Cybersecurity

Cybersecurity has become a pervasive issue affecting all aspects of our digital lives, from financial institutions to healthcare systems (Prince, 2018). It encompasses technical, organizational, and executive measures to protect electronic information and communication systems from unauthorized access and misuse (Mijwil, 2023). The healthcare sector is particularly vulnerable to cybercrime due to its valuable data and weak defenses, with potential breaches impacting patient safety and trust (Coventry & Branley, 2018). To address these challenges, artificial intelligence, specifically machine learning and deep learning techniques, plays a significant role in predicting and understanding malicious software behavior (Mijwil, 2023). Researchers and practitioners from academia, government, and industry are developing emerging technologies and methodologies to mitigate various cyberthreats (Hsu et al., 2015). As cybersecurity becomes integral to patient safety and overall digital security, a holistic approach involving changes to human behavior, technology, and processes is necessary (Coventry & Branley, 2018).

Reference:

1. Prince, D. (2018). Cybersecurity: The security and protection challenges of our digital world. Computer, 51(4), 16-19.
2. Mijwil, M. M., Salem, I. E., & Ismaeel, M. M. (2023). The significance of machine learning and deep learning techniques in cybersecurity: A comprehensive review. Iraqi Journal For Computer Science and Mathematics, 4(1), 10.
3. Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. Maturitas, 113, 48-52.
4. Hsu, D. F., Marinucci, D., & Voas, J. M. (2015). Cybersecurity: Toward a Secure and Sustainable Cyber Ecosystem. Computer, 48(4), 12-14.
5. Coventry, L., & Branley, D. (2018). Cybersecurity in healthcare: A narrative review of trends, threats and ways forward. Maturitas, 113, 48-52.

The Internet of Things (IoT)

The Internet of Things (IoT) is a rapidly evolving paradigm that connects physical objects to the internet, enabling seamless integration and communication between devices and humans (Carretero & García Sánchez, 2013). IoT combines sensor, embedded, computing, and communication technologies to provide ubiquitous services (Narasimha Swamy & Kota, 2020). This interconnected network of devices aims to improve various aspects of daily life, including energy efficiency, healthcare, and comfort (Carretero & García Sánchez, 2013). The IoT architecture encompasses multiple layers and technologies, such as fog computing, wireless sensor networks, and data analytics (Din et al., 2019). However, the widespread adoption of IoT faces challenges in energy efficiency, scalability, interoperability, and security (Narasimha Swamy & Kota, 2020). As IoT continues to evolve, it is expected to transform the real world into intelligent virtual objects, unifying everything under a common infrastructure (Madakam et al., 2015). This technological advancement promises to revolutionize various industries and improve the overall quality of life.

Reference:

1. Carretero, J., & Garcia, J. D. (2014). The Internet of Things: connecting the world. Personal and Ubiquitous Computing, 18, 445-447.
2. Swamy, S. N., & Kota, S. R. (2020). An empirical study on system level aspects of Internet of Things (IoT). IEEE Access, 8, 188082-188134.
3. Din, I. U., Guizani, M., Hassan, S., Kim, B. S., Khan, M. K., Atiquzzaman, M., & Ahmed, S. H. (2018). The Internet of Things: A review of enabled technologies and future challenges. Ieee Access, 7, 7606-7640.
4. Madakam, S., Ramaswamy, R., & Tripathi, S. (2015). Internet of Things (IoT): A literature review. Journal of Computer and Communications, 3(5), 164-173.

Expert systems for Grape

Expert systems in agriculture, particularly viticulture, have shown significant potential for improving crop management and decision-making. As an example Expert systems for Grape. These systems can capitalize on heterogeneous knowledge and predict grape maturity with high accuracy (Perrot et al., 2015). They aim to elevate average worker performance to expert levels in various agricultural domains (McKinion & Lemmon, 1985). Specific applications include diagnosing pests, diseases, and disorders in apple crops (Kemp et al., 1989), which could be adapted for grapevines. Recent advancements integrate computer vision and machine learning techniques for viticulture, addressing challenges such as harvest yield estimation, vineyard management, grape disease detection, and quality evaluation (Seng et al., 2018). The development of specialized databases, like GrapeCS-ML, facilitates research in smart vineyard technologies, including color-based berry detection for different cultivars (Seng et al., 2018). These innovations promise to enhance sustainability and efficiency in the wine industry through improved prediction and management capabilities.

Reference:

1. Perrot, N., Baudrit, C., Brousset, J. M., Abbal, P., Guillemin, H., Perret, B., ... & Picque, D. (2015). A decision support system coupling fuzzy logic and probabilistic graphical approaches for the agri-food industry: prediction of grape berry maturity. PloS one, 10(7), e0134373.
2. McKinion, J. M., & Lemmon, H. E. (1985). Expert systems for agriculture. Computers and Electronics in Agriculture, 1(1), 31-40.
3. Kemp, R. H., Stewart, T. M., & Boorman, A. (1989). An expert system for diagnosis of pests, diseases, and disorders in apple crops. New Zealand Journal of Crop and Horticultural Science, 17(1), 89-96
4. Seng, K. P., Ang, L. M., Schmidtke, L. M., & Rogiers, S. Y. (2018). Computer vision and machine learning for viticulture technology. IEEE Access, 6, 67494-67510.

Expert Systems

Expert systems are artificial intelligence applications designed to emulate human expert decision-making in specific domains (Á. Rocha & S. Anwar, 2019; T. Todorov & J. Stoinov, 2019). These systems solve complex problems by reasoning through knowledge bases, often represented as if-then rules (Á. Rocha & S. Anwar, 2019). They are applied in various fields, including milk quality monitoring and animal health (T. Todorov & J. Stoinov, 2019), as well as information management, organizational models, and software systems (Á. Rocha & Lima, 2018). As technology advances, expert systems are evolving to demonstrate superb decision-making skills and conform to social norms for expertise, behaving more like human experts (Papageorgiou, 2021). This development has implications for various fields, including bioinformatics, and raises questions about the concept of "expert generalist" (Papageorgiou, 2021). The continued research and development of expert systems contribute to their growing capabilities and applications across diverse domains.

Reference:

1. Rocha, Á., & Anwar, S. (2019). The journal of knowledge engineering special issue on WorldCist'17—fifth world conference on information systems and technologies. Expert Systems, 36. https://doi.org/10.1111/exsy.12414
2. Todorov, T., & Stoinov, J. (2019). Expert System for Milk and Animal Monitoring. International Journal of Advanced Computer Science and Applications.https://doi.org/10.14569/ijacsa.2019.0100604
3. Rocha, Á., & Lima, S. (2018). Expert systems: The journal of knowledge engineering special issue on WorldCist'16 ‐ 4th world conference on information systems and technologies. Expert Systems, 35. https://doi.org/10.1111/exsy.12260
4. Papageorgiou, K.G. (2021). Expert Characteristics: Implications for Expert Systems. Advances in experimental medicine and biology, 1338, 155-164 . https://doi.org/10.1007/978-3-030-78775-2_18

I want to be good at Cyber Security, what should I do?

To excel in cybersecurity, a field that is constantly evolving and expanding, you should focus on a blend of education, practical skills, staying updated with the latest trends, and networking. I want to be good at Cyber Security, what should I do? 

Here are some steps to guide you:

1. Educational Foundation: Start with a strong foundation in computer science or a related field. Consider degrees or courses in cybersecurity, network security, information technology, or computer engineering.
2. Certifications: Obtain industry-recognized certifications. Some key certifications include Certified Information Systems Security Professional (CISSP), Certified Ethical Hacker (CEH), CompTIA Security+, and Cisco's CCNA Security. These certifications not only provide knowledge but also demonstrate your skills to potential employers.
3. Practical Skills: Hands-on experience is crucial. Engage in practical activities like setting up your own lab, participating in Capture The Flag (CTF) challenges, and using platforms like Hack The Box or TryHackMe for practice.
4. Stay Informed: Cybersecurity is a rapidly changing field. Stay informed about the latest threats, trends, and technologies. Follow cybersecurity blogs, attend webinars and conferences, and join relevant forums and online communities.
5. Networking: Build a professional network. Connect with experienced professionals through LinkedIn, join cybersecurity groups, attend industry conferences, and participate in community events. Networking can lead to mentorship opportunities and potential job offers.
6. Soft Skills: Develop soft skills such as problem-solving, critical thinking, and effective communication. These skills are vital in understanding complex systems and explaining technical issues to non-technical stakeholders.
7. Ethical and Legal Knowledge: Understand the ethical and legal implications of cybersecurity. This includes knowing what constitutes ethical hacking, the laws related to digital data, and privacy regulations.
8. Specialization: As you progress, consider specializing in areas like penetration testing, network security, incident response, or digital forensics, depending on your interests and career goals.
9. Internships and Entry-level Jobs: Gain experience through internships or entry-level positions in IT or cybersecurity. Real-world experience is invaluable and can significantly boost your career.
10. Continuous Learning: The field is always evolving, so continuous learning is essential. Attend workshops, take advanced courses, and consider advanced degrees if they align with your career goals.

Starting a career in cybersecurity requires a mix of technical knowledge, practical experience, and continuous learning. It's a challenging yet rewarding field with a high demand for skilled professionals.

Contoh Lain dari contoh percabangan dengan penambahan syarat

 Contoh Lain dari Contoh percabangan dengan penambahan syarat

Gambar Output

/*catatan
 * jika BMI dan Usia>=40 munculkan pesan "Obesitas II" dan "Awas Bahaya"
 * dll
 */

mulai:-
    write("Masukkan Berat Badan: "),read(BB),
    write("Masukkan Tinggi Badan: "),read(TB),
    write("Masukkan Usia: "),read(Usia),
    
    BMI is BB/((TB/100)*(TB/100)),
    
    write("Berat Badan: "),write(BB),nl,
    write("Tinggi Badan: "),write(TB),nl,
    write("Usia: "),write(Usia),nl,
    write("BMI: "),write(BMI),nl,
    (
        BMI>=30,Usia>=40,write("Obesitas II"),write(", Awas Bahaya");
        BMI>=30,Usia<40,write("Obesitas II"),write(", Lakukan Diet!");
        BMI>=25,Usia>=40,write("Obesitas I"),write(", Hati-Hati");
        BMI>=25,Usia<40,write("Obesitas I"),write(", Kurangi Lemak");
        BMI>=23,Usia>=40,write("Berlebih"),write(", Waspada");
        BMI>=23,Usia<40,write("Berlebih"),write(", Banyakin Olah Raga");
        BMI>=18.5,Usia>=40,write("Normal"),write(", Pertahankan");
        BMI>=18.5,Usia<40,write("Normal"),write(", Tetap Imbangi dengan olah raga");
        BMI<18.5,Usia>=40,write("Kurang"),write(", Tingkatkan Nutrisi");
        BMI<18.5,Usia<40,write("Kurang"),write(", Tingkatkan BB Anda");
        mulai
    )
    .

Contoh percabangan dengan penambahan syarat

 

Ini adalah  Contoh percabangan dengan penambahan syarat. 

Seseorang dinyatakan lulus jika:

1. Nilai bagus dan Tinggi Badan terpenuhi,
2. Nilai bagus dan Tinggi Badan gagal,
   

Seseorang dinyatakan TDK lulus jika:

1. Nilai gagal dan Tinggi Badan terpenuhi,
2. Nilai gagal dan Tinggi Badan gagal, 
   

/*programs lulus
 * UTS:30
 * UAS:30
 * TUGAS:20
 * PRESENSI:20
 * Rerata>=80 dan Tinggi Badan >= 160
 *
 */

mulai:-
    nl,
    write("*************************************"),nl,
    write("Program Perhitungan Grade"),nl,
    write("*************************************"),nl,
    write("Input UTS Kamu: "),read(UTS),
    write("Input UAS Kamu: "),read(UAS),
    write("Input TUGAS Kamu: "),read(TuG),
    write("Input Presensi Kamu: "),read(Pre),
    write("Input Tinggi Badan: "),read(TB),
    Rerata is ((UTS*30) + (UAS*30) + (TuG*20) + (Pre*20))/100,
    write("Nilai UTS Kamu: "),write(UTS),nl,
    write("Nilai UAS Kamu: "),write(UAS),nl,
    write("Nilai TUGAS Kamu: "),write(TuG),nl,
    write("Nilai Presensi Kamu: "),write(Pre),nl,
    write("*************************************"),nl,
    write("Nilai Rerata Kamu: "),write(Rerata),nl,
    write("Input Tinggi Badan: "),write(TB),nl,
    (
        (   Rerata >=80,TB>=160), write("LULUS");
        (   Rerata <80,TB<160), write("TIDAK LULUS");
        (   Rerata >=80,TB<160), write("LULUS");
        (   Rerata <80,TB>=160), write("TIDAK LULUS")
    
    ).