The development of modern biotechnology has brought fundamental changes to the life sciences through the application of molecular biology, genetic engineering, and bioinformatics techniques. This article aims to examine the transformation of life sciences through modern biotechnology approaches and its impact on health, agriculture, the environment, and industry. The method used is a descriptive-analytical literature review based on international journal sources and relevant scientific publications. The results of the study indicate that technologies such as CRISPR-Cas9 enable precise gene editing for genetic disease therapy, while the mRNA vaccine platforms developed by Pfizer and Moderna demonstrate the acceleration of biomedical innovation in response to the global pandemic. In the agricultural sector, genetic engineering increases crop productivity and resilience, while in the environmental sector, biotechnology supports environmentally friendly bioremediation processes. However, ethical and regulatory challenges remain important concerns in its implementation. In conclusion, modern biotechnology plays a key role as a catalyst for sustainable, innovation-driven transformation in the life sciences.
Public complaint services are an essential part of public service delivery in supporting the government’s rapid response to various social issues and emergency situations. In West Tanjung Jabung Regency, public complaint services are provided through the HALO USTAD 112 Call Center managed by the Department of Communication and Informatics. However, the existing service still faces several limitations, including the lack of optimal integration in complaint data management, inadequate documentation of reports based on regional classifications, and limited capabilities in storing and retrieving complaint data. This study aims to optimize the HALO USTAD 112 Call Center service through the design of a mobile-based public complaint information system, so that the processes of receiving, managing, and monitoring reports can be carried out more effectively and in a structured manner. The system development applies the Waterfall method, which consists of requirement analysis, system design, implementation, and testing stages. The designed information system includes key features such as user and admin login, complaint submission, report management and verification, report monitoring, statistical visualization of complaint data, and regional-based report recapitulation. The application is developed using the Flutter framework with the Dart programming language, while Supabase is utilized as the backend integrated with a PostgreSQL database. The results of this study are in the form of a system design and prototype that are expected to improve the quality of public complaint services and support more accurate, integrated, and efficient data management.
Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.
This study analyzes bottled water consumer traits and the market share of various brands over five years at the University of Udayana using the Markov chain method. Primary data from questionnaires show most consumers are female informatics students in dorms, consuming over 2 liters daily, mostly purchasing from stores. Decisions consider quality and brand, influenced by TV ads over peer recommendations. Market share in period 1: Aqua led with 52%, followed by Le Minerale (28%), Club (13%), Cleo (7%), and others (0%). In period 2, Aqua maintained 52%, Le Minerale rose to 36%, while Club and Cleo declined to 2% and 3%. Period 3 saw Aqua at 49%, Le Minerale at 33%, and Club/Cleo at 2% and 1%. In period 4, Aqua led with 45%, Le Minerale at 31%, and Club/Cleo/others at 2%, 1%, and 7%. Finally, in period 5, Aqua remained at 41%, Le Minerale fell to 28%, while others decreased to 6%, and Club/Cleo remained at 2% and 1%.
Knowledge must be managed effectively to facilitate transfer between individuals, groups, and organizations. The Informatics Engineering study program currently lacks a system for knowledge management. Currently, the study program facilitates offline discussion forums for the sharing of knowledge gained by lecturers and students. These offline discussion forums require significant costs, time, and space, often resulting in delays in knowledge sharing. This research focused on the analysis and design of a Knowledge Management System to meet the needs of Informatics Engineering students at Universitas PGRI Madiun. The system development method used was the Knowledge Management System Lifecycle (KMSL). In this study, the TIF KMS system using the KMSL method has been successfully built. The results of testing using the Blackbox Testing method showed that 5 scenarios and 18 cases were successfully executed as expected with a 100% success rate. Based on the system test results, the TIF KMS is ready to proceed to the implementation stage. Future implementation can be done by developing additional features such as a digital library
In Iranian Traditional Medicine, some herbs like Achillea millefolium, commonly known as yarrow, are implicated as appetite enhancers. However, there is not enough research evidence to prove their actual effect. Achillea millefolium is a dicot which belongs to the family Asteraceae. The purpose of this study was the analysis Achillea millefolium based on a bioinformatic study and toxicity test on the chicken embryo. This research method consisted of conversion of nucleotides into amino acids, analysis of the three-dimensional structure of Achillea millefolium protein, epitope and Allergen Proteins, antigens and toxins and toxicity test on the chicken embryo. In addition, this study also obtained the results of proteins that are epitope, antigenic, non-allergenic and non-toxic and toxicity test on the chicken embryo was 250 ng/egg.. Morphological description of the embryo on the 21st day after injection, at a concentration of 250 ng of Mentha piperita/egg product, an abnormal embryological picture was obtained. Chicken Embryo Weight and Body Length Measurements were carried out in chicken embryos. Need research for other species of plant.
Hawthorn extract has been used for ameliorating cardiac disorders and pulmonary hypertension. The main chemical constituents of hawthorn flavonoid extract (HFE) include flavonoids (1-2%), oligomeric proanthocyanidins (1-3%), and other bioactive components (e.g., triterpene acids, organic acids, sterols, and cardioactive amines). These compounds are reported to have many pharmacological effects, including neuroprotective, hepatoprotective, cardioprotective, and nephroprotective effects. This study was aimed the analysis Crataegus oxyacantha based on a bioinformatic study and toxicity test on the chicken embryo. This method consisted of analysis of the three-dimensional structure of Crataegus oxyacantha Protein, Epitope and Allergen Proteins, Crataegus oxyacantha Proteins that were antigens and toxins and toxicity test on the chicken embryo. The results of research conducted on 3 three-dimensional structures of Crataegus oxyacantha protein, GQME value and QmeanDisCo value. In addition, this study also obtained the results of proteins that are epitope, antigenic, non-allergenic and non-toxic and toxicity test on the chicken embryo was 250 ng/egg..Morphological description of the embryo on the 21st day after injection, at a concentration of 250 ng of Crataegus oxyacantha /egg product, an abnormal embryological picture was obtained. Chicken Embryo Weight and Body Length Measurements were carried out in chicken embryos. Need research for other species of plant.
This study examines the public communication strategy implemented by the broadcasting division of the Bogor City Communication and Informatics Office (Diskominfo) in producing and disseminating video content through social media as a means of public information and education. A descriptive qualitative approach was used, using observation, interviews, and document study techniques to gain an in-depth understanding of contextual communication practices. The results indicate that the public communication strategy was implemented through three main stages: pre-production, production, and post-production. In the pre-production stage, messages relevant to community needs were planned. The production stage focused on creating content that was informative, engaging, and easily understood by the audience, while the post-production stage included the dissemination and evaluation of message effectiveness. Challenges encountered included limited resources, technical issues, and sometimes suboptimal coordination between departments. However, these obstacles were overcome through teamwork, technical training, and more efficient equipment management. This study confirms that video content plays a strategic role in strengthening the relationship between the government and the community and encouraging transparency and public participation. Continuous evaluation based on audience feedback is recommended to improve the effectiveness of future communication.
In the midst of increasingly rapid and complex information flows in the digital era, the ability of government institutions to establish effective communication has become crucial in shaping a positive public image. This study aims to examine the public communication strategies implemented by the South Jakarta Department of Communication, Informatics, and Statistics in delivering information to the public in a transparent, accurate, and participatory manner. Employing a qualitative approach with a descriptive method, data were obtained through in-depth interviews, observation, and documentation. The findings reveal that the department actively utilizes social media and digital channels, fosters two-way communication, and crafts messages that are adaptive to public needs. Despite these efforts, challenges persist, such as limited human resources, disruptions in digital infrastructure, and the spread of disinformation that undermines public trust. This study underscores the importance of communication strategies that are not only technical but also ethical and humanistic, positioning the public as partners in dialogue. These findings are expected to contribute to strengthening the communication capacity of government institutions that are oriented toward service, transparency, and collaboration.
The daily exchange of informatics over the Internet has both eased the widespread proliferation of resources to ease accessibility, availability and interoperability of accompanying devices. In addition, the recent widespread proliferation of smartphones alongside other computing devices has continued to advance features such as miniaturization, portability, data access ease, mobility, and other merits. It has also birthed adversarial attacks targeted at network infrastructures and aimed at exploiting interconnected cum shared resources. These exploits seek to compromise an unsuspecting user device cum unit. Increased susceptibility and success rate of these attacks have been traced to user's personality traits and behaviours, which renders them repeatedly vulnerable to such exploits especially those rippled across spoofed websites as malicious contents. Our study posits a stacked, transfer learning approach that seeks to classify malicious contents as explored by adversaries over a spoofed, phishing websites. Our stacked approach explores 3-base classifiers namely Cultural Genetic Algorithm, Random Forest, and Korhonen Modular Neural Network – whose output is utilized as input for XGBoost meta-learner. A major challenge with learning scheme(s) is the flexibility with the selection of appropriate features for estimation, and the imbalanced nature of the explored dataset for which the target class often lags behind. Our study resolved dataset imbalance challenge using the SMOTE-Tomek mode; while, the selected predictors was resolved using the relief rank feature selection. Results shows that our hybrid yields F1 0.995, Accuracy 0.997, Recall 0.998, Precision 1.000, AUC-ROC 0.997, and Specificity 1.000 – to accurately classify all 2,764 cases of its held-out test dataset. Results affirm that it outperformed bench-mark ensembles. Result shows the proposed model explored UCI Phishing Website dataset, and effectively classified phishing (cues and lures) contents on websites.
Development of a Canva-Based Informatics E-book on ICT Material for Grade X at SMA Negeri 5 Samarinda. This research aims to determine the feasibility of a product in the form of an E-book as a learning medium and to assess the response toward the Canva-based Informatics E-book for Grade X students at SMA Negeri 5 Samarinda. This study employs a Research and Development (R&D) method with the PPE model, which consists of three stages: Planning, Production, and Evaluation. The research subjects involved 35 students from Grade X (I) at SMA Negeri 5 Samarinda, along with 3 media experts and 1 material expert. The research object is the Canva-based Informatics E-book on ICT material. The results showed that the development of the Canva-based E-book, as evaluated by 3 media expert validators, obtained a percentage score of 92.19%, which falls into the “Highly Feasible” category. The Evaluation conducted by 1 material expert achieved a percentage score of 100%, also in the “Highly Feasible” category. Furthermore, students’ responses to the product were measured and obtained an average percentage score of 82.57%, which falls into the “Very Interesting” category. Thus, the development of the Canva-based E-book on ICT material for Grade X at SMA Negeri 5 Samarinda, developed using the PPE model, is declared highly feasible based on media and material expert validation and very interesting based on students’ responses.
Development of a Canva-Based Informatics E-book on ICT Material for Grade X at SMA Negeri 5 Samarinda. This research aims to determine the feasibility of a product in the form of an E-book as a learning medium and to assess the response toward the Canva-based Informatics E-book for Grade X students at SMA Negeri 5 Samarinda. This study employs a Research and Development (R&D) method with the PPE model, which consists of three stages: Planning, Production, and Evaluation. The research subjects involved 35 students from Grade X (I) at SMA Negeri 5 Samarinda, along with 3 media experts and 1 material expert. The research object is the Canva-based Informatics E-book on ICT material. The results showed that the development of the Canva-based E-book, as evaluated by 3 media expert validators, obtained a percentage score of 92.19%, which falls into the “Highly Feasible” category. The Evaluation conducted by 1 material expert achieved a percentage score of 100%, also in the “Highly Feasible” category. Furthermore, students’ responses to the product were measured and obtained an average percentage score of 82.57%, which falls into the “Very Interesting” category. Thus, the development of the Canva-based E-book on ICT material for Grade X at SMA Negeri 5 Samarinda, developed using the PPE model, is declared highly feasible based on media and material expert validation and very interesting based on students’ responses.
Adapting to students’ learning styles is a key factor in enhancing the effectiveness of higher education, particularly in Informatics programs where learning preferences vary widely. This study aims to segment students based on their learning styles using the K-Means clustering algorithm, guided by the VARK model (Visual, Auditory, Read/Write, Kinesthetic). Data were collected from 130 Informatics students, including information on their learning preferences, and processed through normalization techniques. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, and subsequent cluster interpretation was conducted. The results identified three dominant clusters, each representing distinct learning behavior patterns. These clusters were analyzed to recommend tailored instructional strategies for each group. Specifically, Visual learners were found to benefit from graphic-heavy materials, Auditory learners preferred lectures and discussions, Read/Write learners thrived on written content and detailed notes, while Kinesthetic learners responded best to hands-on activities. The findings support the development of adaptive, data-driven teaching approaches that align with the actual learning tendencies of students in Informatics. Moreover, the study demonstrates that the K-Means method is effective in systematically identifying student learning profiles, which can be used to inform instructional improvements. This personalized approach to teaching could significantly enhance learning outcomes by providing students with the most effective educational experiences tailored to their individual learning styles
Sleep is a fundamental human need that plays a crucial role in maintaining both physical and mental health. Poor sleep quality can trigger a variety of health problems, ranging from decreased concentration to an increased risk of chronic diseases. The complexity of factors influencing sleep quality—such as stress levels, heart rate, blood pressure, physical activity, and lifestyle—makes its assessment difficult through direct observation alone. Therefore, data mining approaches are increasingly utilized to identify relevant patterns in sleep-related data. This study aims to compare the performance of the C4.5 (Decision Tree) algorithm and the Naïve Bayes algorithm in predicting sleep quality using the Sleep Health and Lifestyle dataset, which contains information from 374 respondents. The research method applied is a quantitative comparative approach employing classification techniques with 10-fold cross-validation to ensure robust evaluation. Model performance is assessed using accuracy, precision, and recall metrics to provide a comprehensive understanding of the effectiveness of each algorithm. The findings indicate that the C4.5 algorithm achieves an accuracy of 96.26% and offers advantages in terms of interpretability through its decision tree visualization, enabling easier understanding of variable relationships. In contrast, the Naïve Bayes algorithm demonstrates superior predictive performance, achieving an accuracy of 98.66% along with consistently high precision and recall across nearly all classes. These results suggest that Naïve Bayes is more effective for predictive tasks involving sleep quality, while C4.5 remains highly valuable when the goal is to interpret variable interactions and decision rules. Overall, this research highlights the potential of data mining techniques in health informatics, particularly in improving the understanding and prediction of sleep quality, which in turn can contribute to better prevention and management of sleep-related health issues.
The development of information and communication technology (ICT) has encouraged the implementation of Electronic-Based Government Sistems (SPBE) aimed at improving the quality of public services to become more transparent, accountable, and efficient. In line with this, the Department of Communication and Informatics (Diskominfo) of Central Java Province developed the Integrated Secretariat and Supporting Unit Sistem (Si-SEKRUP) application as a digital innovation specifically designed to support internal administrative management. The presence of this application represents a concrete step in bureaucratic digital transformation, particularly in realizing administrative services that are fast, integrated, and oriented toward the principles of good governance. This study aims to evaluate the effectiveness of the Si-SEKRUP application in supporting digital-based administrative governance. The research method applied is descriptive qualitative, utilizing secondary data obtained from literature, official reports, and public documents, with validation through data triangulation. The findings show that the implementation of Si-SEKRUP has significantly improved administrative efficiency, as reflected in the acceleration of document processing by up to 50%, the achievement of 100% digital documentation, and a substantial reduction in paper use that supports a paperless office sistem. Furthermore, the integration of digital and real-time asset monitoring has enhanced organizational transparency and accountability. The implementation of this application has not only provided technical benefits but has also fostered a shift in employee work culture toward more adaptive, collaborative, and productive practices in line with digital-era demands. These findings affirm that the success of bureaucratic digital transformation requires sustainable development, both in terms of technological infrastructure and human resource competencies, so that SPBE utilization can run optimally and deliver tangible value to public services.
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This study aims to determine the implementation of the Smart Society 5.0-based digital literacy program in Ciamis Regency, implemented by the Communication and Informatics Office (Diskominfo). This program is part of the local government's digital transformation to create a smart, inclusive, and adaptive society to the development of information technology. This study uses a qualitative approach with descriptive methods, and data is obtained through in-depth interviews, field observations, and documentation. The analysis is conducted using Charles O. Jones's theory of public policy implementation, which includes three main components: organization, interpretation, and implementation. The results show that organizationally, Diskominfo has a supportive structure and relatively competent human resources, although there are still limitations in the mastery of digital technology among employees. From an interpretation perspective, the digital literacy strategy is implemented through public education, hoax prevention, digital content provision, and social media utilization. Meanwhile, in terms of implementation, supervision is carried out through monitoring, regular reporting, the use of digital applications, and evaluation forums. Some obstacles faced include budget limitations, the digital divide between regions, uneven technological infrastructure, and the lack of specific evaluation indicators. Nevertheless, improvement efforts continue through human resource training, optimization of digital facilities and infrastructure, and cross-sector synergy. This study concludes that the implementation of the Smart Society 5.0-based digital literacy program in Ciamis Regency has been quite successful, but still requires strengthening human resources, evaluation policies, and equitable digital access in rural areas.
The effectiveness of reporting within the reporting information system at Community Health Centers (Puskesmas) is crucial for supporting data‑driven decision‑making, health‑program planning, and performance monitoring. Accurate and timely reporting enables healthcare administrators to analyze trends, allocate resources efficiently, and improve patient outcomes through evidence‑based interventions. This article analyzes the factors influencing reporting effectiveness at Puskesmas in Serang Regency, including technological, human‑resource, and organizational factors. The study employs a literature‑review and case‑analysis approach, highlighting challenges and offering recommendations to improve reporting effectiveness. The results indicate that limitations in infrastructure, staff competency, digital literacy, as well as management support and policy, are key factors that must be addressed to optimize the reporting system in Serang Regency’s Puskesmas. Technological barriers—such as inadequate hardware, software, and internet connectivity—often hinder the seamless collection and transmission of health data. Human‑resource challenges, including insufficient training and low digital literacy among staff, can lead to data‑entry errors and delays. Organizational factors—such as clear policies, standard operating procedures, and a supportive management culture—are also essential for sustaining high‑quality reporting practices. Furthermore, the integration of health informatics and continuous quality‑improvement initiatives can enhance the reliability and usability of reported data, ultimately supporting better health outcomes at the community level. The study concludes that a holistic approach—encompassing technological upgrades, ongoing staff development, and strong organizational leadership—is necessary to ensure the effectiveness and sustainability of reporting systems in primary‑healthcare settings.
Digital transformation in government governance demands an integrated information system capable of increasing efficiency, transparency, and accountability. This study aims to analyze the effectiveness of the use of the Secretariat and Supporting Elements Integration System Application (Si-SEKRUP) at the Communication and Informatics Office of Central Java Province. The research method used a qualitative descriptive approach with data triangulation techniques in the form of documentation, observation, and literature studies. The results of the study indicate that the implementation of Si-SEKRUP has a positive impact on administrative management. This application is able to accelerate administrative processes by up to 50% compared to the previous manual system. In addition, electronic document recording encourages transparency, while digital and real-time asset monitoring strengthens institutional accountability. Resource efficiency has also increased, as seen from the significant reduction in paper use, in line with the principles of green office. The research findings are linked to the theory of organizational effectiveness and Management Information Systems (MIS), where Si-SEKRUP is assessed to have met the indicators of accuracy, timeliness, relevance, and efficiency of information. This indicates that the application can function as a supporting tool in strategic and operational decision-making within government organizations. However, optimizing the application's use still faces challenges, particularly related to the need for employee training and consistent managerial support. With strengthened human resource capacity and leadership commitment, Si-SEKRUP has the potential to become an effective and sustainable integrated information system model supporting digital-based bureaucratic reform.
The issue of determining the number of students' graduation times is one of the important indicators in transmitting the quality and effectiveness of the higher education process in universities. The rate of on-time graduation not only impacts accredited institutions, but also becomes a concern for campus management in designing learning strategies and academic guidance. This study aims to apply and compare two classification algorithms in data mining, namely C4.5 and K-Nearest Neighbor KNN, in predicting the accuracy of students' graduation times. Predictions are made based on academic attributes such as Grade Point Average GPA, number of credits that have been achieved, and Semester Grade Point Average IPS as input variables. The method used in this study is Knowledge Discovery in Database KDD which includes data selection, preprocessing, transformation, data mining, and evaluation of results. The study was conducted using the RapidMiner tool, with a dataset of 279 Informatics Study Program students from the 2015 to 2019 intake. The data was classified into two categories: "graduated on time" and "not graduated on time". The test results showed that the KNN algorithm provided better performance compared to C4.5. KNN produced an accuracy of 76.08%, with a precision of 73.11% and a recall of 41.92%. Meanwhile, the C4.5 algorithm produced an accuracy of 73.49%, with a precision of 64.62% and a recall of 41.89%. This difference in accuracy indicates that KNN is more effective in capturing patterns in the data and providing more accurate predictions in this context. Thus, the KNN algorithm can be considered a more optimal method to assist universities in predicting potential student admissions in a timely manner, thus enabling early intervention for students at risk of late graduation. This research also contributes to the development of data mining-based academic decision support systems in higher education.