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Coal hauling activities are an important part of mining operation because they affect production continuity, cycle time efficiency, and operational safety. This study aims to analyze the requirements of road support equipment on the coal hauling road from Sector 4 to the new Coal Processing Plant (CPP) at PT. Asmin Bara Bronang, Central Kalimantan. Based on road geometry, traffic density, California Bearing Ratio (CBR), and Unsurfaced Road Condition Index (URCI). The research method used was applied research with a quantitative approach. Primary data ware collected through field measurements consisting of road geometri observations, traffic density observations, Dynamic Cone Penetrometer (DCP) testing to obtain CBR values, and road surface condition assessment using the URCI method. Secondary data were obtained from the company records. The results showed that the hauling road has a total length of 9.1 km with an average width of 16 m, and grade values ranging from -7.68% to 10.52%, which are still below the maximum standard of 12%. Traffic density reached 184 dump trucks/day, for coal hauling and 62 units/day for construction material transportation, indicating high traffic intensity. CBR values ranged from 7% to 100%, showing variations in subgrade bearing capacity. The URCI value ranged from 72,50 to 91.00, indicating fair to good road conditions. Based on the analysis of road conditions and maintenance area requirements, the recommended support equipment for maintaining the hauling road consists of 1 motor grader unit, 1 compactor unit, 1 bulldozer unit, and 1 water truck unit.

This research was motivated by the importance of public relations management in building cooperation among schools, parents, and communities to support the improvement of educational quality in elementary schools. This study aimed to analyze public relations management in increasing parent and community participation in elementary schools. The research used a qualitative approach with a descriptive research design. Data collection techniques were conducted through interviews, observations, and documentation involving the principal, teachers, parents, and community members as informants. Data analysis was carried out through data reduction, data presentation, and conclusion drawing. The results showed that public relations management was implemented through direct communication in parent meetings, the use of digital media such as WhatsApp Groups, parental involvement in school activities, and collaboration with the community in supporting educational programs. Parent participation was reflected in mutual cooperation activities, assisting students’ learning, and supporting school programs. The obstacles found included parents’ limited time, low digital literacy among some parents, and the lack of optimal evaluation of school public relations programs. The implications of this study indicate that effective public relations management can increase parent and community involvement in supporting the educational process in elementary schools.

This research aims to analyze students' learning climate through the application of the Project Based Learning (PjBL) in mathematics learning in class II of Public Elementary School 002 Palembang. This study used a qualitative descriptive method with the study subjects of grade II students. Data collection techniques are conducted through observation, interviews, and documentation. Data analysis is conducted through the stages of data reduction, data presentation, and conclusions drawn. Research results show that the application of the PjBL model is able to form a more conducive learning climate, characterized by increased focus, attention, and student involvement in the learning process. In addition, interactions between students are becoming more directed, group collaboration is getting better, and activities that interfere with learning can be minimized. Project-based activities involving kinesthetic activities such as shearing and sticking help students become more active, creative, and easier to understand the concept of right triangles more concretely. Student-centered learning also provides an opportunity for them to learn independently and in groups so that the learning experience becomes more meaningful. Thus, the application of the PjBL model can be an effective learning alternative in creating an active, conducive, and enjoyable learning atmosphere for elementary school mathematics and supporting the achievement of learning goals optimally.

. Penelitian ini bertujuan untuk menganalisis pengaruh motivasi dan disiplin kerja terhadap kinerja pegawai pada Badan Pengembangan Sumber Daya Manusia (BPSDM) Provinsi Sulawesi Selatan secara parsial dan simultan. Fokus utama penelitian ini adalah untuk mengetahui sejauh mana motivasi dan disiplin kerja mampu meningkatkan kinerja pegawai dalam melaksanakan tugas dan tanggung jawabnya. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis penelitian asosiatif. Populasi dalam penelitian ini adalah seluruh pegawai BPSDM Provinsi Sulawesi Selatan, dengan teknik pengambilan sampel menggunakan probability sampling dan penentuan jumlah sampel menggunakan rumus Slovin dengan tingkat kesalahan 5%, sehingga diperoleh sebanyak 91 responden . Pengumpulan data dilakukan melalui kuesioner, observasi, dan dokumentasi. Analisis data menggunakan regresi linear berganda dengan pendekatan statistik untuk menguji pengaruh variabel independen (motivasi dan disiplin kerja) terhadap variabel dependen (kinerja pegawai). Hasil penelitian menunjukkan bahwa motivasi dan disiplin kerja berpengaruh terhadap kinerja pegawai, baik secara parsial maupun simultan, sehingga peningkatan motivasi dan disiplin kerja akan berdampak pada peningkatan kinerja pegawai di lingkungan BPSDM Provinsi Sulawesi Selatan.

This research was designed to explore the extent to which public beliefs influence the implementation of Digital traceability  systems in the halal industrial sector. The approach used was quantitative with a survey method, where questionnaires were distributed to 60 respondents who were consumers of halal products in Indonesia. Data were analyzed using simple linear regression via Microsoft Excel. Research findings indicate that public confidence has a positive and significant influence on the adoption of Digital traceability  systems, with a regression coefficient of 0.476 and a significance level of 0.000 (<0.05). In addition, the coefficient of determination (R Square) value of 0.219 indicates that public confidence contributes 21.9% to the implementation of the Digital traceability  system, while the rest is determined by other factors that were not researched. These findings confirm that public trust is an important element in encouraging acceptance of digital technology, especially in the halal industry which relies heavily on transparency and consumer confidence. Thus, implementing a Digital traceability  system that is supported by information openness and easy access to technology can be an effective strategy to strengthen consumer trust while expanding technology adoption.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

According to the Regulation of the Minister of ATR/BPN of the Republic of Indonesia No. 14 of 2022 concerning the Provision and Utilization of Green Open Space, an area must have at least 30% Green Open Space of its total area with details of 20% as Public Green Open Space and 10% as Private Green Open Space. The availability of Public Green Open Space in Pangkalpinang City is ± 1,486 ha or ± 14% of the total area, less than 6% of the applicable provisions. The purpose of writing this scientific article is to determine the potential distribution of Public Green Open Space in Pangkalpinang City as an effort to fulfill the availability of Green Open Space in the area. The variables or data used are open land by considering residential / non-residential areas, building density, distance from roads and distance from rivers, as well as land ownership or control status, content in the Spatial Plan, and Green Open Space typology. This study uses overlay analysis techniques to determine the distribution of potential Public Green Open Space in the Pangkalpinang Region. The results of the study indicate that there is still a distribution of potential land that can be developed into public green open space including green open space areas/zones in the form of road borders and other areas/zones in the form of areas that provide protection to the area below, spring borders, underpasses, beaches, and rivers. The distribution of potential public green open space can be used as a consideration in fulfilling the availability of public green open space in the Pangkalpinang area of ​​20% of its area. The fulfillment of Public Green Open Space can provide ecological and social functions for the surrounding environment.

Penelitian ini dilatarbelakangi oleh kesenjangan minat belajar yang melekat di kalangan mahasiswa. Penelitian ini bertujuan untuk membandingkan minat belajar pada mahasiswa STAI Sebelas April mengacu pada tiga tipe gaya belajar, yaitu auditory, visual dan kinestetik berdasar gender. Penelitian ini menerapkan pendekatan kuantitatif dengan metode komparasional menggunakan analisis data statistik melalui uji Two Way Anova dengan General Linear Model. Hasil penelitian ini mengungkap temuan yaitu: Terdapat pengaruh gaya belajar auditory, visual dan kinestetik terhadap minat belajar mahasiswa (p = < 0.001); Namun tidak terdapat pengaruh gender terhadap minat belajar (p = 0.610); Dan tidak terdapat interaksi gaya belajar dengan gender dalam membangun minat belajar mahasiswa (p = 0.704 > 0.05). Penelitian ini berimplikasi bahwa keragaman gaya belajar mahasiswa terkait erat dalam memupuk minat belajarnya. Penelitian merekomendasikan agar dosen mengakomodir semua keragaman gaya belajar di dalam proses perkuliahan.

This study aims to analyze a comparison of coal stockpile volumes using the DJI Mavic 3 Pro Unmanned Aerial Vehicle (UAV) method versus the truck count method at PT. Mitra Barito. Data collection was conducted through aerial photography using a UAV at altitudes of 60 meters and 70 meters, as well as Ground Control Point (GCP) measurements using GPS. The aerial imagery data was processed using photogrammetry software to generate orthophotos and a Digital Elevation Model (DEM), followed by a geometric accuracy test based on the Geospatial Information Agency Regulation No. 6 of 2018, using the Circular Error 90% (CE90) and Linear Error 90% (LE90) parameters. The research results show that high-quality processing at an altitude of 60 meters yields a CE90 value of 2.1619 meters and an LE90 value of 4.3656 meters, thereby meeting the accuracy standards for RBI maps at a scale of 1:5,000, Class 3 for horizontal accuracy, and a scale of 1:10,000, Class 3 for vertical accuracy. Volume calculations of the stockpile using UAVs yielded a result of 22,750.900 m³, while the truck count method produced a volume of 23,503.300 m³. The volume difference between the two methods was 753.400 m³, with a deviation percentage of 3.2%. Based on the research results, the UAV method is considered capable of providing relatively accurate calculations of coal stockpile volume.

This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

The article entitled “Gender Construction and Representation of Social Actors in the Drama Lutung Kasarung: A Critical Discourse Analysis by Theo van Leeuwen” aims to describe the representation of gender-based social actors through inclusion and exclusion strategies and to reveal the construction of gender ideology built in the drama text. This study uses a qualitative approach with a descriptive-analytical design. The research data source is the drama text Lutung Kasarung, while the analysis unit includes dialogue, narrative, and the depiction of characters who represent gender-based social actors. Data collection techniques are carried out through documentation by reading, identifying, and grouping data according to Theo van Leeuwen's analysis categories. The results of the study show that the inclusion strategy is more dominantly used to present male characters as strong, rational figures, and have authority in determining the course of the story. In contrast, female characters are represented in two patterns, namely the ideal passive woman and the dominant woman who is constructed negatively. In addition, the exclusion strategy is used to obscure the role of women in decision-making, thereby reinforcing gender marginalization. This study concludes that the drama Lutung Kasarung represents patriarchal ideology through discourse practices that shape power relations between men and women.

The Merdeka Curriculum, as a new educational policy in Indonesia, emphasizes the cultivation of the Pancasila Student Profile, which is rich in citizenship values. This study aims to analyze the application of citizenship values in Pancasila and Civic Education (PPKn) learning within the framework of the Merdeka Curriculum at the high school level. This research employs a qualitative descriptive method, collecting data through classroom observations, semi-structured interviews with PPKn teachers, and analysis of teaching modules and project-based learning documents (P5). The findings indicate that the application of citizenship values is primarily integrated through Project-Based Learning to Strengthen the Pancasila Student Profile (P5). Values such as mutual cooperation (gotong royong), critical reasoning (bernalar kritis), and global diversity (berkebinekaan global) are the most frequently emphasized. However, teachers face challenges, including the need to adapt pedagogical approaches, difficulties in assessing value-based outcomes authentically, and a lack of specific reference materials aligned with the new curriculum. The implication of this research is the urgent need for structured professional development for teachers and the development of comprehensive teaching guides to ensure the effective and standardized implementation of citizenship values in PPKn learning across all educational units.

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.

Tujuan dari penelitian ini adalah untuk mengevaluasi bagaimana penerapan Standar Akuntansi Pemerintahan (SAP) memengaruhi kualitas laporan keuangan pemerintah daerah. Selain itu, penelitian ini juga menyelidiki peran Sistem Informasi Pemerintahan Daerah (SIPD) sebagai faktor moderasi. Dengan menggunakan teknik purposive sampling, penelitian kuantitatif ini mengumpulkan 31 responden dari Organisasi Perangkat Daerah (OPD) di bawah pemerintah Kota Medan. Data yang digunakan berasal dari data awal yang dikumpulkan melalui kuesioner. Structural Equation Modeling berbasis Partial Least Square (SEM-PLS), yang dibantu oleh SmartPLS, digunakan untuk melakukan analisis data. Hasil penelitian menunjukkan bahwa penggunaan SAP berdampak positif dan signifikan terhadap kualitas laporan keuangan pemerintah daerah, tetapi SIPD tidak dapat mengontrol hubungan antara penggunaan SAP dan kualitas laporan keuangan. Studi ini meningkatkan penelitian teori institusional, terutama tentang bagaimana tekanan regulasi dan normatif membantu meningkatkan kualitas pelaporan keuangan sektor publik dengan menerapkan standar akuntansi. Penelitian ini memiliki manfaat bagi pemerintah daerah untuk meningkatkan konsistensi penerapan SAP dan mengoptimalkan SIPD melalui peningkatan kemampuan sumber daya manusia dan dukungan teknologi informasi. Penelitian ini terbatas pada jumlah sampel yang relatif kecil serta ruang lingkup yang hanya mencakup OPD di satu daerah, sehingga hasil penelitian belum dapat digeneralisasi secara luas.

This study aims to examine the debate regarding the effectiveness of traditional learning methods in science education at Madrasah Ibtidaiyah (MI) amid the development of educational digitalization. The study employed a qualitative approach with a case study design conducted in Jember Regency for three months, from February to April 2026. The research informants consisted of 16 participants, including madrasa principals, teachers, parents, and community members. Data collection techniques were carried out through interviews, observations, and documentation, which were then analyzed using descriptive qualitative techniques. The findings revealed that traditional methods are still considered effective in helping students understand basic science concepts because the learning process is systematic and easy to comprehend. However, limited access to technology in several schools remains an obstacle to the equal implementation of digital learning. In addition, although digital learning can increase students’ motivation and engagement, it does not necessarily lead to an optimal improvement in conceptual understanding. Therefore, this study concludes that a combination of traditional and digital learning methods is the most appropriate approach in science learning at elementary schools and Madrasah Ibtidaiyah, considering students’ needs as well as the availability of facilities and infrastructure. structure.

The development of digital technology has driven significant changes in learning practices, including Indonesian language learning, which increasingly utilizes online platforms such as YouTube. In this context, digital literacy is not only related to the technical ability to access information but also encompasses ethical, critical, and character-building aspects of students. This study aims to analyze the integration of digital literacy and character education values in online learning through Indonesian language educational videos on YouTube. The research employed a qualitative approach using content analysis methods. The research data consisted of Indonesian language learning videos selected purposively based on the relevance of the material, channel popularity, and consistency of educational content. The findings reveal that Indonesian language learning videos on YouTube contain various character education values, such as responsibility, academic honesty, hard work, independence, tolerance, and communicative attitudes. These values are integrated through the presentation of materials, the use of polite language, contextual examples, and reflective invitations that encourage students to think critically. In addition, aspects of digital literacy are reflected in the ability of video content to guide students toward the wise, selective, and responsible use of digital media. However, the study also identified several limitations, particularly the lack of systematic explicitness of character values and the suboptimal two-way interaction between educators and students.

This study was motivated by the widespread use of Indonesian language that does not conform to standard linguistic rules in Instagram content created by adolescents aged 17–20 years. As a social media platform that emphasizes speed and self-expression, Instagram encourages users to employ slang, nonstandard abbreviations, code-mixing, and various other forms of language deviation. This study aims to analyze the forms of Indonesian language errors found in adolescents’ Instagram content and to identify the factors contributing to these errors. The research employed a descriptive qualitative approach using content analysis. Data were collected through questionnaires distributed to 37 respondents and through an analysis of their language use habits on Instagram. The findings reveal that the most dominant forms of language errors include letter repetition to emphasize emotion (78.3%), nonstandard abbreviations (56.7%), code-mixing between Indonesian and foreign languages (45.9%), and the use of slang (45.9%). The main factors influencing the use of nonstandard language are the desire to appear relaxed and informal (86.5%), the need for fast and practical communication (67.6%), and the influence of peer groups and social media trends. Nevertheless, most respondents are aware that these habits may negatively affect their formal language skills. This study highlights the importance of digital language literacy to help adolescents use Indonesian appropriately according to different communication contexts.