Metode Penelitian Kualitatif Pendekatan Naratif

Metode Penelitian Kualitatif Pendekatan Naratif. Pendekatan naratif merupakan salah satu dari lima desain utama dalam metodologi penelitian kualitatif (bersama dengan fenomenologi, etnografi, grounded theory, dan studi kasus). Secara etimologis, kata "naratif" berasal dari bahasa Latin narrare, yang berarti menceritakan atau mengisahkan.

Secara harfiah, metode ini dilakukan dengan cara mendeskripsikan suatu peristiwa, kehidupan, atau pengalaman individu secara terperinci dan kronologis untuk menemukan makna di balik fenomena tersebut. 

Berikut adalah rincian deskripsi mengenai pendekatan naratif:

Fokus dan Tujuan Utama. 

Penelitian naratif berfokus pada eksplorasi kehidupan seorang individu atau sekelompok kecil individu. Tujuannya adalah untuk memahami bagaimana seseorang memaknai pengalamannya, pandangan dunianya, serta identitasnya melalui cerita-cerita yang ia tuturkan. Peneliti tidak hanya mencari tahu apa yang terjadi, tetapi bagaimana dan mengapa peristiwa tersebut diceritakan dengan cara tertentu.

Karakteristik Penelitian Naratif. 

• Fokus pada Individu Tunggal: Berbeda dengan survei yang membutuhkan banyak sampel, pendekatan ini sering kali hanya meneliti satu atau dua orang untuk menggali cerita yang sangat mendalam.
• Urutan Kronologis: Data dan cerita biasanya disusun berdasarkan urutan waktu (masa lalu, masa kini, dan masa depan) sehingga alur peristiwa terlihat jelas.
• Pengumpulan Data: Sangat bergantung pada data lisan dan tulisan. Peneliti biasanya menggunakan wawancara mendalam yang bersifat terbuka (open-ended), observasi partisipatif, dokumen pribadi (buku harian, surat), hingga foto atau arsip.
• Restorying (Menceritakan Kembali): Peneliti kualitatif mendengarkan cerita partisipan, menyusun ulang data tersebut berdasarkan elemen naratif (seperti plot, latar, tokoh, klimaks, dan resolusi), lalu menceritakannya kembali secara ilmiah.
• Kolaborasi: Terjadi hubungan dan kolaborasi yang erat antara peneliti dan partisipan dalam menegosiasikan makna cerita agar laporan akhir mencerminkan pengalaman yang otentik tanpa bias dari peneliti.

Jenis-Jenis Pendekatan Naratif. 

Terdapat beberapa variasi dalam desain penelitian ini, di antaranya: 

• Autobiografi: Kisah hidup yang ditulis sendiri oleh subjek penelitian. 
• Biografi: Kisah kehidupan seorang individu yang ditulis atau diteliti oleh orang lain (peneliti). 
• Riwayat Hidup (Life History): Menggambarkan perjalanan kehidupan seseorang secara utuh dari waktu ke waktu. 
• Cerita Pengalaman Pribadi (Personal Experience Story): Fokus pada satu episode, kejadian spesifik, atau fase tertentu dalam kehidupan seseorang, bukan keseluruhan hidupnya.

Daftar Referensi:

Creswell, J. W., & Poth, C. N. (2018). Qualitative Inquiry and Research Design: Choosing Among Five Approaches (4th ed.). Sage Publications. 

Clandinin, D. J., & Connelly, F. M. (2000). Narrative Inquiry: Experience and Story in Qualitative Research. Jossey-Bass.

Webster, L., & Mertova, P. (2007). Using Narrative Inquiry as a Research Method: An Introduction to Using Critical Event Narrative Analysis in Research on Learning and Teaching. Routledge.

Daiute, C., & Lightfoot, C. (Eds.). (2004). Narrative Analysis: Studying the Development of Individuals in Society. Sage Publications.

Chase, S. E. (2005). "Narrative Inquiry: Multiple Lenses, Approaches, Voices". Dalam N. K. Denzin & Y. S. Lincoln (Eds.), The Sage Handbook of Qualitative Research (3rd ed., pp. 651-679). Sage Publications.

Schreiber, J., & Asner-Self, K. (2011). Educational Research: The Interrelationship of Questions, Sampling, Design, and Analysis. John Wiley & Sons.

Thresholding in Image Processing

Thresholding in image processing is a segmentation technique used to separate objects from the background by comparing each pixel’s intensity to a threshold value.

If a pixel value is above or below that threshold, it is assigned to a class.

For a simple binary threshold:

$$g(x,y)=\{\begin{array}{ll}1,& f(x,y)\ge T\\ 0,& f(x,y)<T\end{array}$$

Where:

• $f(x,y)$ = original pixel value
• $g(x,y)$ = output pixel value
• $T$ = threshold

Example:

• grayscale pixel = 180, threshold = 128 → output = white
• grayscale pixel = 70, threshold = 128 → output = black

Common types:

• Global thresholding: one threshold for the whole image
• Adaptive thresholding: threshold changes by local region
• Otsu thresholding: automatically finds an optimal threshold

It is commonly used for:

• object detection
• document binarization
• medical image segmentation
• shape extraction

Tutorial JFLAP7.1

Apa itu JFLAP7.1?

JFLAP adalah perangkat lunak pembelajaran untuk memodelkan, menguji, dan mengonversi berbagai konsep bahasa formal. Dari menu tutorial resminya, cakupan utamanya meliputi Finite Automata, Pushdown Automata, Turing Machine, Grammar, Regular Expressions, serta fitur seperti Convert to DFA, Minimize DFA, Convert CFG to PDA, CYK Parser, dan lain-lain.

2. Cara instalasi JFLAP 7.1

Di halaman perangkat lunak resmi, file utama yang dibutuhkan untuk menjalankan program adalah JFLAP7.1.jar. Situs resmi juga memberi catatan bahwa bila sistem operasi mengunduhnya sebagai .zip, file itu perlu diubah namanya kembali menjadi .jar. Pada Windows dan macOS, JFLAP bisa dijalankan dengan double click pada file .jar, sedangkan pada Linux/Unix dapat dijalankan dengan perintah java -jar JFLAP.jar.

Langkah instalasi yang disarankan

1. Pastikan Java terpasang. Untuk JFLAP 7.1, rujukan resmi menyebut penggunaan Java 8.0.
2. Unduh JFLAP7.1.jar dari halaman software resmi.
3. Jika file berubah menjadi .zip, ubah lagi menjadi .jar.
4. Jalankan:

• Windows: double click JFLAP.jar
• macOS: double click JFLAP.jar
• Linux: java -jar JFLAP.jar

Setelah berhasil dibuka, JFLAP akan menampilkan daftar struktur yang bisa dibuat, misalnya Finite Automaton, Pushdown Automaton, Turing Machine, Grammar, dan Regular Expression.

3. Mengenal antarmuka dasar JFLAP

Pada editor Finite Automaton, toolbar utama berisi empat alat penting: Attribute Editor, State Creator, Transition Creator, dan Deletor. Pola kerja editor untuk FA ini juga menjadi dasar bagi editor automata lain di JFLAP.

Fungsi keempat alat itu:

1. Attribute Editor: mengatur initial state dan final state
2. State Creator: membuat state baru
3. Transition Creator: membuat transisi
4. Deletor: menghapus state atau transisi.

Untuk menjadikan sebuah state sebagai initial, aktifkan Attribute Editor, lalu klik kanan pada state dan centang Initial. Untuk menjadikannya final, lakukan hal yang sama dan centang Final. State final akan digambar dengan lingkar ganda.

Decision Support Systems (DSS): Enhancing Better Decisions in the Digital Era

In today’s fast-changing world, individuals and organizations are constantly required to make decisions quickly and accurately. Whether in business, healthcare, education, agriculture, or government, decision-making has become more complex due to the large amount of data available. This is where Decision Support Systems (DSS) play an important role.

A Decision Support System (DSS) is a computer-based information system designed to support decision-makers in solving semi-structured or unstructured problems. Rather than replacing human judgment, DSS helps users analyze information, compare alternatives, and choose the most appropriate course of action.

What is a Decision Support System?

A Decision Support System is an interactive system that collects, processes, and presents data in a way that assists decision-making. It combines data, analytical models, and user-friendly software to help managers, professionals, or other users make better decisions.

DSS is especially useful when decisions involve uncertainty, multiple criteria, or a large number of possible alternatives. For example, a company may use DSS to select the best supplier, a hospital may use it to determine treatment priorities, or a farmer may use it to choose the most suitable crops based on land and weather conditions.

Main Components of DSS

A typical Decision Support System consists of three main components:

1. Data Management

This component stores and manages the data needed for decision-making. The data may come from internal databases, external sources, or real-time systems.

2. Model Management

This part contains mathematical, statistical, or logical models used to analyze problems and generate possible solutions. Examples include forecasting models, optimization models, and scoring methods.

3. User Interface

The user interface allows decision-makers to interact with the system easily. A good DSS should present information clearly through tables, dashboards, charts, or reports.

Characteristics of DSS

Decision Support Systems have several important characteristics:

• They support, not replace, human decision-makers.
• They are flexible and adaptable to changing conditions.
• They can handle both qualitative and quantitative data.
• They assist in semi-structured and unstructured decision problems.
• They provide alternative solutions for comparison.

These characteristics make DSS highly valuable in environments where decisions must be made carefully and efficiently.

Types of Decision Support Systems

DSS can be classified into several types based on how they support decisions:

1. Data-Driven DSS

Focuses on collecting and analyzing large volumes of data. It is commonly used in business intelligence and reporting systems.

2. Model-Driven DSS

Uses analytical models and simulations to support decisions. This type is useful for forecasting, planning, and optimization.

3. Knowledge-Driven DSS

Provides recommendations based on expert knowledge, rules, or artificial intelligence.

4. Document-Driven DSS

Helps users retrieve and analyze documents, reports, and written information relevant to decision-making.

5. Communication-Driven DSS

Supports group decision-making by enabling collaboration, discussion, and information sharing among team members.

Benefits of DSS

Implementing a Decision Support System offers many advantages, such as:

• Improving the quality of decisions
• Saving time in analyzing alternatives
• Reducing human error
• Supporting more objective and consistent decisions
• Helping organizations respond quickly to changes
• Increasing productivity and efficiency

By providing structured analysis, DSS allows decision-makers to focus on strategy rather than spending too much time processing raw data manually.

Applications of DSS in Real Life

Decision Support Systems are widely used in many fields:

Business

Companies use DSS for budgeting, market analysis, supplier selection, inventory control, and customer relationship management.

Healthcare

Hospitals and clinics use DSS to support diagnosis, patient treatment planning, and medical resource allocation.

Education

Educational institutions use DSS for student performance evaluation, scholarship selection, and academic planning.

Agriculture

Farmers and agricultural agencies use DSS to determine planting schedules, fertilizer recommendations, pest control strategies, and crop selection.

Government

Governments use DSS for policy planning, disaster management, public service improvement, and resource distribution.

Challenges in Using DSS

Although DSS offers many benefits, there are also challenges in its implementation. These include:

• High development and maintenance costs
• Dependence on accurate and updated data
• Need for user training
• Resistance to adopting new technology
• Difficulty in selecting the most suitable model for specific problems

Therefore, successful DSS implementation requires not only technology but also good planning, user involvement, and proper management support.

The Future of DSS

As technology continues to evolve, Decision Support Systems are becoming more intelligent and powerful. The integration of Artificial Intelligence (AI), Machine Learning, Big Data, and Cloud Computing has significantly expanded the capabilities of DSS.

Modern DSS can now provide predictive insights, real-time analytics, and personalized recommendations. In the future, DSS will likely become even more essential for organizations seeking to remain competitive and make informed decisions in complex environments.

Conclusion

Decision Support Systems are valuable tools that help individuals and organizations make better, faster, and more informed decisions. By combining data, models, and user interaction, DSS transforms raw information into useful knowledge for solving real-world problems.

In an era where data is growing rapidly and decision-making is becoming more challenging, DSS serves as a bridge between information and action. For this reason, understanding and implementing DSS is increasingly important across many sectors.

Rumus SUM di Ms Excel

Rumus SUM adalah rumus digunakan untuk menjumlahkan beberapa data

Contoh Percabangan Menghitung Grade Nilai dengan Prolog

hitung:-

    write("UAS: "),read(Uas),

    write("UTS: "),read(Uts),

    write("Tugas: "),read(Tugas),

    write("Presensi: "),read(Presensi),

    Nilai is ((Uas*40)+(Uts*30)+(Tugas*20)+(Presensi*10))/100,

    write(Nilai),nl,

    (   Nilai >= 80,write("Grade A");

            Nilai >= 65,write("Grade B");

        Nilai >= 55,write("Grade C");

        Nilai >= 40,write("Grade D");

        write("Grade E")

        )

    .

Si Budi

iden(n01,andi).

iden(n02,budi).

iden(n03,ina).

iden(n04,ina).

item(i01,anggur).

item(i02,jeruk).

item(i03,donut).

warna(w01,merah).

warna(w02,hijau).

warna(w03,coklat).

warna(w04,kuning).

rasa(r01,manis).

rasa(r02,masam).

menyukai(n01,i01,w01,r01).

menyukai(n02,i01,w02,r02).

menyukai(n03,i02,w04,r02).

menyukai(n04,i03,w03,r01).

manis1(Nama):-

   iden(No,Nama),rasa(Ra,manis),

   menyukai(No,_,_,Ra).