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Siti Maesaroh
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Journal Collabits
Published by Universitas Mercu Buana
ISSN : 30628601     EISSN : 30466709     DOI : http://dx.doi.org/10.22441/collabits
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Journal Collabits adalah jurnal yang membahas strategi keamanan cyber untuk meningkatkan kinerja dan keandalan dalam implementasi teknologi kecerdasan buatan (AI), kecerdasan bisnis (BI), dan sains data, yang di kelola oleh Fakultas Ilmu Komputer (FASILKOM) terdiri dari dua prodi yaitu Teknik Informatika (TI dan Prodi Sistem Informasi (SI). Dengan pertumbuhan pesat dalam penggunaan teknologi ini, keamanan cyber menjadi semakin penting dalam menjaga integritas, kerahasiaan, dan ketersediaan data. Tulisan ini mengeksplorasi berbagai pendekatan, alat, dan praktik terbaik dalam mengamankan sistem AI, BI, dan sains data, termasuk deteksi ancaman, enkripsi data, manajemen akses, dan pemulihan bencana. Jurnal ini juga menganalisis dampak kebijakan keamanan cyber pada inovasi teknologi dan memberikan rekomendasi untuk meningkatkan keamanan dalam ekosistem digital yang terus berkembang
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 102 Documents
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Optimisation of the Competency Assessment System Through Matrix Applications and Linear Algebra Using the AHP Method Nabil Ahmad Furqon; Ius Andre Virganata; Maulana Arvian Wibisana; Qalbiridha Albarra; Mohamad Yusuf
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.32523

Abstract

Competency-based assessment systems are increasingly important in education and industry to objectively assess individual abilities, overcoming the subjectivity issues inherent in traditional assessment methods. This study aims to develop an innovative competency assessment system by combining Assessment Matrix and Linear Algebra, specifically using the Analytic Hierarchy Process (AHP) method to systematically and accurately determine the weight of criteria. The research data were taken from a dataset of college students, with five main criteria of competence, including technical skills, cooperation, and creativity. The data normalization process was carried out using Min-Max Scaling and Z-Score Normalization to ensure consistency, followed by the construction of an AHP comparison matrix based on the level of importance between criteria. The weight of the criteria was calculated using the eigenvector method, and the consistency test was carried out through the Consistency Ratio (CR) to ensure the validity of the matrix (CR < 0.1). The final assessment was obtained by multiplying the AHP weights by the student's scores for each criterion. The results showed that this approach resulted in a more objective, transparent, and accurate assessment system than conventional methods, with the potential to improve fairness in evaluation in the academic environment. This research provides a new contribution in the application of linear algebra to the development of competency assessment systems, as well as offering practical solutions for educators and human resource managers in improving performance evaluation.
Evaluation of the Effectiveness of Hybrid Learning Based on Linear Algebraic Hybrid Model in the Online-Offline Lecture System in the Digital Era Andre Meyro Ritonga; Nicholas Sulistio; Guruh Pandhu Anggriawan; Mohamad Yusuf
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.32548

Abstract

Optimal class division is a crucial aspect of academic planning to ensure the effectiveness of the learning process. The main challenges in class division lie in the limited capacity of space, balanced distribution of students, and the fulfillment of varied academic needs. This study proposes a Linear Programming-based approach to optimize class division by considering various constraints, such as the maximum capacity of the room, the number of students, and the distribution of subjects according to curriculum needs. The developed applications are designed to produce optimal solutions that minimize student distribution gaps and ensure efficient classroom utilization. A case study is applied to an educational institution to evaluate the performance of the application in real situations. The results of the experiment show that this approach is able to improve the efficiency of classroom allocation, reduce imbalances in the distribution of students, and optimize the use of educational facilities. Thus, this research contributes to more effective and data-based academic management in decision-making related to class division.
Design of a Website-Based Employee Attendance Information System at PT. TOP AIRCONDITIONER Destie Lia Syifa; Tia Yuniar; Malik Fajar; Sukanto Sukanto; Galuh Ardianti
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.33796

Abstract

PT. Top Airconditioner is one of the companies engaged in AC service and air conditioning providers located at Permata Tangerang Blok DA No.11. PT. Top Airconditioner is currently using an outdated employee attendance system that involves collecting attendance data on paper, which is prone to loss and damage. This leads to delays in data recapitulation and ineffective management. To address this issue, a research was conducted to develop a web-based employee attendance system. The research utilized RAD analysis research method, UML system design, and PHP programming language with MySQL database. The main objectives were to create a system that allows employees to easily record their attendance daily and enables HRD to monitor attendance of employee more efficiently. The result of the research is a web-based employee attendance system that simplifies the process of recording attendance and enhances overall management. This new system will streamline the attendance process for PT. Top Airconditioner and improve HRD's ability to monitor attendance accurately.
Design of a Web-Based Air Conditioner Service Information System Using the Fifo (First In First Out) Method: A Case Study at PT. TOP AIRCONDITIONER Mohamad Iqbal Nawari; Rafiyudin Rafiyudin; Refki Heriyansyah; Vikri Assidiqie; Nur Cahyo Darmawan
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.33797

Abstract

PT. Top Airconditioner is one of the companies engaged in AC service and air conditioning providers located at Permata Tangerang Blok DA No.11. PT. Top Airconditioner already has a website that contains information about PT. Top Airconditioner, the types of AC service services available along with their prices, but do not have a special website that functions for booking AC service services because currently the process of booking AC service services is still using WhatsApp media. The system that is currently running is not running well because there is still a problem, namely there is no media to store AC service booking request data because currently the request is made using WhatsApp media so that data is vulnerable to being lost and AC repair reports have not been properly documented because the data of the repair results is only recorded using a book. This study uses the PIECES analysis method, RAD development, UML design, and system testing using blakbox testing. This research results in an ac service system that is built using Java, spring boot, for the Front End using react js postgreSql database and its deployment using google cloud platform.
Designing User Interface and User Experience for Habit Tracker Application for Android Mobile Devices Nila Natalia
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.35091

Abstract

Positive habit formation is one of the main challenges in everyday life that requires consistency and continuous motivation. This study aims to design and develop an Android-based habit tracker application with a focus on optimizing the User Interface (UI) and User Experience (UX) to increase user engagement and effectiveness in building positive habits. The research methodology uses the User-Centered Design (UCD) approach which includes the stages of user needs analysis, wireframe and mockup design, prototyping, and usability testing evaluation. Data collection was carried out through a survey of 150 respondents to identify user needs and preferences for the habit tracker application. The UI/UX design process integrates the principles of Material Design Guidelines and the psychology of habit formation theory to create an intuitive and motivating interface. The main features of the application include habit tracking with progress visualization, reward and gamification systems, reminder notifications, and analytics dashboards. The results of the usability testing evaluation showed a user satisfaction level of 87% with a System Usability Scale (SUS) score of 82.5, which is included in the "excellent" category. A/B testing on various UI elements showed an increase in user retention rate of 34% and completion rate of 28% compared to conventional design. The application successfully implemented a consistent responsive design across different screen sizes with an average loading time of 2.3 seconds. This research contributes to the development of user-friendly mobile applications for habit tracking, provides insights into the importance of a psychological approach in UI/UX design, and serves as a reference for the development of similar applications in the future.
Design and Construction of a Web-Based Machine Ordering Application Using the Codeigniter Framework Bebi Rahmawati; Siti Fauziyah; Siti Iis Istianah; Anas Fadhlulloh; Alwan Mukhlisinardi
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.35092

Abstract

The company PT Engineering is engaged in manufacturing machinery and machine tools, repairing metal products, and repairing electrical equipment. The company was founded with the aim of meeting the needs of the industry in terms of providing high-quality equipment. the process of ordering production machines, such as tanks and mixers, which is currently running is still carried out using whatsapp or email media, so it is less effective and can cause various problems such as data recording errors, and less optimal service to customers. The absence of an integrated ordering system also makes it difficult for companies to track order status so that they cannot provide fast and accurate information to customers. The purpose of this research is to facilitate the production department and planner to monitor incoming orders so that orders can be completed on time. This research uses RAD analysis research method, diagram design using UML, system coding using PHP programming language and Mysql database. This research produces a web-based ordering system that is connected to a database so that order data can be stored properly.
Design and Construction of a Web-Based Crowdfunding Application Using the Laravel Framework Foezi Arisandi SJ
Journal Collabits Vol. 2 No. 2 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i2.35925

Abstract

This research uses RAD analysis research method, diagram design using UML, system coding using PHP programming language and Mysql database. This research produces a web-based ordering system that is connected to a database so that order data can be stored properly. This research aims to design and build a web-based crowdfunding application to address inefficiencies in conventional ordering and donation systems, particularly in industrial environments. The study highlights the increasing importance of digital fundraising and the role of crowdfunding as an effective platform for connecting fund-seekers with potential donors. Using the Laravel framework and MySQL database, the system was developed to improve transparency, accessibility, and transaction management. The research applies the Waterfall development method and includes structured interviews and observation as part of the requirement-gathering process. The result is a functional, secure, and scalable web application that enables users to create, manage, and donate to campaigns with real-time tracking and integrated payment systems. This system enhances organizational outreach and improves the efficiency of donation and fund distribution processes.
Comparison of Random Forest and Naive Bayes Algorithms in Classification of Song Popularity on the Spotify Platform Janu Ilham Saputro; Rian Fantomi; Saoloan Simbolon; Puput Nur Arizka; Berlina Ramadani
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37578

Abstract

The purpose of this study is to use machine learning to rank Spotify songs based on how popular they are. Because there is so much music data out there, musicians and artists need to know if a song will be popular or not. The dataset has 8,778 songs, each with different features like how popular the artist is, how many followers they have, and other song details. This research evaluates the efficacy of two classification algorithms: Random Forest and Naive Bayes. Artist popularity, artist followers, explicit album total tracks, and track number are the main things that are used to make models. The results of the experiment show that the Random Forest algorithm works better than the Naive Bayes algorithm. The Random Forest algorithm was right 76.54% of the time, but the Naive Bayes algorithm was only right 72.21% of the time. The f1-score for both popularity classes is also better for Random Forest. This finding shows that ensemble-based models, like Random Forest, work better with the features of music popularity data than basic probabilistic models do.
Implementation of DBSCAN Clustering and Random Forest Algorithm for Mapping and Predicting Shooting Incidents in New York Azka Niaji Rangkuti; Samoedra Cakra Arifin; Muhammad Ramadansyah Kurnia Putra; Nila Natalia
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37587

Abstract

Shooting incidents in crowded, heavily populated areas of cities cause serious threats to public safety and social security. New York State, which includes large metropolitan areas and suburban regions, experiences complex spatial and temporal crime patterns that are difficult to identify using traditional crime analysis methods that rely only on descriptive statistics and manual hot spot identification. This study proposes a data-driven quantitative approach to mapping and predicting shooting incidents by integrating spatial clustering and machine learning techniques. Density-based clustering methods are applied to the geographic coordinates of shooting incidents to identify areas with high incident concentrations while filtering out isolated events as noise. The resulting spatial clusters are then interpreted as hotspot locations and used as reference labels for a supervised classification model. A Random Forest algorithm is then used to predict hotspot and non-hotspot locations using spatial and temporal features, including geographic position and time of occurrence. The model is evaluated using standard classification performance measures, including accuracy, precision, recall, F1 score, and confusion matrix analysis.
Analysis and Prediction of Customer Churn in the Telecommunications Industry Using Logistic Regression and Random Forest Celsi Alisa Nabila; Ryno Julian Santoso; Sabila Alya Nafisa; Yuni Roza
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37599

Abstract

Customer churn represents a major challenge for telecommunication companies because of its significant influence on revenue stability and customer retention efforts. Intense competition among service providers has increased the need for reliable predictive models capable of identifying customers with a high probability of terminating their subscriptions. This study focuses on the analysis and prediction of customer churn by applying machine learning techniques to the Telco Customer Churn dataset. The research workflow includes data preprocessing stages such as duplicate removal, treatment of missing values, and transformation of both categorical and numerical features. Exploratory data analysis supported by visualization techniques is employed to examine customer behavior and feature relationships. Subsequently, the dataset is partitioned into training and testing subsets using an 80:20 stratified split. A preprocessing pipeline is applied, incorporating feature scaling for numerical variables and one-hot encoding for categorical variables. Predictive models are developed using Logistic Regression and Random Forest algorithms, and their performance is assessed through accuracy measurements and classification reports. The results indicate that the Random Forest model delivers better predictive performance than Logistic Regression, demonstrating its effectiveness in modeling complex data patterns. Overall, the study confirms that machine learning-based approaches can serve as effective tools for churn prediction and offer meaningful insights to support strategic decision-making in customer retention within the telecommunication sector.

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