Daftar Jurnal Penerbit Universitas Negeri Semarang
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Dinamika Abrasi terkait Perubahan Garis Pantai di Desa Pantai Bahagia Kabupaten Bekasi
No full textDesa Pantai Bahagia merupakan salah satu pesisir di Kabupaten Bekasi tepatnya di Kecamatan Muaragembong yang telah mengalami abrasi dengan sangat signifikan. Kejadian abrasi ini disebabkan oleh naiknya permukaan air laut dan adanya faktor pemicu berupa konversi lahan kawasan mangrove oleh masyarakat untuk keperluan pertambahan lahan tambak. Tujuan dari penelitian ini adalah menganalisis dinamika abrasi secara multitemporal selama 30 tahun di Desa Pantai Bahagia dari tahun 1993, 2000, 2007, 2012, 2020, 2023. Metode yang dilakukan adalah tahapan ekstraksi garis pantai dengan algoritma MNDWI (Modified Diference Water Index). Data garis pantai yang didapatkan lalu dihitung statistik dengan bantuan tools extension DSAS (Digital Shoreline Analysis System) pada ArcMap 10.8 untuk pengolahan data luas perubahan garis pantai yang memiliki nilai abrasi. Statistik yang digunakan yaitu EPR (End Point Rate) dengan membandingkan dua garis pantai serta LRR (Linear Regression Rate). Hasil yang dilakukan uji ground check di area garis pantai pada transek yang mengalami abrasi meningkat setiap tahunnya menyebabkan pengurangan daratan menjadi laut di Dukuh Muara Bendera Timur, Gobah Timur, Kampung Beting dan Blukbuk Timur. Rata –rata pengurangan daratan yang terjadi sebesar -18,77m/tahun pada 1992-2000, di tahun 2000-2007 terjadi sebesar -16,19 m/tahun, pada tahun 2007-2012 terjadi sebesar -31,86 m/tahun, pada tahun 2012-2020 memiliki rata-rata pengurangan daratan sebesar -130,13 m/tahun, serta di tahun 2020-2023 terjadi pengurangan daratan sebesar -60,32 m/tahun
Valorization of Empty Palm Oil Fruit Bunch Fiber as Hydrophobic Cement Board
No full textThe penetration of property business and technology has increased significantly increased due to economic improvement. One of the materials in high demand is cement board. Cement board compost of agricultural waste limits its application due to its easily absorbing water. Herein, we used the empty palm oil fruit bunch as a filler in cement board and coated it with polystyrene from styrofoam waste. The board shows good mechanical strength and fulfill the minimum requirement for cement board standard of Indonesian National Standard (SNI). The board also shows hydrophobic properties, and no water droplets permeate the surface even after 4 h. These findings open a new application for cement board not only for the interior but also for the exterior
Enhanced Biogas Production from Tapioca Wastewater Through the Microbial Electrolysis Cell-Assisted Anaerobic Digestion Process at Various Urea Additions
No full textIn Indonesia, tapioca wastewater is one of the most abundant organic wastewater. It has a great deal of potential for use as a substrate for biogas, but it contains a high ratio of chemical oxygen demand (COD) to nitrogen (N). For this reason, adding nitrogen sources, such as urea, is crucial. Meanwhile, microbial electrolysis cell-assisted anaerobic digestion (MEC-AD) is a novel technology that can be applied to enhance biogas production. Thus, the purpose of this study was to ascertain how adding urea affected the biogas yield from tapioca wastewater through the MEC-AD process. There were six digesters, namely A (MEC-AD urea 0.25 g), B (MEC-AD urea 0.5 g), C (MEC-AD urea 1 g), D (MEC-AD urea 1.5 g), E (MEC-AD without urea), F (AD without urea). The MEC-AD process was carried out at room temperature using a batch system. The results revealed that the MEC-AD (without urea) generated a biogas yield 2.3-fold higher than AD alone (without urea). Then, an increase in urea addition in the MEC-AD process from 0 to 1.5 g enhanced biogas yield from 106.4 to 268.8 mL/g-COD. It means that in MEC-AD, the urea addition of 1.5 g generated 2.5 times more biogas yield than without urea addition. The MEC-AD with urea addition of 1.5 g had the most stable substrate pH and the highest volatile fatty acids during the process. The MEC-AD (without urea) gave a higher COD removal efficiency (21%) than AD alone (without urea), namely 14%. Then, an increase in urea addition from 0 to 1.5 g in MEC-AD increased COD removal from 21% to 38%. Therefore, the best variable was MEC-AD with a urea addition of 1.5 g. This innovation is expected to reduce environmental pollution and provide biogas as alternative energy to substitute the use of fossil fuels.
The Relationship of Agility, Reaction Speed, and Eye-Hand Coordination to Volleyball Forearm Passing Ability
No full textVolleyball, a dynamic team sport, relies heavily on mastering fundamental techniques such as forearm passing to ensure seamless gameplay and effective team strategies. The proficiency in forearm passing is influenced not only by technical skills but also by physical components, including agility, reaction time, and eye-hand coordination. This study aims to analyze the relationship between these three physical factors and forearm passing ability among volleyball players. Conducted at the Tunas Brilliant Volleyball Club, vocational high school Ma’arif NU 01 Limpung, Batang, Central Java, in December 2024, the research employed a quantitative correlational approach. A purposive sampling technique was used to select 30 active players aged 15–18 years. Data were collected through standardized tests: the Illinois Agility Run Test for agility, the Whole Body Reaction Test for reaction time, the ball throw-catch test for eye-hand coordination, and the volleyball forearm passing test for passing ability. Pearson correlation analysis revealed significant relationships: agility showed a strong negative correlation (r = -0,553, p = 0,002), reaction time a moderate negative correlation (r = -0,378, p = 0,039), and eye-hand coordination a strong positive correlation (r = 0,564, p = 0,001) with passing ability. These findings underscore the critical role of integrated physical training in enhancing passing skills. Coaches are recommended to design holistic training programs incorporating agility drills, reaction exercises, and coordination activities to optimize player performance and support long-term volleyball development.
Sustainable Empowerment Strategies in Rural Areas: A Qualitative Study on Women Farmer Groups
No full textBackground: Community empowerment plays an important role in driving sustainable development, especially in rural areas. The Bejiharjo Farmer Women Group (KWT) is an example of community-based empowerment initiatives that have succeeded in developing social entrepreneurship. Through women\u27s active participation, capacity building, and utilization of local resources, this KWT has been able to generate significant economic and social impacts for its communities.
Research Urgency: It is important to understand the empowerment pattern that KWT Bejiharjo applies in building sustainable social enterprises, in order to replicate similar models in other villages. Identification of key success factors can make a major contribution to the development of local potential-based women\u27s and community empowerment programs.
Research Objectives: This study aims to explore the patterns of community empowerment implemented by the Bejiharjo Women Farmers Group (KWT) and identify the key elements that drive the success and sustainability of community-based social entrepreneurship.
Research Method: A qualitative case study approach was employed, involving in-depth interviews, participatory observation, documentation, and focus group discussions. Thematic and narrative analyses were conducted to identify key empowerment patterns, with data triangulation ensuring validity and reliability.
Research Findings: The results of the study show that the empowerment carried out by KWT Bejiharjo has succeeded in increasing members\u27 ownership and sense of responsibility for group activities, along with the growth of collective commitment and social bonds. In addition, intensive training from various partners also improves members\u27 skills and knowledge in the fields of production, marketing, and business management. This effort is supported by the utilization and diversification of local resources, which not only strengthens the household economy, but also creates added value through processed products and expands the potential of educational agrotourism in the local area.
Research Conclusion: The empowerment model applied by KWT Bejiharjo has succeeded in creating a sustainable social entrepreneurship ecosystem. Shared ownership, capacity building, and utilization of local potential are the main foundations in building economic and social independence at the community level
Research Novelty/ Contibution: This research contributes to the development of a community-based empowerment model that is oriented towards social entrepreneurship. The findings in this study can serve as a reference for the development of sustainable empowerment strategies in other rural communities with a participatory and local potential-based approach
Digital Transformation of the e-Mental App and Police Service Quality: Mental Health Role
No full textBackground - Digital transformation has become a strategic approach to improving public service performance, including in law enforcement institutions. The e-Mental application was introduced as part of digital innovation to enhance both the performance and mental health of police officers. However, the direct impact of digital transformation on service quality, particularly when considering internal psychological readiness, remains underexplored.
Research Urgency - Given the growing demands for responsive public services and increasing work-related stress among officers, understanding how digital initiatives contribute to service quality and how mental health interacts with these effects is crucial for sustainable innovation.
Research Objectives - This study aims to analyze the influence of digital transformation via the e-Mental application on the quality of police services, with mental health as a moderating factor. It also seeks to examine the effect of digital transformation on officers\u27 mental health and how this moderates the relationship with service quality.
Research Method - The research was conducted at the Police Resort (Polres) of Donggala Regency, Central Sulawesi. It involved 205 police officers across nine sectoral police stations (Polsek) as respondents for digital transformation and mental health variables, and 205 community members as evaluators of service quality. Data were analyzed using multiple linear regression with an interaction term.
Research Findings - Digital transformation significantly improves both mental health (t = 6.250, p < 0.001) and service quality (t = 7.878, p < 0.001). However, mental health significantly moderates this relationship, weakening the effect of digital transformation on service quality (B = -0.019, t = -3.890, p < 0.001).
Research Conclusion - Digital transformation alone is insufficient for improving service quality without psychological readiness and institutional support.
Research Contribution - This study highlights the importance of integrating psychological well-being into digital innovation strategies, particularly in public service sectors like the police.
Key words: Digital transformation, e-Mental app, mental health, Service quality
Applying User Centered Design and System Usability Scale to Design Knowledge Management System for Exam Proctors in Higher Education
No full textPurpose: This research aims to develop a system interface design solution to address the university’s knowledge management problem with exam proctors. Developing a knowledge management system is expected to maintain the integrity of examinees and reduce the risk of plagiarism. This research identifies user needs in business processes and maps them to relevant features based on previous research.
Methods: This research adopted a user-centered design methodology in developing interface design solutions, which consisted of four stages: understanding the context of use, specifying user requirements, designing solutions, and evaluating against requirements. Semi-structured interviews were used for data collection, and a system usability scale (SUS) questionnaire was employed for design solution evaluation.
Result: This research identified the needs of business processes in higher education in the context of exam proctors and mapped them to a suitable feature solution. Recommendations for information architecture and knowledge management system design implementation in higher education were also provided. This research achieved a SUS score of 74.8, indicating that the developed system met users’ needs.
Novelty: This research provides a practical implementation of developing a knowledge management system in higher education with user-centered design
Brain Tumor Detection Using Improved Fuzzy Logic Classifier Model Based on K-folds Validation
No full textPurpose: This study aims to improve brain tumor detection by integrating Fuzzy Logic with K-folds validation to enhance classification accuracy and robustness. The research addresses the challenge of distinguishing between normal and abnormal brain MRI images.
Methods: This study utilized a public dataset from Kaggle comprising 2,660 MRI images, initially categorized into four classes: Glioma, Meningioma, Pituitary, and No Tumor. For the study, Glioma, Meningioma, and Pituitary were combined into one abnormal label, resulting in two classes: Normal and Abnormal. The methodology involved pre-processing the images, applying Fuzzy Logic with K-folds validation (K=3), and evaluating the model’s performance using single prediction tests.
Result: The proposed approach achieved an exceptional accuracy of 99.88% during the K-folds validation process. The model demonstrated strong performance across all test samples, accurately classifying both normal and abnormal cases, with true positive results in single prediction tests.
Novelty: This study introduces a novel combination of Fuzzy Logic with K-folds validation, demonstrating a significant improvement in classification accuracy compared to existing methods. The integration of these techniques offers a robust framework for brain tumor detection, enhancing diagnostic precision and addressing the challenge of distinguishing between various tumor types in MRI images
Sentiment Analysis on SocialMedia Using TF-IDF Vectorization and H2O Gradient Boosting for Student Anxiety Detection
No full textPurpose: Mental health issues are now a concern for many people. Anxiety or often called Anxiety that is excessive and prolonged has also become the forefront of various psychological disorders that trigger impacts such as stress to suicide. People using social media platforms tend to be a medium for expressing opinions sharing information and even expressing daily emotions. Many studies have shown a correlation between expressing emotional statements on social media and mental disorders. This research aims to conduct sentiment analysis of Anxiety on social media using H2O Gradient Boosting by implementing TF-IDF Vectorization which is set to max feature.
Methods: This research utilizes 6980 post data from social media. The method applied is by conducting Exploratory Data Analysis then Data preprocessing, Tf-Idf Vectoriztion with max feature experiments 100, 250, 500, 1000 and 2000, H2O Gradient Boosting Model, Cross Validation, and Model performance evaluation.
Result: The results of this study show good model performance through max feature TF-IDF = 250 with an accuracy value of 99%, Specificity 99.57%, and Eror Rate of 0.0106.
Novelty: So that the use of the H2O Gradient Boosting model succeeded in providing good performance in classifying anxiety sentiment
Development of a Mental Health Classifier Using LSTM and Text Preprocessing Techniques
No full textPurpose: This study aims to address undiagnosed mental health conditions using social media for early detection. By applying advanced preprocessing techniques and LSTM models, the research improves classification accuracy for depression and PTSD. It highlights deep learning’s potential to process unstructured data and provides a scalable solution for real-world mental health monitoring.
Methods: Data was collected from Twitter using keywords like "depression" and "anxiety." Preprocessing included normalization, tokenization, stemming, and stopword removal. An LSTM-based model with GloVe embeddings, LSTM layers, and dropout was developed. The model’s performance was evaluated using metrics like accuracy, precision, recall, and F1-score to ensure robust and applicable results.
Result: The LSTM model achieved 90% accuracy, outperforming Random Forest (89%) and SVM (89%). Preprocessing steps like tokenization and stemming boosted performance by 15%. The model effectively captured temporal dependencies in text, showcasing its ability to analyze unstructured social media content for mental health detection.
Novelty: This study integrates advanced text preprocessing with LSTM to enhance mental health detection. Unlike traditional methods, it captures temporal nuances using GloVe embeddings. The scalable framework provides a reliable solution for real-world applications, paving the way for multilingual and cross-platform research in mental health analytics