Nowadays, video games are getting popular due to the rapid growth of technology, especially mobile technology. Video games are now able to be played on mobile devices whether in single or multiplayer mode by using the internet and it's free and available everywhere. Understanding player experience in mobile video games is essential for developers and researchers. This research evaluates player experience in a mobile video game using the Game Experience Questionnaire (GEQ) and Player Experience Questionnaire (PENS). The evaluation was performed using an online questionnaire form to the participants (N = 110) who had played the specific mobile game. Using the Core Questionnaire from the Game Experience Questionnaire and Player Experience of Need Satisfaction module, this research found that the Game Experience Questionnaire shows high scores values in the aspects of Immersion (4.22), Competence (3.25), and Positive Aspects (4.38) which indicates a good player experience, and the Player Experience of Need Satisfaction shows a rather balanced yet high score for each value that indicates a satisfying player experience.

The development of artificial intelligence (AI) and the increased curiosity about how large language models (LLMs) may maximize an organization's opportunities and the ethical implications of LLMs, such as the ability to generate human-like text, give rise to concerns regarding disinformation and fake news. As a result, it is crucial to develop evaluation benchmarks that take into account the social and ethical implications involved. The great challenges of LLMs lack awareness of their own limitations, yet they persist in producing responses to the best of their capabilities. This often results in seemingly plausible but ultimately incorrect answers, posing challenges to the implementation of reliable generative AI in industry. This paper aims to delve into the evaluation metrics of machine-learning models' performance, specifically focusing on LLM. Therefore, bibliometric analysis utilized to explore and analyze various techniques and methods used in evaluating large language models. Additionally, it sheds light on the specific areas of focus when evaluating these models. The results show that natural language processing systems, classification of information, and computational linguistics are some of the techniques used to evaluate large language models. This work paves the way for future investigations employing extensive language models.

Diabetes merupakan penyakit metabolik dengan gejala hiperglikemia akibat gangguan sekresi insulin dan aksi insulin. Diabetes gestasional adalah gangguan toleransi glukosa pada wanita hamil. Saat kehamilan, plasenta menghasilkan hormon baru seperti human placental lactogen (HPL), hormon estrogen, dan hormon peningkat resistensi insulin. Gejala diabetes gestasional tidak selalu mudah dikenali, dan seringkali penderitanya mengalami gejala awal secara tidak sadar. Penelitian ini bertujuan untuk membandingkan performa dua algoritma yaitu K-NN dan Naïve Bayes dengan Feature Selection dalam mengklasifikasikan penderita diabetes gestasional. Hasil error terendah dari feature selection dengan iterasi K=4, memperoleh MAE 0.317, MSE 0.142, dan RMSE 0.377. Hasil akurasi pada model KNN dengan K=5 , tanpa Feature Selection sebesar 80% dan K-NN dengan Feature Selection sebesar 77%. Sementara itu, Naïve Bayes tanpa Feature Selection sebesar 77% dan Naïve Bayes dengan Feature Selection sebesar 80%. Dari hasil tersebut K-NN tanpa Feature Selection dan Naïve Bayes dengan Feature Selection mendapatkan hasil yang lebih baik.

Pengembangan permainan "Ngabdi: The Beginning" menggunakan Unity Engine dan bahasa pemrograman C#. Dengan fokus pada integrasi skrip sederhana yang diaktifkan melalui input mouse, teknik ray casting digunakan untuk mendeteksi benturan objek dalam scene permainan. Pengujian dilakukan untuk menentukan panjang optimal interactionRayLength, parameter kunci dalam ray casting. Hasil implementasi dan pengujian menunjukkan kesuksesan dalam mencapai kinerja optimal dan respons yang diinginkan. Dengan melakukan uji coba sebanyak 7 kali, paper ini menyajikan landasan yang kokoh untuk pemahaman lebih lanjut tentang parameter kritis dalam teknik ray casting 3D dan pengaplikasiannya dalam pengembangan permainan. Kesimpulan ini dapat menjadi panduan bagi pengembang permainan untuk memanfaatkan teknik ray casting 3D secara efektif dalam lingkungan Unity Engine.

Evaluasi lahan dapat dilakukan untuk meningkatkan kualitas dan kuantitas komoditas pertanian. Salah satunya dengan persyaratan penggunaan lahan dengan mempertimbangkan karakteristik lahan. Namun, Dinas Pertanian selaku koordinator sulit mendapatkan informasi terkait karakteristik lahan yang sesuai dengan jenis tanaman berdasarkan faktor cuaca. Anomali cuaca menyebabkan turunnya produktitivitas tanaman. Tujuan penelitian ini adalah mengembangkan sistem informasi kesesuaian lahan untuk menentukan jenis tanaman pangan beradasarkan karakteristik lahan serta evaluasi kesesuaian lahan tanaman. Metode dalam penelitian ini adalah Framework for the Application of System Thinking (FAST). Tahapan FAST yaitu scope definition, problem analysis, requirement analysis, decision analysis, design, contruction and testing, dan instalation and delivery. Berdasarkan hasil uji kelayakan aplikasi menghasilkan nilai 87% dengan kriteria baik. Hasil ini menunjukkan bahwa sistem informasi kesesuaian lahan tanaman pangan dapat digunakan dengan baik.

In the current industrial era, clothing companies face increasingly complex challenges in maintaining the quality and sales of their products. To overcome these obstacles, companies must have a good vision, mission, and decision-making capabilities supported by efficient data management. Data is becoming a valuable asset in Industry 4.0, pivotal in process management, increased productivity, and competitive advantage. This research explores the implementation of Data Warehouse (data warehouse) and ETL (Extraction, Transformation, and Load) processes on star schema for PT Golden Flower Tbk. A data warehouse is a container for storing company data, supporting decision-making, and providing business insights. Meanwhile, the ETL process ensures that the data entered into the warehouse is clean, structured, and ready for analysis. PTA garment manufacturing and exporter company, USPT Golden Flower Tbk, uses the Pentaho application to carry out the ETL process. Pentaho, a Business Intelligence tool, facilitates easy data processing and analysis. This research also explains the use of dimension tables and fact tables in star schemas, which form the basis for more in-depth data analysis. Through visualization and analysis using star schema, Mondrian, and schema workbench, companies can identify sales patterns in each branch and understand which products are most in demand by consumers. The results of the star schema, moMondrianand schema workbench provide a better understanding of the company's sales performance, enabling more informed decision-making and more effective strategies.

Agama dan politik saling terkait dalam konteks pemilihan umum di Indonesia. Penggunaan isu agama dalam kegiatan politik merambah luas di pemilu Indonesia tahun 2014 dan 2019 di media sosial. Paper ini mengisi kekosongan di literatur untuk mengkaji fenomena penggunaan isu agama dalam politik secara kuantitatif. Data diambil menggunakan dari media sosial twitter menggunakan API secara legal dan menjaga privasi pengguna. Pengambilan data dilakukan dengan menggunakan kata kunci terkait agama seperti Islam, Al-Qur’an, Hadits, Halal, Shalat, dan sebagainya lalu di filter dengan berbagai kata kunci terkait politik. Melalui berbagai teknik eksplorasi data seperti analisis korelasi Spearman dan visualisasi geospasial, penelitian ini menemukan adanya hubungan signifikan antara banyaknya isu agama terkait politik dan perolehan suara calon presiden. Pada tahun 2014 korelasi untuk Prabowo dibandingkan korelasi untuk Jokowi lebih tinggi yaitu sebesar 0.72, lalu menurun pada tahun 2019 menjadi 0.56. Penelitian ini dapat dijadikan inspirasi untuk penurunan dan pencegahan terjadinya polarisasi di masyarakat akibat penggunaan isu agama dalam kegiatan politik.

Floods have a significant socio-economic impact on Indonesian society. Much of this information is sourced from online news articles and social media. This research investigates whether the Support Vector Machine (SVM) method can be used for flood disaster level classification (low, medium, and high). Our methodology involves preparing data extracted from textual news articles on the National Disaster Management Agency (BNPB) website on the topic of flooding. We then labeled the data according to Regulation No. 02/2012 on general guidelines for disaster assessment and used the Support Vector Machine (SVM) method. Training and testing were conducted using different datasets, followed by accuracy and error evaluation. In addition, we considered the performance comparison of SVM with other classification methods, including Decision Tree, Naive Bayes, Adaboost, Random Forest, and Xgboost. The experimental results show that SVM still does not get good accuracy results for flood disaster level classification. The SVM accuracy level result of (52%) is still low compared to Random Forest (78%), and Xgboost (68%). Further research is expected to increase the accuracy of SVM for flood level classification.

This paper is the result of an extensive study, comparison, and analysis of academic articles and research related to smart grids and the solutions they offer to enhance the efficiency, reliability, and sustainability of modern electrical power systems. It presents a comprehensive analysis of the progress made in recent years in smart grid technologies, including architecture, communication infrastructure, control strategies, and the integration of renewable energy sources. Additionally, the benefits and advantages of adopting smart grids are explored and presented, along with the challenges arising from their use. There is also a reference to the future prospects of using smart grids and how they can impact human life. Following this, there is an analysis of the two most critical elements of smart grids, namely Energy Management Systems (EMS) and Advanced Metering Infrastructure (AMI). Finally, some conclusions are drawn regarding the use of smart grid systems, and a personal opinion is expressed.

Dalam konteks meningkatkan Pelayanan Bursa Kerja Khusus di SMKN 2 Purwakarta, diperlukan suatu sistem informasi yang mampu mengatasi permasalahan dalam pendataan alumni dan lowongan kerja. Tujuan dari penelitian ini adalah untuk mengembangkan sebuah aplikasi web yang tidak hanya mempermudah proses pendataan, tetapi juga memberikan peluang untuk perbaikan di masa depan. Penelitian ini melibatkan langkah-langkah seperti studi literatur, pengumpulan data, analisis sistem, pengembangan sistem, dan pembuatan laporan. Data dikumpulkan melalui kombinasi penelusuran literatur dan kegiatan lapangan, termasuk wawancara dengan subjek yang relevan. Dalam pengembangan sistem, digunakan metodologi SDLC (Software Development Life Cycle) dengan model Waterfall yang terstruktur dan sesuai untuk merancang sistem informasi alumni. Untuk aplikasi pengembang sistem informasi ini, dipilih PHPRad sebagai solusi yang menawarkan fitur-fitur kuat, kemudahan penggunaan, dan dukungan untuk pengembangan berkelanjutan. Selain itu, PHPRad juga menghasilkan dokumentasi yang berharga untuk perbaikan di masa depan, termasuk hasil pengujian Blackbox yang memfokuskan pada fungsionalitas fitur.