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Security Of Dynamic Domain Name System Servers Against DDOS Attacks Using IPTABLE And FAIL2BA: Security Of Dynamic Domain Name System Servers Against DDOS Attacks Using IPTABLE And FAIL2BA Ibnu Muakhori; Sunardi Sunardi; Abdul Fadlil
Jurnal Mantik Vol. 4 No. 1 (2020): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (933.248 KB)

Availability, integrity and confidentiality are the main objectives of information security and server security. These three elements are links that are interconnected in the concept of information protection.Distributed Denial of Service (DDoS) is an attack to make online services, networks and applications not available by flooding data traffic so that services is unvailable or availability aspects disrupted. This attack resulted in huge losses for institutions and companies engaged in online services and web-based applications being one of the main targets of attackers to carry out DDoS attacks. Countermeasures that take a long time and large recovery costs are a loss for the institution or company that owns the service due to loss of integrity. NDLC (Network Development Life Cycle) is a method that has stages namely analysis, design, simulation, prototyping, implementation, monitoring and management. The NDLC method used aim for the results obtained focused and detailed. Snort IDS applied on the DDNS server functions to record when there is a DDoS attack. Implemention fail2ban as realtime preventation tool on the server by configuring based on the rules applied to fail2ban. The results showed Snort IDS managed to detect DDoS attacks based on the rules applied to Snort IDS. Realtime prevention using Fail2ban successfully functions as a DDoS attack by blocking the attacker's IP Address.

Web Server Security Analysis Using The OWASP Mantra Method: Web Server Security Analysis Using The OWASP Mantra Method Bambang Subana; Abdul Fadlil; Sunardi Sunardi
Jurnal Mantik Vol. 4 No. 1 (2020): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (434.86 KB)

Higher Education has been using web-based academic information system, for all academic administration process in this academic system such as study plan, academic transcipt, lecturers and Curriculum and student data. So that required maintenance in database and system management whith well-maintained and scheduled. It is necessary to apply the system to determine the level of vulnerability in order to avoid attacks from irresponsible parties. OWASP (Open Web Application Security Project) is one of the methods for testing the web-based applications released by owasp.org. Using OWASP may indicate that authentication management, authorization and session management.The STMIK Jakarta website often has problems on the web and the loss of some important data that interferes with lectures. At the end of 2016, around September when preparing for the first semester of the Study Plan, the website experienced programmed data loss, consequently the academic system was disrupted. The STMIK Jakarta has used a web-based academic information system, for all academic administrative processes such as study plans, academic transcripts, lecturers, curriculum and student data.This system requires data base and system management. It is important to implement a security system to determine the level of vulnerability to avoid attacks from irresponsible parties. OWASP (Open Web Application Security Project) is one method for testing web-based applications released by owasp.org. The results of the research have been carried out with the results reaching around 90% management authentication, authorization, and session management not being implemented properly.

Klasifikasi Loyalitas Pengguna Data Alumni Pada Forlap Dikti Menggunakan Metode Net Promotore Score Abdul Fadlil; Rusydi Umar; Fitrah Juliansyah
JURIKOM (Jurnal Riset Komputer) Vol 9, No 3 (2022): Juni 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i3.4363

As one of the private universities, STMIK Muhammadiyah Jakarta provides electronic-based services to students and alumni using website with the domain address https://pddikti.kemdikbud.go.id. website aims as a means to convey graduation data validation information to alumni in particular by utilizing information technology. The low level of use of the website by alumni in knowing the status of graduation is the background of the need for usability to determine the level of truth of the data and user satisfaction with the website. This study aims to measure the extent to which the website is used by users to achieve its goals. In this study, the test must be carried out using the Net Promoter Score method. So that the results of the NPS calculation will be converted into a percentage that provides information on the extent of loyalty to students in using the Forlap Dikti page to validate alumni data in the STMIK Muhammadiyah Jakarta campus. Then the results obtained from calculations using NPS are: %Promotore - % Dectractor = 53% - 13% = 40. In determining the NPS value is not calculated based on percentages, because NPS calculations are not percentage calculations but integer numbers (consisting of whole numbers) and not contains a fraction or a decimal value

Penerapan Algoritma Winnowing dan Word-Level Trigrams Untuk Mengidentifikasi Kesamaan Kata Rezki Ramdhani; Abdul Fadlil; Sunardi Sunardi
JURIKOM (Jurnal Riset Komputer) Vol 9, No 2 (2022): April 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i2.4060

Identifying the same words in two or more texts is the first step in the process of detecting plagiarism. Plagiarism detection software are commercially available but relatively expensive. Although some software is offered for free, the features provided are very limited. Therefore, a word similarity detection system is needed to be used as an alternative for users that can be freely accessed. The application of the pattern matching method is one of the solutions that can be used to find the similarity of words between documents. There are several algorithms that can be used as a method to find the similarity of words in the text, including the Winnowing algorithm which is known to have good performance in detecting similarity of words. Winnowing is a hashing-approach based algorithm that applies hash-function and window formation to obtain fingerprints during pattern matching. Based on these fingerprints, the word similarity level can be calculated. Previous studies have only calculated the level of similarity of words based on the character (character-level), while the calculation of the level of similarity based on words (word-level) is still limited. This research was carried out with the aim of measuring the level of similarity of words using the Winnowing algorithm and word-level trigrams. The results showed that the Winnowing algorithm which was applied using word-level trigrams could detect similarities in the text of 76.84%, 52.29%, 37.40%, and 19.29%, respectively. From the results of the study, it can be concluded that the pattern matching method with the Winnowing algorithm and word-level trigrams can be used to measure the level of similarity of the text

Analisis Keamanan Sistem Informasi Akademik Menggunakan Open Web Application Security Project Framework Muh. Amirul Mu'min; Abdul Fadlil; Imam Riadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4099

Information system security is one of the important things in the development of technology to protect comprehensive and structured data or information. The Academic Information System (SIA) has a service to receive requests in the form of HTTP or HTTPS protocol website pages from clients called browsers. Intruders can hack websites without the owner's knowledge. This research was conducted to find the vulnerability of SIA STIKES Guna Bangsa Yogyakarta. The framework used is the Open Web Application Security Project (OWASP) which is usually used to evaluate systems or applications. The tools used are WhoIs, SSL Scan, Nmap, and OWASP Zap. The results obtained were finding 12 vulnerabilities with four vulnerabilities at the medium level, namely Absence of Anti-CSRF Tokens, Cross-Domain Misconfiguration, Missing Anti-clickjacking Header, and Vulnerable JS Library, six at the low level namely Cookie Without Secure Flag, Cookie without SameSite Attribute, Cross-Domain JavaScript Source File Inclusion, Server Leaks Information via "X-Powered-By" HTTP Response Header Field(s), Timestamp Disclosure – Unix, and X-Content-Type-Options Header Missing, and two at the informational level namely Content-Type Header Missing and Information Disclosure - Suspicious Comments.

Penerapan Clustering K-Means untuk Pengelompokan Tingkat Kepuasan Pengguna Lulusan Perguruan Tinggi Dikky Praseptian M; Abdul Fadlil; Herman Herman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4191

One way to evaluate the quality of graduates is to provide questionnaires to graduate users, namely agencies / companies in the world of work in order to assess the quality of graduates of each university. Questionnaires for graduates are generally carried out by filling out the questionnaire form physically and then returning to the college. The K-Means method is one of several non-hierarchical clustering methods. Data clustering techniques are easy, simple and fast. Many approaches to creating clusters or groups, such as creating rules that dictate membership in the same group/group based on the level of similarity between the members of the group. Other approaches such as creating a set of functions to measure multiple criteria from grouping as a function of some parameters of clustering/grouping. From the results and discussions, K-Means clustering succeeded in grouping graduate user satisfaction data into three clusters where the results shown by manual calculations and applications showed the same results where clusterS C1 as many as 48 alternatives, C2 as many as 1 alternative, and C3 as many as 2 alternatives. In the sense that the application that is built successfully implements K-Means clustering is evidenced by the comparison of applications with weka tools has similar percentage results. In terms of the percentage of graduate users or alumni from STMIK PPKIA Tarakanita Rahmawati 94.12% Very satisfied, 1.96% Satisfied and 3.92% Quite Satisfied.

Sistem Pendukung Keputusan Penerimaan Peserta Didik Baru dan Pemilihan Jurusan dengan Metode AHP dan SAW Yuniarti Lestari; Sunardi Sunardi; Abdul Fadlil
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4227

The activity of admitting new students (PPDB) is an administrative process that is repeated every year. This activity is the starting point for the process of finding quality resources according to the criteria of each school. Selection is done manually, such as using spreadsheets or number processing, causing problems, including the length of the selection process. This study develops a PPDB selection system that facilitates the process of accepting new students. The development of this research uses Javascript Node JS, React JS framework, MySQL database from Xampp, and visual studio code editor. The system was built using two methods, namely Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW). AHP is used to select prospective students, while SAW is used as a way to map the majors of each prospective student. The input criteria are National Achievement Test (NUN), School Achievement Test (NUS), Academic Potential Test (TPA), entry path, and major interest. Research has succeeded in building an application that produces rankings and majors that are 100% the same as the calculation simulations carried out manually. The test was carried out with a black box test with 100% valid results. The results of the selection were then tested using the alpha test and beta test. Respondents gave responses strongly agree 83% and agree 17%, while the responses do not know/undecided, disagree, and strongly disagree each 0%.

Perbandingan Metode AHP dan TOPSIS untuk Pemilihan Karyawan Berprestasi Musri Iskandar Nasution; Abdul Fadlil; Sunardi Sunardi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4194

This study designed a system to determine outstanding employee selection using a Decision Support System (DSS) with the Analytical Hierarchy Process (AHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The purpose of this study is to analyze the accuracy in making decisions. The stages of this research are collecting employee data and criteria data, then weighting the criteria and assessment, after that the calculation uses the AHP and TOPSIS methods, and the last step is the analysis of the calculation results and the calculation of accuracy. The criteria used are attendance, years of service, permission, and discipline. Implementation for building applications using the PHP programming language and MySQL database. The results of the calculation of the accuracy obtained by the AHP method are 100%, as well as the TOPSIS method at 100%. The results of the AHP calculation show that the first rank results are obtained with a value of 0.02525, namely employees with code K8, while the results of the TOPSIS calculation show that the first rank results are obtained with a value of 0.955236913, namely employees with code K8. This shows that the two methods have the same results in determining the first rank of employees, however the TOPSIS method is better than AHP because the TOPSIS calculation process is carried out twice normalization so that it does not produce the same value.

Implementasi Data Mining dengan Algoritma Naïve Bayes untuk Profiling Korban Penipuan Online di Indonesia Sunardi Sunardi; Abdul Fadlil; Nur Makkie Perdana Kusuma
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.3999

Profiling of victims of crime is intended to facilitate the targeting of information dissemination and carry out prevention efforts. Profiling is helpful to increase the awareness of internet users against cybercrime. This study aims to create a sociodemographic profile based on online fraud victims using Instant Messengers in Indonesia based on the sociodemography of online fraud victims, namely age, gender, education level, domicile, occupation, duration of using the internet in a day, and Instant Messenger media used. The method used in this research is the descriptive statistical method, namely Data Mining using the snowball sampling method by sharing a link via WhatsApp. Participants were given a link to fill out several survey questions about the sociodemographic of the victim, such as age, gender, occupation, domicile, and online fraud that had been experienced through the IM application. The survey was created using GoogleForms and sent online via WhatsApp to participants who had been victims of online fraud. The Data Mining technique was used to analyze the responses of 1910 participants and then classified using the Naïve Bayes Algorithm. The results showed that the Naïve Bayes Algorithm has an accuracy percentage of 75.28%. The prediction model for the vulnerability of online fraud victims is a female respondent, aged 27.3 years, using Instagram and WhatsApp, currently living in Central Java Province, education background is high school, and the duration of using the internet more than eight hours a day, and status as a Student/College Student.

Analisis Sentimen HateSpeech pada Pengguna Layanan Twitter dengan Metode NaÃ¯ve Bayes Classifier (NBC) Murni Murni; Imam Riadi; Abdul Fadlil
JURIKOM (Jurnal Riset Komputer) Vol 10, No 2 (2023): April 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i2.5984

In January 2023, Twitter users experienced a significant increase of 27.4% compared to the previous year. The social media platform Twitter is commonly used to share various types of information. One type of information frequently shared by users is Hate Speech. Hate Speech involves the dissemination of messages that nurture feelings of hatred and hostility towards specific individuals or groups, including ethnicity, religion, race, and other categories. Forms of Hate Speech encompass insults, defamation, blasphemy, provocation, incitement, and the spread of fake news. In order to address the potential for division and threats to Indonesia's unity, sentiment analysis capable of categorizing tweets as Hate Speech or Non-Hate Speech is necessary. This research aims to conduct sentiment analysis on Hate Speech tweets posted by Twitter users using the NaÃ¯ve Bayes Classifier method. The dataset consists of 5000 samples processed using the Python programming language. Data processing stages involve preprocessing (including case folding, tokenization, stopword removal, normalization, and stemming), labeling, and assigning word weights (Term Weighting) using the Term Frequency (TF) and Inverse Document Frequency (IDF) methods. The data is then divided into training and testing sets, with three different data splits: 70% training and 30% testing, 30% training and 70% testing, and 50% training and 50% testing. Evaluation using the Confusion Matrix yields the highest accuracy of 81%, precision of 81%, recall of 100%, and F1-Score of 90% in the 70% training and 30% testing data split.

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