Penelitian ini bertujuan untuk merancang sistem monitoring deteksi dini kebakaran pada ruang server. Kebakaran merupakan ancaman serius yang dapat mengakibatkan kerusakan fatal pada peralatan di ruang server dan potensi kehilangan data pada storage. Untuk menunjang penelitian yang memanfaatkan otomatisasi dan hal yang bersifat real time, maka diperlukan komunikasi antara sensor-sensor dan software yang juga disebut teknologi Internet of Things (IoT). Dalam penelitian ini, sensor-sensor yang dimaksud yaitu Flame Sensor yang berguna sebagai pendeteksi radiasi dari nyala api, Sensor MQ 2 sebagai  pendeteksi asap dan Sensor DHT 11 untuk mendeteksi perubahan suhu yang  signifikan. Untuk mempermudah dalam pemantauan, juga di lengkapi dengan laman web server yang dapat diakses dengan mudah serta menggunakan beberapa output, antara lain buzzer sebagai sirine bahaya, pilot lamp untuk memberikan sinyal visual adanya suhu yang tidak normal serta outpur exhaust fan untuk membantu sirkulasi udara di dalam ruang server. Implementasi dalam rancang bangun sistem monitoring ini diharapkan dapat membantu memastikan performa server dalamkondisi yang selalu optimal dan mengurangi resiko kebakaran yang lebih fatal serta dapat mengurangi beban kerja staf pengelola di Dinas Kependudukan dan Pencatatan Sipil Kabupaten Semarang.

Effective drug inventory management is essential for hospital pharmacy management to prevent stock shortages and waste due to expiration. Bina Kasih Hospital faces obstacles such as manual recording and reporting difficulties. This study designs a web-based drug inventory information system with waterfall methodology, using PHP, MySQL, and Bootstrap interface. This system allows real-time data access, stock tracking, automatic notification, and data integration.
Testing using black-box and user acceptance testing showed increased operational efficiency, with a reduction in recording errors of up to 80% and an acceleration of reporting of up to 50%. This system supports auditing, reporting, and data integration. This solution is expected to optimize Bina Kasih Hospital's pharmacy management, with the potential for further development for integration with electronic medical record modules and procurement systems.

This study aims to describe teachers' strategies in improving reading skills through the implementation of PJBL in Indonesian language learning. This study uses a descriptive qualitative approach with the research subject being a grade 1 teacher at SDN Kedung Rawan II Krembung. The data collection techniques used in the research are observation, interviews, and documentation. The data obtained is then analyzed through several stages, including data collection, data condensation, data presentation, and data verification. The results showed that teachers used eight main strategies to improve initial reading skills, including understanding reading concepts, using reading techniques, and individualized support. In addition, to improve the character of reading enthusiasts, teachers integrate innovative and creative literacy activities in the learning process. The results of this study are expected to provide practical guidance for teachers in implementing PJBL learning to develop reading skills and reading interest effectively.

This research focuses on optimizing the speed of Big Data processing using Artificial Intelligence (AI) in healthcare applications. The study integrates Random Forest (RF) and Deep Learning (DL) algorithms with cloud-based computing systems to improve data processing efficiency. The dataset includes both structured data, such as Electronic Health Records (EHR), and unstructured data, like medical images. The results show that RF performs better with structured data, achieving a lower Mean Squared Error (MSE) and higher R-squared (R²) than traditional methods. Meanwhile, DL achieves superior accuracy and Area Under the Curve (AUC) in processing unstructured data. By utilizing the distributed computing power of Spark on a cloud platform, the processing speed was significantly enhanced, as demonstrated by a statistically significant reduction in processing time (p < 0.05) observed through a t-test analysis comparing Spark-based computing with traditional methods. Despite these improvements, challenges such as data privacy and infrastructure costs remain. Despite these improvements, challenges such as data privacy and infrastructure costs remain. This research provides a robust framework for real-time healthcare data analysis, highlighting its potential to improve decision-making processes and patient outcomes in medical services.

The types of personality disorders often associated with criminal behavior in prisoners are antisocial personality disorder and narcissistic personality disorder. Antisocial Personality Disorder is a personality disorder characterized by a pattern of indifference and violation of the rights of others, which usually begins in childhood or early adolescence and continues into adulthood. Meanwhile, Narcissistic Personality Disorder (NPD) is a psychological disorder characterized by patterns of excessive superiority, fantasies of power or extraordinary importance, and the need for admiration or special treatment. The method in this study uses a descriptive method with literature study data collection techniques involving the study of theories and references related to values, culture, and norms that apply in the social context under study. Literature study is an important step in research because research always depends on scientific literature. Literature study was conducted for data analysis in this research. Research data was obtained from relevant sources, such as books, journals, articles, and previous research, through literature study.

This research focuses on optimizing the speed of Big Data processing using Artificial Intelligence (AI) in healthcare applications. The study integrates Random Forest (RF) and Deep Learning (DL) algorithms with cloud-based computing systems to improve data processing efficiency. The dataset includes both structured data, such as Electronic Health Records (EHR), and unstructured data, like medical images. The results show that RF performs better with structured data, achieving a lower Mean Squared Error (MSE) and higher R-squared (R²) compared to traditional methods. Meanwhile, DL achieves superior accuracy and Area Under the Curve (AUC) in processing unstructured data. By utilizing the distributed computing power of Spark on a cloud platform, the processing speed was significantly enhanced, as demonstrated by a statistically significant reduction in processing time (p < 0.05) observed through a t-test analysis comparing Spark-based computing with traditional methods. Despite these improvements, challenges such as data privacy and infrastructure costs remain. Despite these improvements, challenges such as data privacy and infrastructure costs remain. This research provides a robust framework for real-time healthcare data analysis, highlighting its potential to improve decision-making processes and patient outcomes in medical services.

Currently, the Amikom University Purwokerto student portal is still not equipped with a student data search feature that can make it easier to access related information. The unavailability of this feature can provide significant obstacles to various students' daily activities, especially in activities such as academic research, collaborative group work across study programs, and interactions between departments. The aim of this research is to apply the TOGAF framework which is expected to be able to help identify the root of the problem and provide a more comprehensive, systematic and effective solution in an effort to improve the functionality of this portal, so that it can better support students' academic needs. This research uses the TOGAF method which includes Understanding Business Context, Vision Architecture, Definition Architecture, and Transition Architecture. The result of this research is a flowchart design for the student data search feature which was created and analyzed using the Bizagi Modeler application which was compared between the design and the current process. Based on the results, it shows that the design of the student data search flow through Student Amikom Purwokerto is much faster and more efficient because the data search time is reduced from 40 minutes to 3 minutes.
 

The problem of hypertension increases every year, this is caused by many factors, one of which is still low dietary requirements. Actions to support diet compliance require family support and self-motivation. The aim of this research was to determine the relationship between family support and self-motivation with diet compliance in adult hypertensive sufferers at the Dinoyo Community Health Center, Malang City. The research design used cross sectional. The study population was 511 hypertensive sufferers with a total sample of 84 hypertensive sufferers. Samples were taken using the Accidental Sampling technique. The independent variables are family support and self-motivation, the dependent variable is compliance with hypertension. The instrument used is a questionnaire sheet. Data analysis used Fisher's exact test. The research results showed that the majority of respondents had good family support (67.9%). Almost all respondents have high self-motivation (91.7%). Most respondents had high diet compliance (50.7%). There is a relationship between family support (p=0.024; OR=3.3) and self-motivation (p=0.000; OR=0.3) with diet compliance in hypertension sufferers. Further research can conduct research directly examining family data regarding the family's.Masalah hipertensi setiap tahunnya mengalami peningkatan, hal ini disebabkan oleh banyak faktor salah satunya kepatuhan diet yang masih rendah. Tindakan untuk mendukung kepatuhan diet diperlukan dukungan keluarga dan motivasi diri. Tujuan penelitian ini untuk mengetahui hubungan dukungan keluarga dan motivasi diri dengan kepatuhan diet pada penderita hipertensi usia dewasa di Puskesmas Dinoyo Kota Malang. Desain penelitian menggunakan cross sectional. Populasi penelitian sebanyak 511 penderita hipertensi dengan jumlah sampel sebanyak 84 orang penderita hipertensi. Sampel diambil dengan teknik accidental sampling. Variabel independen adalah dukungan keluarga dan motivasi diri, variabel dependen adalah kepatuhan diet hipertensi. Instrumen yang digunakan berupa lembar kuesioner. Analisis data menggunakan uji fisher's exact test. Hasil penelitian menunjukkan sebagian besar responden memiliki dukungan keluarga yang baik (67,9%). Hampir seluruh responden memiliki motivasi diri yang tinggi (91,7%). Sebagian responden memiliki kepatuhan diet yang tinggi (50,7%). Ada hubungan dukungan keluarga (p=0,024; OR=3,3) dan motivasi diri (p=0,000; OR=0,3) dengan kepatuhan diet pada penderita hipertensi. Penelitian selanjutnya dapat melakukan penelitian tentang mengkaji secara langsung data keluarga.  

Traditional culture is a vital part of a community's identity, yet in the era of globalization, cultural elements are often threatened by modernization, which can blur local identities. This study explores the application of the Design Thinking method in integrating the Surjan Jogja motif into visual communication design, focusing on how the design can preserve cultural meaning while remaining relevant in contemporary times. The research employed a qualitative approach, utilizing the five stages of Design Thinking: empathy, problem definition, ideation, prototyping, and testing. Data were collected through in-depth interviews with 15 respondents, including designers, batik artisans, and cultural figures, as well as direct observations of the creative process. The results show that 80% of respondents appreciated designs that integrated the Surjan motif, with many stating that the design offered deeper meaning compared to products without cultural context. The study found that the application of Design Thinking produced innovative visual solutions and strengthened local cultural identity amidst globalization. The conclusion of this study emphasizes the importance of integrating traditional elements in visual communication design to preserve local culture. The practical implications of this research are significant, as it provides a framework for the development of culture-based design and opens opportunities for further research on the application of this method in other cultural contexts, thereby offering a practical guide for designers and cultural researchers interested in preserving local culture through design.

In the rapidly evolving era of big data, predictive analytics has become a crucial approach in supporting data-driven decision-making across various sectors such as finance, healthcare, and marketing. However, the effectiveness of predictive models is highly dependent on the quality of features utilized in model training. This study aims to evaluate and compare various feature engineering techniques to enhance the accuracy of predictive models based on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms. The research employs a quantitative experimental approach by applying different feature engineering techniques, including SHAP-based feature importance, Principal Component Analysis (PCA), and categorical variable encoding. The evaluation results indicate that the implementation of SHAP-based feature importance yields the best outcomes, with a Mean Squared Error (MSE) of 0.150 and a Root Mean Squared Error (RMSE) of 0.387 in the XGBoost model. These values outperform those without feature engineering, which recorded an MSE of 0.230 and an RMSE of 0.479. The combination of PCA and encoding techniques also shows a significant performance improvement with an MSE of 0.160 and an RMSE of 0.400. The XGBoost algorithm consistently demonstrates superior performance compared to RF across various testing scenarios. The contribution of this study lies in its recommendation of appropriate feature engineering techniques to improve the predictive quality of Machine Learning (ML)  models. This research provides insights for researchers and practitioners in developing more effective feature engineering strategies and opens opportunities for exploring advanced techniques in more complex data domains.