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The rapid development of smartphone technology today creates challenges for consumers and manufacturers in determining an objective price range based on highly varied technical specifications. This study aims to implement the Random Forest algorithm in classifying smartphone price ranges into four main categories, namely low, mid-range, high, and flagship. The research method was carried out systematically through the stages of loading a dataset of 2,000 entries, exploratory data analysis (EDA) to ensure data integrity, and model training with a training and testing data split of 80:20. The results showed that the Random Forest model achieved a significant overall accuracy rate of 89%. Based on feature importance analysis, it was found that RAM capacity was the most dominant determining factor, contributing 47% to prediction accuracy, followed by battery power and screen resolution as supporting features. These findings have strategic implications for manufacturers to prioritize memory capacity upgrades in determining product pricing in the market, as well as providing guidance for consumers in assessing the fairness of a device's price based on its technical capabilities.

This article examines the typology and classification of regional government apparatus in Indonesia by analyzing their structure and functions within local governance. The study draws on recent theoretical literature (2020–2025) and legal frameworks to map how different types of regional apparatus such as executive agencies (“dinas”), supporting agencies (“badan daerah”), secretariat, inspectorate, and territorial units are organized and classified. Using a normative-juridical and conceptual approach, the paper reviews relevant laws, regulations, and academic studies to identify patterns of structural typology and functional differentiation within local governments. The findings reveal that many local governments still apply structural-heavy models rather than functionally tailored organizations, leading to excessive bureaucracy and inefficiency. The analysis suggests that a clearer classification aligned with functional roles can improve governance effectiveness and administrative efficiency. The article concludes by recommending that local governments re-evaluate their organizational structures to better reflect the functional needs of governance, rather than merely replicate structural models.                                                               

This study aims to design and build a population distribution application in Demak Regency in 2025 using a Geographic Information System (GIS) approach. The study focuses on three main variables: population, population density, and population growth rate per sub-district. The author used the research method of collecting data and references that can later strengthen the results of this study and the application design using the waterfall model. Non-spatial data, namely data in the form of population information, was obtained from the Central Statistics Agency of Demak Regency, while spatial data is data related to regional administrative boundaries. Data processing was carried out using QGIS 2.18 through the stages of joining attributes, classification using the Natural Breaks (Jenks) method, and thematic map creation. The results show that population distribution is uneven. Demak Kota, Karangtengah, and Sayung sub-districts have the highest number and density, while coastal sub-districts such as Wedung and Bonang have low densities. The highest population growth rate is in Karangtengah sub-district at 0.8%. The application of GIS has proven effective in visualizing population distribution and supporting spatial-based regional development planning.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

This Community Service activity reflects university students' social concern for their environment, particularly in elementary education. The activity was conducted at SDN 032 Tarakan to provide students with an understanding of the differences between needs and wants. The method used was face-to-face socialization, interactively designed through simple material delivery, ice-breaking, and an image classification game. The results showed high student enthusiasm and their ability to correctly classify most of the provided images into 'needs' and 'wants'. This activity not only enriched students' knowledge but also trained critical thinking skills in setting priorities. It is hoped that similar activities can be continued to foster economic literacy from an early age among elementary school students.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.”  The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique.  Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.

Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang dapat membantu tim Marketing Officer (MO) PT. Alvarel Technology Innovation dalam menentukan status pelanggan secara objektif dan terstruktur. Sistem ini dirancang menggunakan kombinasi metode Analytical Hierarchy Process (AHP) dan Weighted Sum Model (WSM). Metode AHP digunakan untuk menentukan bobot kriteria yang meliputi Potensial Pasar, Urgensi, Finansial, serta Hubungan dan Reputasi, dengan memastikan konsistensi matriks perbandingan berpasangan. Hasil pembobotan kemudian digunakan dalam metode WSM untuk melakukan perhitungan skor total pelanggan dan menyusun pemeringkatan status berdasarkan nilai tertinggi hingga terendah. Data penelitian diperoleh dari catatan internal perusahaan dan wawancara dengan Marketing Officer, dengan jumlah sampel 30 pelanggan. Hasil pengujian menunjukkan bahwa sistem dapat menghasilkan peringkat status pelanggan dalam lima kategori, yaitu potensial, prospek, pending, pasif, dan skip. Temuan utama memperlihatkan bahwa kategori prospek memperoleh skor tertinggi dan menjadi prioritas tindak lanjut. Dengan demikian, sistem pendukung keputusan berbasis AHP–WSM ini mampu mengurangi subjektivitas, meningkatkan efisiensi, serta memberikan rekomendasi yang lebih akurat dan terukur untuk mendukung pengambilan keputusan strategis perusahaan dalam pengelolaan pelanggan.

This research is based on the awareness that music functions both as an artistic expression and as a cosmological representation embedded within the cultural structures of traditional societies. The Siwaluh Jabu traditional house in Lingga Village, North Sumatra, was selected as the object of study to examine the relationship between music and the cosmological views of the Karo people. The purpose of this study is to reveal the meaning of music within cultural practices and rituals carried out in the Siwaluh Jabu House, as well as to explore musical elements that reflect the continuity between humans, ancestors, and the universe. This research employs a qualitative approach, with data collected through literature review, participatory observation, and interviews with traditional leaders and local artists. The data analysis was conducted through data reduction, classification, data presentation, and drawing of conclusion. The results of the study show that music both the vocal mantra “ole…ah…ole” and the Gendang Lima Sendalanen ensemble contains symbolic values that interpret wood as a natural element, sustain social connections between groups, and reinforce the social system embedded in the spatial organization of the Siwaluh Jabu traditional house. Music serves as a medium of spiritual and cultural communication that unites the physical and metaphysical dimensions of life. The implications of this research enrich the perspective of music culture and emphasize the importance of preserving traditional music as a local knowledge system integrated with the cosmology of the Karo people.

This study aims to analyze user sentiment toward the integration of Artificial Intelligence (AI) in online learning platforms, which are increasingly expanding in the digital era. With the growing use of AI technologies in education—such as learning chatbots, material recommendation systems, and automated assessments—it is essential to understand users’ perceptions and reactions to these implementations. The research employs sentiment analysis based on text mining using user review data collected from various online learning platforms. The analysis process includes data preprocessing, sentiment classification using machine learning algorithms, and interpretation of results based on the proportion of positive, negative, and neutral sentiments. The findings indicate that most users express positive sentiments toward AI integration, as it enhances learning efficiency and personalization. However, some users raise concerns regarding data privacy and the lack of human interaction. This study is expected to serve as a reference for educational platform developers to design AI systems that are more adaptive, transparent, and user-centered

This research proposes an alternative model for predicting the plasticity potential of clay soils, updating the previous model developed by Firincioglu and H. Bilsel. The alternative lies in the use of the fine fraction content (FC) as the predictive variable, replacing the percentage of sand fraction content (SC) previously suggested. The analysis was conducted on 61 low-plasticity clay (CL) soil samples, classified using the Casagrande and Moreno-Maroto systems, by examining the relationship between consistency limits (LL, PI), grain fractions (sand, silt, clay), and related parameters (FC, sand-silt-clay spectrum, and sand fraction ratio). The correlation analysis results show significant findings, including a strong positive correlation between sand content and the sand ratio (SR), as well as a negative correlation between sand content and FC, and between FC and SR. The performance of the substitute Casagrande quadratic model [2] reveals the best predictive accuracy among the proposed models ( [1], R² =0.90; [3], R²  0.89; [4], R² =0.43; [5], R² =0.93; [6], R² =0.93; [7], R² =0.89; [8], R² =0.89), with the highest R2 value of 0.93, MSE of ≈4.01, and MAE of ≈1.44–1.47. The equation is .

The rapid growth of Indonesia’s digital economy has created opportunities while simultaneously increasing the risk of monopolistic practices and unfair business competition. To address these issues, the Business Competition Supervisory Commission (KPPU) introduced behavioral remedies as an alternative dispute settlement mechanism. This study aims to analyze the effectiveness of behavioral remedies in cases of market control and abuse of dominant position. The research employs a doctrinal legal method. Effectiveness is assessed through two main indicators, namely the restoration of market conditions and the prevention of repeated violations. The findings show that this mechanism is generally effective in restoring competition and preventing recurrence, as demonstrated in several KPPU cases, including Case No. 04/KPPU-I/2024. However, its effectiveness remains conditional, as the mechanism is reactive, does not impose fines, and is monitored only for a relatively short period. Moreover, although KPPU Regulation No. 2 of 2023 regulates this mechanism, it does not provide sufficient legal explanation regarding the classification of articles that are permitted. Therefore, stronger monitoring and more detailed legal clarification are required to ensure the sustainable effectiveness of this mechanism in maintaining fair competition.

In the era of digital transformation, modern, systematic, and integrated archive management has become a crucial element in creating effective, efficient, and accountable education governance. Office Management and Business Services (MPLB) teachers in East Java still face obstacles in implementing technology-based archive management due to limited digital literacy, lack of supporting facilities, and minimal ongoing training relevant to 21st-century needs. This Community Service (PKM) activity aims to improve the professional competence of MPLB teachers in modern archive management through interactive workshop-based training, intensive mentoring, and direct simulation of the use of digital archiving applications. The implementation methods included participant needs analysis, problem-solving-based module development, digital archive management practice using a cloud system, and pre-test and post-test-based evaluation to measure competency improvement. The results of the activity showed an average increase in participant understanding of 42%, a 58% increase in operational skills in using archiving applications, and a 35% increase in archive management time efficiency. In addition, teachers are able to implement an electronic file classification system in schools and build a database of digitized documents. These activities strengthen transparent and sustainable educational administration, as well as supporting the realization of inclusive and globally competitive education in line with the Asta Cita vision towards Indonesia Emas 2030.

This literature study evaluates the accuracy of the Slope Mass Rating (SMR) method for coal mine slope stability in Indonesia through a systematic descriptive synthesis of 25 empirical studies from 2020 to 2025. The objectives of the study were to identify the level of SMR prediction accuracy, factors affecting the method's performance, and modifications required for local Indonesian conditions. The research method involved a systematic search with inclusion criteria for empirical studies reporting SMR and/or Safety Factor (SF) values ​​for coal mines and associated slopes in Indonesia. Quantitative analysis showed a range of reported SMR values ​​between 41 and 96 with a median of 72, while SF values ​​ranged from 1.137 to 4.09 for normal operational conditions. The synthesis results indicated that SMR provides a consistent stability classification for initial slope design and failure mode identification (planar, wedge, toppling), with historical validation showing a correlation of up to 91.23% between SMR-based hazard zoning and actual field events in some cases. Key limitations include dependence on discontinuity data quality, sensitivity to groundwater conditions and tropical weathering, and variation in the interpretation of adjustment factors F1-F4. Modifications such as NAAF23 and integration with numerical modeling have been shown to improve prediction reliability. It is recommended that coal mining practitioners combine SMR with kinematic analysis and limit equilibrium modeling as standard operating procedures, and develop adjustment factors specific to Indonesian geological conditions. Further research should focus on standardizing parameter reporting and cross-site quantitative validation to enable more robust statistical meta-analyses.  

Swimming performance is strongly influenced by aerobic endurance, which enables athletes to maintain speed, technique efficiency, and physiological stability during prolonged activity. However, quantitative data regarding aerobic endurance levels among club-level swimmers in Indonesia remain limited. This study aimed to describe the aerobic endurance level of athletes from the Science Swimming Team. A descriptive research design was employed involving 11 swimmers selected through purposive sampling. Data were collected using the Cooper Swimming Test conducted over a 15-minute freestyle swimming session, and aerobic capacity was estimated through VO₂max values. The collected data were analyzed descriptively to classify aerobic endurance levels based on established normative categories by sex and age. The findings indicated that most athletes achieved good to very good performance in swimming distance; however, VO₂max classifications showed that aerobic capacity was predominantly in the moderate category, with only one female athlete reaching an excellent level. This disparity suggests that favorable distance performance does not necessarily reflect optimal aerobic capacity. The results imply the need for more targeted training programs focusing on improving VO₂max through structured aerobic and interval-based training. These findings provide practical input for coaches in designing data-driven and individualized training strategies to enhance aerobic endurance and competitive performance in swimming athletes.