Organizational culture is a system of values, norms and beliefs held by a group of people in an organization that differentiates it from other organizations. This definition can also be seen as a pattern of basic assumptions discovered or developed by a group of people as they interact with each other. Organizational culture can also be interpreted as the characteristics or guidelines implemented by each member of an organization or company. Micro, Small, Small and Medium Enterprises (UMKM) are independent productive business units, carried out by individuals or business entities in all economic sectors which greatly assist the process of recovery or improvement of the Indonesian economy. Therefore, play a very important role in the rate of economic growth in Indonesia. The title of this research is "Organizational Culture Analysis of Employee Performance in the Umkm Scope of Tamansari Region, Tasikmalaya City".

The purpose of this research is to describe the types of figurative language found in Adele's songs in album 30. The research methodology used is literature study with a qualitative descriptive approach. The results show that there are several types of figurative language in the lyrics of these songs, including symbolic (36%), personification (9%), metaphor (4.5%), simile (18%), hyperbole (4.5%), and allegory (28%). Thus, it can be concluded that the most used figurative language in the album is the symbolic type.

Purpose: Face recognition has become an increasingly intriguing field in artificial intelligence research. In this study,   This study aims to explore the application of CNNs, implemented through TensorFlow, to develop a robust model for enhancing facial recognition accuracy in student attendance systems. The focus of this research is the development of a model capable of recognizing student faces under various lighting conditions and poses in an academic environment, using a multi-class dataset of student images collected from internship attendance records at the Computer Science Department.
Methods: The dataset, comprising facial images from 19 students, served as the foundation for training and validating the CNN model. The dataset, sourced from the computer science department's internship attendance records, included a total of 231 images for training and 59 images for validation. The preprocessing phase included facial area detection and categorization, resulting in a well-organized dataset for training and validation. The CNN architecture, consisting of seven layers, was meticulously designed to achieve optimal performance.
Result: The model exhibited exceptional accuracy, reaching 93% on the validation dataset after 300 training epochs. Precision, recall, and F1-score metrics were employed for a detailed evaluation across diverse classes, highlighting the model's proficiency in accurately categorizing facial images. Comparative analyses with a VGG-16-based model showcased the superiority of the proposed CNN architecture. Moreover, the implementation of a web service demonstrated the practical applicability of the model, providing accurate predictions with a remarkable response time of less than 0.3 seconds.
Novelty: This comprehensive study not only advances face recognition technology but also presents a model applicable to real-world scenarios, particularly in student attendance systems.

Pengembangan Aplikasi Pemilihan Umum RT02 RW12 di Kelurahan Meteseh, Semarang, oleh tim mahasiswa Universitas Dian Nuswantoro bertujuan meningkatkan efisiensi dan partisipasi masyarakat dalam pemilihan ketua RT. Aplikasi ini fokus pada literasi digital, melibatkan masyarakat RT02 RW12, dan memastikan partisipasi maksimal. Tahap pelaksanaan mencakup negosiasi, perancangan, konfirmasi, pengembangan, dan pengujian aplikasi. Sosialisasi berhasil meningkatkan pemahaman dan literasi digital peserta. Aplikasi berbasis Laravel menyajikan calon ketua RT dan visi misi mereka. Tim mahasiswa juga berperan sebagai fasilitator pada pemilihan, menciptakan kesuksesan dalam meningkatkan partisipasi masyarakat. Hasilnya, aplikasi ini bukan hanya meningkatkan efisiensi dan transparansi dalam pemilihan ketua RT, tetapi juga mendorong partisipasi aktif masyarakat serta memberikan kontribusi positif terhadap demokratisasi tingkat lokal. Dengan demikian, pengembangan aplikasi ini berhasil menciptakan dampak positif dalam proses pemilihan umum di tingkat RT.

There are so many companies nowadays, some are large companies and some are small companies. Every company certainly hopes to continue to exist in an era of intense competition. Without realizing it, technology is also experiencing rapid development too. The problem faced by companies is how to improve employee performance with current technology. The company realizes that employee performance is very important so that the company can compete with other companies. One way to improve company performance is by digitalizing the company. Steps that companies can take in implementing company digitalization are: creating a business website, carrying out digital marketing, implementing cloud computing, utilizing big data, implementing AI technology, and participating in digital training for their employees.

Penyakit Kardiovaskular (Cardiovascular Diseases) adalah faktor utama kematian global, dengan jumlah korban mencapai 17,9 juta jiwa setiap tahun atau sekitar 32% dari total kematian global (World Health Organization, 2021). Faktor risiko penyakit kardiovaskular diantaranya faktor usia, semakin bertambahnya usia seseorang, maka semakin tinggi risiko terkena penyakit kardiovaskular. Faktor lain yaitu memiliki riwayat penyakit kardiovaskular dalam keluarga, diabetes, tekanan darah tinggi, obesitas (kegemukan), Pola hidup tidak sehat, dan Stres. (Kemenkes RI, 2021). Masalah pada penelitian ini adalah bagaimana mengetahui model K-Nearest Neighbors (KNN) dengan baik melalui perhitungan accuracy, recall, precision, dan f1-score pada klasifikasi penyakit kardiovaskular. Tujuan penelitin ini adalah mengetahui kinerja K-Nearest Neighbors (KNN) dengan baik melalui perhitungan accuracy, recall, precision, dan f1-score pada klasifikasi penyakit kardiovaskular. Metode K-Nearest Neighbor (KNN) adalah salah satu metode klasifikasi yang memanfaatkan pola-pola data yang ada dalam dataset untuk mengklasifikasi kategori atau kelas dari suatu sampel yang belum diketahui. Hasil klasifikasi data pelatihan menunjukkan akurasi sebesar 85.49%, dengan precision 84,43%, recall 87,04%, dan f1-score 85,71%. Melalui uji coba menggunakan KNN, diperoleh hasil dengan akurasi sebesar 91% dan nilai presisi 90%, recall 93%, dan f1-score 92%. Kesimpulan dari penelitian ini adalah metode K-Nearest Neighbor (KNN) memiliki hasil yang baik untuk melakukan klasifikasi pada penyakit kardiovaskular yaitu akurasinya 91%.

Pembuatan model klasifikasi memerlukan beberapa hal yang penting untuk diperhatikan demi mendapatkan model yang memiliki performa terbaik. Indikator suatu model disebut baik dapat dilihat salah satunya dari tingginya nilai akurasi dan f1-score yang dihasilkan dari model tersebut. Rendahnya nilai loss juga merupakan salah satu indikator model tersebut memiliki performa yang baik. Untuk dapat membuat model yang baik, diperlukan beberapa syarat seperti arsitektur yang tepat dan data yang berkualitas. Pemilihan model yang terlalu sederhana akan mengakibatkan model memiliki performa yang buruk, begitupun jika model terlalu kompleks tidak akan menghasilkan performa yang baik pula, oleh karena itu model yang dipilih haruslah model yang tepat  dan sesuai dengan jenis data yang digunakan. Data yang berkualitas juga merupakan faktor penting agar model memiliki performa maksimal. Data dapat dikatakan berkualitas jika memenuhi syarat seperti jumlahnya cukup, distribusi datanya seimbang tiap kelas, memiliki keanekaragaman dan memiliki kebersihan yang baik.  Pada penelitian ini, dilakukan pembuatan model klasifikasi CT Kidney Stone dengan dataset yang imbalance. Dataset diperoleh dari sumber publik yaitu Kaggle. Pembuatan model menggunakan algoritma CNN karena CNN merupakan salah satu algoritma yang terbaik dalam membuat klasifikasi gambar. Pembuatan model menggunakan 3 cara untuk melihat model yang memiliki performa paling baik. Model pertama dibuat dengan data train yang imbalance. Model kedua dibuat dengan melakukan augmentasi data untuk menambah keragaman data. Model ketiga dibuat dengan SMOTE oversampling untuk menyeimbangkan distribusi data. Setelah itu ketiga model tersebut akan diuji dengan menggunakan data privat untuk melihat performa pengujian dan melihat tingkat overfitting yang terjadi. Penelitian ini menghasilkan bahwa model yang memiliki performa terbaik adalah model ketiga yang menggunakan SMOTE.

Fires in residential areas usually occur in densely populated areas. Rawa Buaya Village is a sub-district in West Jakarta City which has a fairly high population density and there is also a densely populated settlement located at RT.016/RW.04 with 445 families. This settlement experienced fire disasters five times in 2008, 2009, 2015, and twice in 2022. The aim of the research is to describe community preparedness to reduce the risk of fire as well as factors that support and inhibit community preparedness. This research uses descriptive methods through a qualitative approach. The results of the research show that 40% of the community knows that the cause of fires is due to human negligence, 81% have the desire to prevent fires, 76% have an agreement between the Head of the RT and the community, 74% have determined the evacuation location for victims, 67% do not know the evacuation route board, 91% have the desire to to help their relatives, 62% only knew that a fire simulation had been carried out once, 74% had no early warning system, and 60% had not prepared an emergency fund. So it was concluded that the community was sufficiently prepared to face fires by knowing the causes and things to prevent fires, having an agreement to prepare evacuation locations, helping relatives and participating in disaster simulations, but the vulnerability of the living environment was a factor that triggered fires.
 

As an important means of sea transportation for the distribution of goods and human mobility between islands, ships require a reliable prime mover to avoid wear or damage, especially when overspeeding occurs which can result in fire. Therefore, a prototype was created that uses an IR Obstacle sensor to monitor the RPM speed of the rotating engine. By using Arduino Uno as a processor, this prototype is able to display the RPM value on the LCD and provide a signal in the form of a green light if the speed is below 4000 Rpm, as well as a red light and buzzer sound if overspeed occurs above 4000 Rpm, followed by the engine turning off after 15 seconds. Testing shows that this prototype has a high level of accuracy, with a difference between 0.08% to 2.73% and an average difference of 1.003%.

Furniture raw materials are still a major challenge in the industry, in line with the wishes of consumers to get good quality raw materials and soaring export demand, so there is a need for a control process to monitor the value of products using forecasting. The purpose of this study was to predict gross domestic product in the furniture industry in Indonesia in 2022. This study used secondary data on the quarterly trend of gross domestic product in the furniture industry in Indonesia 2010-2021 taken from the research industry data processed by BPS and Bank Indonesia, The method used is Double Exponential Smoothing-Holt. The results of the calculation using the double exponential smoothing-holt method obtained a value of ? of 0.658 and ? of 0.008 where the forecasting results for the 2022 period, namely the 1 quarter of 7.602 billion rupiah, quarter 2 of 7.676 billion rupiah, quarter 3 of 7.749 billion rupiah, and quarter 4 of 7.822 billion rupiah. Where the MAPE value is 0.737% which means forecasting has very good results.