Traditional electrical load forecasting often relies on macro approaches to analyzing historical data (time series) and energy consumption patterns (sectoral), which do not account for geographic variations and complicate the mapping of load centers. To address this, the study integrates Geographically Weighted Regression (GWR) model with Geographic Information Systems (GIS), providing a more nuanced analysis. GWR extends linear regression by incorporating geographic factors, resulting in location-specific regression coefficients that capture local variations in load density. GIS complements this by offering detailed spatial analysis and visualizing the distribution of electrical loads. The application of GWR revealed that land use for industry, land use for social purposes, GDP, and electrical load for business significantly affect load density in most subdistricts. The GWR model achieved a high R2 of 99.82%, indicating that these variables explain almost all of the variance in load density, while GIS-based plots illustrated the spatial distribution and significance of these variables. The model has been validated as suitable for representing all subdistricts within the cluster, with an average MAPE of 3.36%. Therefore, the study demonstrates that the integration of GWR and GIS significantly improves the precision of load growth projections and the estimation of load centers. Additionally, it allows for a precise determination of the location and distribution of load centers, aligning forecasts with the underlying geographic structure and facilitating better-informed decision-making for energy management and infrastructure planning

Load demand forecasting is crucial in energy supply planning due to economic progress and territorial expansion, where land utilization transforms dynamically. An accurate sectoral load prediction can preclude the loss of beneficial opportunities arising from excessive load demand or excessive investment at a low-growth juncture. However, the particular area in this sectoral approach is still relatively large, rendering it incapable of precisely projecting load at minor points (micro-spatial). This study has proposed a micro-spatial load prediction strategy that categorizes identified areas into smaller grids or districts. This procedure includes clustering similar sites together for improved accuracy. K-Means is one of the partitional clustering approaches, a clustering algorithm utilizing object-based centroid-based partitioning approaches. The algorithm determines a cluster's centroid or centre as the average point for the cluster. This technique is advantageous as it can process extensive data efficiently and is appropriate for circular data. This technique can divide the data into multiple partitions, ensuring that each object belongs to precisely one cluster. Subsequently, mathematical modelling is used to predict the load of each cluster, which can then be utilized to more accurately evaluate the positions and sizes of prospective substations, transmission, and distribution facilities.

Abstract: This study aims to describe the quality of the explanatory text learning videos for the events of November 10 based on validity, practicality, and effectiveness. Validity is based on the assessment of material experts and media experts. Practicality based on user assessment and student responses. Effectiveness based on the results of trials in class. This study uses research and development methods. The research model uses ADDIE (Raiser and Mollenda). Data collection instruments use questionnaires and tests. The results of the study show that the learning video of the explanatory text of the events of 10 November has good quality reading and viewing elements. The results of the expert's assessment stated that all aspects of the learning video material were in the very good category (80.68%). The results of the media expert's assessment stated that the learning video design was in the very good category (89.28%). The results of the user assessment stated that all aspects of the learning videos were in the very good category (93%). The results of student responses stated that all aspects of learning videos were in the very good category (83.8%). The trial results stated that all aspects of the learning video were effective and in the very good category (71.42%). Based on data analysis, it can be stated that the learning videos are of high quality and can be used as learning media for explanatory texts on the events of November 10 in class VII-B at SMP Muhammadiyah 17 Surabaya. Keywords: explanatory text, learning video quality Abstrak: Penelitian ini bertujuan untuk mendeskripsikan kualitas video pembelajaran teks eksplanasi peristiwa 10 November berdasarkan kevalidan, kepraktisan, keefektifan. Kevalidan berdasarkan penilaian ahli materi dan ahli media. Kepraktisan berdasarkan penilaian pengguna dan respon peserta didik. Keefektifan berdasarkan hasil uji coba di kelas. Penelitian ini menggunakan metode penelitian dan pengembangan. Model penelitian menggunakan ADDIE (Raiser dan Mollenda). Instrumen pengumpulan data menggunakan angket dan tes. Hasil penelitian menunjukkan video pembelajaran teks eksplanasi peristiwa 10 November elemen membaca dan memirsa berkualitas. Hasil penilaian dari ahli menyatakan bahwa seluruh aspek materi video pembelajaran dalam kategori sangat baik (80,68%). Hasil penilaian dari ahli media menyatakan bahwa desain video pembelajaran dalam kategori sangat baik (89,28%). Hasil penilaian dari pengguna menyatakan bahwa seluruh aspek video pembelajaran dalam kategori sangat baik (93%). Hasil respon peserta didik menyatakan bahwa seluruh aspek video pembelajaran dalam kategori sangat baik (83,8%). Hasil uji coba menyatakan bahwa seluruh aspek video pembelajaran efektif dan dalam kategori sangat baik (71,42%). Berdasarkan analisis data dapat dinyatakan bahwa video pembelajaran berkualitas dan dapat digunakan sebagai media pembelajaran teks eksplanasi peristiwa 10 November di kelas VII-B di SMP Muhammadiyah 17 Surabaya. Kata Kunci: kualitas video pembelajaran, teks eksplanasi

ABSTRACT One of the efforts to prevent infectious diseases is by giving immunization infants and toddlers. Immunization is one of the factors that affect the body's resistance to various diseases or immunity, which will influence an anthropometric nutritional status and child survival. To determine the relationship between basic immunization and nutritional status in children aged by 1-3 years old. The design of this research is using correlational quantitative with cross sectional design. The number of samples is 114 children aged by 1-3 years old using purposive sampling technique. The results of the univariate analysis showed that most of the children aged by 1-3 years old in Citangkil II Community Health Center (Puskesmas) in Cilegon City were given complete basic immunization (78.1%) and had good nutritional status (71.9%). The results of the bivariate analysis obtained p value: 0,001, it means there is relationship between the provision of basic immunization and the nutritional status of children by aged 1-3 years old. There is a relationship between basic immunization and nutritional status in children aged by 1-3 years old. Keywords: Immunization, Nutritional Status, Toddler ABSTRAK Salah satu upaya pencegahan penyakit infeksi adalah dengan pemberian imunisasi pada bayi dan balita. Imunisasi merupakan salah satu faktor yang mempengaruhi daya tahan tubuh terhadap berbagai penyakit atau kekebalan tubuh yang selanjutnya akan berpengaruh pada status gizi antropometri dan survival anak. Untuk mengetahui hubungan pemberian imunisasi dasar dengan status gizi pada anak usia 1-3 tahun. Desain penelitian adalah kuantitatif korelasional dengan desain cross sectional. Jumlah sampel adalah 114 anak usia 1-3 tahun diambil dengan menggunakan teknik purposive sampling. Hasil analisis univariat menunjukkan anak usia 1-3 tahun di Puskesmas Citangkil II Kota Cilegon sebagian besar memiliki status gizi baik (71,9%) dan sebagian besar diberikan imunisasi dasar lengkap (78,1%). Hasil analisis bivariat didapatkan nilai p: 0,001, hal tersebut berarti ada hubungan antara pemberian imunisasi dasar lengkap dengan status gizi pada anak usia 1-3 tahun. Ada hubungan antara pemberian imunisasi dasar lengkap dengan status gizi pada anak usia 1-3 tahun di Wilayah Kerja Puskesmas Citangkil II Kota Cilegon. Kata Kunci: Balita, Status Gizi, Perkembangan

The obligation to reduce carbon emissions due to conventional generators increases the necessity of renewable energy power plant installation. Floating Photo-Voltaic (PV) can be a solution to overcome the problem of land acquisition inconstructing renewable energy. Nevertheless, the renewable energy injection does not come without problems. The traditional rules which permit renewable energy release during disturbances can cause instability in the system. Thus, several countries, including Indonesia, have started implementing lowvoltage ride-through (LVRT) and high-voltage ride-through (HVRT) regulations. This paper evaluates the LVRT performance of Floating-PV injection for 60 MW capacity scenarios in the 10 GW Sumatran system. Voltage dip simulation is carried out with 3-phase faults on the point of common coupling (PCC) by varying impedance values and two different droop controller value. The PCC’s voltage dip to 0 pu and 0.5 pu forces the Floating-PV to supply 1 pu of reactive current for both scenarios. However, when a three-phase fault causes the voltage drop to 0.85 pu, the reactive current response cannot meet the LVRT in the 2 droop value scenarios. A proper adjustment of droop value is employed in scenario 2 and able to fulfill the LVRT requirement to supply 0.6 pu reactive current using 4 as droop value. Under all scenarios PCCs voltage able to bounce back into allowable range and ensure PV is capable of dynamic voltage support.

In this article, we demonstrated the utilization of digital poster media of a fantasy fairy tale in learning activities for Indonesian students. By observations, we have found that the participants had a low ability to understand the material. This research was conducted to validate the usability of digital poster media in learning activities. We have designed a digital poster containing fantasy fairy tale material to satisfy our objective. The mentioned poster is then used to achieve the elements of phase D of the prototype curriculum. We used the study method of the Hannafin and Peck development model with three phases (needs assessment, designing, and development and implementation). We have conducted evaluation and revision stages in all phases on the corresponding poster. Based on the calculation of the two validation aspects (i.e., subject and media experts), we obtained the value of subject experts as 87.5%; and media experts as 89.58%. The average of the two validators was 88.54 %. Therefore, both validators stated that the mentioned poster is valid. Thus, this poster has the potential to be applied and to improve the competence of the viewer element.

Minimum Competency Assessment (AKM) as a new assessment system needs to be addressed by the organized school by making various preparations. This study aims to describe the preparation in dealing with AKM in Elementary Schools (SD). This research is qualitative research with a phenomenological approach.The subjects in this study were principals and teachers of a public elementary school in the Eromoko sub-district, Wonogiri district. Data was collected by using observation, interview, and documentation techniques. Data analysis techniques using interactive analysis include data collection, reduction, presentation, and conclusion. The results showed that the AKM organizing education unit had prepared rooms and equipment by preparing a waiting room, AKM room, Chromebook, electricity and stable internet. Administrative preparation was done by printing and pasting the required documents. The preparation of students was done by providing socialization, practice about AKM questions using books, and training on the use of computers.

Physiological direct current (DC) potential shifts in electroencephalography (EEG) can be masked by artifacts such as slow electrode drifts. To reduce the influence of these artifacts, linear detrending has been proposed as a pre-processing step. We considered quadratic detrending, which has hardly been addressed for ultralow frequency components in EEG. We compared the performance of linear and quadratic detrending in simultaneously acquired DC-EEG and transcutaneous partial pressure of carbon dioxide during two activation methods: hyperventilation (HV) and apnea (AP). Quadratic detrending performed significantly better than linear detrending in HV, while for AP, our analysis was inconclusive with no statistical significance. We conclude that quadratic detrending should be considered for DC-EEG preprocessing.

This paper discusses the performance of existing resilience matrix. The calculation of the resilience metrics is simulated on channel 6 of the RBTS bus using the Typhoon Vicente disturbance event in 2012. The estimation of the resilience index employed the sequential Monte Carlo method and is based on the transmission line's fragility curve. The two parameters considered in estimating resilience are the ratio of restoration speed to length of disturbance and the area of the comparison area on the system performance curve under fault conditions and normal conditions. Combining these two equations utilizes the weighted sum method, in which the weighting arrangement is carried out by simulating disturbance events in 3 scenarios. Scenario variations that are consider in this study are transmission line designed wind speed, repair speed, and the number of repair teams. Based on the simulation, it was found that the most appropriate weighting for the parameter area is 0.5, and for the speed of repair per length of time of disturbance is 0.5.

Current discovery indicates that Type 2 Diabetes (T2D) could be categorized into many sub-clusters, which is a step towards precision medicine. Implementation of feature scaling to cluster T2D into subgroups is crucial, aiming to transform and fit the data within a specific scale. This paper aims to compare the differences between cleaned, normalized, and standardized data in k-means clustering using T2D patient data. Two data transformation approaches were applied on the clustering algorithm, namely data normalization and data standardization. By comparing the clustering analysis results, normalized data (Elbow method (EM) =435.63) illustrates the best data point distribution to form clusters and good internal clustering validation scores in comparison to cleaned data (EM =502,254.97) and standardized data (EM = 23,518.82). We concluded that data normalization in the k-means clustering algorithm is the best method for data transformation for T2D sub-clustering compared to cleaned data and standardized data.

Depression is a debilitative disease that affects over 300 million people all around the globe. It affects the functionality of people suffering from it, which implicates to socioeconomic burden to individual, families and societal levels. The subjectivity symptoms and signs in diagnosing depression on patients is a great problem among psychiatrists and psychologists. By building a depression risk model, it helps physician to identify depression with higher efficiency, accuracy and specificity. Healthcare will be improved in terms of cutting costs, time of service and energy to serve the patients. By Machine Learning, specifically Supervised Learning uses classifiers and feature extraction tools to identify what are the most significant factors to diagnose depression. This method helps to build a risk model which helps to improve in identifying depression among liable patients.

High-density electroencephalography (HD-EEG) is currently limited to laboratory environments since state-of-the-art electrode caps require skilled staff and extensive preparation. We propose and evaluate a 256-channel cap with dry multipin electrodes for HD-EEG. We describe the designs of the dry electrodes made from polyurethane and coated with Ag/AgCl. We compare in a study with 30 volunteers the novel dry HD-EEG cap to a conventional gel-based cap for electrode-skin impedances, resting state EEG, and visual evoked potentials (VEP). We perform wearing tests with eight electrodes mimicking cap applications on real human and artificial skin. Average impedances below 900 k omega for 252 out of 256 dry electrodes enables recording with state-of-the-art EEG amplifiers. For the dry EEG cap, we obtained a channel reliability of 84% and a reduction of the preparation time of 69%. After exclusion of an average of 16% (dry) and 3% (gel-based) bad channels, resting state EEG, alpha activity, and pattern reversal VEP can be recorded with less than 5% significant differences in all compared signal characteristics metrics. Volunteers reported wearing comfort of 3.6 +/- 1.5 and 4.0 +/- 1.8 for the dry and 2.5 +/- 1.0 and 3.0 +/- 1.1 for the gel-based cap prior and after the EEG recordings, respectively (scale 1-10). Wearing tests indicated that up to 3,200 applications are possible for the dry electrodes. The 256-channel HD-EEG dry electrode cap overcomes the principal limitations of HD-EEG regarding preparation complexity and allows rapid application by not medically trained persons, enabling new use cases for HD-EEG.

We designed cranial electro stimulation with a low current intensity, which is applied to the head indirectly using direct current intensity. Unidirectional transcranial stimulation is a non-invasive brain stimulation method that has been shown to be effective in modulating cortical excitability and guiding human perception and behavior. The purpose of this research is to design a low-current-intensity cranial electro-stimulation therapy device that is affordable, dependable, and feasible. The CES has a frequency range of 10, 13, and 15 Hz and treatment times of 15, 30, and 45 minutes. The CES generates a current intensity of 0.25, 0.5, 0.75, and 1 mA. The design of CES prototypes was tested at Balai Pengamanan Fasilitas Kesehatan (BPFK) Jakarta, Indonesia, which includes electrical safety measurement, performance testing, and battery reliability. The BPFK Jakarta declares that the test meets the requirements of the testing method and that the tool has passed the test. In addition, the tool has been issued a certificate with no YK.01.03/XLVII.2/PK/2022.

Objective: This study aims to determine the effect of leadership, competence, and innovation on employee performance. Methodology/Technique: The study was conducted using primary data obtained from a survey of 160 employees of Bank Indonesia Department of Money Management (DPU). The data analysis method used is SEM with Lisrel 8.80 statistical software. Findings: Results show that leadership had no positive and significant effect on employee performance, while competency, and innovation had a positive and significant effect on the employee performance of Bank Indonesia. Department of Money Management (DPU). Novelty: Data processing proves that leadership, competence, and innovation simultaneously affect the performance of Bank Indonesia employees (DPU), but leadership does not have a significant impress on employee performance, it shows that the performance of Bank Indonesia employees (DPU) prioritizes the system. From the results of competencies and innovations that show a positive and significant impress, it shows that the achievement performance of DPU employees which is driven by competence and innovation possessed by DPU employees is more dominant and also encourages the birth of breakthroughs that can make the work process more effective and efficient.

The most prevalent disease is type 2 diabetes mellitus (T2DM), a chronic metabolic disorder. T2DM is linked to fat buildup in the lower torso around the abdomen, which leads to fat buildup in the belly region. As a result, it’s important to categorize and forecast diabetes patients based on their dietary intake. In this study, we used the pre-trained Inception V3, Keras, and Tensorflow convolutional neural network (CNN) model to identify different food categories. Comparing the CNN model’s accuracy to other methods from earlier studies, it achieved 96.6%, which is fairly high. Additionally, there is a correlation between calories with fat, carbs, protein, and sugar related with T2DM via linear regression between nutrition classes.

Purpose of the study: The purpose of this study is to design and formulate the practice of optimization strategies performed by Islamic schools to improve school quality and customer satisfaction. Methodology: This study used field research method as the data obtained through interviews and direct observation. The object of the research is SMP Muhammadiyah Sinar Fajar Cawas and SMP Islam Terpadu Muhammadiyah An Najah Jatinom Klaten, Central Java, Indonesia Main Findings: The results of the study show that the optimization of quality improvement performed by two schools under study is a marketing mix strategy through 7P application: (1) product of a variety of programs; (2) price of school fees offered to parents; (3) places of both schools are strategic and accessible by public and private transportation; (4) promotion through printed, electronic, and social media; (5) people including educators and office staff are young and enthusiastic; (6) physical evidence of school buildings and student reports and; (7) process of teaching and learning in both schools have met national education standards. The seven concepts are very influential in improving and optimizing the progress and quality of schools. The most influential concept applications in SMP Muhammadiyah Sinar Fajar are product, price, place, and people. Meanwhile, product, price, place, and physical evidence are five major applied concepts carried out in SMP Islam Terpadu Muhammadiyah An Najah. Applications of this study: This research is expected to be applied in schools, universities, and the wider community that focuses on education issues and optimizing the quality of Islamic schools Novelty/Originality of this study: Therefore, the impacts of the seven concepts are the high public interest; young, enthusiastic, and high creativity teachers; a variety of programs; teachers who are admired by prospective students and parents and; professional and certified school principals.

During an in vitro fertilization (IVF), an egg cell and sperm are combined outside of the body. The selection of embryos during IVF is very important. The quality of the embryo needs to be evaluated before it may be transferred. At this moment, the quality of embryos is evaluated visually. The morphological judgment is dependent on the expertise and experience of the attending physician or embryologist. The evaluation of embryo images can be done with the use of artificial intelligence (AI), which can be utilized to achieve unbiased automatic embryo segmentation. Both supervised and unsupervised methods can be used to complete the segmentation process. CNN is utilized in this study to perform the segmentation of embryo pictures. The model that performs the best in this research makes use of typical training data and divides it up into two classes. It has an accuracy of 93.8 percent, and by using it, the research can assess whether an embryo is usable.

In high workload areas such as the Intensive or Critical Care Units (ICU/CCU), clinicians are burdened with too many alarms and false alarms, leading to poor user response or no response to alarm signals, which in turn leads to serious patient safety concerns, adverse events such as injury and deaths. Even with the implementation of the IEC international alarm standard, alarm hazards are still seen as the top health technology hazard in healthcare institutions. There are numerous new developments in alarm technology, including in the areas of alarm detection and smart alarm design, aimed at improving the sensitivity and performance of alarm systems. However, there is still a lack of studies on the application of Human Factors Engineering (HFE) principles and AI in designing alarms for medical devices that could improve user response and ensure patient safety. This research aims to develop a fuzzy logic base multimodality clinical alarm monitor software to improve alarm response among the clinicians in ICU/CCU and the performance of the clinical alarm. The research involves testing, verifying, and validating the fuzzy-based multimodality clinical alarm to improve the performance of the alarm system. The proposed fuzzy alarm is compared to the medical professional interpretation of a patient physiological condition extracted from the MIMIC II database. The results show that the proposed fuzzy alarm can match the interpretation of medical professionals with high accuracy. In terms of sensitivity and specificity, the proposed alarms achieve good performance with blood pressure and heart rate specificity and sensitivity at 100%. Meanwhile, sensitivity and specificity for respiratory rate are at 97.59% and 99.68%, while sensitivity and specificity for oxygen saturation are at 100% and 98.04%, respectively. The research concluded that incorporating alarm information systems based on risk, human factor engineering principles, and fuzzy logic into the alarm system significantly improves user response while reducing alarm hazards and optimising the performance of the alarm system.

Majority of strokes are brought on by an unanticipated obstruction of blood flow to the brain and heart. Stroke severity can be reduced by being aware of the various stroke warning signs in advance. Previous study on stroke prediction had an accuracy less than 90%. Sample size of 1000 – 2000 for that study was insufficient to justify the results obtained by the trained model. In this study, comparisons are made among different approaches to the stroke prediction model, include four different classification methods, which are logistic regression, Random Forest, Decision Tree and Support Vector Machine (SVM). The results obtained by the classifiers were trained with 2000 samples and 3109. All the classifiers were then tested individually. The accuracy for each model are, 91% for Decision Tree, 95% for Random Forest, 95% for Logistic Regression and 100% Support Vector Machine (SVM). As a conclusion, our study suggested that SVM approach is fit well for stroke prediction model as it achieved the highest accuracy compared to the others.