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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Candra Naya
Universitas Pelita Bangsa, Bekasi
Computer Science & IT Decision Sciences, Operations Research & Management

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Journal : bulletin of computer science research

 1 
Implementasi Algoritma Apriori dalam Menemukan Pola Asosiasi pada Data Penjualan Produk Retail Sufajar Butsianto; Candra Naya; Anggi Muhammad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.731

Abstract

This study aims to implement the Apriori algorithm in finding association patterns in retail product sales data, using the Association Rule Mining approach. Evaluating the ruler or association rules formed based on the support, confidence, and lift parameters, in finding association patterns in retail product sales data with a focus on the relationship between product categories. The data used consists of 500 sales data as sample data and 5,972 transactions as test data. The data mining process was carried out on the main product categories such as Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks, to find association rules that appear simultaneously with the Bulk Products category in one transaction time. The minimum support parameter was set at 0.02 and the minimum confidence was set at 0.5. By using these parameters, several significant association rules were obtained. One of the strongest rules shows that if products in the Milk, Coffee, Tea, Drinks, Detergent, and Biscuit/Snacks categories are purchased together, then there is a 64.3% probability (confidence) that products in the Bulk Products category are also purchased at the same time. The support value of this rule reached 3.8%, and the lift value was 1.49, indicating a positive association and not a coincidence. Evaluation of the test data showed that this pattern was consistently found across 5,972 transactions, with a repeatability rate of 61.7%. The results of this study demonstrate that the Apriori algorithm is effective in identifying consumer purchasing patterns that can be utilized for product placement strategies, bundling offers, and inventory planning in retail management.
Analisis Tingkat Sentimen Opini Publik Terhadap Kebijakan TV Digital di Platform X Menggunakan Multinomial Naïve Bayes Asep Arwan Sulaeman; Candra Naya; Muhtajuddin Danny; M. Makmun Effendi
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.951

Abstract

The migration from analog to digital television broadcasting is part of the transformation of the broadcasting system aimed at improving broadcast quality and spectrum efficiency. However, the implementation of the digital television policy has generated diverse public responses, ranging from support to criticism. This study aims to analyze public opinion on the digital television policy in Indonesia using social media data from platform X. A quantitative approach was employed using text mining and supervised machine learning techniques. Data were collected through a crawling process using the keyword “tv digital”, resulting in 1,855 tweets. After data selection and cleaning, 789 tweets were obtained as the final dataset. The analysis stages included text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF–IDF), and sentiment classification using the Multinomial Naïve Bayes algorithm. The results indicate that positive sentiment dominates public opinion, with 478 tweets (60.58%), while negative sentiment accounts for 311 tweets (39.42%). Model performance evaluation shows an accuracy of 79.21%, precision of 82.45%, and recall of 85.06%, indicating that the model performs well and consistently in classifying sentiment. These findings demonstrate that social media–based sentiment analysis can serve as an empirical approach to understanding public perceptions of digital television policy.
Analisis Sentimen Publik Terhadap Progres Pembangunan IKN di TikTok Menggunakan Naïve Bayes dan SVM Candra Naya; Ermanto
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.969

Abstract

The rapid development of social media has created new spaces for the public to express opinions regarding various public policies, including the development of Indonesia’s new capital city, Nusantara (IKN). TikTok, as one of the platforms with high user interaction, provides a valuable data source for analyzing public perceptions of this national development project. This study aims to analyze sentiment in TikTok comments related to the progress of IKN development and to compare the performance of the Naïve Bayes and Support Vector Machine (SVM) classification algorithms. The research employs a quantitative approach using a data mining framework based on the SEMMA methodology, which includes the stages of Sample, Explore, Modify, Model, and Assess. The dataset was collected through web scraping using Apify, resulting in 2,000 comments, of which 1,850 valid comments remained after the selection process. Text preprocessing was performed through cleaning, case folding, tokenizing, stopword removal, and filtering, followed by feature extraction using the TF-IDF method. The dataset was divided into training and testing sets using an 80:20 ratio. Model evaluation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results show that the SVM algorithm outperformed Naïve Bayes with an accuracy of 91.25%, precision of 90.70%, recall of 92.86%, and F1-score of 91.77%, while Naïve Bayes achieved an accuracy of 84.25%, precision of 83.87%, recall of 86.67%, and F1-score of 85.24%. The sentiment distribution indicates that positive sentiment toward the development of IKN slightly dominates negative sentiment. These findings suggest that SVM is more effective for classifying sentiment in informal social media text such as TikTok comments.
Prediksi Konsentrasi CO(GT) Menggunakan Long Short-Term Memory pada Data Sensor Kualitas Udara IoT Asep Arwan Sulaeman; Candra Naya; Ahmad Turmudi Zy; Riyadi Riyadi
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1162

Abstract

Air quality deterioration has become a major challenge for public health and environmental management in urban areas. Internet of Things (IoT)-based monitoring systems continuously generate sensor data that can be exploited for air quality prediction; however, these datasets commonly contain missing values, noise, and temporal dependencies that may reduce prediction accuracy. This study proposes a Long Short-Term Memory (LSTM)-based model to predict carbon monoxide (CO(GT)) concentrations using the Air Quality UCI dataset, which consists of 9,357 observations and 15 attributes. During preprocessing, -200 values were identified as missing-value indicators, followed by invalid-data handling, Min-Max normalization, and sequence generation using a sliding-window approach with a window size of four. The processed data were divided into training and testing sets using an 80:20 ratio. The prediction model employs a single LSTM layer with 50 hidden units and a Dense output layer and is trained using the Adam optimizer for 50 epochs. Experimental results achieved a Mean Absolute Error (MAE) of 0.0389 and a Root Mean Squared Error (RMSE) of 0.0567, indicating that the proposed model effectively captures temporal patterns in air quality observations with relatively low prediction errors. These findings are consistent with previous studies reporting the effectiveness of LSTM for air quality forecasting and demonstrate its potential to support continuous IoT-based environmental monitoring systems. Future work may incorporate hyperparameter optimization and comparative evaluations with alternative deep learning architectures to further improve predictive performance.
Co-Authors Ahmad Turmudi Zy Anggi Muhammad Rifa'i Asep Arwan Sulaeman Ermanto Ermanto Ermanto M. Makmun Effendi Muhtajuddin Danny Riyadi Riyadi Sufajar Butsianto Unggul Prima Dhani