In 2024, Indonesia will hold simultaneous general elections dominated by the participation of young people, especially Generation Z and Millennials, who seek political information primarily through the Internet, highlighting the crucial role of digital media in shaping public opinion. Detik.com is actively reporting on the 2024 elections, evidenced by a special election subchannel. However, the lack of topic categorization in this subchannel makes it difficult for readers to find in-depth information, and tracking and analyzing the large volume of news articles published daily is a significant challenge. This study employs Topic Modelling techniques, specifically the BERTopic method, to analyze topics related to the 2024 elections from Kompas.com news articles. The dataset, sourced from the detik.com election sub-channel, was collected via scraping from September 1, 2023, to February 14, 2024, totalling 15,019 articles. The text preprocessing involves case folding, cleaning, tokenizing, and stopword removal. Topic modelling using BERTopic includes embeddings with sentence-transformers "distiluse-base-multilingual-cased-v1," dimensionality reduction with UMAP, clustering with K-Means using optimal k=5 value evaluated by Elbow, tokenizer with CountVectorizer, and weighting scheme using c-TF-IDF. Based on the Silhouette Score of 0.566 and Silhouette plot results, the clustering results using the K-Means model with a value of k equal to 5 produce good clustering with clear inter-cluster distances. For other evaluations, the SSE value of 70223.257 provides an overview of the cluster distribution, the Davies-Bouldin Index of 0.758 shows that the cluster has a relatively good level of inter-cluster separation with good closeness within the cluster, the Calinski-Harabasz Index of 20083.489 shows good and compact inter-cluster separation, and the Dunn Index of 0.003 shows outliers that cause overlapping clusters and lack of clear separation. The evaluation results show that implementing the K-Means model with a value of k equal to 5 again emphasizes that the clustering results are good. The modelling results show an average topic coherence value of 0.0902 and produce five main topics in the 2024 election news on Detik.com topic 0: about presidential and vice presidential candidates (5,215 articles) with the representation of the words 'ganjar', 'prabowo', 'anies' and 'imin', topic 1: about general elections and related surveys (3,191 articles) with the representation of the words '2024', 'pemilu', 'pilpres' and 'suara', topic 2: news about Joko Widodo President (2,604 articles), topic 3: news about presidential and vice presidential debates (2046 articles) with representations of the words 'presiden', 'jokowi', 'demokrat' and 'politik' and topic 4: news about the figure Gibran Rakabuming Raka and related issues (1963 articles) with representations of the words 'raka', 'rakabuming', 'nomor' and 'urut'. Using the results of this research, readers can gain insights into the most discussed issues and the attention given to key figures in the 2024 election news on the detik.com news portal.

Berkebun tanaman hias adalah salah satu hobi yang sangat digemari masyarakat Indonesia saat ini. Salah satu yang paling diminati adalah berkebun tanaman hias Aglonema sp. Namun, biasanya permasalahan yang muncul adalah sering kali kita tidak mempunyai waktu atau lupa untuk melakukan penyiraman, karena masih dilakukan secara manual. Berdasarkan dari permasalahan tersebut maka penelitian ini bertujuan untuk membuat suatu alat yang dapat membantu proses penyiraman tanaman secara otomatis. Alat yang dikembangkan memanfaatkan sistem berbasis Internet of Things yang dapat bekerja secara otomatis menggunakan Soil Moisture Sensor untuk mengukur nilai kelembaban tanah yang dikontrol oleh NodeMCU dan dikoneksikan ke Telegram. Soil Moisture Sensor berfungsi sebagai pendeteksi kelembaban dalam tanah yang dikontrol oleh NodeMCU digunakan untuk mengolah inputan yang kemudian yang akan ditampilkan pada Liquid Crystal Display nilai kondisi tanah tanaman. Alat ini menggunakan Pompa Air untuk mengalirkan air ke tanaman dengan perintah dari NodeMCU. Sistem Sprinkler yang telah dibuat mampu menyirami tanaman secara otomatis. Aplikasi Telegram akan menerima dan menampilkan nilai-nilai kondisi tanah yaitu kering, lembab atau basah sesuai hasil pembacaan dari sensor kelembaban tanah.

Disability issues are still a major concern in society due to the discrimination often faced by people with disabilities. Many of them have abilities that are equal to individuals without physical limitations. Through this case study, this research aims to Cluster disability data by considering three types of disabilities: physical, visual and hearing, and hearing and speech using agglomerative hierachical Clustering and kmeans methods. This research was conducted by analyzing data from people with disabilities in 7 provinces in Indonesia. K-means to group data and agglomerative hierarchical Clustering as a centroid determinant in k-means. to enrich the results of data analysis, the EDA (Exploratory Data Analysis) process is used to identify outliers and anomalies. The results of the data analysis show that there are three main Clusters. The first Cluster has a high level of disability and includes 62 cities and districts, the second Cluster has a medium level of disability with 37 cities and districts, and the third Cluster has a low level of disability with 27 cities and districts. The best evaluation using the Davies Bouldin Index method resulted in two Clusters, indicating a better quality of Cluster division. The results of this study provide a better understanding of the distribution of disability in Indonesia, which can be used as a foundation to improve inclusion and accessibility for people with disabilities. Further recommendations can be made based on these findings to improve their situation in terms of employment and education.

Migraines are recurring and highly painful headaches with multiple associated symptoms that severely affect millions of people around the world. This condition is considered quite serious from a neurologist’s perspective because it is highly debilitating. Effective treatment of migraines begins with its diagnosis but the subjective nature of clinical evaluations along with class imbalance in patient datasets makes this very complicated. This paper attempts to tackle these issues by developing a machine-learning framework for automated migraines classification by utilizing a Kaggle dataset of 400 samples with 23 independent attributes and 1 dependent attribute representing different types of migraines. Our framework starts with a detailed cleansing of the data, which includes filtering out all missing values. Then, through the use of SMOTE (Synthetic Minority Oversampling Technique), the issue of an imbalanced dataset is tackled. This is followed by optimized feature selection through forward selection and cross-validation with Naïve Bayes. Supervised machine-learning classifiers such as Random Forest (RF), decision tree (DT), K-nearest Neighbors (KNN), and Naïve Bayes (NB) are evaluated and voted on to predict the outcome.

Penelitian ini bertujuan untuk mengimplementasikan arsitektur MobileNet dalam klasifikasi jenis kulit wajah, termasuk kulit normal, kering, dan berminyak, guna menentukan kandungan skincare yang sesuai. Dataset terdiri dari 450 gambar yang dibagi menjadi tiga kelas jenis kulit. Model dilatih menggunakan MobileNet dengan 50 epoch, ukuran citra 224x224 piksel, dan optimizer Adam. Hasil pelatihan menunjukkan peningkatan akurasi seiring bertambahnya epoch, sementara nilai loss menurun. Evaluasi menggunakan data uji menghasilkan akurasi 99% dan loss 3.73%,, sementara itu precision, recall, dan f1-score tertinggi berada pada kelas dry sebesar 100%. Hasil ini menunjukkan bahwa MobileNet mampu memberikan prediksi yang akurat dalam mengklasifikasikan jenis kulit wajah. Selain itu, model juga memberikan rekomendasi produk skincare yang disesuaikan dengan jenis kulit, memberikan informasi yang berguna bagi pengguna.