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The computer laboratory is an essential facility in higher education that requires efficient management of usage and environmental conditions to support the teaching and learning process. However, laboratory management at the Kalimantan Business and Technology Institute is still carried out manually, including scheduling, room condition monitoring, and report creation, which is prone to errors and time-consuming. This study aims to develop an Internet of Things (IoT)-based laboratory monitoring system prototype to improve the effectiveness of computer laboratory management. The approach used is Research and Development (R&D) with a prototype development model, allowing for design adjustments based on user feedback iteratively. Data were collected through observations, interviews, and document studies related to laboratory conditions and analyzed to determine the main system features, such as temperature and humidity monitoring, scheduling, and report generation. The results of the study show that the developed prototype can structure the laboratory workflow, provide real-time monitoring, facilitate schedule management, and simplify report preparation. This prototype is expected to serve as a foundation for developing a more comprehensive application, improving data accuracy, time efficiency, and the quality of laboratory management.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

This research is motivated by the problem of inefficient manual recording of employee attendance at the Central Papua Provincial Bawaslu, which often causes errors and inaccurate attendance data. The main goal of the research is to develop a PHP and MySQL-based digital attendance system that is able to record attendance automatically, real-time, and integrated. The research methods include needs analysis, database and interface design, program code implementation, and system testing. The results of the study show that the digital attendance system developed has succeeded in facilitating the recording of entry times, exit times, and recapitulation of employee attendance with a higher level of accuracy than manual methods. The implications of the implementation of this system are increased administrative efficiency, ease of monitoring, and the provision of more valid attendance data to support fast and appropriate decision-making within the Central Papua Provincial Bawaslu. Thus, this digital attendance system can be a practical solution in improving the quality of human resource management in government agencies.

This community service activity was motivated by the limited capacity of village officials in providing government administration services, particularly in managing correspondence, archiving documents, and implementing standard operating procedures for services. This condition has resulted in the suboptimal quality of public services at the village level. The objective of this activity was to improve the capacity and competence of village officials in providing village government administration services to be more effective, efficient, and accountable. The method used was a participatory approach through the stages of problem identification, joint planning, regulation socialization, technical training, service simulations, and implementation assistance. The subjects of the activity were officials from Kadur Village, North Rupat District, Bengkalis Regency. The results of the activity showed an increase in the officials' understanding and skills in administrative management, the development of a more standardized administrative document format, and a growing collective awareness of the importance of orderly administration as part of good governance. In addition, internal leadership initiatives emerged that encouraged sustainable change within the village government environment. Overall, this activity had a positive impact on improving the quality of village administration services and was the first step towards a more professional transformation of village governance.

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.

This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets and detecting emerging malware variants, the study employed SMOTE to ensure balanced training data and fair model evaluation. The dataset was divided into 80% training and 20% testing subsets, and models were assessed using key performance metrics including accuracy, precision, recall, F1-score, and ROC AUC. The findings revealed that the proposed Random Forest model outperformed the other classifiers, achieving an accuracy of 0.993, precision of 0.992, recall of 1.000, F1-score of 0.996, and a near-perfect ROC AUC of 0.9998 surpassing state-of-the-art approaches. These results affirm the superior predictive capability, consistency, and robustness of the Random Forest algorithm in Android malware detection. The study concludes that base models, when integrated with class-balancing techniques, provide reliable and efficient malware detection across imbalanced datasets. For future research, the study recommends exploring advanced hybrid or ensemble frameworks that integrate Random Forest with deep learning architectures or other meta-heuristic optimization techniques to further enhance detection accuracy, adaptability, and resilience against rapidly evolving Android malware threats.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

This study presents a Graph-Augmented Isolation Forest (GAIF), an unsupervised anomaly-detection framework for analyzing mobile user behavior. The proposed framework represents users and behavioral attributes as a user–feature bipartite graph, enabling the capture of relational dependencies that are not explicitly modeled in conventional vector-based approaches. Low-dimensional user representations are learned through Node2Vec and Graph Sample and Aggregate (GraphSAGE), and the resulting embeddings are subsequently processed by an Isolation Forest to produce anomaly scores. Experiments are conducted on a Mobile Device Usage and User Behavior dataset comprising 700 user profiles derived from application-level behavioral indicators. The dataset is treated as a behavioral abstraction rather than as a malware classification benchmark. A consistent 80:20 stratified train–test split is employed, with all learning-capable operations restricted to the training data to mitigate information leakage. Detection performance is evaluated post hoc using precision, recall, F1-score, and area under the curve (AUC) metrics. Under the evaluated setting, GAIF achieves an F1-score of 0.94 and an AUC of 0.97, demonstrating improved anomaly detection effectiveness relative to representative unsupervised baseline methods. These results are obtained on a static, proxy dataset and should not be interpreted as evidence of real-time deployment capability. Model interpretability is supported through post-hoc Uniform Manifold Approximation and Projection (UMAP) visualizations of the learned embeddings, providing structural insights into anomalous user behavior. Overall, the findings indicate that integrating graph-based representation learning with isolation-based anomaly scoring constitutes a computationally efficient approach for unsupervised mobile user behavior anomaly detection within the scope of this study.

Advances in information technology have changed the way humans obtain and manage information, including in the world of education. School websites have become an important medium for conveying academic, administrative, and school activity information quickly and efficiently. However, not all educational institutions are able to optimize the functions of their websites. This study was conducted to determine user satisfaction with the website of SMP Negeri 5 Kota Jambi using the Webqual 4.0 model and Importance Performance Analysis (IPA). The research method used was a descriptive quantitative approach with data collection through the distribution of questionnaires to 291 respondents from a total population of 1,065 students. The analysis was conducted by measuring the three main dimensions of Webqual 4.0, namely usability quality, information quality, and service interaction quality, then using IPA to map service improvement priorities. The results showed that most users were satisfied with the quality of the website, particularly in  terms of ease of use and service interaction. However, the timeliness of information updates and the responsiveness of the display on mobile devices still needed improvement. Recommendations for improvement focused on the dimensions in the "Concentrate Here" quadrant of the IPA analysis.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

Cashier service plays a crucial role in cooperative operations as it is directly related to members’ financial transactions. Service quality contributes to member satisfaction and loyalty, which in turn affects cooperative performance. This study aims to evaluate the role of cashier services in cooperative performance and member satisfaction at the Koperasi Simpan Pinjam Credit Union Bahtera Sejahtera. A descriptive qualitative approach was employed using observation, interviews, and documentation as data collection techniques. The findings indicate that the cashier service system has generally been implemented well and is positively perceived by members. However, several constraints remain, particularly during peak service hours, including long waiting times caused by the limited number of cashier staff and overlapping responsibilities with customer service functions. These conditions reduce service responsiveness. Nevertheless, cashier services significantly contribute to enhancing member satisfaction and supporting cooperative performance through efficient financial transactions and increased member trust. Improvements in service efficiency and task allocation are therefore recommended to optimize cashier service quality.

Freshwater fish diseases significantly affect aquaculture productivity and economic sustainability, while accurate visual classification remains challenging due to interclass similarity and image variability. This study presents a comparative evaluation of three deep learning architectures—DenseNet201, ResNet50, and EfficientNetV2-S—using a stepwise optimization strategy combined with Gradient-weighted Class Activation Mapping (Grad-CAM) for freshwater fish disease classification. Models were trained through three phases: baseline, optimized, and fine-tuned. Performance was evaluated using accuracy, precision, recall, F1 score, Matthews correlation coefficient (MCC), Cohen’s kappa, and per-class ROC–AUC. Results show consistent performance improvement across all architectures, with EfficientNetV2-S achieving the highest accuracy (97.14%), followed by ResNet50 (96.11%) and DenseNet201 (94.40%). High ROC–AUC values (>0.98) indicate strong discriminative capability. Grad-CAM analysis confirms that all optimized models focus on biologically relevant lesion regions, enhancing model transparency and reliability.

The digital transformation of health services through the Mobile JKN application was introduced by BPJS Kesehatan to provide easier access for the public in obtaining information, managing membership administration, and receiving health services more quickly and efficiently. This study aims to measure the readiness level of patients at Abdul Manap Regional Hospital, Jambi City, in adopting the Mobile JKN application using the HOT-Fit method, which covers three main components: Human, Organization, and Technology. Data were collected from 360 respondents through questionnaires and analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The results indicate that technology factors—including system quality, information quality, and service quality— along with organizational support have a significant effect on system use and user satisfaction, which in turn positively influence the net benefits. The outer loading values of all indicators exceeded 0.7, with Composite Reliability above 0.8 and AVE above 0.6, confirming that the research instruments are valid and reliable. Overall, patients at Abdul Manap Hospital are categorized as ready to adopt Mobile JKN, although improvements in digital literacy and stronger organizational support are still required to optimize its utilization.

Durian Bhineka Bawor is one of the leading local commodities of Alasmalang Village with high economic potential. However, product promotion and marketing activities are still dominated by conventional methods and limited local networks, resulting in restricted market access, low competitiveness, and the absence of structured product information documentation. This community service program aims to address these challenges through the implementation of an interactive website integrated with an AI Agent, serving as a centralized information platform as well as a digital product ordering service. The main objectives of this program are to strengthen local durian branding through the utilization of modern digital technology, expand market reach, and enhance community digital literacy. The implementation method was carried out in several stages, including program coordination and socialization, content needs assessment, website design and development, AI Agent and WhatsApp server integration, system testing, manager training, official deployment, and continuous assistance. The AI Agent provides interactive services in the form of product information delivery, personalized recommendations, and order facilitation directly connected to the admin dashboard and social media platforms, thereby accelerating transaction processes and improving consumer experience. The expected outcomes of this program include the establishment of a more professional, transparent, and efficient promotion and ordering system for Durian Bhineka Bawor products. The developed platform is expected to expand market access, increase product sales, and create new digital-based business opportunities. Furthermore, this program contributes to community empowerment by improving digital skills and technology management capabilities to support local economic independence and competitiveness.

Vaname shrimp (Litopenaeus vannamei) has a fast growth rate, good tolerance to a wide range of salinity and temperature, and resistance to several common shrimp diseases. Vaname shrimp has an efficient feed conversion making it more economical to cultivate, and its international market demand is high. The research method used four treatments of fermented soybean meal doses, namely 0% (control), 15%, 25%, and 35%, each with 3 replications. The feed was formulated using Pearson’s Square method targeting 35% protein, and the soybean meal was fermented using tempe yeast method. The observed parameters included daily growth rate (%), absolute weight gain (grams), survival rate (%), feed utilization efficiency (FUE), and feed conversion ratio (FCR). The results showed that the treatment with 25% fermented soybean meal dose gave the best results with a daily growth rate of 6.42%, absolute weight gain of 0.22 grams, survival rate of 90.66%, and the highest feed utilization efficiency of 0.66.

The white shrimp (Litopenaeus vannamei) is one of the popular marine shrimp species in aquaculture due to its adaptability to various environmental conditions, including a wide range of salinity, and its omnivorous feeding behavior. This study aims to determine the effect of different combinations of stocking density and salinity on the growth rate and post larval survival of vaname shrimp (Litopenaeus vannamei). The research was conducted using factorial planning based on two factors namely stocking density (3, 6, and 9ekor/L) and salinity (15 ppt and 25 ppt). Parameters measured included daily growth rate, absolute weight, survival rate, feed utilization efficiency, and feed conversion ratio (FCR). Water quality observations were also made during the study period including temperature, pH, dissolved oxygen, salinity, and total ammonia. The results showed that the combination of stocking density of 3 fish/L with salinity of 25 ppt gave the best results with a daily growth rate of 0,00664 gram and a survival rate of 86%. This treatment also produced the best feed utilization efficiency of 0.87 and the best FCR value of 1.27, indicating the most efficient use of feed. Water quality parameters during the study were within the appropriate range to support the growth of vanamei shrimp.

This study aims to examine how standard costing can be used as a tool for planning and controlling production costs in salted egg cracker micro, small, and medium enterprises (MSMEs). MSMEs need to manage their production costs effectively to increase their profitability in an era of increasingly fierce business competition. A case study was used to collect data through interviews, observations, and financial document analysis. The study shows that the implementation of standard costing has helped more accurate production cost budget planning. This makes it easier for management to compare standard costs with actual costs, which allows for effective and efficient cost control. Furthermore, the findings indicate that standard costing can serve as a benchmark and evaluation tool to improve operational effectiveness. Furthermore, this study suggests that MSMEs should incorporate a standard costing system into their financial reporting process and educate management and employees about the importance of cost control. Therefore, implementing standard costing can be a long-term strategy to keep the company operational and competitive in an increasingly competitive market.

The management of kWh meter replacement data at PLN ULP Ampera Palembang is still largely handled through manual recording, which often causes data inconsistencies and delays in monitoring activities. This study aims to design an information system that supports the monitoring of kWh meter replacement data based on actual user needs. The research applies a descriptive qualitative method using the User Centered Design (UCD) approach, where users are actively involved throughout the design process. The stages include understanding the work context, identifying user requirements, developing system design solutions, and evaluating the proposed design. The outcome of this research is a kWh meter data monitoring system design that is expected to facilitate data management, improve accuracy, and support more efficient monitoring processes.

This study aims to determine the effect of selling price and operational costs on fishermen’s income in Menawi Village, Angkaisera District, Kepulauan Yapen Regency. The population in this study consisted of all fishermen in Menawi Village, Angkaisera District, Kepulauan Yapen Regency. The research sample included 15 fishermen. The data collection technique used in this study was observation through interviews with fishermen, and the data were analyzed using multiple linear regression analysis with the assistance of SPSS 21.0 for Windows. The results of this study indicate that (a) there is a significant simultaneous effect of selling price and operational costs on fishermen’s income in Menawi Village, Angkaisera District, Kepulauan Yapen Regency; (b) there is a positive and significant partial effect of selling price and operational costs on fishermen’s income in Menawi Village, Angkaisera District, Kepulauan Yapen Regency, which can form the basis for economic policy and natural resource management in the fisheries sector. This research is expected to provide insight for policymakers in formulating strategies to increase fishermen's incomes through improved selling prices and more efficient operational cost management.

This study is based on the need to develop a more effective concentrate ration for lactating dairy cows, as existing formulations in the field are greatly influenced by the availability of ingredients and varying quality. Therefore, this study focuses on optimizing concentrate in dairy cow feed rations to meet SNI standards, which include crude protein (CP), Total Digestible Nutrients (TDN), Calcium (Ca), and Phosphorus (P), with more efficient results in terms of price and nutrition. This study uses the Whale Optimization Algorithm (WOA) metaheuristic approach, which balances the exploration and exploitation processes in finding the best solution to optimization problems. This algorithm has fewer parameters than other metaheuristics such as GA, PSO, and DE. WOA runs naturally in continuous space without the need for genetic operators such as crossover and mutation. The dataset used contains types of dairy cow feed ingredients along with nutritional requirements and prices so that researchers can process the data into efficient feed concentrate that is suitable for lactating dairy cows.