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Tomato (Solanum lycopersicum) is a high-value horticultural commodity in Indonesia, yet its cultivation is frequently disrupted by leaf diseases that are difficult to distinguish visually. Diseases such as Bacterial Spot, Early Blight, and Tomato Yellow Leaf Curl Virus often present overlapping visual symptoms, making early and accurate diagnosis a significant challenge for farmers. The manual identification methods currently in use are inefficient and error-prone, ultimately leading to reduced crop yield  and quality. The general objective of this study is to develop software capable of automatically classifying tomato  leaf diseases. Specifically, this research aims to implement the MobileNetV3 Small architecture based on Convolutional Neural  Network (CNN) with ImageNet pre-trained weights to classify 10 types of tomato leaf diseases. The research methodology encompasses dataset collection from Kaggle comprising 10,000 images (1,000 per class), image pre-processing through resizing to 224x224 pixels, and normalization, as well as hyperparameter optimization (optimizer, learning rate, epoch, batch size) via scheduler. Model performance is evaluated using a confusion matrix encompassing accuracy, precision, recall, and F1-score.

The impact of sighar marriage on the younger generation from the perspective of Islamic law, social, and psychological. The main problem raised is the practice of marriage without dowry which places women as objects of exchange, thus ignoring their rights and causing the invalidity of the contract according to a number of scholars. The purpose of this study is to analyze the social, legal, and religious consequences of sighar marriage and its implications on the formation of children's identity and family resilience. The research method uses a literature study with a normative-comparative approach, referring to classical fiqh literature, hadith, and contemporary academic studies that compare the views of the Shafi'i, Maliki, Hanbali, and Hanafi schools. The results of the study show that nikah syighar has an impact on the neglect of women's rights, the normalization of objectification, household instability, the crisis of children's identity, and the weakening of family institutions. In addition, this practice is contrary to maqasid al-shari'ah, thus undermining the sacred value of marriage as worship and a means of moral development. These findings underscore the need for Islamic family law regulation and education to protect the younger generation from the practice of illegal and harmful marriage.

The drainage system is critical infrastructure for managing stormwater runoff in densely built urban areas, including higher education institutions. This study aims to evaluate the capacity performance of the existing drainage channels in the UPN "Veteran" Jawa Timur campus area. The evaluation was conducted through two main stages: hydrological analysis using the rational method to estimate the design runoff discharge for a 10-year return period, and hydraulic analysis using Manning's equation to calculate the channel cross-sectional capacity in accordance with Permen PU No. 12 of 2014. Based on the assessment of 43 drainage channels, the results showed that 33 channels (76.7%) are still functioning optimally and capable of accommodating the design discharge. Conversely, 10 channels (23.3%) were identified as having insufficient capacity. This capacity deficit was triggered by initial designs that did not accommodate the 10-year return period flood discharge, increased runoff coefficients due to massive pavement development, and effective cross-section narrowing caused by sedimentation. To mitigate inundation issues, this study recommends redesigning the failing channels using an economical hydraulic cross-section, accompanied by periodic normalization and dredging for functional channels. The findings of this study are expected to serve as technical guidelines for the optimal and sustainable management of campus drainage infrastructure.

The wave of digitalization triggers a severe morality crisis among the younger generation through constant exposure to negative content, cyberbullying, and global communication ethics degradation. This descriptive qualitative study aims to analyze the specific internalization of Christian values at GPIB Harapan Kasih Congregation Bekasi to shield youth morality amidst ongoing technological disruption. Employing intensive in-depth interviews and participatory observations, data were analyzed interactively through systemic reduction, structured display, and final verification. The results indicate that the digital morality crisis explicitly manifests as pornography normalization, virtual toxicity, and the severe erosion of academic honesty. In response, pastoral strategies utilizing highly contextualized digital ethics sermons and youth cell groups prove thoroughly effective. The deep internalization of self-control and integrity successfully stimulates a holistic self-censorship mechanism across the youth's cognitive, affective, and behavioral domains. This study concludes that consistent accountability-based mentorship effectively transforms Christian youth from passive digital victims into active agents of change within global cyberspace.

The wave of digitalization triggers a severe morality crisis among the younger generation through constant exposure to negative content, cyberbullying, and global communication ethics degradation. This descriptive qualitative study aims to analyze the specific internalization of Christian values at GPIB Harapan Kasih Congregation Bekasi to shield youth morality amidst ongoing technological disruption. Employing intensive in-depth interviews and participatory observations, data were analyzed interactively through systemic reduction, structured display, and final verification. The results indicate that the digital morality crisis explicitly manifests as pornography normalization, virtual toxicity, and the severe erosion of academic honesty. In response, pastoral strategies utilizing highly contextualized digital ethics sermons and youth cell groups prove thoroughly effective. The deep internalization of self-control and integrity successfully stimulates a holistic self-censorship mechanism across the youth's cognitive, affective, and behavioral domains. This study concludes that consistent accountability-based mentorship effectively transforms Christian youth from passive digital victims into active agents of change within global cyberspace.

This study investigates the implementation of the Gradient Boosting Machine (GBM) algorithm for network intrusion detection using the CICIDS2017 dataset within the CRISP-DM framework. The process encompasses Business Understanding, Data Understanding, and Data Preparation including data cleaning, categorical feature encoding, normalization, and data split (80 % training, 20 % testing). In the Modeling phase, GBM Hyperparameters (learning_rate = 0.1; max_depth = 5; n_estimators = 150) were optimized via Grid Search with 2-fold Cross Validation, and F1-Score  was selected as the primary metric due to class imbalance. Evaluation on the test set yielded accuracy of 99.99 %, precision of 100 %, Recall of 99.98 %, and F1-Score  of 99.99 %, demonstrating exceptional detection capability with minimal false negatives and false positives. Compared to previous studies, this GBM model outperforms in accuracy and stability without overfitting. These findings confirm GBM’s effectiveness for modern Intrusion Detection Systems and its suitability for Deployment in resource-constrained operational environments.

Nature tourism is one of the sectors that plays an important role in supporting the development of regional tourism, including in Lahat Regency, which has significant waterfall tourism potential. Currently, many visitors share their reviews and experiences through digital platforms such as Google Maps. This review can be used as a source of information to understand the public's evaluation of the quality of tourist attractions. This study aims to examine public perception of tourist attractions in Lahat Regency using the Support Vector Machine (SVM) method. Research data were collected through scraping from Google Maps, totaling 500 reviews from five tourist attractions, namely Curup Maung, Curup Buluh, Senyawe Waterfall, Panjang Waterfall, and Green Canyon. The research stages include data preprocessing, consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming. After that, feature extraction was carried out using the TF-IDF method and the classification process using the SVM algorithm. Based on the research results, the Support Vector Machine (SVM) method is able to perform sentiment classification quite well, although the accuracy level varies for each tourist attraction. Curup Maung and Panjang Waterfall achieved the highest accuracy level of 90%. Nevertheless, most visitor reviews were dominated by negative sentiments. This indicates that there are still several aspects that need to be improved, particularly related to tourist facilities and services. This research is expected to serve as a consideration for tourism managers and local governments in efforts to improve management quality as well as the development of tourism in Lahat Regency.

Rapid population growth in Sidoarjo Regency has triggered massive land-use changes, resulting in increased surface runoff and reduced performance of the drainage system. This study aims to evaluate the hydraulic capacity of drainage channels in the Pondok Sidokare Indah Housing area against design flood discharges with return periods of 2, 5, and 10 years. The method used is a descriptive quantitative approach, involving hydrological analysis using maximum daily rainfall data from 2015–2025 and hydraulic modeling of the existing channel along 350 meters. The frequency analysis results indicate that the Log Pearson Type III distribution is the most suitable method based on statistical parameters and the Smirnov-Kolmogorov goodness-of-fit test. The calculation of design flood discharge using the rational method yields values of 0.749 m³/s (2-year), 1.003 m³/s (5-year), and 1.164 m³/s (10-year). Meanwhile, the maximum capacity of the existing channel ranges only between 0.534 m³/s and 0.733 m³/s. The comparison between hydrological load and channel capacity shows that all observation points (Sta 0+000 to Sta 0+350) are in overflow condition, even for the lowest return period flood discharge. This condition confirms that the current channel dimensions are no longer adequate and require normalization to mitigate annual flooding in the area.

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

This article analyzes the security crisis in Haiti through the lens of constructivist theory in International Relations. Haiti represents one of the clearest examples of a failed state in the Western Hemisphere, where state authority has collapsed and been replaced by armed criminal groups known as gangs. Using a constructivist perspective, this article traces how social constructions, identities, and historically formed norms have shaped the fragility of the Haitian state. The study also integrates the concept of human security to illustrate the real impact of this crisis on citizens' security across seven dimensions: economic, food, health, environmental, personal, community, and political. The main finding suggests that Haiti's state failure is not merely a product of weak formal institutions, but the result of a long process of social construction, encompassing the legacy of colonialism, crippling reparation payments, counterproductive foreign intervention, and the normalization of violence in everyday life. From a constructivist perspective, restoring security in Haiti requires narrative reconstruction, rebuilding social trust, and comprehensive reform of institutional norms.

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

The increasing use of mobile applications in the retail industry has generated a large volume of user reviews that contain valuable insights regarding customer experience and service quality. However, the unstructured nature of these reviews requires an automated approach to extract meaningful patterns efficiently. This study aims to perform sentiment analysis on user reviews of the Indomaret Poinku application by integrating lexicon-based labeling with machine learning classification. A total of 10,000 reviews were collected from Google Play Store and processed through a series of text preprocessing steps, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Sentiment labeling was performed using the Indonesian Sentiment Lexicon (InSet), producing three sentiment classes: positive, negative, and neutral. The labeled data were vectorized using CountVectorizer and classified using two algorithms: K-Nearest Neighbors (KNN) and Random Forest (RF). Evaluation results show that Random Forest outperforms KNN, achieving an accuracy of 82.5%, compared to 69% for KNN. Random Forest demonstrates superior performance in handling high-dimensional sparse text features and yields more stable predictions across sentiment classes. This study contributes to the growing body of research on Indonesian sentiment analysis by demonstrating the effectiveness of combining lexicon-based labeling with ensemble learning methods, offering practical implications for developers seeking to improve the quality and user satisfaction of digital retail applications.

Magnetic Resonance Imaging (MRI) is a high-tech medical diagnostic equipment that plays an important role in healthcare, but in its operation it faces problems in the form of very large maintenance costs. This is due to the complexity of MRI technology, where the main unit of MRI is an imported product, while supporting equipment such as UPS, chiller, and AHU system are domestic products, thus impacting the increase in maintenance costs in hospitals and potentially reducing the quality of service. The solution to reduce maintenance costs, because maintenance cost efficiency can increase hospital profits. This study discusses the determination of maintenance priorities on MRI using a multi-criteria-based decision-making model by considering: the age of the MRI, the number of error logs, the condition of supporting equipment, and the expertise of the operator in operating the MRI. The Analytical Hierarchy Process (AHP) method is used with the stages of forming a decision hierarchy, pairwise comparisons, matrix normalization, priority weight calculation, and consistency testing. Data were obtained through questionnaires given to competent respondents, with the object of research at the Orthopedic Hospital, Surakarta, Indonesia. The results of the study indicate that the AHP method can be used to determine MRI maintenance priorities effectively. This is evidenced by the best alternative results, namely the Medium type (0.3372), followed by All Risk (0.3315), and Labor Only (0.3311). The Medium type is the most optimal choice. The AHP method has been proven to provide objective, structured, and accountable recommendations for hospital management in determining maintenance contracts that are appropriate to the technical condition of the equipment and budget constraints.

Indonesian culinary comments on social media platforms such as Instagram are characterized by informal spelling, regional language mixing, slang expressions, and emojis, posing substantial challenges for automated sentiment classification. While IndoBERT has demonstrated strong performance across Indonesian natural language processing tasks, the contribution of individual preprocessing components to fine-tuning performance on informal text remains underexplored, particularly in the culinary domain. This study addresses this gap by conducting a systematic preprocessing ablation study on IndoBERT-Base fine-tuning for Indonesian culinary sentiment classification, accompanied by a comparative evaluation against Naive Bayes with TF-IDF, SVM with TF-IDF, and BiLSTM as representative baselines. A dataset of 3,500 manually labeled Instagram culinary comments across three sentiment classes was used, with a stratified 80/10/10 split. Six preprocessing variants were evaluated under identical experimental conditions to isolate the contribution of each component. The results show that slang normalization is the most impactful single preprocessing step, yielding a macro F1-score gain of +0.0609 over the no-preprocessing baseline, while the full pipeline achieves an accuracy of 0.8800 and a macro F1-score of 0.8465. IndoBERT-Base with the full pipeline outperforms all baselines across all evaluation metrics. Per-class analysis reveals that the negative class achieves the lowest F1-score of 0.7600, with sarcastic expressions and Banjar regional vocabulary identified as primary sources of misclassification. These findings indicate that preprocessing decisions have a measurable and non-uniform effect on IndoBERT fine-tuning performance. In this study, slang normalization provides the most substantial individual contribution in bridging the vocabulary gap between informal user-generated text and the model’s pre-training distribution.

This study aims to examine user opinion tendencies toward Gojek services by integrating Random Forest and K-Means Clustering approaches. The dataset consists of 15,000 user reviews collected throughout 2025 using web scraping techniques. The initial stage focuses on data preprocessing, including text cleaning, case normalization, tokenization, removal of non-informative stop words, and lemmatization to restore words to their base forms. Subsequently, sentiment labels are assigned using a lexicon-based approach. The next phase involves classification modeling through Random Forest to identify sentiment tendencies, while K-Means Clustering is employed to uncover latent patterns within the opinion data. The findings indicate that the Random Forest model achieves an accuracy level of 0.878, demonstrating strong performance in distinguishing positive and negative sentiments, as reflected by f1-scores of 0.932 and 0.818, respectively. However, the model shows limitations in consistently identifying neutral sentiment. In contrast, the implementation of K-Means Clustering successfully categorizes the data into three primary clusters, providing a more structured representation of user opinion characteristics. Overall, these results offer empirical insights that can serve as a strategic reference for enhancing the quality of Gojek’s service delivery.

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

This study aims to explore the shift in students' ethical paradigms regarding the use of Generative Artificial Intelligence (AI) and its relationship to the phenomenon of plagiarism. Using a qualitative approach with the theoretical frameworks of Jean Baudrillard's Simulacra and Pierre Bourdieu's Habitus, this study examines how AI technology is changing the nature of scientific work. The results show the normalization of AI use as a new "digital habitus," where 83% of students consider AI a legitimate research tool, but on the other hand, it creates a condition of "Aesthetics Without Substance." The main findings reveal a reduction in originality where academic honesty is only measured through technical scores (such as Turnitin), rather than intellectual depth. The comparison between authentic and AI-based writing indicates the risk of systemic intellectual atrophy. In conclusion, this study recommends the need for a redesign of educational evaluation systems that focus on processes and verbal dialectics to mitigate the impact of pseudo-competence on college graduates.

Automatic facial expression recognition is a significant challenge in human-computer interaction with broad relevance in mental health, security, and behavioral analysis. This study proposes the implementation of Deep Learning using a custom Convolutional Neural Network (CNN) architecture to classify seven basic emotion categories: angry, disgust, fear, happy, sad, surprise, and neutral. Key challenges such as lighting variations and visual feature ambiguity in the FER2013 dataset are addressed through image pre-processing techniques, data augmentation, and the use of Batch Normalization and Dropout layers to prevent overfitting. The research methodology involves a systematic architectural design with three main convolution blocks optimized for computational efficiency. Experimental results show that the proposed model achieved a validation accuracy of 68.2%. Performance analysis based on F1-Score reveals that the "Happy" emotion has the highest detection rate (0.85) due to contrasting facial geometric features, while the "Fear" emotion is the most difficult class to identify (0.41). This study concludes that the use of an optimized standalone CNN architecture provides competitive and efficient performance compared to heavier transfer learning models, making it feasible for implementation on devices with mid-range hardware specifications.

Career selection is an important process for students at Darussalam Gontor University (UNIDA) because it influences their academic development and future employment. However, many UNIDA students experience difficulties in determining suitable careers due to a lack of understanding of their psychological characteristics. This study aims to build a Decision Support System (DSS) for career recommendations for UNIDA students based on psychological test results using the Simple Additive Weighting (SAW) method. The psychological data used are non-clinical test results collected through a structured questionnaire from six respondents and converted into numerical scores. The research stages include determining criteria and weights, compiling a decision matrix, normalization process, calculating preference values, and ranking career alternatives using SAW. The career alternatives used consist of academics, corporate professionals, entrepreneurs, managers, and social/public services. The results show that the managerial career alternative obtained the highest preference value of 0.861, followed by entrepreneurs at 0.824, corporate professionals at 0.778, social/public services at 0.737, and academics at 0.703. These findings demonstrate that the SAW method is capable of providing objective and systematic career recommendations based on the psychological profiles of UNIDA students. This research is expected to assist UNIDA students and academics in making more informed career decisions tailored to individual characteristics

The selection process for students eligible for the National Selection Based on Achievement (SNBP) requires objective and structured assessment because it involves various academic and non-academic criteria. This study aims to develop a Decision Support System (DSS) to determine the ranking of SNBP eligible students at SMAN 8 Surabaya using the Additive Ratio Assessment (ARAS) method. The ARAS method is used to evaluate student alternatives based on their report card scores for semesters 1-5, academic ability tests (TKA), academic achievements, non-academic achievements, discipline, organizational activity, and attendance through a normalization process to obtain relative Ki values. The results of the study show that the system is capable of producing objective student rankings with relative utility values (Ki) ranging from 95.15 to 89.38, where the highest value indicates the best alternative from all alternatives. The application of ARAS-based DSS can improve the efficiency, transparency, and consistency of the SNBP student selection process.