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Traditional association rule analysis is effective at uncovering co-purchase patterns but fails to provide a global structural view of the market, which often results in fragmented and isolated insights. This study proposes a hybrid framework that integrates the Apriori algorithm with a Minimum Spanning Tree (MST) in order to validate and contextualize association rules within a single structural backbone. Transaction data from a retail store are transformed into a weighted, undirected product graph using an inverse-support function, and an MST is then extracted to represent the market backbone, while frequent itemsets and strong rules are obtained using Apriori. Experimental results on 236 multi-item transactions show that the MST backbone comprises 10 products and 9 fundamental links, with 66.67% of these links being confirmed by strong association rules, indicating a substantial coherence between statistical and structural evidence. The proposed model identifies 41 Apriori patterns that can be embedded in the MST and ranks them using a new metric, Structural Distance, which enables the categorization of Core Patterns, Bridge Patterns, and Complex Patterns according to their structural tightness. This hybrid perspective distinguishes dense, strategically meaningful bundles from anomalous but frequent combinations that are structurally peripheral, thereby offering a more holistic and actionable alternative to conventional Market Basket Analysis. The validated framework can support various applications, including store layout optimization, cross-selling strategies, and the design of path-based recommender systems, and it opens avenues for future extensions based on dynamic graphs and Graph Neural Networks.

Penelitian ini bertujuan untuk merancang dan merealisasikan antena mikrostrip patch berbentuk segitiga (triangular) yang bekerja pada frekuensi 1800 MHz, sebagai penguat jaringan internet di kawasan Kebun Raya Balikpapan. Lokasi tersebut diketahui memiliki kualitas sinyal yang kurang baik untuk beberapa provider. Antena yang dirancang menggunakan dua konfigurasi, yaitu MIMO 8x8 dengan teknik pencatuan langsung dan array 8x1, yang kemudian disimulasikan menggunakan CST Studio Suite 2019 dan diuji performansinya menggunakan metode Speedtest. Hasil simulasi menunjukkan bahwa antena 8x8 memiliki nilai VSWR sebesar 1,50, return loss -12,96 dB, dan gain 4,06 dBi dengan pola radiasi omnidirectional. Sementara itu, konfigurasi 8x1 array menunjukkan nilai VSWR 1,24, return loss -19,21 dB, dan gain 6,46 dBi, juga dengan pola radiasi omnidirectional. Hasil pengujian di lapangan dengan tiga kondisi tanpa antena eksternal, antena MIMO dan antena array 8x1 didapatkan hasil bahwa antena array 8x1 memberikan kecepatan unduh tertinggi mencapai 17,227 Mbps, unggah 4,072 Mbps, Jitter 34,9 ms  dan packet loss 14,18 % sedangkan antena MIMO 8x8 memberikan kecepatan unduh tertinggi mencapai 13,77 Mbps, unggah 2,462 Mbps, Jitter 49,9 ms  dan packet loss 18,43%. Kesimpulannya, kedua jenis antena eksternal yakni MIMO 8x8 dan array 8x1 mampu meningkatkan performa jaringan secara signifikan dan dapat menjadi solusi efektif untuk daerah dengan sinyal lemah. Antena ini diharapkan dapat memberikan kontribusi terhadap akses internet yang lebih stabil di area public yang sangat luas.

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.

The problems in rice fields are complex and varied, depending on geographic location, rice variety, and growing season. Pests often cause serious economic losses. The Solar Sonic Repeller is an innovative portable pest control device designed to address pest problems by utilizing renewable energy, specifically solar energy. This product aims to offer an environmentally friendly and efficient solution. It works by emitting ultrasonic sound waves with a frequency of 30,000–40,000 Hz. The device's advantages lie in its portability and energy independence, thanks to the use of a charging module powered by an integrated photovoltaic (PV) panel with automatic battery charging during the day. The first test measured the output frequency using an oscilloscope to verify that the oscillator circuit produced waves at the specified frequency. The second test measured the device's effectiveness by examining the pest response to the device at various distances. This test was effective within a maximum radius of approximately 14 m from the center point, covering a rice field area of ​​250 m2.

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.

The manual employee attendance process at the Perjuk Village government level often results in inaccurate data, delayed recapitulation, and difficulties in real-time attendance monitoring. This study aims to develop an Internet of Things (IoT) and Radio Frequency Identification (RFID)-based village employee attendance system to simplify the administrative process and improve the efficiency of attendance recording. The development method used is the Research and Development (R&D) model with stages including needs analysis, system design, validation, field trials, and product revisions. The system was built using an ESP32 microcontroller, an RC522 RFID module, and a Wi-Fi connection to transmit attendance data to a web-based server. Testing was conducted using the black box method to ensure all system features run according to design. The results of the black box test show that all features run according to design. The system records attendance automatically with 100% accuracy, saves data to the server database, and displays reports in the form of tables, graphs, and statistical cards. The study concludes that this IoT and RFID-based attendance system is able to improve the accuracy, speed, and efficiency of recording compared to manual methods, and is in accordance with operational needs at the Perjuk Village Office.

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.

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.

Penelitian ini bertujuan untuk menganalisis optimasi parameter Photovoltaic (PV) serta daya keluaran dari pembangkit listrik tenaga surya berbasis Genetic Algorithm. Penelitian ini menggunakan metode pengumpulan data melalui studi literatur, simulasi perangkat lunak, serta pengujian model matematis. Penelitian ini menggunakan pendekatan analisis kuantitatif dan komputasional yang berfokus pada pencarian nilai optimal dari parameter-parameter utama sistem PV, seperti tegangan, arus, dan resistansi. Hasil penelitian menunjukkan bahwa dalam proses optimasi sistem PV, tahapan yang dilakukan meliputi pemodelan karakteristik PV, penyusunan fungsi objektif untuk memaksimalkan daya, penerapan algoritma genetika dalam mencari nilai parameter optimal, serta analisis hasil optimasi. Hasil penelitian memperlihatkan bahwa algoritma genetika mampu meningkatkan efisiensi daya keluaran PV dibandingkan metode konvensional, serta menghasilkan parameter yang lebih sesuai terhadap kondisi iradiasi tertentu. Namun, dalam implementasi metode ini juga ditemukan beberapa kendala, seperti kompleksitas perhitungan dan kebutuhan waktu komputasi yang relatif tinggi. Selain itu, diperlukan penyesuaian lebih lanjut untuk penerapan algoritma ini dalam skala sistem pembangkit yang lebih besar.

Diabetes Mellitus merupakan salah satu penyakit kronis yang jumlah penderitanya terus bertambah setiap tahunnya, termasuk di wilayah Puskesmas Brebes. Banyaknya pasien dengan kondisi klinis yang beragam mendorong perlunya suatu metode untuk mengelompokkan pasien berdasarkan tingkat keparahannya. Penelitian ini bertujuan untuk menerapkan algoritma K-Means dalam proses pengelompokan pasien Diabetes Mellitus dengan menggunakan beberapa parameter klinis, yaitu Gula Darah Puasa (GDP), kadar HbA1c, Kolesterol Total (CHOL), serta tekanan darah sistolik dan diastolik. Pendekatan yang digunakan dalam penelitian ini adalah deskriptif kuantitatif dengan metode data mining berbasis algoritma K-Means. Data yang digunakan diperoleh dari rekam medis Puskesmas Brebes. Proses klasterisasi menghasilkan tiga kelompok, yaitu kategori risiko rendah, sedang, dan tinggi. Hasil penelitian menunjukkan bahwa algoritma K-Means mampu melakukan pengelompokan data pasien secara akurat sesuai tingkat keparahan. Hasil tersebut kemudian divisualisasikan melalui sistem berbasis web yang bertujuan untuk mempermudah pihak puskesmas dalam menganalisis kondisi pasien serta mendukung pengambilan keputusan medis yang lebih efektif.

Mental health is a crucial aspect of modern life, with stress and anxiety being among the most common and impactful psychological disorders. This research proposes a stress and anxiety monitoring system based on the Internet of Things (IoT), integrating biometric sensors and Deep Neural Networks (DNN) for early detection and in-depth analysis. The system is designed using MAX30102 (heart rate and SpO₂), GSR (Galvanic Skin Response), and DS18B20 (body temperature) sensors, managed by an ESP32 microcontroller and communicating through the MQTT protocol. Physiological data is collected in real-time, formatted in JSON, and transmitted to both Android and web-based applications for visualization. The DNN model is developed using the TensorFlow framework with a layered architecture and ReLU activation functions to classify four mental states: relaxed, calm, anxious, and highly stressed. The training dataset comprises both primary and secondary data, including the WESAD dataset. Model performance is evaluated through k-fold cross-validation, showing high accuracy and strong generalization capabilities. The results indicate that the integration of sensor technology and deep learning significantly improves the effectiveness of stress and anxiety detection compared to traditional methods. This system demonstrates great potential for the development of AI-based wearable devices for autonomous, real-time, and adaptive mental health monitoring.

The food security program involving chili cultivation in Pentur Village is hindered by inefficiencies in water use and suboptimal plant growth, primarily due to traditional irrigation methods that fail to consistently maintain ideal soil moisture and temperature. This issue is exacerbated by unpredictable environmental shifts, such as fluctuating weather patterns, and a lack of precise irrigation control stemming from technological limitations. To address this, a system for monitoring and regulating chili plant irrigation using IoT technology was developed. This system employs humidity and temperature sensors connected to an IoT platform like Blynk, enabling real-time observation of plant and environmental conditions. Data on soil moisture, air temperature, and humidity are stored in a database, and irrigation is automated based on soil moisture levels. The goal is to enhance water efficiency, minimize risks associated with over or under-watering due to environmental variations, and improve both yield and quality of the chili crop. This IoT-based system aims to simplify chili plant management for Pentur Village farmers and significantly boost agricultural output.

SMA Negeri 1 Baturiti merupakan salah satu instansi pendidikan di bawah naungan Dinas Pendidikan Kabupaten Tabanan yang memiliki berbagai aset penunjang kegiatan belajar mengajar. Proses pelaporan aset selama ini masih dilakukan secara manual menggunakan Microsoft Word, sehingga kurang efisien dan sering mengalami keterlambatan dalam pelaporan kepada Dinas Pendidikan. Untuk mengatasi permasalahan tersebut, dirancang dan dibangun sistem dengan judul Optimalisasi Pengelolaan Inventaris Sekolah Melalui Sistem Inventory Berbasis Web di SMA N 1 Baturiti. Sistem ini dikembangkan menggunakan bahasa pemrograman PHP dan database MySQL, serta dilengkapi fitur pencetakan laporan dengan PHP Word dan PHP Excel. Sistem ini mempermudah proses input data aset dan menghasilkan laporan yang lebih cepat, akurat, dan terstruktur. Dengan adanya sistem ini, diharapkan pelaporan aset sekolah kepada Dinas Pendidikan dapat dilakukan secara lebih tepat waktu dan efisien.

When producing a perfect product, design process namely 2D modeling and 3D drawing, are closely related. The purpose of this study is to design a 3D model of a gun mastering machine product as a fan drive using CAD applications. The method of this study is decision data on API production (analytical product inspection). Measurement of sterling machine degradation using CPK (deviation) received results of -0.97, -0.22, 0.86, 0.11, 0.11 between zero and what indicates this specification. On average, the results of the stirling fluid fluorescent agent measurement are red numbers indicating that the survey dimensions are absent, especially from the basic dimensions of point 4, with samples 2, 3, and 5 exceeding the specified tolerance limits.

Intuisi Coffee, sebuah kafe di Blang Kolak, Aceh Tengah, masih mengelola proses pemesanan dan transaksi secara manual, yang berdampak pada kurangnya efisiensi dalam pencatatan, pengelolaan stok, dan penyusunan laporan. Penelitian ini bertujuan untuk mengimplementasikan sistem informasi berbasis web guna meningkatkan efisiensi operasional kafe. Pengembangan sistem dilakukan menggunakan metode Waterfall dengan memanfaatkan XAMPP, PHP, dan MySQL sebagai teknologi utama. Evaluasi sistem menggunakan System Usability Scale (SUS) untuk mengukur tingkat kegunaan dari sisi efektivitas, efisiensi, dan kepuasan pengguna. Hasil evaluasi menunjukkan skor SUS sebesar 64,9, yang mengindikasikan sistem berada pada kategori kegunaan yang cukup baik. Implementasi sistem ini terbukti dapat mempercepat proses transaksi, mempermudah pengelolaan data, dan mendukung pengambilan keputusan secara lebih terstruktur

One of the museums found in Bali is Subak museum in Tabanan Regency. The establishment of this Museum aims to preserve the traditional institution called Subak as a noble national cultural heritage, and to introduce to the younger generation and foreign tourists about the unique traditional irrigation system in Bali. Lack of supporting facilities in delivering information, making tourists who visit Subak Museum have minimal information. The solution needed in Subak museum is an Android-based E-Tourism application that is supported by Qr-Code technology so as to provide an interesting presentation of data. In making this application using the development of the Waterfall method and designed using the Unified Modeling Language (UML) with the Java programming language for Android and PHP for web services, and MySQL as a database. So that produced an android-based system in Subak museum Tabanan Regency. Based on the results of the tests carried out it can be concluded that the black box testing system functions have been running according to the planning. Then the result of the testing of users using questionnaires generated Application Benefits scores 90.8%, Ease of 86.93%, Interface 82% and Content 90.2%. 

This study examines the role of Artificial Intelligence (AI) in marketing strategy through a Systematic Literature Review (SLR) approach. AI has shown a transformative impact by outperforming traditional methods, especially in optimizing big data-based marketing strategies. With the ability to analyze consumer behavior in depth, AI enables businesses to personalize marketing efforts and improve user experiences more efficiently and responsively. However, challenges such as data privacy, high initial investment, and reliance on data quality remain major concerns that must be addressed. This study also evaluates the effectiveness of using AI across marketing channels, which shows significant differences in their impact on business strategy. In addition, the integration of AI-based fitness equipment is considered to have a major contribution in increasing consumer satisfaction while driving online business growth. The results of this study provide valuable insights into the effective implementation of AI, as well as highlight the importance of maintaining data security and implementing AI strategically to provide optimal benefits for consumers and business development.

Traditional doors usually consist of a housing or padlock and a flip key to open it. Usually found on office or mall doors, doors can be made more practical. The door will open automatically if there is physical energy (stimulus) that moves it. For example, when someone wants to enter the room, the door will automatically open. Doors like this are designed using automatic control using embedded system equipment. This automatic door system can be designed using automatic control combined with PIR sensors and servo motors. In terms of input equipment, a PIR (Passive Infrared Receiver) sensor is used which will detect humans approaching the door. This PIR sensor will send a signal to the Arduino process unit which contains a microcontroller chip. This microcontroller will send the processed data to the servo motor, so that it can open or close the door automatically.

SION (Sistem Informasi Online) ITB STIKOM Bali is a platform used by students and lecturers to support administrative and academic processes on campus. This research examines how the user experience (UX) level of SION can enhance the quality of services provided by the system. The purpose of this study is to measure user satisfaction with the user experience quality. The study uses the User Experience Questionnaire (UEQ) method, which includes six rating scales: attractiveness, clarity, efficiency, accuracy, stimulation, and novelty. The UEQ method also offers analytic tools that can be downloaded from its official website. Respondents in this study consisted of 75 people from a population of 248. The results of the UEQ analysis show positive evaluations across all aspects, with the following detailed scores: attractiveness received a score of 1.37, perspicuity 1.34, efficiency 1.16, dependability 1.21, stimulation 1.16, and novelty 0.87.

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.