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All Journal Journal of Computer System and Informatics (JoSYC) Eduvest - Journal of Universal Studies Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Setiawan, Ikbal Danu
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Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Neuroscience Social Sciences

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PENGEMBANGAN ABSENSI ONLINE SECARA REAL TIME ALGORITMA SEQUENTIAL SEARCHING MENGGUNAKAN TEKNOLOGI GPS BERBASIS WEB Setiawan, Ikbal Danu; Sari, Ratih Titi Komala
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 3 (2023): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i3.288

Abstract

The attendance application system is important in an era like this, where many large and small companies that initially used fingerprints to be absent or absent manually by admins have now switched to using online attendance. On the one hand, if you are still using manual attendance documents, it is very dangerous to lose or be damaged, therefore there is no web-based use. This attendance is connected to GPS so that the employee's location can be detected when absent. Therefore, Online Attendance in Real Time Sequential Search Algorithm Using Web-Based GPS (Global Positioning System) Technology is very helpful or makes it easier for admins to recap the manpower whether he is not around the office or not. Attendance using this application is very efficient and time-saving and what often happens in small companies is that the employee is no longer there but the employee is not yet in the office, which is detrimental to the company and operations. To make a report using the Online Attendance method in Real Time, Sequential Searching Algorithm Using Web-Based GPS (Global Positioning System) Technology, which requires interviews, research methods, and libraries. Then the software uses the waterfall method which includes: Design, Needs Analysis, Testing, and then Implementation.
Risk Analysis and Mitigation Strategies in Overcoming Cyber Attacks in Information Technology Companies Using the OCTAVE Framework Setiawan, Ikbal Danu; Andryana, Septi
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.49957

Abstract

In the increasingly advanced digital era, information technology companies are the main target of increasingly complex and intense cyberattacks. To address these threats, risk analysis is a crucial step to identify, evaluate, and mitigate risks associated with cyberattacks. This study aims to analyze the risk of cyber attacks on information technology companies using the Octave framework. The Octave framework provides systematic and structured risk management guidelines that can be applied to different types of risks, including cyber risks. Based on the results of the risk analysis, priority is given to risks with high severity to obtain appropriate mitigation measures. The proposed risk mitigation strategy includes the implementation of security technologies, the development of security policies and procedures, and the increase in employee awareness and training on cybersecurity practices. In addition, this study emphasizes the importance of continuous monitoring and periodic evaluation of the effectiveness of the mitigation strategies implemented. In an effort to combat cybercrime and protect digital security, this writing uses the Octave framework method. The results of this study show that by using the Octave framework, companies can effectively identify and manage the risk of cyberattacks. The implementation of this framework not only improves information security, but also provides a systematic and structured approach to cyber threats, thereby strengthening the company's resilience to future attacks.
Penerapan Data Mining Dengan Menggunakan Algoritma Clustering K-Means Untuk Pembagian Jurusan Pada Sekolah Menengah Atas Setiawan, Ikbal Danu; Triayudi, Agung
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4970

Abstract

Senior High School is the last level that must be taken before continuing education at a higher level such as a Diploma or Bachelor's degree. Where in general high schools have class majors for students who will move up to class XI from class Improving the quality of education carried out in the class majoring process means that students will be more focused in accordance with the field of interest of the major that the student/I should take. The process that occurs in determining majors is only based on the wishes expressed by the students without taking into account the academic grades of the subjects that the students have passed or completed in class X. This problem is not a small problem that should be ignored, it This is an important problem that must be resolved immediately because if the problem is not resolved immediately it will have lasting impacts later. The process of determining the division of majors for students can be seen based on the patterns or values of previous students. Data mining is a process used to complete processing of large data. The data that is processed is a collection of data that becomes Big Data from past data that is stored in a storage container and can then be reused by processing it. Clustering is an appropriate way to solve problems. Where in clustering grouping is carried out based on the distance to each data object. The K-Means algorithm is part of Clustering Data Mining, where this algorithm can be used to carry out new groupings based on how clusters are formed. From the results obtained, there are 2 (two) new formation clusters. In cluster 1 there are 9 (nine) students and in cluster 2 there are 6 (six) students.
Penerapan Algoritma Clustering K-Means Data Mining dalam Pengelompokan Mahasiswa Penerima Beasiswa Setiawan, Ikbal Danu; Triayudi, Agung
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4971

Abstract

Scholarships are a program intended to help students with economic problems. For universities, especially private universities, scholarships are an attraction or a campus promotional event to attract prospective students to register at the campus. The scholarships provided by the campus are independent scholarships which are based on funding from the university's foundation. This is very important to pay attention to, where apart from the achievements of prospective students, they must also consider their readiness or ability to participate in the learning process that takes place at the university. Therefore, paying attention to the grades obtained from prospective students is very important to pay attention to. Another problem is that the quota given by the foundation for scholarships is also limited, which is not covered by all prospective students who register or submit scholarship applications. In terms of determining or awarding scholarships, there is not yet a reference standard that is used for determination in the decision-making process, so scholarship awards are often misdirected. Mistakes in awarding scholarships are of course very detrimental to the campus. Therefore, this problem should require special attention and treatment. This problem can be easily resolved by finding a pattern of rules for accepting scholarships. Data mining is a process method that is widely used today, this is because data mining is very helpful in the decision making process. The process carried out by data mining is divided into several techniques such as Clustering. Clustering is a way to group new data. The K-Means algorithm carries out a solution process based on grouping, therefore the K-Means algorithm is classified as a clustering part of data mining. The aim of the research to be carried out is to assist in the process of grouping prospective students who will be prioritized in receiving scholarships. Based on the results of this research, it can later help to find students who are truly worthy of receiving the scholarship. The results obtained from the research are that there are 2 (clusters) obtained from the K-Means algorithm process. Where in cluster 1 there are 10 grouping data and in cluster 2 there are 5 grouping data.
Co-Authors Agung Triayudi Ratih Titi Komalasari Septi Andryana