cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Siti Maesaroh
Contact Email
siti.maesaroh@mercubuana.ac.id
Phone
+6282125242949
Journal Mail Official
collabits-fasilkom@mercubuana.ac.id
Editorial Address
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Location
Kota adm. jakarta barat,
Dki jakarta
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 102 Documents
 7   8   9   10   11 
Laptop Price Prediction Based on Specifications: A Comparison of Random Forest and Linear Regression Bagas Pratama Putra; Ilham Miftahali Mahfuzh; Agus Fahrizal Kurniawan; Ramdani Budiman
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.
Data-Oriented Classification of Red Wine Quality Using Machine Learning Fajar Ammar; Christian Charllo; Raja Wirawidyadana; Nia Rahma
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.
A Data Science Approach to Cancer Patient Classification Using Support Vector Machine and Random Forest Devi Dwi Anggraini; Mutiara Rizky Salsabila; Keisya Rizkia Kamila; Yunita Sartika Sari
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.
Comparative Analysis of Linear Regression and Random Forest for Used Car Price Prediction Muhammad Faris Adjil Syamsudi; Bimo Arya Daffa; Wisnu Jarodi; Nungky Awang Chandra
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.
Analysis of Spotify Song Popularity Based on Audio Features Using Random Forest Anggi Beauty Rahmaputri; Deswita Nindya Putri; Nia Putri Rahmadani; Oleh Soleh
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.
Comparative Analysis of Arima and Facebook Prophet Algorithms for E-Commerce Product Sales Forecasting Ersha Thoriq Ferdinansyah; Muhammad Roffi; Rafi Ramadhan; Elyandri Prasiwiningrum
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.
Analysis of Application-Based Sales System Design to Increase Business Transaction Efficiency Denny Andwiyan; Martono Martono; Dedy Iskandar
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.
Development of an Artificial Intelligence-Based Plant Pest and Disease Inspection Application Using A Convolutional Neural Network Algorithm Widi Pramudiya; Nasril Sany; Firmansyah Apryadhi
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.
Comparative Analysis of Public Sentiment Towards Sri Mulyani and Purbaya as Finance Ministers on the X Platform Using the Indobertweet Model Muhammad Aryaka Zamzami; Siti Maesaroh; Dendy Jonas Managas
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.
Using Tensorflow for Clean and Messy Room Image Classification with Python Gilas Adi Saputra; Damar Pratama Ristadias Hariyanto
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.27274

Abstract

Image classification is a fundamental computer vision task that can support automated visual monitoring in domestic, educational, and workplace environments. This study develops a transparent baseline pipeline using TensorFlow 2.x, Keras, and Python to distinguish clean and messy room images. The dataset contains 192 training images, with 96 images in each class, and 20 validation images, with 10 images in each class. All images are resized to 150 x 150 pixels and normalized to a 0-1 range. Rotation, horizontal flipping, and shearing are applied only to the training data, while validation images are normalized without random transformation. The sequential convolutional neural network contains four convolution-pooling blocks, a fully connected layer, and a sigmoid output for binary classification. Qualitative testing with two external images produced labels that were consistent with visual observation: the cluttered room was classified as messy and the organized room as clean. These demonstrations confirm that the pipeline operates from image input to class prediction, but they do not establish broad generalization or perfect accuracy. The main contribution is a reproducible small-data workflow that documents dataset distribution, preprocessing, augmentation, model parameters, validation procedures, and prediction thresholds. The study is limited by the small validation set, the absence of a large independent test set, and the lack of direct comparison with pretrained models. Future studies should evaluate transfer learning, larger datasets, repeated trials, and metrics such as precision, recall, F1-score, and confusion matrices.

Page 10 of 11 | Total Record : 102

 7   8   9   10   11 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2026) Vol 3, No 1 (2026) Vol. 3 No. 1 (2026) Vol. 2 No. 3 (2025) Vol 2, No 3 (2025) Vol. 2 No. 2 (2025) Vol 2, No 2 (2025) Vol 2, No 1 (2025) Vol 1, No 3 (2024) Vol 1, No 2 (2024) Vol. 1 No. 1 (2024) Vol 1, No 1 (2024) More Issue