Abdul Fadlil

Bio: Abdul Fadlil is an academic researcher from Universitas Ahmad Dahlan. The author has contributed to research in topics: Computer science & Naive Bayes classifier. The author has an hindex of 11, co-authored 91 publications receiving 401 citations. Previous affiliations of Abdul Fadlil include Magister.

Analisis Metode Certainty Factor pada Sistem Pakar Diagnosa Penyakit THT

Abstract: There are two factors that cause a disease, called Congenital and Acquired. Congenital refers to a disease a person is born with, while Acquired refers to a disease acquired after a person was born such as infection, trauma, and neoplasm. The infected person will sometimes require information on the disease before going to the doctor or a hospital. Such information may be found from a system which receives input on the symptoms and gives a clear information on the corresponding disease. This may be achieved via a system of experts, in which the expert refers to an ENT (Ear, Nose, and Throat) specialist. Such information is hoped to provide a solution on the disease. The system of ENT specialists designed and research in this paper used the certainty factor method. The method will overcome the uncertainty in decision making depending on the symptoms described by the user. This paper is successfully applied certainty factor method used as an instrument of decision making in the system of ENT specialists. The system is web-based, enabling the user to access and choose the symptoms of the disease as well as acquiring information on ENT diseases easly.

Sistem Pendukung Keputusan dengan Metode AHP untuk Penilaian Kompetensi Soft Skill Karyawan

Abstract: Karyawan dalam perusahaan merupakan sumber daya utama yang dituntut untuk mampu memberikan pelayanan terbaik dan kinerja yang optimal. Soft skill karyawan adalah keterampilan individu karyawan yang dapat menunjang hubungan individu karyawan dengan karyawan lain, meningkatkan kinerja dan membuka prospek karirnya. Penilaian kinerja karyawan yang dilaksanakan oleh perusahaan umumnya hanya untuk penilaian prestasi kerja yakni bagaimana pekerjaan dapat dikerjakan dengan baik, mencapai target yang ditetapkan dan meraih tujuan akhir yang diinginkan ( hard skill ). Penilaian terkait kemampuan soft skill karyawan belum banyak dilakukan. Ada beberapa kriteria yang diterapkan beberapa perusahaan dalam melakukan penilaian kompetensi soft skill , tetapi kriterianya masih berbeda-beda. Penelitian ini membahas penilaian kompetensi soft skill karyawan dengan menerapkan empat kriteria. Keempat kriteria ini adalah kemampuan komunikasi, kemampuan bekerjasama, kejujuran dan kemampuan interpersonal. Analisis data menerapkan metode Analytical Hierarchical Process (AHP), yang memungkinkan perhitungan matematis dengan berbagai kriteria. Hasil penelitian menunjukan nilai rasio konsistensi 0.053 yang berarti kurang dari nilai rasio konsistensi yang digunakan dalam metode AHP yaitu 0.1, sehingga hasil perhitungan tersebut valid, dan dapat digunakan. Penelitian ini menghasilkan penilaian prioritas kompetensi soft skill yang dibutuhkan perusahaan sebagai berikut: Komunikasi 48%, Kerjasama 27%, Kejujuran 16 % dan interpersonal 10%. Hasil penelitian ini membuktikan bahwa metode AHP dapat digunakan pada penilaian kompetensi soft skill karyawan.

Review of Detection DDOS Attack Detection Using Naive Bayes Classifier for Network Forensics

Abstract: Di s tributed Denial of Service (DDoS) is a type of attack using the volume, intensity, and m ore costs m itigation to increase in this era . A ttack ers used many zombie computers to exhaust the resources available to a network, application or service so that authorize users cannot gain access or the network service is down, and it is a great loss for Internet users in computer networks affected by DDoS attacks. In the Network Forensic, a crime that occurs in the system network services can be sued in the court and the attackers will be punished in accordance with law. This research has the goal to develop a new approach to detect DDoS attacks based on network traffic activity were statistically analyzed using Naive Bayes method. Data were taken from the training and testing of network traffic in a core router in Master of Information Technology Research Laboratory University of Ahmad Dahlan Yogyakarta. The new approach in detecting DDoS attacks is expected to be a relation with Intrusion Detection System (IDS) to predict the existence of DDoS attacks.

Penerapan metode forward chaining dan certainty factor pada sistem pakar diagnosa hama anggrek coelogyne pandurata

Abstract: Coelogyne Pandurata or better known by the general name of black orchid, this orchid species only grows on the island of Borneo Coelogyne Pandurata is an epiphytic orchid attached to other plants but not harmful This orchid is one endemic of Borneo that requires human intervention to maintain its sustainability Orchid plants are very susceptible to various pests and diseases Because many orchid species are cultivated, the disease is difficult to recognize, because the symptoms of disease on orchids vary depending on the variety The methods applied in this calculation are used Forward Chaining and Certainty Factor methods This expert system allows users to diagnose pests that attack the Orchid Coelogyne Pandurata plant (Black Orchid Borneo) from various literature and initial observations The result of application of Forward Chaining and Certainty Factor Method can give pest diagnosis on Orchid Coelogyne Pandurata based on the symptoms given Based on the calculation, the description of confidence level based on the interpretation table of the expert and the final percentage of 930736% is Very Probably both methods are applied To solve existing problems Keywords : Coelogyne Pandurata , C ertainty F actor, Expert system, Forward Chaining Coelogyne Pandurata atau lebih dikenal dengan nama umum anggrek hitam, spesies anggrek ini hanya tumbuh di pulau kalimantan Coelogyne Pandurata merupakan anggrek epifit yaitu menempel pada tanaman lain tetapi tidak merugikan Anggrek ini merupakan salah satu endemik kalimantan yang memerlukan campur tangan manusia untuk menjaga kelestariannya Tanaman anggrek sangat rentan terhadap berbagai serangan hama dan penyakit Karena jenis tanaman anggrek banyak dibudidayakan, menyebabkan penyakitnya sukar dikenal, karena gejala serangan penyakit pada anggrek bervariasi tergantung dari varietasnya Metode yang diterapkan dalam perhitungan ini digunakan metode Forward Chaining dan C ertainty F actor Sistem pakar ini memungkinkan pengguna mendiagnosa hama yang menyerang tanaman Anggrek Coelogyne Pandurata (Anggrek Hitam Kalimantan) dari berbagai literatur dan pengamatan awal Hasil penerapan Metode Forward Chaining dan Certainty Factor dapat memberikan diagnosa hama pada Anggrek Coelogyne Pandurata berdasarkan gejala-gejala yang diberikan Berdasarkan hasil perhitungan, maka keterangan tingkat keyakinan berdasarkan tabel interpretasi dari pakar dan persentase akhir sebesar 93,0736% adalah Sangat Mungkin kedua metode ini diterapkan untuk menyelesaikan masalah yang ada Kata kunci : Coelogyne Pandurata , C ertainty F actor, Forward Chaining , S istem pakar

Ekstraksi Ciri Metode Gray Level Co-Occurrence Matrix (GLCM) dan Filter Gabor untuk Klasifikasi citra Batik Pekalongan

Abstract: Batik merupakan warisan budaya Indonesia yang harus kita jaga dan lestarikan. Proses melestarikannya yaitu dengan pendataan identitas batik tersebut secara komputerisasi. Proses tersebut diawali dengan pengenalan pola untuk mencari informasi dari citra batik tersebut menggunakan proses ekstraksi ciri dengan metode GLCM ( Gray Level Co-Occurrence Matrix ) dan Filter Gabor, kemudian proses klasifikasi menggunakan Jaringan Syaraf Tiruan. Penelitian ini membuat sistem ekstraksi ciri citra batik yang akan digunakan untuk proses selanjutnya yaitu klasifikasi yang dapat digunakan untuk pendataan citra batik, khususnya batik Pekalongan. Pada penelitian ini proses pengumpulan data melalui tiga cara, yaitu observasi, wawancara dan studi pustaka. Dalam pengimplementasiannya menggunakan Matlab 2010a. Pengujian menggunakan empat sampel citra batik tradisional Pekalongan, setiap citra dibagi menjadi beberpa bagian dan selanjutnya diuji dengan metode tersebut. Hasil penelitian ini telah menghasilkan beberapa niai metode GLCM dan hasil citra proses ekstraksi ciri metode Filter Gabor yang dapat digunakan untuk proses klasifikasi citra batik.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

State of the art automated black-box web application vulnerability testing

Abstract: Black-box web application vulnerability scanners are automated tools that probe web applications for security vulnerabilities In order to assess the current state of the art, we obtained access to eight leading tools and carried out a study of: (i) the class of vulnerabilities tested by these scanners, (ii) their effectiveness against target vulnerabilities, and (iii) the relevance of the target vulnerabilities to vulnerabilities found in the wild To conduct our study we used a custom web application vulnerable to known and projected vulnerabilities, and previous versions of widely used web applications containing known vulnerabilities Our results show the promise and effectiveness of automated tools, as a group, and also some limitations In particular, "stored" forms of Cross Site Scripting (XSS) and SQL Injection (SQLI) vulnerabilities are not currently found by many tools Because our goal is to assess the potential of future research, not to evaluate specific vendors, we do not report comparative data or make any recommendations about purchase of specific tools

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

Abstract: The exponential growth in computer networks and network applications worldwide has been matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 were created to train predictive models on network-based intrusion detection. These datasets are not meant to serve as repositories for signature-based detection systems, but rather to promote research on anomaly-based detection through various machine learning approaches. CSE-CIC-IDS2018 contains about 16,000,000 instances collected over the course of ten days. It is the most recent intrusion detection dataset that is big data, publicly available, and covers a wide range of attack types. This multi-class dataset has a class imbalance, with roughly 17% of the instances comprising attack (anomalous) traffic. Our survey work contributes several key findings. We determined that the best performance scores for each study, where available, were unexpectedly high overall, which may be due to overfitting. We also found that most of the works did not address class imbalance, the effects of which can bias results in a big data study. Lastly, we discovered that information on the data cleaning of CSE-CIC-IDS2018 was inadequate across the board, a finding that may indicate problems with reproducibility of experiments. In our survey, major research gaps have also been identified.

Attack classification using feature selection techniques: a comparative study

Abstract: The goal of securing a network is to protect the information flowing through the network and to ensure the security of intellectual as well as sensitive data for the underlying application. To accomplish this goal, security mechanism such as Intrusion Detection System (IDS) is used, that analyzes the network traffic and extract useful information for inspection. It identifies various patterns and signatures from the data and use them as features for attack detection and classification. Various Machine Learning (ML) techniques are used to design IDS for attack detection and classification. All the features captured from the network packets do not contribute in detecting or classifying attack. Therefore, the objective of our research work is to study the effect of various feature selection techniques on the performance of IDS. Feature selection techniques select relevant features and group them into subsets. This paper implements Chi-Square, Information Gain (IG), and Recursive Feature Elimination (RFE) feature selection techniques with ML classifiers namely Support Vector Machine, Naive Bayes, Decision Tree Classifier, Random Forest Classifier, k-nearest neighbours, Logistic Regression, and Artificial Neural Networks. The methods are experimented on NSL-KDD dataset and comparative analysis of results is presented.