Konsultan Arsitektur Perusahaan: KPK, RistekDikti, INSW, BPPT, Kementerian Sosial, Kementerian Keuangan (Itjend, DJBC, DJPK), Telkom, FIF, PLN, PJB, Pertamina EP, dll. Penelitian dalam bidang rekayasa perangkat lunak bukanlah penelitian mengenai pengembangan perangkat lunak yang akhirnya menjadi produk perangkat lunak, melainkan. Ilmu Komputer: untuk melayani siswa yang ingin melanjutkan sebagai generalis ilmu komputer atau yang ingin melanjutkan studi pascasarjana.
Software Engineering: To serve students who have the intellectual and technical aptitude to excel as software. Information Systems: to serve students who desire a career that focuses on the information needs of organizations and who are primarily interested in technology as a vehicle for meeting such needs. Computer Engineering: to serve students who want a career focused on developing computer-based devices (embedded systems).
IS specialists focus on integrating information technology solutions and business processes to meet the information needs of businesses and other businesses so that they can achieve their goals in an effective and efficient manner. IS specialists deal with the information that computer systems can provide to help a business define and. Untuk teştiğan nilai parameter (panjang, tinggi, lebar, dalam, benuk, jenis material, etc.) yang bleaching optimal pada desain.
5 Penerapan kerangka TOGAF yang dimodifikasi untuk pengembangan arsitektur perusahaan di perusahaan kecil dan menengah.
Sarjana)
Magister)
Doktor)
- Penelitian Tindakan
- Eksperimen
- Studi Kasus
- Survei
- Structured Design (SD)
- Rapid Application Development (RAD)
- Agile Development
- SD: Waterfall Method
- RAD: Phased Development
Kontribusi kepada masyarakat tidak dapat diukur secara langsung, karena tidak termasuk dalam tujuan penelitian, melainkan pada manfaat penelitian. Kajian berupa pemantauan dan pencatatan secara cermat terhadap pelaksanaan sesuatu yang dilakukan peneliti, yang tujuannya untuk memecahkan masalah dan mengubah situasi (Herbert, 1990).
Versi 2
RAD: Prototyping
Agile Values
Agile Principles 3. Agile Practices
Communication: Building software requires communicating requirements to the developers
Simplicity: Encourages starting with the simplest solution, extra functionality can then be added later
Feedback
Courage: Several practices embody courage. One is the
Agile: Extreme Programming
Metode yang saat ini digunakan untuk memperkirakan nilai tukar adalah regresi linier, jaringan saraf, dan mesin vektor pendukung. Keunggulan mesin support vector adalah dapat menyelesaikan permasalahan B (pada regresi linier) dan D (pada jaringan syaraf tiruan). Lampu lalu lintas yang ada bersifat statis (waktu tetap), sehingga tidak dapat mengatasi kepadatan kendaraan pada waktu yang berbeda-beda.
Literature review is a critical and in-depth evaluation of previous research (Shuttleworth, 2009) (https://explorable.com/what-is-a-literature-review). A good literature review evaluates the quality and findings of previous research (State-of-the-Art Methods).
Paper dari Journal
Paper dari Book Chapter
Paper dari Conference (Proceedings) 4. Thesis dan Disertasi
Report (Laporan) dari Organisasi yang Terpercaya
Buku Textbook
Traditional Review
Systematic Literature Review or Systematic Review
Systematic Mapping Study (Scoping Study) 4. Tertiary Study
Romi Satria Wahono, A Systematic Review of Software Defect Prediction Literature: Research Trends, Datasets, Methods and Frameworks, Journal of Software Engineering, Vol. Intervention Software defect prediction, fault prediction, fault prone, detection, classification, estimation, models, methods, techniques, datasets.
PICOC
Estimating the number of defects remaining in software systems using estimation algorithm
Discovering defect associations using association rule algorithm (Association)
Classifying the defect-proneness of software
Clustering the software defect based on object using clustering algorithm (Clustering)
Analyzing and pre-processing the software defect datasets (Dataset Analysis)
Journal of Systems and Software Journal of Systems and Software Journal of Systems and Software. International Conference on Natural Computation IEEE Transactions on Knowledge and Data Engineering IEEE Transactions on Systems, Man and Cybernetics IEEE Transactions on Reliability. LOC_code_and_comment NCSLOC The number of lines containing both code and comment in a module.
The accurate and reliable classification algorithms to build a better prediction model is an open problem in software defect prediction.
Menzies Framework
Lessmann Framework
Song
The comparisons and benchmarking result of the defect prediction using machine learning classifiers indicate
Noisy attribute predictors and imbalanced class distribution of software defect datasets result in
Neural network and support vector machine have strong fault tolerance and strong ability of nonlinear
Software defect datasets contain noisy data points that cannot be reliably assumed to be false using such a simple method (Gray, Bowes, Davey, & Christianson, 2011). The performance of software defect prediction improved when irrelevant and redundant attributes were removed (Wang, Khoshgoftaar, & Napolitano, 2010). The software error prediction performance is significantly reduced because the data set contains noisy attributes (Kim, Zhang, Wu, & Gong, 2011).
Software defect data has an unbalanced nature with very few defective modules compared to non-defective ones (Tosun, Bener, Turhan, & Menzies, 2010). Imbalance can lead to a model that is not practical in predicting software defects because most cases will be predicted as defect-prone (Khoshgoftaar, Van Hulse, & Napolitano, 2011). Noisy attribute predictors and unbalanced class distribution of software defect datasets result in inaccurate classification models.
Neural network has strong fault tolerance and strong ability of non-linear dynamic processing of software error data, but it is practically feasible. Romi Satria Wahono, Nanna Suryana Herman, and Sabrina Ahmad, A Comparative Framework of Classification Models for Predicting Software Defects, Advanced Science Letters, Vol. Romi Satria Wahono and Nanna Suryana Herman, Selection of genetic traits for predicting software errors, Advanced Science Letters, Vol.
Romi Satria Wahono and Nanna Suryana, Combining Feature Selection Based on Particle Particle Optimization and Packaging Technique for Software Defect Prediction. Romi Satria Wahono, Nanna Suryana and Sabrina Ahmad, Metaheuristic neural network parameter optimization based on genetic algorithm for software. 20, Romi Satria Wahono, Nanna Suryana and Sabrina Ahmad, Metaheuristic optimization based feature selection for software defect prediction, Journal of.
A Comparison Framework of Classification Models for
Software Defect Prediction (CF SDP)
- Romi Satria Wahono, Nanna Suryana Herman and Sabrina Ahmad, A Comparison Framework of Classification
- Romi Satria Wahono, Nanna Suryana Herman and Sabrina Ahmad, A Comparison Framework of Classification
If the P-value is < 0.05 (bold), it indicates that there is a significant difference between two classifiers. Based on the significant difference results, there is no significant difference between the LR, NB, BP, and SVM models. Romi Satria Wahono, Nanna Suryana Herman and Sabrina Ahmad, A Comparison Framework for Classification Ahmad, A Comparison Framework for Classification.
SCOPUS SJR: 0.240)
How does the integration between genetic algorithm based trait selection and bagging technique affect the accuracy of software error prediction. Which metaheuristic optimization techniques perform best when used in the selection of software defect prediction functions.
A Hybrid Particle Swarm
Optimization based Feature Selection and Bagging
Technique for Software
Defect Prediction (PSOFS+B)
Although there are two classifiers that have no significant difference (P > 0.05), the results have shown that the remaining eight classifiers have significant difference (P < 0.05). Romi Satria Wahono and Nanna Suryana, Combining Particle Swarm Optimization based Feature Selection and Bagging Technique for Software Defect Prediction, International Journal of Software Engineering and Its Applications, Vol 7, No. 5, September 2013. To develop a hybrid particle swarm optimization based feature selection and bagging technique to improve the accuracy of software failure prediction.
Although there are two classifiers that show a significant difference (P < NB and SVM), the results indicated that those of the remaining eight classifiers do not show a significant difference (P > 0.05). Romi Satria Wahono, Nanna Suryana and Sabrina Ahmad, Metaheuristic Optimization Based Feature Selection for Software Defect Prediction, Journal of Software, Vol.
SCOPUS SJR: 0.260)
Memperbaiki C4.5
Memperbaiki Use Case Points
Memperbaiki Genetic Algorithms
Kutipan (Quotation): Kata-kata yang diambil persis sama dengan apa yang dituliskan (tanpa
Paraphrase: Menyusun kembali pemikiran penulis dan mengungkapkannya dengan kata-kata sendiri
Evaluasi: Interpretasi dalam bentuk komentar, baik setuju atau tidak dengan menyebutkan alasannya
The equations and benchmarking result of the defect prediction using machine learning classifiers indicates prediction using machine learning classifiers. Noisy attribute predictors and an unbalanced class distribution of data sets with software defects result in the distribution of data sets with software defects and result in inaccuracy of classification models. Neural network and support vector machines have strong fault tolerance and strong nonlinear strong fault tolerance capability and strong nonlinear software fault data dynamic processing capability, but.
Romi Satria Wahono, Nanna Suryana and Sabrina Ahmad, Metaheuristic Optimization-Based Feature Selection for Software Defect Prediction, Journal of.
