendukung pilar smart city: smart governance, smart economy, smart people, smart mobility, smart environment, smart society, dan smart living. Hasil kajian menunjukkan bahwa pemanfaatan kecerdasan buatan mampu mendorong efisiensi layanan publik, memperkuat sistem pendidikan berbasis personalisasi, mendukung analitik data untuk pengambilan keputusan, serta menciptakan ekosistem inovatif yang merangsang pengembangan kompetensi SDM. Selain itu, sinergi antara pemerintah, sektor industri, dan lembaga pendidikan menjadi kunci dalam memastikan bahwa transformasi digital yang dilakukan mampu menjangkau seluruh lapisan masyarakat secara adil dan berkelanjutan. Dengan pemanfaatan kecerdasan buatan secara inklusif dan etis, kota cerdas tidak hanya menjadi solusi teknologi, tetapi juga platform strategis dalam mencetak manusia yang unggul dan siap bersaing dalam era global.

Technological advancements have brought fundamental changes in the way we interact with digital images and photography. One significant milestone in this development is the Photoshop Express Photo Editor, which has become a primary platform for image processing and editing. Datasets are used to analyze sentiment and are utilized during the accuracy testing phase. Based on the testing results, the Convolutional Neural Network (CNN) algorithm achieved an average accuracy value of 86.50%, compared to the Naïve Bayes (NB) algorithm, which achieved an average accuracy value of 75%. The results of the research conclude that the choice of sentiment analysis method should be tailored to the needs and limitations of the system. If a fast, light, and easy-to-understand process is required, the Naive Bayes method is the right choice. However, if accuracy and context understanding are the top priorities, then CNN is a superior approach, although it requires more resources. Additionally, based on the Wordcloud data, it is known that the majority of comments are positive, indicating that the reviews or texts analyzed contain many positive expressions related to quality, usability, and ease of use.