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Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 889 Documents
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Penentuan Pola Pada Dataset Penjualan Dalam Data Mining Menggunakan Metode Apriori Utami, Ulfa; Irmayani, Deci; Bangun, Budianto
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7498

Abstract

In everyday life and the business world, buying and selling activities play a central role. For companies, daily transaction data is not just a record, but an important asset that holds the potential to increase sales through analysis. The volume of sales data generated daily is enormous, making manual processing inefficient and prone to errors. The complexity of the number of products sold also makes it difficult to gain a comprehensive understanding of purchasing patterns. Dynamic changes in consumer preferences further complicate demand forecasting and may lead to inventory issues. This study aims to address these issues by analysing sales data to identify products that are frequently purchased together. This information will be utilised in designing more effective marketing strategies, such as cross-promotions or product bundling. Additionally, this data is useful for demand forecasting and optimising inventory management. The ultimate goal is to provide relevant product recommendations to customers and enhance their satisfaction. To achieve this objective, this study applies data mining techniques, specifically the Apriori Association method. Data from 15 types of items in 28 weekly transactions at TOKO BANGUNAN MAJU BERSAMA will be analysed as an initial sample to identify the most frequently purchased combinations of construction tools. The Apriori method will associate each item based on a minimum support value of 0.25 and a minimum confidence value of 0.80. The application of this method resulted in 4 rules from 3-item patterns with confidence values ranging from 0.88 to 0.89.
Multi-Aspect Sentiment Analysis of Movie Reviews Using BiLSTM on Platform X Data Sinaga, Astria M P; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7509

Abstract

The film industry generates scores of movie reviews annually, reflecting viewer opinion towards various aspects of movies such as story, music, performances, and so on. They are a good source to publicly analyze opinion automatically. Aspect-based and sentiment analysis of movie reviews based on a multitask classification model rooted in the Bidirectional Long Short-Term Memory (BiLSTM) structure is the theme of this study. The objective of this research is to develop and evaluate a multitask BiLSTM-based model capable of simultaneously classifying sentiment polarity and movie review aspects to enhance fine-grained opinion mining. Data was collected from Platform X through web crawling and subjected to various text preprocessing steps before feeding them into the model. Unlike traditional approaches that treat sentiment and aspect classification as independent operations, the method proposed in this work is performing both simultaneously—sentiment prediction (positive, neutral, negative) and aspect categories (plot, music, actors, others). The model was compared between three different sizes of BiLSTM layers—32, 64, and 128 units—to investigate the influence of model capacity on performance. A 10-fold cross-validation scheme also implemented to confirm the reliability and robustness of results. Experiment findings reveal that the 128-unit BiLSTM model outperformed other models across the board, particularly at picking up subtle contextual relationships, to achieve the highest accuracy score in both tasks. Although this model significantly longer to train, its improved generalization—most notably for difficult sentiment- aspect pairs such as neutral or low-resource categories—validated the trade-off. The findings validate the effectiveness of BiLSTM-based multitask learning for comprehensive movie review analysis, demonstrating the importance of model capacity in tackling complex language patterns and fine-grained opinion identification.
Multi-Aspect Sentiment Analysis on Gojek Application Reviews Using CNN-LSTM Method Saragih, Pujiaty Rezeki; Sibaroni, Yuliant; Prasetyowati, Sri Sulyani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7511

Abstract

Since its initial release in 2010, Gojek has remained the most used online transport service by Indonesians. Multi-aspect sentiment analysis is a method applied to determine user sentiment towards various specific aspects in their comments. By applying this method, there will be deeper understanding of user views regarding various components of the Gojek service. The method employed was data scraping from web crawling of Google Play Store user reviews and data preprocessing, i.e., cleaning, case folding, tokenizing, stopword removal, normalization, and stemming. A hybrid CNN-LSTM model was employed since it is capable of extracting spatial features using CNN and long-term dependencies using LSTM. The seven most crucial aspects of the Gojek service, i.e., access, time, comfort, information, customer service, availability, and safety, were the central themes of this research. The main objective of this research is to analyze user sentiment across these key aspects using a deep learning-based multi-task approach, in order to gain actionable insights for improving service quality. The performance of the models was evaluated on accuracy as the primary metric, and the experiments attempted three model sizes: 32, 64, and 128 hidden units. Among them, the 64-unit model performed best overall consistently, with both aspect and sentiment classification accuracy being satisfactory. While the 128-unit model achieved slightly better accuracy on some sentiment tasks, it exhibited overfitting. The 64-unit model, however, gave the most balanced results and the best trade-off between model complexity and performance. The findings show the potential of multi-task deep learning approaches to extract valuable insights from user reviews. Such findings can be highly valuable to aid business strategy formulation and service quality improvement, and ultimately greater customer satisfaction, as well as consolidate Gojek's market dominance in Indonesia's online transport business.
Aspect-Based Sentiment Classification of iPhone 15 YouTube Reviews Using VADER-Augmented LSTM Alya, Hasna Rafida; Sibaroni, Yuliant
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7514

Abstract

This research investigates the effectiveness of the Long Short-Term Memory (LSTM) model in performing aspect-based sentiment classification on English-language reviews of the iPhone 15 sourced from the YouTube platform. The study focuses on five key product aspects frequently mentioned by users: charger port, camera, screen, design, and battery. To evaluate the model’s performance, two distinct labeling strategies were employed. The first involved manual annotation, where human annotators identified both the relevant aspects and the associated sentiment in each review. The second strategy integrated additional sentiment cues derived from a lexicon-based method, Valence Aware Dictionary and sEntiment Reasoner (VADER). In this approach, the polarity output from VADER was prepended to each review to enrich the input with emotional context. The experimental results demonstrate that supplementing review texts with sentiment polarity information from VADER contributes to a modest but measurable improvement in sentiment classification accuracy. Specifically, using the micro-average accuracy metric, defined as the ratio of correct predictions to the total number of test instances, the model's performance improved from 67% under the manual only annotation to 68% with VADER enhanced input. Additionally, aspect classification remained consistently strong, showing a slight improvement from 90% to 91% after incorporating VADER. Furthermore, based on macro-average accuracy an evaluation metric that calculates the mean performance across all classes regardless of class distribution, accuracy improvements were observed in several aspects, particularly the camera, screen, and design. However, a minor decline in performance was noted for the battery and charger port aspects. These results suggest that enriching review data with sentiment polarity information derived from lexicon-based tools like VADER can enhance the model’s ability to comprehend emotional nuance, leading to more accurate identification of user sentiments within aspect-specific reviews.
Implementasi Data Mining dengan Menggunakan Algoritma Apriori untuk Mengoptimalkan Pola Penjualan Produk Elektronik Dewi, Rahayu Kusnita; Juledi, Angga Putra; Irmayani, Deci
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7515

Abstract

This study discusses the application of the Apriori algorithm in analyzing electronic product sales data. The results show that the Apriori algorithm is effective in finding consumer purchasing patterns through association analysis, which allows the identification of product combinations that are often purchased together. Combinations of products with strong purchasing relationships, such as AAA Batteries (4-pack) and USB-C Charging Cable (confidence 0.9), and Wired Headphones and USB-C Charging Cable (confidence 0.7), can be utilized for bundling strategies and increasing sales. Of the 18 types of electronic products analyzed, seven products met the minimum support requirements, indicating high potential for further analysis. The Apriori algorithm also proved suitable for medium-scale datasets due to its simplicity, although it is less efficient than FP-Growth on big data. This study concludes that the application of the Apriori algorithm supports data-based business decision making, especially in understanding consumer behavior, stock management efficiency, and marketing strategy development.
Classification of High Risk of Obesity in Women using Decision Tree Methods Rizawanti, Riftika; Rajiansyah, Rajiansyah
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7518

Abstract

Excessive body fat accumulation characterizes obesity, a medical condition primarily caused by an energy imbalance. This excess fat is stored throughout the body, including the abdomen, thighs, and arms. Obesity is a global health concern, prevalent in Indonesia, impacting physical, psychological and social well-being. Women are more susceptible to obesity due to a combination of biological and lifestyle factors. A community health center study of 156 patients revealed that 71.20% exhibited central obesity, with women comprising 76.60% of this group and men 31.6%. This study focuses on the disproportionate impact of obesity on women. To better understand and address obesity, classification is crucial. This study uses a Decision Tree method to classify 898 women based on 14 assessments for high obesity risk, comparing its performance using three attribute selection criteria. The Decision Tree (Gini Index) model achieved 77.22% overall accuracy (Figure 12). The Normal category has 83% precision and 88.30% recall. The Overweight category had 62.50% precision and 63.83% recall. The Obese category had 75% precision and 66.67% recall. The Underweight category achieved 100% precision and recall. While the model demonstrates good classification performance, particularly for Normal and Underweight categories, it requires further refinement to better differentiate between Overweight and Obese individuals.
Sistem Pendukung Keputusan Pemilihan Calon Ketua Komite Sekolah Menggunakan Metode CoCoSo Bilatasya, Yolanda; Juledi, Angga Putra; Irmayani, Deci; Harahap, Syaiful Zuhri
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7530

Abstract

The election of the school committee chairperson is one of the important decision-making processes that directly impacts the sustainability and effectiveness of collaboration between the school and parents/guardians. At SMP Negeri 1 Rantau Selatan, the selection process for the committee chairperson has been conventional, subjective, and lacks a structured and standardised assessment system. This results in the selection process being based on popularity and personal connections rather than objective competence and qualifications. To address this issue, this study aims to design and implement a Decision Support System (DSS) based on the Combined Compromise Solution (CoCoSo) method, which can accommodate various quantitative evaluation criteria and generate more objective, transparent, and accountable decision recommendations. The CoCoSo method was chosen for its ability to integrate a compromise approach to conflicting criteria and produce consistent alternative rankings through three aggregation techniques: arithmetic mean, relative sum, and compromise programming. This study uses five main criteria to assess the suitability of committee chair candidates: experience in the field of education, communication skills, leadership, understanding of education policy, and integrity. Data was obtained from 15 committee chair candidates based on observation and questionnaire results, which were then processed through the CoCoSo method stages, including decision matrix formation, value normalisation, positive and negative ideal solution calculations, and final score aggregation. The data processing results show that the candidate named Eko Prasetyo obtained the highest compromise value in all CoCoSo calculation approaches with a final Ki value of 2.505, consistently placing him as the top-ranked candidate in the system's recommendations. This demonstrates that the CoCoSo method is effective in evaluating and determining the best candidate based on a data-driven and scientifically rational approach. Additionally, the system built can also serve as a strategic tool to enhance the quality of participatory educational governance at the school unit level.
SMOTE and BERT Approaches for Handling Class Imbalance in Sentiment Analysis of the CoreTax Application on Big Data Ginting, Meiliyani Br; Surbakti, Asprina Br; Ilham, Safarul; Utomo, Dito Putro; Ginting, Raheliya Br
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8310

Abstract

Coretax is a tax information system developed by the Directorate General of Taxes (DJP) to support digital and integrated tax administration processes, covering everything from taxpayer registration to reporting and auditing. Although it was designed to improve efficiency, transparency, and accuracy in tax management, its implementation has sparked mixed reactions among the public due to various technical challenges and the complexity of the annual tax reporting process. This situation highlights the need for a sentiment analysis that can objectively capture public perceptions of the system’s performance. In this study, Natural Language Processing (NLP) and Machine Learning techniques were applied to analyze 3,000 tweets from Twitter (X) related to Coretax. One of the main issues identified in the dataset is class imbalance, where positive sentiments significantly outnumber negative and neutral ones, leading to biased classification results. To address this issue, the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset by generating synthetic samples for the minority classes. The BERT model was then employed for sentiment classification because of its strong ability to understand contextual meaning through its transformer-based architecture. Experimental results show that before applying SMOTE, the BERT model achieved an accuracy of 77%, which increased to 80% after SMOTE was implemented, along with improvements in precision, recall, and F1-score, particularly for the minority classes. These findings demonstrate that the combination of SMOTE and BERT significantly enhances the performance of sentiment analysis in understanding public responses to Coretax. This approach can serve as a valuable reference for evaluating and improving tax digitalization policies, ensuring they are more effective, inclusive, and responsive to public needs.
Pengembangan Sistem Kotak Amal Berbasis ESP32 Dan Sensor TCS3200 Untuk Monitoring Dan Analisis Donasi Real-Time Andriyanti, Reza; Arbansyah, Arbansyah; Sumadi, Muhammad Taufiq
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.5898

Abstract

The development of a donation box system based on ESP32 and TCS3200 sensor aims to enhance the efficiency and transparency of donation management previously conducted manually. This system is designed to detect and classify banknote denominations, display donation status on an OLED LCD, and send notifications through a Telegram bot. Testing of the TCS3200 sensor revealed that the RGB value ranges for Indonesian Rupiah banknotes from Rp 1,000 to Rp 100,000 vary, with minimum and maximum values for each color component. For example, Rp 1,000 banknotes have a blue color range of 6-9, green 7-9, and red 7-9. The sensor can detect banknote denominations accurately despite some instability in readings influenced by lighting conditions and color interference. The OLED LCD successfully displays messages corresponding to system conditions, and notifications via the Telegram bot are received properly under stable network conditions. This system functions effectively, facilitating monitoring and enhancing transparency in donation management.
Sentiment Analysis of Digitalization of Small and Medium Enterprise on Social Media X Using SVM and KNN Methods Haidar, Muhammad Dzakiyuddin; Lhaksmana, Kemas Muslim
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6723

Abstract

The rapid digitalization of Small and Medium Enterprises (SMEs) has led to significant shifts in business operations, especially in their adaptation to digital platforms. Public perception towards this digital transformation is crucial to understand, as it reflects the success and acceptance of these efforts. This research conducts sentiment analysis on social media platform X to classify public opinions regarding the digitalization of SMEs. The analysis employs two machine learning algorithms, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The study compares the performance of both models under baseline and hyperparameter-tuned conditions. The results show that the SVM model consistently outperforms KNN in terms of accuracy, precision, recall, and F1-score. The highest accuracy achieved by the SVM model is 81.97% after hyperparameter tuning with a sigmoid kernel. Meanwhile, the best KNN model records an accuracy of 81.31% using Manhattan distance with 11 neighbors. This study demonstrates that SVM provides better stability and performance in sentiment classification related to SME digitalization. The findings are expected to help policymakers better understand public sentiment and formulate more effective strategies for supporting SME digital transformation.

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