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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Intelligent Software Systems
Domy Kristomo
Universitas Teknologi Digital Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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A Decision Model to Support the Selection of SENKOM Personnel Using the Profile Matching Method with the Capability of Cyber Security I Nyoman Oka Semadi; Domy Kristomo; Bambang Purnomosidi
Journal of Intelligent Software Systems Vol 2, No 2 (2023): December 2023
Publisher : LPPM UTDI (d.h STMIK AKAKOM) Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiss.v2i2.1135

Abstract

The very rapid development of information technology has brought tactical and strategic advantages, but it can also be a potential attack from opposing parties on the information and communication systems and networks used, thus opening the way for the emergence of a new war, namely cyber warfare. Cyber attacks are a new threat to Adisutjipto Air Base, which targets vital parts that can impact the organization and make the command and control system ineffective and inefficient. One of the important elements of Adisutjipto Lanud in facing cyber attacks is the readiness of data and communication network security personnel. In the direct or conventional personnel selection process, it is not possible to see the abilities possessed by prospective data security personnel, both in terms of skills, management aspects, analytical aspects, competency weight, and so on. A decision support system can be used to assist decision-making based on existing criteria. This research is limited to only considering the selection of personnel who will become members of komlek or senkom who are responsible for data security and communications networks at Adisutjipto Air Base. In this research, the method used is the profile matching method. The concept of the profile matching method is to compare the selection using the conventional method with the decision support system method in selecting komlek/senkom personnel as cyber security personnel so that differences in competency can be identified, also called GAP (Gross Across Product). The smaller the GAP produced, the greater the weight of the value. large, this means that personnel who meet the requirements have a greater chance of someone occupying that position. The final result of this research is to obtain ranking information for each cyber security candidate based on profile matching calculations to be able to carry out tasks optimally in securing data and networks at Adisutjipto Air Base.
Mapping Research on the Use of Algorithms in Commerce: A Bibliometric Analysis Based on Scopus Ahmad Bilal Almagribi; Fikky Ardianto; Anas Taufan; Domy Kristomo
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3441

Abstract

Algorithms had garnered widespread attention across various scientific disciplines, including the commercial sector. According to data from Scopus, over 600 documents exploring the application of algorithms in commerce were identified. However, no comprehensive bibliometric analysis had been conducted to deeply examine the implementation of algorithms within this sector. This research aimed to fill this gap by analyzing the contributions of authors, affiliations, countries, and journals within the literature on commercial algorithms. Employing bibliometric methods on 645 Scopus-indexed documents, this study revealed that 2022 marked the peak of publications with 112 documents, indicating significant growth in this area. Li, Y. from Wuhan College, China, was recognized as the most productive author. Additionally, several universities in China were noted as the most productive affiliations. The ACM International Conference Proceeding Series was the most prolific source on this topic. The study also identified Computer Science, Engineering, and Mathematics as the most popular subject areas. These results indicate a need for further research into aspects such as data privacy, User Experience (UX), Dynamic Pricing Algorithms, and blockchain technology to enhance efficiency and security in commercial applications. This research paves the way for a broader understanding of algorithm utilization in commerce and provides recommendations for future studies.
Prediction of Unpaid Student Fees at Muhammadiyah Ahmad Dahlan University Cirebon using the Random Forest Algorithm Suherman Herman; Domy Kristomo
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5411

Abstract

This study aims to develop a predictive model for student fee payment arrears at Universitas Muhammadiyah Ahmad Dahlan Cirebon using the Random Forest algorithm. The dataset was obtained from the Academic Information System and consisted of 490 student records from four cohorts (2018–2021), which were divided into 80% training data and 20% testing data. The data processing stages included data cleaning, transformation, and feature selection using Recursive Feature Elimination (RFE). The model was optimized using GridSearchCV to obtain the best configuration. The evaluation results indicate strong performance, with an AUC of 0.980, accuracy of 88.8%, precision of 90.4%, recall of 88.8%, and an F1-score of 0.875. Feature importance analysis identified the amount of arrears variable as the most dominant factor influencing prediction outcomes. Strategic recommendations for university implementation include: (1) deploying a data-driven early warning system to identify at-risk students, (2) offering payment relief or installment programs for students with high arrears, and (3) conducting regular financial monitoring through a dashboard to support timely decision-making. Therefore, this study not only produces an effective predictive model but also provides practical solutions for improving university financial management.
Co-Authors Ahmad Bilal Almagribi Anas Taufan Bambang Purnomosidi Dwi Putranto Fikky Ardianto I Nyoman Oka Semadi Suherman Herman