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Perbandingan Metode Smart dan Maut untuk Pemilihan Karyawan pada Merapi Online Corporation Musri Iskandar Nasution; Abdul Fadlil; Sunardi Sunardi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 6: Desember 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021863583

Penelitian ini merancang sistem untuk menentukan pemilihan karyawan terbaik menggunakan Sistem Pendukung Keputusan (SPK). Perhitungan sistem menggunakan metode SMART dan MAUT. SMART merupakan metode pengambilan keputusan multiatribut yang setiap alternatif terdiri dari sekumpulan atribut dan setiap atribut mempunyai nilai-nilai. Sedangkan MAUT didasarkan pada konsep dimana pembuat keputusan dapat menghitung utilitas dari setiap alternatif menggunakan fungsi MAUT dan dapat memilih alternatif dengan utilitas tertinggi. Metode SMART digunakan karena perhitungannya lebih sederhana dan memungkinkan penambahan serta pengurangan alternatif tanpa mempengaruhi perhitungan pembobotan mengingat jumlah karyawan bisa berkurang dan bertambah secara tidak teratur. Sedangkan metode MAUT digunakan karena memunculkan hasil urutan peringkat dimana akan muncul hasil nilai terbesar sampai nilai terkecil sehingga dapat diketahui karyawan dengan terbaik dengan nilai tertinggi. Sehingga dapat mengambil keputusan dengan efektif atas persoalan yang kompleks dengan menyederhanakan dan mempercepat proses pengambilan keputusan. Metode penelitian yang digunakan adalah metode pengembangan sistem model waterfall, metodologi ini terdapat tahapan-tahapan kegiatan yang harus dilakukan dalam merancang suatu sistem. Perhitungan menggunakan 30 sampel data karyawan dan empat kriteria penilaian. Empat kriteria tersebut adalah presensi dengan bobot 40, masa kerja dengan bobot 30, ijin dengan bobot 20, dan disiplin dengan bobot 10. Data karyawan yang digunakan adalah karyawan yang sama dalam kedua metode serta mempunyai data penilaian yang sama. Hasil perhitungan menggunakan SMART dan MAUT menunjukkan bahwa keduanya dapat diimplementasikan dan berfungsi dengan baik untuk menentukan karyawan terbaik. Dengan menggunakan data alternatif, nilai alternatif, dan bobot kriteria yang sama diperoleh hasil bahwa metode SMART memberikan hasil yang lebih baik dengan 22 peringkat, sedangkan metode MAUT menghasilkan 18 peringkat. Semakin banyak jumlah peringkat yang muncul maka semakin baik karena mampu meminimalisir nilai preferensi yang sama, sehingga perankingan alternatif dapat dilakukan dengan baik. AbstractThis study designed a system to determine the best employee selection using a Decision Support System (SPK). System calculations using the SMART and MAUT methods. SMART is a multi-attribute decision making method in which each alternative consists of a set of attributes and each attribute has values. Whereas MAUT is based on the concept where decision makers can calculate the utility of each alternative using the MAUT function and can choose alternatives with the highest utility. The SMART method is used because the calculation is simpler and allows the addition and subtraction of alternatives without affecting the weighting calculation given the number of employees can be reduced and increased irregularly. While the MAUT method is used because it raises the ranking order results in which the largest value will appear until the smallest value so that it can be known by the employee with the highest value. So that they can make decisions effectively on complex issues by simplifying and accelerating the decision making process. The research method used is the method of developing the system waterfall model, this methodology there are stages of activities that must be carried out in designing a system. The calculation uses 30 employee data samples and four assessment criteria. The four criteria are presence with a weight of 40, tenure with a weight of 30, permission with a weight of 20, and discipline with a weight of 10. Employee data used are the same employees in both methods and have the same assessment data. The results of calculations using SMART and MAUT indicate that both can be implemented and function properly to determine the best employees. By using alternative data, alternative values, and the same criteria weights, the results obtained that the SMART method gives better results with 22 ratings, while the MAUT method yields 18 ratings. The more number of ratings that appear, the better because it is able to minimize the same preference value, so that alternative ranking can be done well.

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.

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