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Siti Maesaroh
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INDONESIA
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 68 Documents
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Finding the Closest Distance to a Minimarket Using the Dijkstra Algorithm Zaki, Mirza Aufa
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27286

Abstract

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Laptop Price Prediction Based on Specifications: A Comparison of Random Forest and Linear Regression Putra, Bagas Pratama; Mahfuzh, Ilham Miftahali; Kurniawan, Agus Fahrizal; Budiman, Ramdani
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37603

Abstract

This study investigates the prediction of laptop prices based on hardware specifications by comparing the performance of Linear Regression and Random Forest algorithms. The dataset consists of both numerical and categorical features, including brand, processor type, RAM capacity, storage configuration, screen size, and other relevant attributes that influence pricing. Data preprocessing was conducted through data cleaning, handling missing values, and transforming categorical variables using one-hot encoding. The dataset was then divided into training and testing sets with a 70:30 ratio to evaluate model generalization. Exploratory data analysis was performed using visualizations such as average price per brand, correlation heatmaps of numerical features, and scatter plots comparing actual and predicted prices. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²) on both training and testing data. The results indicate that the Random Forest model achieves higher predictive accuracy compared to Linear Regression, as it is more effective in capturing non-linear relationships and complex feature interactions. In contrast, Linear Regression tends to underperform due to its linear assumptions when applied to heterogeneous laptop specification data. These findings suggest that ensemble-based models are more suitable for laptop price prediction tasks involving diverse and non-linear feature patterns.
Optimisation of the Competency Assessment System Through Matrix Applications and Linear Algebra Using the AHP Method Furqon, Nabil Ahmad; Virganata, Ius Andre; Wibisana, Maulana Arvian; Albarra, Qalbiridha; Yusuf, Mohamad
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.
Data-Oriented Classification of Red Wine Quality Using Machine Learning Ammar, Fajar; Charllo, Christian; Wirawidyadana, Raja; Rahma, Nia
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37621

Abstract

This study examines the use of supervised machine learning to classify the quality level of red wine based on measurable physicochemical properties. The analysis is conducted using the winequality-red.csv dataset, which contains laboratory-based measurements such as acidity components, alcohol percentage, and sulfur dioxide levels. The primary goal of this research is to explore the contribution of these attributes to wine quality and to compare the classification results produced by different machine learning models. The research procedure involves initial data inspection, feature preparation, exploratory analysis, model training using Logistic Regression and Random Forest, and performance assessment through accuracy, precision, recall, and F1-score indicators. The results show that the Random Forest classifier yields more consistent and reliable classification outcomes than Logistic Regression. These findings suggest that machine learning techniques can support objective quality evaluation processes in the food and beverage industry.
APPLICATION OF CAESAR CIPHER CRYPTOGRAPHY TO ENCRYPT MESSAGES USING C++ PROGRAMMING LANGUAGE Ramadhan, Rayhan Anugrah
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27294

Abstract

Cryptography is a method used to maintain the confidentiality and integrity of messages. One of the simple but effective cryptographic techniques is the Caesar Cipher. Caesar Cipher is a character replacement algorithm that uses shifts or shifts in the alphabet. This study aims to apply the Caesar Cipher in encrypting messages. The research method used is experimentation by implementing the Caesar Cipher algorithm using the C++ programming language. Testing is done by encrypting text messages that are given using a certain shift or shift. The results showed that the implementation of Caesar Cipher cryptography succeeded in encrypting messages properly. Entered text messages are converted into encrypted forms that can only be read by recipients who have the same shift key. Message security depends on the strength of the shift key used.Keywords: cryptography, Caesar Cipher, encryption, messages, security, C++
Comparative Analysis of Public Sentiment Towards Sri Mulyani and Purbaya as Finance Ministers on the X Platform Using the Indobertweet Model Zamzami, Muhammad Aryaka; Maesaroh, Siti; Managas, Dendy Jonas
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37962

Abstract

The development of social media has positioned platform X (Twitter) as a primary source for expressing public opinion toward government figures and policies. This study aims to analyze public sentiment toward two Indonesian public figures, Sri Mulyani Indrawati and Purbaya Yudhi Sadewa, by utilizing the transformer-based IndoBERTweet model. The data were collected from January 1, 2025, to November 1, 2025. A total of 11,000 tweets related to Sri Mulyani were collected; however, only 2,500 tweets were used for data processing and model training, with a maximum limit of 1,000 tweets per month. Meanwhile, 650 tweets were obtained for Purbaya Yudhi Sadewa. This research employs a supervised learning approach with labeled data consisting of positive, negative, and neutral sentiment classes. Minimal preprocessing was applied, considering that IndoBERTweet is specifically designed to handle the characteristics of social media text. The model was trained for five epochs and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the IndoBERTweet model can classify sentiment effectively, particularly on the Sri Mulyani dataset, which contains a larger volume of data and achieves an accuracy of over 82%. In contrast, the model’s performance on the Purbaya Yudhi Sadewa dataset shows a lower accuracy of 71%, influenced by the limited amount of data. This study confirms that the quantity and distribution of data significantly affect the performance of transformer-based sentiment analysis models. Based on the sentiment classification results, public sentiment toward Sri Mulyani Indrawati tends to be dominated by negative and neutral sentiments, while sentiment toward Purbaya Yudhi Sadewa shows a distribution dominated by neutral and positive sentiments.
Analysis of Application-Based Sales System Design to Increase Business Transaction Efficiency Andwiyan, Denny; Martono, Martono; Iskandar, Dedy
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.37914

Abstract

Design and Implementation of Mobile Application-Based Sales System to Increase Business Transaction Efficiency is a research that aims to develop a comprehensive digital solution to overcome the inefficiency of conventional sales systems in Micro, Small, and Medium Enterprises (MSMEs). This research uses a mixed-method methodology with the PIECES Framework, Fishbone Diagram, and SWOT Analysis analysis approaches to identify existing system problems, followed by system design using Unified Modeling Language (UML) which produces a System Framework with five integrated components, Activity Diagrams for transaction workflow optimization, and Use Case Diagrams with four main actors (Admin, Cashier, Customer, Supplier). The results of the research provide theoretical contributions in the development of a mobile information system framework for MSMEs and practical contributions in the form of an adaptable implementation model for various types of retail businesses, proving that a mobile application-based sales system can be an effective solution for MSME digital transformation in increasing competitiveness and business operational efficiency.
Comparative Analysis of Arima and Facebook Prophet Algorithms for E-Commerce Product Sales Forecasting Ferdinansyah, Ersha Thoriq; Roffi, Muhammad; Ramadhan, Rafi; Prasiwiningrum, Elyandri
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37651

Abstract

Uncertainty in market demand poses a fundamental challenge in e-commerce supply chain management. This study evaluates the accuracy of daily sales forecasting for the "Set" product category in the Amazon Sales Report dataset by comparing the traditional ARIMA model with the modern additive Facebook Prophet model. Inventory management in e-commerce is often hindered by unpredictable demand fluctuations, which are difficult to forecast manually. The findings reveal that Prophet outperforms ARIMA, achieving a mean absolute error (MAE) of 35.412 and a root mean square error (RMSE) of 48.723, corresponding to an 18.82% improvement in forecasting efficiency. Prophet’s ability to capture weekly seasonal patterns demonstrates its suitability as a more reliable approach for operational stock management.

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