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Siti Maesaroh
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siti.maesaroh@mercubuana.ac.id
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collabits-fasilkom@mercubuana.ac.id
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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 68 Documents
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Improving E-commerce Platforms with Collaborative Filtering algorithms for Product Recommendations Maesaroh, Siti; Nabila, Putri; Ramadhan, Faiz Muhammad
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27299

Abstract

Online product reviews play a major role in the success or failure of an e-commerce business. In a transaction, buyers will usually find out information on the use of the product or service from online reviews posted by previous customers to get detailed product recommendations and make purchase decisions. Many reviews are created by users who often include strong sentimental opinions. This review of data is very promising and can be used by both customers and the Company. Customers can read reviews to know more about the quality of a product. However, due to the large number of reviews, it is difficult to see and read all consumer evaluations personally to get useful information. One effective approach in providing such recommendations is using Collaborative Filtering (CF) algorithms. This research aims to improve e-commerce platforms by applying Collaborative Filtering algorithms to provide more accurate and relevant product recommendations to users.
Comparative Analysis of Linear Regression and Random Forest for Used Car Price Prediction Syamsudi, Muhammad Faris Adjil; Daffa, Bimo Arya; Jarodi, Wisnu; Chandra, Nungky Awang
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37646

Abstract

Manual estimation is often subjective and prone to human bias because the used car market has a complex pricing structure with non-linear depreciation. Objective: This study conducted a comparative analysis between Linear Regression and Random Forest algorithms to develop a more objective pricing model. Methods: The Kaggle dataset contains 5,000 entries indicating features such as manufacturer, model, engine size, and mileage for this study. The methodology included data cleaning, feature engineering, and outlier removal using the IQR method. For training and testing, the data was split 80:20. Results: "Year of Manufacture" was identified as the feature that most significantly influences price, and the evaluation results showed a significant difference in performance. Linear Regression achieved 82.33% accuracy, while Random Forest achieved 99.60% accuracy. Conclusion: Random Forest captures non-linear patterns and complex relationships in used car pricing better than Linear Regression, although it remains quite reliable for general trends.
Development of an Artificial Intelligence-Based Plant Pest and Disease Inspection Application Using A Convolutional Neural Network Algorithm Pramudiya, Widi; Sany, Nasril; Apryadhi, Firmansyah
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.37915

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.
Implementation of DBSCAN Clustering and Random Forest Algorithm for Mapping and Predicting Shooting Incidents in New York Rangkuti, Azka Niaji; Arifin, Samoedra Cakra; Putra, Muhammad Ramadansyah Kurnia; Natalia, Nila
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.
Sentiment Analysis of Reviews Grab Application on Google Playstore Based on Methods Naïve Bayes Nugroho, Alvian; Febriyani, Nisa; Kurniawan, Heri
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.30263

Abstract

This research aims to conduct sentiment analysis of user reviews for the Grab application in the Google Play Store using the Naïve Bayes method. The research uses data in Indonesian language and analyzes sentiment in three classes: positive, neutral, and negative. The Naïve Bayes method is used to classify user reviews into the appropriate sentiment categories. The research utilizes the Google Play Store API and the Google_play_scrapper library to collect user review data. A total of 1195 reviews were successfully collected. The results of the sentiment analysis are expected to provide valuable insights for Grab in improving user experience and the quality of their application services.
Analysis of Spotify Song Popularity Based on Audio Features Using Random Forest Rahmaputri, Anggi Beauty; Putri, Deswita Nindya; Rahmadani, Nia Putri; Soleh, Oleh
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37647

Abstract

The rapid growth of digital music streaming platforms such as Spotify has significantly increased competition among songs, making popularity an important yet difficult aspect to predict. Understanding the factors that influence song popularity is essential for musicians, producers, and digital platforms in developing effective promotion strategies and recommendation systems. This study aims to analyze the relationship between Spotify audio features and song popularity using a data science approach. The dataset used in this study consists of songs described by various audio features, including danceability, energy, loudness, tempo, acousticness, instrumentalness, valence, and track duration, with popularity serving as the target variable. An exploratory data analysis (EDA) was conducted to examine the distribution of popular and non-popular songs, analyze correlations among audio features, and visualize the relationships between selected audio features and popularity. The results show that the dataset is highly imbalanced, with non-popular songs dominating the overall distribution. Correlation analysis indicates strong relationships between certain audio features, particularly between energy and loudness, while the linear correlation between individual audio features and popularity is relatively weak. Scatter plot visualizations suggest that popular songs tend to have higher levels of danceability, energy, and loudness compared to non-popular songs. However, no single feature can adequately explain popularity on its own, suggesting that a combination of multiple audio characteristics influences song popularity. This research provides an initial insight into the relationship between Spotify audio features and song popularity and serves as a foundation for future studies applying machine learning models, such as Random Forest, for popularity prediction.
Making Simple Calculator Programming Using Java Language Azzam, Muhammad Musyaffa
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27269

Abstract

This research aims to develop a simple calculator application using Java Socket-based client-server connection. The research methodology involves the process of application development and functional testing of the calculator. The application allows users to perform basic mathematical operations such as addition, subtraction, multiplication, and division. The implementation using Java Socket enables communication between the client and server, allowing users to send operation requests to the server and receive the results back. The testing results show that the application functions well and is capable of producing accurate results. This journal can serve as a reference for developers interested in creating a simple calculator application using the Java programming language.
Analysis and Prediction of Customer Churn in the Telecommunications Industry Using Logistic Regression and Random Forest Nabila, Celsi Alisa; Santoso, Ryno Julian; Nafisa, Sabila Alya; Roza, Yuni
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.
Comparative Analysis of Google Dialogflow and Rule-Based NLTK Chatbots for Application FAQ Yasin, Raihan Nur; Cherid, Ali Hadi; Prihandi, Ifan; Sari, Yunita Sartika
Journal Collabits Vol 2, No 3 (2025)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v2i3.27345

Abstract

This study presents a comparative analysis of two chatbot frameworks, Google Dialogflow and rule-based NLTK (Natural Language Toolkit), for the development of chatbots to handle frequently asked questions (FAQ) in applications. The study focuses on Blender, a popular 3D modeling software, as a case study. Ten testing questions were used to evaluate the chatbots' accuracy, precision, recall, and F1-score. The results showed that Dialogflow achieved an accuracy of 80%, precision of 80%, recall of 100%, and an F1-score of 88.9%. In contrast, the rule- based NLTK chatbot achieved an accuracy of 60%, precision of 66.7%, recall of 80%, and an F1-score of 72.8%. The study concluded that Dialogflow is a more effective and reliable chatbot for handling Blender FAQs due to its ability to retrieve relevant information from a large knowledge base and its use of machine learning algorithms to improve its performance over time. However, the rule-based NLTK chatbot may still be useful in certain situations where a more simple and customizable chatbot is required.
A Data Science Approach to Cancer Patient Classification Using Support Vector Machine and Random Forest Anggraini, Devi Dwi; Salsabila, Mutiara Rizky; Kamila, Keisya Rizkia; Sari, Yunita Sartika
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37642

Abstract

The increasing availability of healthcare data has encouraged the application of data science and machine learning techniques in medical research. Cancer patient datasets contain numerical demographic and clinical attributes that can be utilized for classification tasks; however, complex feature relationships and limited feature relevance remain key challenges. This study aims to analyze cancer patient data and compare the performance of Support Vector Machine and Random Forest algorithms for gender classification. The dataset used in this study consists of numerical features, including patient age, tumor size, number of examined lymph nodes, number of positive lymph nodes, body mass index, and survival duration measured in months. The research methodology includes data preprocessing, exploratory data analysis, model development, and performance evaluation. Feature normalization and data splitting are applied to ensure a fair comparison between models, while exploratory analysis is conducted to examine data distribution and relationships among variables. Both classification models are trained under identical experimental settings and evaluated using accuracy as the primary performance metric. The results indicate that both algorithms can classify cancer patients with satisfactory accuracy. Support Vector Machine demonstrates slightly better performance compared to Random Forest, suggesting its effectiveness in handling numerical data with complex decision boundaries. The findings highlight the importance of appropriate algorithm selection and feature utilization in healthcare data analysis.

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