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Intelligent Computing and Advanced Data Science
Published by Universitas Satya Terra Bhinneka
ISSN : -     EISSN : 31244688     DOI : -
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Neural networks Reasoning and evolution Intelligent search Intelligent planning Intelligence applications Computer vision and speech understanding Multimedia and cognitive informatics Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning Technology and computing (like particle swarm optimization); intelligent system architectures Knowledge representation Bioinformatics Natural language processing Automated reasoning Logic programming Machine learning Visual/linguistic perception Evolutionary and swarm algorithms Derivative-free optimisation algorithms Fuzzy sets and logic Rough sets Simulated biological evolution algorithms (like genetic algorithm, ant colony optimization, etc) Multi-agent systems Data and web mining Emotional intelligence Hybridisation of intelligent models/algorithms Parallel and distributed realisation ofintelligent algorithms/systems Application in pattern recognition, image understanding, control, robotics and bioinformatics Application in system design, system identification, prediction, scheduling and game playing Application in VLSI algorithms and mobile communication/computing systems
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Articles 10 Documents
 1 
Modeling Travel Distance to the Nearest Mosque Using the Euclidean Distance Algorithm Dicky Dharmawan; Mikha Sinaga; Nita Sembiring
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 1 (2026): February 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

The study will calculate the travel distance to the closest mosque through the Euclidean Distance formula, which is a standard formula for the straight-line distance between two geographic points. The overall goal is to calculate the accessibility of mosques within an area by using geographical coordinates and provide valuable data for city planning and religious facility planning. Applying the Euclidean Distance formula, studies estimate closeness between given set of points and nearest mosque and thus is a straightforward yet efficient way to quantify closeness. Findings from research indicate high variability in mosque accessibility based on geographical distribution in the study area. The model indicates areas with inadequate mosque access, and it may be utilized by planners and policymakers in responding to the need for enhanced religious service coverage. The Euclidean Distance model is limited in the sense that it does not account for actual conditions such as the road network, traffic, or terrain that may influence actual travel distance and time. This research contributes to the existing literature in spatial analysis by demonstrating how the Euclidean Distance algorithm can be applied in geographic research on access to public services. More advanced algorithms and actual data sets can be employed by future researchers to continue enhancing the precision and applicability of the model in actual environments.
Analisis pandangan dan tanggapan pengguna terhadap chatbot berbasis ai: studi kasus chatgpt Jordan Kelvin Gabriel Siregar; Oswald Siahaan; Airin Manik
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 1 (2026): February 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

Perkembangan teknologi Artificial Intelligence (AI) telah mendorong pemanfaatan chatbot sebagai media interaksi digital, salah satunya ChatGPT yang banyak digunakan oleh masyarakat. Penelitian ini bertujuan untuk menganalisis persepsi dan tingkat kepuasan pengguna terhadap penggunaan ChatGPT berdasarkan ulasan pengguna. Metode penelitian yang digunakan adalah pendekatan mixed-method, yang mengombinasikan analisis kuantitatif dan kualitatif. Data kuantitatif diperoleh dari rating pengguna dan dianalisis menggunakan statistik deskriptif serta uji distribusi dengan bantuan IBM SPSS, sedangkan data kualitatif berupa komentar pengguna dianalisis menggunakan analisis isi untuk mengidentifikasi tema dan pola persepsi pengguna. Hasil penelitian menunjukkan bahwa mayoritas pengguna memberikan persepsi positif terhadap ChatGPT, terutama dari aspek kemudahan penggunaan, kecepatan respons, dan manfaat fungsional. Namun demikian, masih ditemukan beberapa keluhan terkait akurasi jawaban dan pemahaman konteks yang kompleks. Temuan ini menunjukkan bahwa meskipun ChatGPT dinilai mampu meningkatkan pengalaman pengguna, peningkatan kualitas respons dan stabilitas sistem masih diperlukan. Penelitian ini diharapkan dapat memberikan kontribusi teoretis dalam pengembangan kajian kepuasan pengguna chatbot berbasis AI serta kontribusi praktis bagi pengembang dalam meningkatkan kualitas layanan chatbot.
Analisis ketergantungan mahasiswa terhadap penggunaan ai tools dengan pendekatan statistik deskriptif Daniel Syaputra Sianipar; Dinel Emka Primanta Tarigan; Giovani Pasyah
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 1 (2026): February 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

Perkembangan teknologi yang pesat, khususnya dalam proses pembelajaran pemrograman di perguruan tinggi. Pemanfaatan AI tools semakin meluas di kalangan mahasiswa karena membantu memahami materi dan menyelesaikan tugas akademik. Namun demikian, penggunaan AI tools yang intensif juga berpotensi menimbulkan ketergantungan yang dapat memengaruhi kemampuan berpikir kritis mahasiswa. Penelitian ini bertujuan untuk menganalisis tingkat ketergantungan mahasiswa terhadap penggunaan AI tools dalam pembelajaran pemrograman pada Program Studi Informatika Universitas Satya Terra Bhinneka. Pendekatan kuantitatif digunakan dalam penelitian ini, dengan pengumpulan data dilakukan menggunakan kuesioner berisi 15 pernyataan yang disebarkan kepada sebanyak 50 responden. Data yang diperoleh dianalisis menggunakan statistik deskriptif dengan aplikasi SPSS. Sebelum menganalisis, instrumen penelitian diuji validitas dan reliabilitasnya. Hasil uji validitas menunjukkan seluruh item pernyataan memiliki nilai r hitung lebih besar dari r tabel (0,279) dan nilai signifikansi kurang dari 0,05, sehingga seluruh item dinyatakan valid. Uji reliabilitas menghasilkan nilai Cronbach’s Alpha sebesar 0,947 yang menunjukkan tingkat reliabilitas sangat tinggi. Hasil analisis statistik deskriptif menunjukkan nilai rata-rata skor ketergantungan sebesar 45,72 yang berada pada kategori ketergantungan sedang. Penelitian ini menunjukkan bahwa mahasiswa menggunakan AI tools secara cukup intensif sebagai pendukung pembelajaran, namun belum menunjukkan tingkat ketergantungan yang berlebihan.
Modeling Student Task Group Preferences Using Graph Theory and Spectral Clustering Hafiz Zulkhairi
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 1 (2026): February 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

This study aims to model student preferences in forming task groups using graph theory and clustering algorithms. The research object involves 2024 cohort students of the IF A Siang class at Universitas Satya Terra Bhinneka. Preference data were collected through questionnaires and transformed into numerical representations for analysis. Graph theory was applied to model relationships between students based on preference similarity, while spectral clustering was used to form optimal student groups. The results show that spectral clustering is able to identify groups with high internal similarity and clear separation between clusters. This approach provides an objective alternative to conventional group formation methods and helps minimize dissatisfaction among students. The proposed model can support lecturers in forming balanced and effective task groups based on student preferences.
Public sentiment analysis of government subsidy policies on Twitter using the Naïve Bayes classifier Putri Natahsya Amelia; Nayyara Bunga Atiqah; Afrina Hasibuan
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 1 (2026): February 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

Government subsidy policies are one of the important instruments inmaintaining economic stability and improving public welfare; however,they often generate diverse responses in the public sphere. These differingperspectives arise because subsidy policies directly affect the social andeconomic lives of the community. In the digital era, social mediaparticularly the Twitter platform has become a medium for the public toexpress opinions, criticisms, and information in real time regarding suchpolicies. This study aims to analyze public sentiment toward governmentsubsidy policies on the Twitter platform using the Naïve Bayes Classifiermethod with text preprocessing stages. The research data consist ofIndonesian-language tweets collected from the Twitter platform during aspecific period in 2024. The text preprocessing stages include case folding,tokenization, filtering/stopword removal, and stemming to eliminateirrelevant words before the sentiment classification process into positive,negative, and neutral categories. The results show that out of 87 analyzedtweets, neutral sentiment dominates with a percentage of 66.67%,followed by positive sentiment at 19.54% and negative sentiment at13.79%. The dominance of neutral sentiment indicates that most tweetsare informational in nature, while expressed opinions tend to be morepositive than negative. Overall, these findings demonstrate that the NaïveBayes Classifier method is able to provide an objective overview of publicsentiment trends toward government subsidy policies on the Twitterplatform and can be utilized as a references for policy evaluation.
Design of an IoT-Based Heart Rate and Room Temperature Monitoring System Using ESP32 Peter Simanjuntak; Muhammad Imam Zarkasyi
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

Introduction/Main Objectives: Continuous monitoring of heart rate and environmental conditions is essential for supporting remote healthcare services and real-time observation. This study aims to design and implement an Internet of Things (IoT)-based monitoring system using ESP32 for simultaneous monitoring of heart rate, room temperature, and humidity. Background Problems: Conventional monitoring systems generally require separate devices for physiological and environmental measurements and provide limited remote monitoring capabilities. Therefore, there is a need for an integrated, low-cost, and real-time IoT-based monitoring system. Novelty: The proposed system integrates a pulse heart rate sensor and a DHT11 temperature-humidity sensor into a single ESP32-based IoT platform. The system incorporates Wi-Fi communication, cloud-based visualization using the Blynk platform, and a finger-detection mechanism to minimize false heart rate readings caused by sensor noise. Research Methods: The system was developed using an ESP32 microcontroller, a pulse heart rate sensor, and a DHT11 sensor. Sensor data were acquired, processed, and transmitted through a Wi-Fi network to the Blynk cloud platform for real-time visualization. Experimental evaluation was conducted in a laboratory environment under three operating conditions: Danger, Normal, and Not Detected. Finding/Results: The experimental results demonstrate that the proposed system successfully monitored heart rate, room temperature, and humidity in real time. The system accurately classified monitoring conditions into Danger, Normal, and Not Detected states. Furthermore, the implemented finger-detection mechanism effectively prevented false heart rate measurements when no finger was placed on the sensor, while maintaining stable wireless communication and continuous cloud-based monitoring. Conclusion: The proposed ESP32-based IoT monitoring system provides a practical, low-cost, and reliable solution for integrated physiological and environmental monitoring. The successful implementation demonstrates the feasibility of using ESP32 as an IoT gateway for real-time health monitoring applications, particularly for educational purposes, laboratory experiments, remote monitoring, and prototype smart healthcare systems.
Macroeconomic Factor Analysis of Composite Stock Price Index (IHSG) Returns Period (2020-2025) Using Multiple Linear Regression Roy Andreas Sibarani; Steven Sembiring; Mika Riawaty Sibarani; Zepandi Ramadhan
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

The Jakarta Composite Index (JCI) is the main indicator used to describe the performance of the Indonesian capital market. JCI movements are influenced by various macroeconomic factors that can influence investment decisions and financial market stability. This study aims to analyze the influence of macroeconomic factors consisting of the USD/IDR exchange rate, the BI Rate, gold prices, and inflation on JCI returns for the 2020–2025 period. The data used are monthly data obtained from Yahoo Finance, Bank Indonesia, and the Central Bureau of Statistics. The research method used is Python-based multiple linear regression with analysis stages including return transformation, stationarity test using Augmented Dickey-Fuller (ADF), multicollinearity test using Variance Inflation Factor (VIF), and classical assumption testing using Durbin-Watson, Breusch-Pagan, and Jarque-Bera. The results show that changes in the USD/IDR exchange rate have a negative and significant effect on JCI returns. Conversely, changes in the BI Rate, gold prices, and inflation do not show a significant effect at the 5% significance level. The developed model explains 43.6% of the variation in JCI returns and meets the required statistical assumptions. These findings demonstrate that the exchange rate is the most dominant macroeconomic factor influencing JCI returns during the study period. These results are expected to serve as a reference for investors and researchers in understanding the relationship between macroeconomic conditions and the Indonesian capital market.
Sentiment Analysis of youtub Comments on "Cerdas Cermat Empat Pilar MPR RI "Using Lexicon-Based Sentiment Analysis” Nabil Astian; Natalia Situmeang; Sofyh Zack Savira Simarmata; Evi Morina Tarigan
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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The rapid growth of social media has transformed digital communication, enabling citizens to express opinions openly on various public issues. This study examines YouTube user comments on the video "Cerdas Cermat Empat Pilar MPR RI West Kalimantan Province" to understand public sentiment toward civic education content. Manual analysis of large-scale YouTube comment data is inefficient and prone to subjectivity. This study addresses the question: how can Lexicon-Based Sentiment Analysis effectively classify public opinion in Indonesian-language YouTube comments? This study applies a lexicon-based rule-based approach without training data requirements, providing an accessible alternative for sentiment analysis on medium-scale Indonesian-language datasets with full preprocessing pipeline integration. A total of 1,709 comments were collected via web scraping using the YouTube Comment Downloader library on Google Colaboratory. Data preprocessing included case folding, text cleaning, stopword removal, Sastrawi stemming, and negation handling. Sentiment scoring was performed using a customized Indonesian sentiment lexicon. Neutral sentiment dominated with 857 comments (50.15%), followed by negative sentiment at 516 comments (30.19%), and positive sentiment at 336 comments (19.66%). Classification errors were identified in negation and sarcasm processing. Lexicon-Based Sentiment Analysis is a practical and efficient method for public opinion analysis on YouTube. The dominance of neutral comments indicates informative discussion patterns, while high negative proportions reflect a tendency toward public criticism. Future work should integrate machine learning comparisons and expanded lexicon resources.
Deteksi Toxic Comment TikTok dan Auto Filtering pada TikTok Menggunakan Support Vector Machine Dewi Purnama Sari; Dicky Ramadhan Hutasuhut; Nadya Balqis Nasution; Risya Amalia Putri Nasution
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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Abstract

The rapid growth of social media, particularly TikTok, has increased user interaction through its comment feature. However, the large number of comments has also led to the emergence of toxic comments containing hate speech, insults, and cyberbullying. Manual moderation is considered ineffective due to the massive volume of comments and the complexity of informal language commonly used on social media. This study aims to implement a text mining approach using the Support Vector Machine (SVM) algorithm to detect toxic comments and develop an automatic filtering mechanism for TikTok comments. The dataset was collected by scraping comments directly from several TikTok videos. The preprocessing stage included case folding, cleaning, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, followed by text classification using the SVM algorithm. Model performance was evaluated using accuracy, precision, recall, and F1-score. The experimental results show that the SVM model achieved an accuracy of 95.28%, with a precision of 1.00, recall of 0.67, and F1-score of 0.80 for the toxic class. Furthermore, the developed auto-filtering system successfully filters toxic comments automatically, making the content moderation process faster and more efficient. The proposed approach demonstrates that combining TF-IDF feature extraction with the SVM algorithm can effectively support automated content moderation and help reduce the spread of harmful comments on social media platforms.
Banana Leaf Disease Classification Using HSV and LBP Feature Extraction with Support Vector Machine Novriza Rahayu; Farhan Muhammad; Sylvia Indri Yani; Agung Fadillah; Akbar Idaman
Journal of Intelligent Computing and Advanced Data Science Vol. 1 No. 2 (2026): July 2026
Publisher : Universitas Satya Terra Bhinneka

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Banana leaf diseases are one of the primary factors contributing to the decline in the quality and productivity of banana plants, as manual identification remains time-consuming, subjective, and prone to misclassification due to the similarity of symptoms among different diseases. This raises the question of whether a combination of Hue, Saturation, Value (HSV) and Local Binary Pattern (LBP) feature extraction can provide an effective alternative for banana leaf disease classification using a Support Vector Machine (SVM). While previous studies have relied on deep learning methods or combined HSV with Histogram of Oriented Gradients (HOG) features for this task, the combination of HSV and LBP for banana leaf disease classification remains largely unexplored. Using the Banana Leaf Spot Diseases (BananaLSD) dataset, comprising four classes (Cordana, Healthy, Pestalotiopsis, and Sigatoka), the original images were first divided into training and testing sets using an 80:20 ratio to prevent data leakage, after which data augmentation was applied exclusively to the training set. All images were center cropped before HSV and LBP features were extracted, combined, and classified using an SVM with a Radial Basis Function (RBF) kernel. On an independent test set of original, non-augmented images, the proposed model achieved an accuracy of 87.30%, precision of 89.06%, recall of 87.30%, and F1-score of 87.80%, with consistent results confirmed through Stratified Group 5-Fold Cross-Validation and an ablation study showing that the HSV and LBP combination outperformed either feature type alone. These findings indicate that combining HSV and LBP features offers a reliable, feature based alternative to deep learning for automated banana leaf disease identification.

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