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The Decision Support Sistem (DSS) for selecting exemplary teachers at SMP Negeri 2 Loli aims to assist the school in determining teachers who deserve to be recognized as exemplary teachers. The evaluation of exemplary teachers involves various criteria such as teaching performance, discipline, creativity, and communication skills with students. However, the selection process conducted manually tends to be subjective and time-consuming. Therefore, a sistem is needed to simplify and accelerate the selection process while producing objective and transparent results. This sistem is developed using the Simple Additive Weighting (SAW) method, which is one of the techniques in multi-criteria decision-making. The SAW method works by assigning weights to each predetermined criterion and calculating the weighted scores for each alternative (teacher). These weighted scores are then summed to determine the ranking order of teachers who meet the criteria as exemplary teacher candidates. The results of this study indicate that the implementation of the SAW method can produce a more structured, objective, and efficient exemplary teacher selection process. The sistem facilitates the school in conducting evaluations and decision-making, while also reducing the potential for errors that may occur during manual selection. Thus, this decision support sistem is expected to make a significant contribution to improving the quality and accuracy of exemplary teacher assessments at SMP Negeri 2 Loli.

The Family Welfare Empowerment (PKK) is a community organization that plays a vital role in improving family welfare in Indonesia. Through various programs, the PKK contributes to family empowerment, particularly in education, health, and economics. However, many PKK activities are still conducted manually, including recording and managing member data and activities. This creates several challenges, such as difficulty in archiving, time-consuming information searches, and difficulties in preparing accurate and timely reports. This research aims to design and develop a web-based information system that can simplify member data management, activity recording, attendance management, and report generation more quickly, efficiently, and accurately. This system will be designed using an object-oriented approach, involving several stages, starting from needs analysis, system design, implementation, and system testing. This information system is expected to improve service efficiency, facilitate PKK administrators in monitoring member activity, and support a more precise and weighted data-based decision-making process. Another advantage is that this system will also facilitate the PKK in producing faster, more comprehensive, and more structured activity reports, thereby increasing organizational transparency and accountability. Therefore, this web-based information system is expected to be a solution to improve the PKK's performance in carrying out its function of empowering family welfare.

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disorder that presents significant diagnostic challenges due to its heterogeneous clinical manifestations and symptom overlap with other neurological conditions. Early and accurate diagnosis is critical for initiating timely interventions and improving patient outcomes. Traditional diagnostic approaches rely heavily on clinical expertise and manual interpretation of neuroimaging data, such as structural MRI, Diffusion Tensor Imaging (DTI), and functional MRI (fMRI), which are inherently time-consuming and prone to interobserver variability. Recent advances in Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated potential for automating neuroimaging analysis, yet existing models often suffer from limited generalizability across modalities and datasets. To address these limitations, we propose a Transformer-augmented deep learning ensemble framework for automated ALS diagnosis using multi-modal neuroimaging data. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Vision Transformers (ViTs) to leverage the complementary strengths of spatial, temporal, and global contextual feature representations. An adaptive weighting-based fusion mechanism dynamically integrates modality-specific outputs, enhancing the robustness and reliability of the final diagnosis. Comprehensive preprocessing steps, including intensity normalization, motion correction, and modality-specific data augmentation, are employed to ensure cross-modality consistency. Evaluation using 5-fold cross-validation on a curated multi-modal ALS neuroimaging dataset demon-strates the superior performance of the proposed model, achieving a mean classification accuracy of 94.5% ± 0.7%, precision of 93.9% ± 0.8%, recall of 92.9% ± 0.9%, F1-score of 93.4% ± 0.7%, spec-ificity of 97.4% ± 0.6%, and AUC-ROC of 0.968 ± 0.004. These results significantly outperform baseline CNN models and highlight the potential of transformer-augmented ensembles in complex neurodiagnostic applications. This framework offers a promising tool for clinicians, supporting early and precise ALS detection and enabling more personalized and effective patient management strategies.

This study focuses on the weight of broiler chicken carcass components. This study aims to see the extent to which this treatment has an effect on the weight of broiler chicken carcass components. The study used 80 chickens, with feed given CP 11 and CP 12. This study with a Completely Randomized Design includes 4 treatments 5 replications. Treatments are P0 = P0 = Level 0% in 1 L of drinking water, P1 = Level 7.5% in 1 L of drinking water, P2 = Level 15% in 1 L of drinking water. P3 = Level 22.5% in 1 L of drinking water. The study used Analysis of Variance (ANOVA) to see significance (P <0.05) and continued with the Duncan test to see significant differences between treatments. The results of the study statistically provide a significant effect (P <0.05) on the breast weight variable, but not significant (P>0.05) on the thigh weight, wing weight, and back weight variables. It can be concluded that the addition of ginger extract has a positive effect on breast weight, thigh weight, wing weight, and back weight of broiler chickens.

Chronic diseases such as chronic kidney disease (CKD), diabetes, and heart disease remain major causes of mortality worldwide, highlighting the need for accurate and interpretable diagnostic models. However, conventional machine learning methods often face challenges of limited generalization, feature redundancy, and class imbalance in medical datasets. This study proposes an integrated classification framework that unifies three complementary feature paradigms: classical tabular attributes, deep latent features extracted through an unsupervised Long Short-Term Memory (LSTM) encoder, and quantum-inspired features derived from a five-qubit circuit implemented in PennyLane. These heterogeneous features are fused using a feature-wise attention mechanism combined with an AdaBoost classifier to dynamically weight feature contributions and enhance decision boundaries. Experiments were conducted on three benchmark medical datasets—CKD, early-stage diabetes, and heart disease—under both balanced and imbalanced configurations using stratified five-fold cross-validation. All preprocessing and feature extraction steps were carefully isolated within each fold to ensure fair evaluation. The proposed hybrid model consistently outperformed conventional and ensemble baselines, achieving peak accuracies of 99.75% (CKD), 96.73% (diabetes), and 91.40% (heart disease) with corresponding ROC AUCs up to 1.00. Ablation analyses confirmed that attention-based fusion substantially improved both accuracy and recall, particularly under imbalanced conditions, while SMOTE contributed minimally once feature-level optimization was applied. Overall, the attention-guided AdaBoost framework provides a robust and interpretable approach for clinical risk prediction, demonstrating that integrating diverse quantum, deep, and classical representations can significantly enhance feature discriminability and model reliability in structured medical data.

This study evaluated the population status of the conch Strombus luhuanus at Asol Beach, , Haruku Island, Maluku, through an analysis of density, size structure, length–weight relationship, and spatial distribution. Data were collected using transect–quadrat methods in the intertidal zone during low tide, complemented by measurements of environmental parameters (temperature, salinity, pH, and dissolved oxygen). The results revealed a population density of 2.76 ind/m², classified as moderate compared to other sites in Maluku. Shell length distribution encompassed nine size classes, with dominance in the mid-size class (3.93–4.38 cm), indicating successful recruitment of juveniles to early adults. The length–weight relationship followed the equation W = 1.871L0.0521 (R² = 0.857) with a negative allometric growth pattern, suggesting that individuals allocate more energy to shell elongation than body mass increase. The Morisita index (Id = 211.07) indicated an aggregated distribution pattern, consistent with reproductive strategies of broadcast-spawning gastropods. Environmental conditions (temperature 28.7 °C; salinity 32.66‰; pH 6.44; DO 6 mg/L) were within the tolerance range of tropical intertidal mollusks. These findings highlight the critical role of sandy seagrass habitats in supporting S. luhuanus populations and emphasize the need for management strategies based on minimum catch size, harvest regulation, and coastal habitat protection.

The purpose of this study was to determine the effect of pulley angle variations of 13.5ᵒ and 14ᵒ and roller weight variations of 12 g, 14 g, and 16 g on the torque of a 150 cm3 automatic motorcycle. The method with a quantitative approach the method used is the experimental method. For the sample of this research is a type of pulley and 3 types of rollers with different slopes and weights where the angle varies to test the 150 cm3 matic motorcycle torque used. From the results of this study results in a value where the Fcount value is greater than the Ftable means that the null hypothesis (H02) is rejected and the alternative hypothesis (H22) is accepted or the mass of the roller has an influence on torque, the Fcount value is greater than the Ftable means that the hypothesis (H01) is rejected and the alternative hypothesis (H11) is accepted or the mass of the roller has an influence on torque, and that the interaction between pulley angle and roller mass does not affect, and the Ftable value is lower than the Fcount value that the hypothesis (H03) is accepted and the alternative hypothesis (H33) is rejected, roller mass affects torque.

This study aims to analyze the role of Penta-Helix stakeholders in developing tourism potential in Kupang City. The method used is descriptive qualitative with a qualitative approach. The focus of this research is to observe and find out how the application of the Penta-Helix model in the development of tourism potential in Kupang City, using data collection techniques in the form of Interviews, Questionnaires and Documentation analyzed using Analytichal Hierarchy Process (AHP) analysis techniques. The results of this study show that the Penta-Helix Collaboration in developing the tourism potential of Kupang City has been implemented, but the results to advance tourism in Kupang City are still not optimal. This is due to the lack of awareness from the public and also coordination from the government in maintaining the safety and cleanliness of tourism places is lacking. Factors that become the scale of importance to the application of the Penta-Helix model of tourism in Kupang City based on the level of priority are aspects of government, private sector, society, media, and academia. For the government aspect, the indicator with the greatest weight is the construction of tourism facilities. For the private sector, the priority indicator is the improvement of service services. In the community aspect, the most priority indicator is the indicator of cleanliness of the tourist environment. For the media aspect, the priority indicator is tourism propotion. For the academic aspect, the indicator with the greatest weight is the development of research contribution facilities.

This study discusses the effect of valve weight and clearance on the efficiency of a hydraulic ram pump, with a focus on optimizing water discharge efficiency in the Sumberingin Tourism Area, Wringinsongo Village, Tumpang, Malang Regency. The method used in this study is an experiment with variations in valve weight consisting of 10 kg, 11 kg, and 12 kg, as well as valve clearances varying between 16.5 mm, 24.5 mm, and 32.5 mm. The results showed that the combination of a valve weight of 10 kg and a clearance of 16.5 mm produced the highest water discharge efficiency, namely 69.7 liters per minute, with an optimal efficiency of around 33%. In the optimization process, the application of Design of Experiment (DOE) revealed that a valve weight of 11 kg and a clearance of 16.5 mm can produce a maximum discharge efficiency of 63.155 with a desirability value reaching 0.7671. These findings indicate that proper settings on valve weight and clearance can contribute significantly to increasing the efficiency of a hydraulic ram pump. This research provides valuable insights into improving the performance and reliability of hydraulic ram pumps in rural water distribution systems. Understanding the influence of these variables is expected to yield more effective solutions for water resource management, particularly in areas requiring efficient irrigation systems. Furthermore, this research also opens up opportunities for further research on hydraulic ram pump optimization by considering other factors that can influence the efficiency and performance of the overall water distribution system. It is hoped that the results of this research can serve as a reference for the development of more efficient and environmentally friendly hydraulic ram pump technology, as well as supporting the sustainability of water resource management in rural areas.

Pemilihan jurusan di Sekolah Menengah Kejuruan (SMK) merupakan tahap penting yang mempengaruhi arah pendidikan dan karier siswa di masa depan. Di SMK XYZ Kabupaten Tegal, pemilihan jurusan masih dilakukan secara konvensional dengan mempertimbangkan nilai rapor dan saran dari guru atau orang tua, yang cenderung bersifat subjektif dan tidak sepenuhnya memperhatikan potensi serta minat individual siswa. Untuk itu, penelitian ini bertujuan mengembangkan sistem pendukung keputusan (SPK) yang dapat memberikan rekomendasi jurusan secara objektif dan sistematis menggunakan dua metode, yaitu Naive Bayes dan Simple Additive Weighting (SAW). Naive Bayes, yang berbasis pada algoritma probabilistik, telah terbukti efektif dalam memprediksi ketuntasan belajar siswa, sementara SAW banyak digunakan dalam pengambilan keputusan multikriteria dengan memperhitungkan beberapa aspek kriteria seperti nilai rapor, hasil psikotes, dan minat siswa. Penelitian ini mengkomparasi kedua metode tersebut untuk menentukan mana yang lebih optimal dalam memberikan rekomendasi jurusan yang akurat dan sesuai dengan karakteristik siswa di SMK XYZ. Hasil penelitian diharapkan dapat memberikan solusi alternatif bagi sekolah dalam proses pemilihan jurusan yang lebih objektif, efisien, dan minim subjektivitas, serta dapat diterapkan pada proses pendidikan di SMK lainnya.

This study aims to analyze the growth of tilapia (Oreochromis niloticus) cultivated in a concrete pond system in Dusun IV, Fadoro Lasara Village, Gunungsitoli City. The concrete pond system was chosen for its advantages in land efficiency, ease of management, and ability to maintain stable water quality. The research method employed a quantitative experimental approach over 60 days, with 200 Nile tilapia as the study subjects. The parameters observed included absolute weight and length gain, specific growth rate (SGR), feed conversion ratio (FCR), and survival rate (SR). The results showed that the average weight of the fish increased from 5.12 grams to 98.46 grams, with a length increase of 12.5 cm. The SGR was recorded at 4.29% per day, FCR at 1.38, and SR at 96.5%. These results indicate that the concrete pond system can support optimal tilapia growth. This study recommends the application of concrete ponds as an efficient aquaculture alternative, particularly in areas with limited land and resources. This system has proven to be adaptive to local conditions and contributes to increased productivity and income for aquaculture farmers.

This study aimed to evaluate the effect of broiler concentrate-based rations combined with corn and rice bran on the performance of native chickens. A total of four dietary treatments were formulated, namely P1 (100% broiler concentrate), P2 (75% broiler concentrate, 10% corn, 15% rice bran), P3 (50% broiler concentrate, 30% corn, 20% rice bran), and P4 (25% broiler concentrate, 55% corn, 20% rice bran). The parameters observed included feed intake, body weight gain, and feed conversion ratio (FCR). Data were analyzed using analysis of variance (ANOVA), and significant differences among treatments were further tested with Duncan’s New Multiple Range Test (DMRT). The results showed that dietary treatments significantly affected the growth performance of native chickens (P<0.05). Chickens fed diets with higher proportions of broiler concentrate (P1 and P2) exhibited better body weight gain and more efficient feed conversion compared to those receiving lower concentrate levels (P3 and P4). In contrast, increasing corn and rice bran levels tended to increase feed intake but did not proportionally improve body weight gain, resulting in poorer FCR. It can be concluded that native chickens perform optimally when fed rations containing at least 75% broiler concentrate, which provides a balance between growth and feed efficiency.

The purpose of this study was to determine the effect of Potassium Fertilizer and Chicken Manure on the Growth, Production and Potassium Absorption of Sweet Corn Plants. This study used a Randomized Block Design (RAK) consisting of 2 factors. The first factor is Potassium Fertilizer (K) consisting of 4 levels, namely:K0 = Control (Without fertilizer), K1 = 7.2 g/plot, K2 = 14.4 g/plot, K3 = 21.6 g/plot. The second factor of Chicken Manure Fertilizer (C) consists of 3 treatment levels, namely: C1 = 2160 g/plot, C2 = 2880 g/plot, C3 = 3600 g/plot. The results of this study indicate thatPotassium fertilizer treatment had a significant effect onplant height, weight of cobs with husks per plant, weight of cobs per plot.The treatment of chicken manure has a significant effect onplant height, weight of cobs with husks per plant, and weight of cobs per plot.The interaction between potassium fertilizer and chicken manure had no significant effect on all observation parameters.

The research aims to determine red onion plants' growth and production response (Allium cepa L.) on Planting Distance Treatment and Eco enzyme Concentration. This study used a Randomized Block Design (RBD) with two treatment factors. The first factor is the Planting Distance treatment consisting of 3 levels, namely J1 = 15 cm x 15 cm, J2 = 20 cm x 15 cm, and J3 = 25 cm x 15 cm. The second factor is the Eco enzyme Concentration, consisting of 3 levels, namely E1 = 0.8 ml ee / 240 ml water, E2 = 1.6 ml ee / 240 ml water, and E3 = 2.4 ml ee / 240 ml water. The study results showed that the Planting Distance treatment significantly affected root length and tuber diameter per sample. However, they had no significant effect on fresh tuber weight on the growth and production of shallots. The Eco enzyme concentration treatment had no significant effect on all observation treatments. The interaction between Planting Distance and the Eco enzyme concentration had no significant effect on all observation treatments.

Background: Obesity has become a global epidemic, with substantial implications for musculoskeletal health, particularly in weight-bearing joints like the knee and ankle. Aim: To determine the prevalence of obesity with respect to age and gender and to assess its effect on the knee and ankle joints among adults in Ekpoma, Edo State. Materials and Methods: A descriptive cross-sectional study involving 60 obese adults was conducted using a structured, validated questionnaire. Ethical approval and informed consent were obtained. Results: Respondents were predominantly aged 40–45 years (35%) and female (80%). 65% reported pain in the knee and/or ankle joints; 56.7% reported joint stiffness or reduced range of motion. Conclusion: Obesity significantly affects the knee and ankle joints, contributing to pain, stiffness, and a reduced range of motion. These results reinforce the need for targeted public health interventions aimed at obesity prevention and management, particularly in populations at risk of joint-related complications.

Diabetes Mellitus is a metabolic disorder caused by the pancreas failing to produce insulin hormone adequately. Based on the cause, diabetes mellitus is classified into three types, including type 1 DM, type 2 Diabetes Mellitus, and gestational diabetes mellitus. Hyperglycemia can also occur due to food consumed that is not balanced with physical activity carried out so that fat accumulates in the body and causes blood to be unable to enter the body's cells, ultimately increasing blood sugar levels. Signs and symptoms that often appear in Diabetes Mellitus patients are polydipsia (excessive thirst), polyuria (frequent urination due to excessive urine production), polyphagia (quickly feeling hungry even though you eat a lot and want to eat continuously) and weight loss without a clear cause. Symptoms of diabetes mellitus can be controlled by increasing physical activity, following a healthy diet, and getting enough rest. Several studies have proven that bay leaves have the potential as an antidiabetic. This study aims to determine the content of secondary metabolic compounds and the antidiabetic activity of bay leaf extract using α-amylase enzyme inhibition in vitro. This research method uses laboratory experiments. This study uses α-amylase enzyme inhibition to determine the antidiabetic activity contained in bay leaf extract. The method used is DNSA. The results of this study obtained a yield of bay leaf extract of 30%, water content of simplicia of 6.24%, drying loss of simplicia of 5.3%, ash content test of 5.6%, water content test of extract of 6.34%, drying loss test of extract of 7.03%. Bay leaf extract positively contains secondary metabolite compounds, namely saponins, tannins, flavonoids, and alkaloids. The results of IC50 in bay leaf extract of 66.705 ppm are classified as strong antidiabetic. The results of acarbose of 56.818 ppm are classified as strong antidiabetic.

This study aims to determine the effect of cow manure and liquid organic fertilizer eco farming on the growth and production of shallots (Allium ascalonicum L.). This research method uses a Factorial Randomized Block Design (RAK) consisting of 2 factors. The first factor is cow manure (P) consisting of 4 treatment levels, namely: P0 = Control (Without Compost), P1 = 0.5 kg/plot (5 tons/ha), P2 = 1.0 kg/plot (10 tons/ha) and P3 = 1.5 kg/plot (15 tons/ha). The second factor: Eco Farming consists of 3 treatment levels: C1 = 150 ml/plot, C2 = 250 ml/plot, and C3 = 350 ml/plot. The study's results showed that the provision of cow manure significantly affected plant height, number of leaves, wet weight of tubers per sample, and wet weight of tubers per plot. Providing eco farming impacts plant height, number of leaves, dry weight of tubers per sample, and wet weight of tubers per plot. The interaction between cow manure and eco farming had no significant effect on plant height, number of leaves, dry weight of tubers per sample, and wet weight of tubers per plot.

The research aims to determine red onion plants' growth and production response (Allium cepa L.) on Planting Distance Treatment and Eco enzyme Concentration. This study used a Randomized Block Design (RBD) with two treatment factors. The first factor is the Planting Distance treatment consisting of 3 levels, namely J1 = 15 cm x 15 cm, J2 = 20 cm x 15 cm, and J3 = 25 cm x 15 cm. The second factor is the Eco enzyme Concentration, consisting of 3 levels, namely E1 = 0.8 ml ee / 240 ml water, E2 = 1.6 ml ee / 240 ml water, and E3 = 2.4 ml ee / 240 ml water. The study results showed that the Planting Distance treatment significantly affected root length and tuber diameter per sample. However, they had no significant effect on fresh tuber weight on the growth and production of shallots. The Eco enzyme concentration treatment had no significant effect on all observation treatments. The interaction between Planting Distance and the Eco enzyme concentration had no significant effect on all observation treatments.

The verification and validation process for land application data at the Southwest Sumba Regency Land Office has been carried out manually, which often results in delays, data inconsistencies, and the potential for errors in decision-making. These problems impact the low efficiency of public services and the accuracy of managed data. Therefore, this study aims to develop a Decision Support Sistem (DSS) that can assist the verification and validation process for land application data sistematically, quickly, and objectively. This sistem is built using the Simple Additive Weighting (SAW) method, which functions to determine the level of eligibility of an application based on several criteria, namely completeness of documents, land status, legality of ownership, suitability of applicant data, and dispute history. The sistem design process is carried out through several stages, including needs analysis, design, implementation, testing, and evaluation. The results show that the implementation of a SAW-based DSS can increase the efficiency of the verification process by up to 65%, and increase the accuracy of validation results by up to 90% compared to manual methods. This sistem provides automatic decision recommendations, thus assisting officers in assessing the eligibility of land applications in a transparent and consistent manner. Thus, this sistem has the potential to be an effective solution to support the digitalization of public services in the land sector in Southwest Sumba Regency.

The development of information technology has accelerated digitization in various sectors, including in the decision-making process. In the DayTrans Shuttle service, the selection of the best route is still done manually using Microsoft Excel. This process is time-consuming, inefficient, and has the potential to cause errors and subjectivity. The purpose of this study is to design and develop a web-based decision support system by applying the Weighted Product (WP) method to determine the most optimal shuttle route objectively and efficiently. The research data was obtained through interviews, observations, and literature studies, then analyzed according to system requirements. The development was carried out through the stages of requirements analysis, database and interface design, implementation, and testing. The developed system is equipped with features for managing criteria data, alternative routes, weight calculations, and real-time presentation of recommendation results. The research results show that the system functions well, is able to speed up the route selection process, and produces accurate and transparent recommendations. Thus, this system is expected to improve DayTrans' operational efficiency while supporting the quality of inter-city transportation services.