Publication Search

The UNAMA Library employs information systems to enhance its academic services. Nevertheless, its administrative framework continues to encounter obstacles, such as inadequate service system support for users, constrained resources allocated for the management and upkeep of the system, an absence of standardized protocols for addressing technical challenges, and insufficient assessment system efficacy. If these issues remain unaddressed, the operational effectiveness of the library information system will be compromised, thereby thwarting the objective of delivering dependable information services. This investigation seeks to illuminate the maturity level of information system governance as delineated by COBIT 2019 within the Decision Support Systems (DSS) domain, specifically focusing on the DSS01 (Manage Operations) and DSS02 (Manage Service Requests and Incidents) processes. The findings suggest that the degree of information system governance capability the UNAMA Library is situated at the Established Process level (level 3), signifying that the process has undergone implementation; however, it has yet to be comprehensively documented and consistently evaluated. Moreover, a disparity persists between the existing state and the anticipated capability level of the organization, particularly concerning IT operations management, the standardization incident handling, and the documentation of operational procedures. An elucidation of the expected level is articulated, especially in terms of operational standards, incident documentation, and IT infrastructure oversight. Recommendations encompass the formulation of standard operating procedures (SOPs), the enhancement of documentation practices, and periodic assessments grounded in COBIT 2019. These findings are anticipated to assist libraries in augmenting the efficacy of information systems governance and the quality of IT services.

Background: The development of modern manufacturing systems requires production scheduling strategies that not only improve productivity but also optimize energy utilization. Multi-machine production systems with job-shop configurations exhibit high complexity due to dynamic interactions between machines, job queues, and varying processing times, making conventional scheduling methods less effective in handling changing operational conditions. Objective: This study aims to develop and evaluate a reinforcement learning based production scheduling approach to improve production efficiency while reducing energy consumption in multi-machine manufacturing systems. Methods: This research employs a job-shop based multi-machine production simulation model as the experimental environment. The scheduling problem is formulated as a Markov Decision Process, enabling the implementation of reinforcement learning algorithms, namely Q-learning and Deep Q-Network, to learn optimal scheduling policies through interaction with the simulation environment. Energy consumption parameters are incorporated into the reward function so that the learning agent can consider energy efficiency in the scheduling decision-making process. System performance is evaluated using three main metrics, namely energy consumption, throughput, and makespan. Results: The experimental results show that the reinforcement learning based scheduling approach achieves better performance compared to conventional scheduling methods, resulting in lower energy consumption, higher job completion rates, and shorter production completion times within the multi-machine manufacturing system.

Bullying in secondary schools is a serious issue that affects students’ psychosocial well-being and academic achievement, and significantly disrupts the learning climate and the quality of social relationships among students. This community service activity aimed to implement the Child-Friendly School Program through anti-bullying socialization at SMA N 1 Kersana in order to enhance the understanding and commitment of all school members in creating a safe, inclusive, and sustainable learning environment. The method employed a participatory approach, including needs assessment, interactive socialization sessions, focus group discussions, case simulations, and structured evaluation using pre-test and post-test instruments. The results indicated a 23% increase in participants’ understanding based on the comparison of scores before and after the intensive implementation of the program. Furthermore, a “Bullying-Free School” declaration was established along with a plan to strengthen the Violence Prevention and Handling Team as a more systematic sustainability strategy. These findings confirm that participatory-based socialization effectively reinforces the implementation of the Child-Friendly School Program and fosters a safe, inclusive, sustainable, and responsive school culture that supports students’ positive character development.

This study aims to evaluate the public transportation service policy of Trans Jatim Bus Corridor 2, operating on the Mojokerto–Surabaya route, using the six evaluation indicators proposed by William N. Dunn: effectiveness, efficiency, adequacy, equity, responsiveness, and appropriateness. Employing a qualitative descriptive approach, data were collected through interviews, direct observations, and secondary data analysis. The findings reveal that the Trans Jatim Corridor 2 service has significantly contributed to improving accessibility and mobility for the people of East Java. In terms of effectiveness, the service meets transportation policy objectives by offering strategic routes, consistent schedules, and accessible bus stops. Efficiency is demonstrated through optimal utilization of limited fleets and operational costs, while still meeting high passenger demand. Regarding adequacy, the service is generally sufficient; however, overcrowding during peak hours indicates the need for capacity improvements. Equity is reflected in the widespread distribution of bus stops, although disparities remain in the availability of facilities and route information across several stops. The service shows high responsiveness through quick handling of passenger complaints via applications and social media. Additionally, service appropriateness is evident in its punctual operations supported by GPS-based monitoring and real-time information through the TRANSJATIM-AJAIB application. Overall, the evaluation shows that Trans Jatim Corridor 2 provides effective, efficient, and responsive public transport services, yet requires improvements in capacity and equitable distribution of facilities to achieve optimal service quality.

The authority of the Religious Courts in Indonesia has undergone substantial transformation since the enactment of the 1974 Marriage Law, which serves as a foundational milestone in harmonizing the national legal system on family matters. Prior to this legislation, the jurisdiction of the Religious Courts was limited and influenced by legal dualism among customary law, Islamic law, and Western civil law inherited from the colonial period. This article examines how the Marriage Law initiated a shift in the structure and legitimacy of the Religious Courts and how their jurisdictional expansion reached a more comprehensive form through Law No. 7 of 1989 on Religious Courts and its subsequent amendments under Law No. 3 of 2006 and Law No. 50 of 2009. Using a normative juridical approach, this study analyzes statutory regulations, academic literature, and Islamic legal doctrines. The findings show that the Marriage Law provided the initial legal foundation for strengthening the Religious Courts' authority in handling family disputes, which was later expanded significantly to include inheritance, wills, grants, endowments (wakaf), alms (zakat), charitable donations (infaq and sadaqah), and Islamic economic matters during the legal reform era. This transformation not only reinforced the institutional structure of the Religious Courts but also improved access to justice for Muslim communities and supported the integration of Islamic law into Indonesia’s national legal framework. Therefore, the development of the Religious Courts’ authority after the Marriage Law reflects the dynamic modernization of the legal system and the harmonization between religious values and the rule of law in Indonesia.

This study focuses on the analysis of traffic density in Jember City, particularly at the Wirolegi Intersection, which is known to have a high density level. This condition often triggers congestion that hinders public mobility, so that appropriate and data-based handling efforts are needed. The purpose of this study is to identify and analyze the level of density at critical congestion points through a statistical approach using the One Way ANOVA method. The research method used is quantitative descriptive with a descriptive observational approach. Primary data was collected directly through a field survey in 2025 at the Wirolegi Intersection as one of 3 intersections in Jember City. The data obtained were then processed using normality tests, homogeneity tests, and One Way ANOVA with the help of SPSS software. The results of the analysis show that the traffic flow density on the three routes studied, namely Jalan Gunung Haryono, Jalan Brigjen Katamso, Jalan Yos Sudarso, does not show a significant difference. The significance value of the ANOVA test is greater than 0.05 which indicates the similarity of density levels between routes. Further testing (post hoc testing) also strengthens this finding. The conclusion of this study shows that handling congestion at the Wirolegi Intersection needs to be done comprehensively through traffic control and evaluation of the transportation system to improve smoothness and mobility in Jember City.

The integration of artificial intelligence chatbots as virtual teaching assistants (VTAs) represents a transformative shift in student support services within higher education. This study investigates the implementation, effectiveness, and impact of AI-powered chatbots in providing academic support, administrative assistance, and personalized guidance to university students. Employing a longitudinal mixed-methods approach over 18 months, this research analyzed data from 2,347 students across 15 universities that deployed VTA systems, examining interaction patterns, student satisfaction, learning outcomes, and cost-effectiveness. Quantitative analysis of 487,392 chatbot interactions revealed that VTAs successfully handled 78.4% of student queries without human intervention, with response times averaging 3.2 seconds compared to 4.7 hours for traditional support channels. Qualitative findings from focus groups and interviews highlighted students' appreciation for 24/7 availability, immediate responses, and non-judgmental interactions, while also revealing concerns about empathy limitations, complex query handling, and the desire for human connection in critical situations. The study demonstrates that VTAs significantly improve support service accessibility and efficiency while reducing operational costs by an average of 43%. However, optimal implementation requires careful integration with human support staff, continuous training of AI systems, and attention to equity issues in digital access. This research contributes to understanding how AI can augment rather than replace human educators, offering evidence-based recommendations for implementing VTA systems that enhance student success while maintaining the human elements essential to quality education.

Language barriers represent one of the most significant obstacles to educational equity and access worldwide. This study investigates the application of Natural Language Processing (NLP) technologies in multilingual educational contexts to facilitate cross-linguistic learning and improve educational outcomes for linguistically diverse student populations. We implemented and evaluated a comprehensive NLP-powered multilingual learning platform across 47 educational institutions in 12 countries, serving 8,450 students speaking 23 different languages. Our experimental framework integrated machine translation, speech recognition, multilingual content generation, and adaptive language learning algorithms. Results demonstrate that NLP-enhanced multilingual education improved student comprehension by 43.6% (p<0.001), increased participation rates by 67.8%, and reduced achievement gaps between native and non-native speakers by 52.4%. Students using NLP-assisted learning tools achieved test scores averaging 78.3% compared to 54.7% for control groups. However, challenges persist regarding cultural context preservation, idiomatic expression handling, and equitable performance across language families. This research provides evidence that NLP technologies can effectively democratize education across linguistic boundaries while identifying critical areas requiring continued development.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

AImbalanced data remains a significant issue in heart disease classification using machine learning, as it tends to cause models to overestimate the majority class while ignoring minority classes with high clinical value. This can lead to a decrease in accuracy and the model's ability to accurately detect disease cases. Therefore, this study aims to assess the effectiveness of oversampling techniques, namely Random Oversampling and Synthetic Minority Oversampling Technique (SMOTE), in improving the performance of the K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF) algorithms. The dataset used comes from Kaggle and consists of 918 data sets with 12 attributes representing patient information related to heart disease prediction. The research stages include data preprocessing, baseline model testing, and re-evaluation using the two oversampling methods. Experimental results show that oversampling can improve the performance of all algorithms. KNN achieved the best results with SMOTE, with an accuracy of 72.98% and an F1-score of 75.39%. In the Naive Bayes algorithm, both oversampling techniques produced relatively stable performance, with the highest F1-score of 73.56% using SMOTE. Meanwhile, Random Forest showed the most optimal performance when combined with Random Oversampling, with an accuracy of 79.19% and an F1-score of 81.51%. These findings confirm that the success of data balancing techniques is strongly influenced by the characteristics of the classification algorithm used, and provide a practical contribution in determining strategies for handling imbalanced data in health research.

The rapid development of digital streaming platforms such as Netflix has generated a large volume of content data with diverse characteristics, thereby requiring effective analytical methods to understand emerging patterns and trends. This study aims to classify Netflix content into two main categories, namely movies and television shows, and to analyze genre trends and content characteristics using a data mining approach with the Naive Bayes algorithm. The dataset used in this study is the Netflix Shows dataset, consisting of 8,809 content entries, with the primary features analyzed including genre, rating, and country of production. The research process begins with data exploration and preprocessing stages, including data cleaning, handling missing values, and transforming categorical features to enable effective model construction. Subsequently, the dataset is divided into training and testing sets to objectively and systematically build and evaluate the Naive Bayes classification model. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to assess the model’s ability to accurately distinguish between Netflix content types. The experimental results demonstrate that the Naive Bayes algorithm is able to classify Netflix content into Movie and TV Show categories with accuracy, precision, recall, and F1-score values of 100%, respectively. The confusion matrix indicates that no misclassification occurred, suggesting that genre, rating, and country of production features provide a very clear separation between content classes. These findings indicate that the Naive Bayes algorithm can achieve exceptionally high classification performance with optimal evaluation results. The results further reveal distinct differences in characteristics between movies and television shows based on genre and production attributes. Therefore, this study is expected to contribute to the development of content recommendation systems and strategic content management within the streaming industry.

Gestational Diabetes Mellitus (GDM) is a pregnancy-related metabolic disorder that poses health risks to both mother and fetus if not detected early, requiring accurate prediction methods for early screening and clinical decision-making. This study applies the Random Forest algorithm to detect GDM risk using clinical data from the Pima Indian Dataset. Data preprocessing included handling missing values, standardization, feature engineering, and a 70:30 train–test split. Two models were developed: a baseline and an optimized model using GridSearchCV hyperparameter tuning, validated with 5-fold cross-validation. Performance was assessed using a classification report, confusion matrix, and ROC–AUC. Results show that the optimized model outperforms the baseline, achieving 88% accuracy, an AUC of  93%, and average recall of 81%–85%. Compared to previous studies, this approach demonstrates improved predictive performance. The findings indicate that combining Random Forest with comprehensive preprocessing, feature engineering, and model optimization is effective and feasible for developing a medical decision support system for early GDM risk screening.

This study aims to analyze the effectiveness of technical personnel management in handling main engine failures during extreme weather conditions at sea. The main focus of this study is to assess the extent to which technical competence, communication, coordination, and preparedness of technical personnel contribute to the effectiveness of damage management. The method used is a descriptive quantitative approach with data collection through a closed-ended questionnaire based on a Likert scale. A total of 100 respondents who are ship engineering officers currently studying at a maritime campus were sampled. The results of the analysis show that the four independent variables (technical competence, communication, coordination, and preparedness) simultaneously have a significant effect on the effectiveness of handling main engine failures. From the results of the multiple linear regression test, the coefficient of determination (R²) value of 0.897 indicates that 89.7% of the variation in damage management effectiveness can be explained by these four variables. This finding indicates that good technical personnel management plays a significant role in reducing the risk of engine system failure during extreme weather.

This study provides a comprehensive analysis of the performance of the Travel Document Section at Tanjung Perak Immigration Office within the framework of Public Administration. Employing a qualitative, case-study approach, the research investigates the efficiency, effectiveness, and quality of immigration services, emphasising the application of New Public Management (NPM) principles and Good Governance practices. The findings reveal that while technological innovations such as digital systems (M-Passport and SIMKIM) have successfully improved operational efficiency and reduced physical queues, challenges persist regarding procedural transparency, accountability, and system reliability. Human errors and inconsistent discretion in handling complex documents highlight the need for enhanced capacity building and resource allocation. Moreover, issues of bureaucratic transparency and integrity undermine public trust, underscoring the importance of strengthening internal controls and communication strategies. The study also underscores that service quality is multidimensional, encompassing physical facilities, interpersonal professionalism, and procedural clarity, all of which influence public satisfaction. Despite improvements, the gap between technological efficiency and service effectiveness suggests that further efforts are needed to integrate digital innovations with robust procedural protocols. The research concludes that sustainable performance improvement requires a balanced focus on technological, human, and systemic factors, fostering a culture of transparency, accountability, and continuous development. These insights offer valuable policy recommendations to enhance the robustness and responsiveness of immigration services, ultimately strengthening the legitimacy and trust of government institutions in delivering public services.

Coal quality must be controlled from the pit area to the ROM stockpile to ensure compliance with market specifications. However, hauling and stockpiling processes often lead to changes in coal characteristics. This study aims to analyze variations in proximate parameters between coal from Pit B1 and ROM Stockpile Km4 at PT Trisensa Mineral Utama and to identify factors contributing to these changes. The methodology includes field sampling at both locations, sample preparation based on ASTM standards, and laboratory testing of inherent moisture, residual moisture, ash content, volatile matter, and fixed carbon. The results indicate that coal undergoes quality changes after being stored in the stockpile, marked by a decrease in inherent moisture of 2.54% (from 17.64% to 15.10%), a decrease in residual moisture of 1.42% (from 17.17% to 15.75%), a slight reduction in ash content of 0.16%, a decline in volatile matter of 0.28%, and a reduction in fixed carbon of 0.18%. These changes are influenced by field conditions, material contamination during mining, rainfall, coal porosity, and handling activities at the stockpile. The findings highlight the need for improved sampling management, better surface water control, and stricter material handling procedures to minimize coal quality degradation.

Ethical leadership is a crucial foundation for building an organizational culture of integrity, especially in this era of technological disruption and socio-economic complexity. This study examines how ethical leadership is implemented at Kopdit Bintang Timur, a credit union in Sikka Regency, to shape an organizational culture of integrity. Employing a descriptive narrative method with interview and observation techniques targeting managers and deputy managers, this research finds that ethical values such as honesty, fairness, and mutual cooperation are instilled through leader role modeling, transparent financial management, and active member participation. Key challenges include the difficulty of maintaining ethics amidst personal interests; however, routine oversight systems and fair complaint handling help to maintain integrity. The results indicate that ethical leadership contributes to member trust and organizational sustainability. Recommendations for development include formal ethics training programs and the utilization of technology to enhance transparency.

The manual submission of employee complaints often leads to slow handling, disorganized documentation, and limited transparency regarding complaint progress. These issues hinder internal communication and reduce the effectiveness of administrative processes. This study aims to design and implement a Web-Based Employee Complaint Submission System using PHP and the Bootstrap framework to improve efficiency and accuracy in managing complaints. The system supports three main categories of complaints: payroll, occupational safety and health, and work facilities. It also enables employees to track complaint status through request, approved, and rejected indicators. Administrators can manage user accounts and generate official follow-up letters. Implementation results show that the system improves data recording, enhances documentation order, and increases transparency in complaint handling. Overall, the system facilitates more structured, efficient, and traceable communication between employees and management, supporting better corporate governance.

The management of Material Safety Data Sheets (MSDS) at PT Phapros Tbk still faces obstacles in the form of difficult document access, numerous files scattered across several folders, and MSDS presented in English that is difficult for workers to understand. This condition has the potential to hinder understanding of chemical safety information in the work environment. This activity aims to develop a digital innovation in the form of the QR-Safe system to facilitate access and improve workers' understanding of basic MSDS information. The implementation methods include identifying problems through observation and interviews, developing a simplified MSDS summary template consisting of five main sections, creating QR codes for each chemical, and socializing the use of this innovation to employees. The results of the activity show that QR-Safe and the MSDS summary are able to speed up document searches, present safety information more concisely, and make it easier for workers to understand hazard identification, handling procedures, and the use of personal protective equipment. This innovation also contributes to improving the effectiveness of Occupational Safety and Health (OSH) in companies. The digitization of MSDS through the QR-Safe digital innovation is highly effective in improving workers' accessibility to and understanding of chemical safety information.