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Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jalan Raya Pakuan PO. BOX 452, Bogor, Indonesia
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Komputasi
Published by Universitas Pakuan
ISSN : 16937554     EISSN : 26543990     DOI : https://doi.org/10.33751/komputasi.v22i2.29
Core Subject : Science, Education, Engineering,
Computer Science & IT Control & Systems Engineering Mathematics
Komputasi is a journal that publishes scientific papers in the fields of computer science and mathematics. This journal, published by the Department of Computer Science, Faculty of Mathematics and Natural Sciences, Pakuan University, Bogor. This journal provides an opportunity for researchers or academics to submit papers in the field of computer science, as well as management policies related to all aspects of computers and their subdisciplines. The journal is published twice a year, is well-documented in book form, which includes a wide range of computer science and mathematics papers by authors from various backgrounds. In addition, we also have partners from local editors who graduated as professors from several universities who will review each article before it is published. Every article or paper published in this Journal will definitely be useful for all visitors and readers. Articles submitted to this journal will be reviewed by reviewers before being published by a blind review.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 36 Documents
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Implementation of Fuzzy Search and Audio Player for Web-Based Quranic Verse Retrieval Dewi Primasari; Firman Hakim; Muhammad Danise Raditya Saneistha; Ihsan Wahid Cahya
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 22 No. 2 (2025): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

This study aims to develop a web-based Quranic search system integrating fuzzy search and audio player features to address typographical errors in user inputs and enhance accessibility. Employing the Prototype Development Model, we implemented the Levenshtein Distance algorithm via Fuse.js for tolerance-based text matching and integrated an interactive audio player for verse recitation. Testing with typo variations revealed optimal performance at an 80–85% similarity threshold, achieving 92% user satisfaction in handling common misspellings (e.g., "Albaqara" → "Al-Baqarah"). The system’s client-side processing ensured rapid responses (<1s), while Progressive Web App (PWA) architecture enabled offline functionality. Key contributions include: (1) a typo-resistant Quranic search interface, (2) empirical validation of threshold settings, and (3) holistic user engagement through audio-text integration. This work bridges gaps in prior studies that focused solely on exact matching or audio features, offering a novel synergy of adaptive search and multimedia interaction for Islamic digital tools.
Application of Proportional Integral Derivative system as speed control on Mobile Robot Line Follower: submission of Journal Thesis Raka Pramudiya Ramadani; Arie Qur'ania; Agus Ismangil
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 22 No. 2 (2025): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

The development of the world of robotics in Indonesia is experiencing very rapid development compared to a few years ago, the use of PID Control-based technology is growing in the field of robotics. This line follower robot uses IR Line follower sensor to detect the black line and PID control to regulate the movement of the robot automatically, so that the robot can follow the line that has been made with direction. The Arduino Uno microcontroller in this robot design is the main part of the input and output control system. In this robot design, Arduino Uno is used as a microcontroller to control sensors that function as sensing / detection tools for robots. The working system of this robot is: how to run it by activating the power button on the back of the robot. The number of sensors and the accuracy of the PID control value can affect the comparison of the robot's travel time value from the starting point to the end of the test with two paths that have been tested, the results obtained 4 IR LF Sensors with an average travel time of 6.7 seconds on the first path and 13.5 seconds on the second path, better than 2 or 3 IR LF Sensors which have a difference of 0.64 seconds on the first path and 0.7 seconds on the second path. The above values can change according to the influence of component specifications, circumstances and robot environment during testing.
Recent Advances in Deep Learning for MRI-Based Brain Tumor Identification: A Systematic Review (2020 - 2025) adly maulana; Suherman
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 22 No. 2 (2025): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

The utilization of deep learning (DL) technology in brain MRI image analysis has seen significant advancements over the past five years. This study presents a systematic review of literature from 2020 to 2025, evaluating DL progress in automated tumor lesion segmentation, tumor type classification, genetic biomarker prediction, and treatment response monitoring. Various DL architectures, such as nnU-Net and ensemble models, dominate segmentation tasks, while transformer-based methods and foundation models are emerging as new pathways for large-scale medical image management. However, technical challenges including cross-institutional MRI protocol variations, underrepresentation of pediatric data, and model bias remain primary concerns. Initiatives like BraTS and federated learning approaches offer potential solutions to enhance DL model validity and scalability. This review highlights future directions for developing more adaptive, accurate, and ethical DL systems to support individualized and sustainable brain tumor diagnosis and management.
Application of Principal Component Analysis on Factors Causing Inflation in West Kalimantan Asri Rahmawati; Yuyun Eka Pratiwi; Onelia Rochmah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 22 No. 2 (2025): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

Inflation is an important indicator in assessing the economic stability of a region. Inflation fluctuations in West Kalimantan are influenced by various economic and structural factors. This study aims to identify the main factors causing inflation in West Kalimantan using Principal Component Analysis (PCA). Secondary data for the 2024 timeframe was obtained from West Kalimantan's Central Statistics Agency (BPS). Economic variables that are suspected of influencing inflation are analyzed using PCA to be reduced to new dominant factors. The main components obtained are then interpreted economically to understand the structure of the causes of inflation. The results of the analysis show that the cumulative proportion of the two components reaches 90%, so the two main components are sufficient to represent the main structure of the data. This means that most of the information from the original variables can be effectively reduced into two main components. Keywords: Inflation; PCA; reduction; economic factors.
Implementation of Convolutional Neural Network with VGG-16 Architecture in Digital Hiragana Handwriting Image Recognition Hendra Bayu Suseno; Fitri Mintarsih; Victor Amrizal; Rheditia Ferdiansyah; Tjut Awaliyah Zuraiyah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.65

Abstract

The number of Japanese language learners in Indonesia ranks second at 711,732 people. Hiragana is the first letter to be learned, especially at the beginner level and is usually learned before Katakana and Kanji. Some characters in Hiragana have similar main forms such as nu (ぬ) and me (め), ne (ね) and wa (わ), thus adding complexity to the recognition process. Like previous research that created a Hiragana pronunciation learning application and previous research that was an English writing learning application, allowing people to learn on their own, by applying CNN (Convolutional Neural Network) to recognize written characters, researchers were inspired to apply this in learning to write Hiragana letters. Therefore, researchers created a digital Hiragana handwriting recognition model using the VGG-16 CNN Architecture method so that the model created can later be used in a Hiragana learning application for writing. This study used a dataset in the form of digital Hiragana handwriting images totaling 1518 data with 33 data for each label (46 types of letters). The hyperparameters used in this study to train the model were 5 epochs, a batch size of 32, the Adam Optimizer, and a Learning rate of 0.001. Based on the test results with the aforementioned parameters, the Accuracy value was 98.55%, Precision was 98.91%, Recall was 98.55%, and the F1-Score was 98.51%.
Assessing Assessing Students’ Ethical Concerns in AI-Integrated Online Learning Systems: A Study in Batam City Hendi Sama; Julianto; Surya Tjahyadi
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.69

Abstract

The adoption of Artificial Intelligence (AI) in online education introduces ethical concerns related to accuracy, fairness, and accountability. This study examines students’ ethical concerns of AI-integrated learning systems, focusing on AI-generated materials, attendance monitoring, and chatbot interactions. A mixed-method approach combining qualitative and quantitative techniques was used, involving interviews with 48 students from three different educational levels in Batam City. Thematic analysis identified five dominant themes. These included AI as a useful yet unreliable learning assistant, concerns about fairness in AI-based monitoring, uncertainty regarding responsibility and accountability, the need for transparent institutional policies, and limited AI literacy leading to overreliance. The study reports its findings using percentage-based distributions to illustrate the prevalence of these concerns across educational levels. The results indicate that students’ acceptance of AI in online learning is closely tied to the presence of human oversight, transparent institutional policies, and clearly defined accountability mechanisms. The novelty of this study lies in its focus on a pre-adoption educational environment, where AI is not yet fully institutionalized. Unlike prior studies examining post-implementation contexts, this research captures students’ anticipatory ethical expectations, highlighting concerns often overlooked in retrospective evaluations. The study contributes by providing empirical evidence across educational levels, offering localized insights from a developing Indonesian city, and extending AI ethics research beyond technology-advanced settings. The findings emphasize the importance of human oversight, accountability mechanisms, and transparent institutional policies for ethically grounded AI governance in education.
Application of Linear Regression and Random Forest Algorithms in Predicting Human Development Index (HDI) Mulyati Mulyati; Nur Aynun Siregar; Khairunnisa
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.76

Abstract

The Human Development Index (HDI) is an important indicator for assessing the welfare and quality of life of the population in a region. The different growth of the HDI between regions indicates the need for accurate data-based analysis and prediction. One of them is a predictive analysis technique using the Linear Regression Algorithm and Random Forest. This study compares the two algorithms to predict the Human Development Index based on Expected Years of Schooling, Average Years of Schooling, Life Expectancy and adjusted Per Capita Income. The research stages include data collection, data pre-processing, data analysis and model evaluation. The results show that the use of the K-Fold Cross Validation method with a value of K = 5 produces a more optimal linear regression model compared to the Random Forest model. This is indicated by a higher coefficient of determination (R²) value and lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE).
A Analysis and Modeling Simulation King Kuphi Cafe Queuing System With Customer Arrival Variations Using Python Nurul Fikria Nurul_Fikria; Risky Ananta Pradana; Jelita Rahmah Zebua; Fathi Athallah Z
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

Queueing system modeling and simulation is an effective approach for analyzing service performance in business environments with dynamic customer arrival rates, such as at King Kuphi Cafe. This study aims to model the queueing system at the cafe with various variations in customer arrival rates using the queueing theory approach and simulate it using the Python programming language. The models used are the M/M/1 and M/M/c queueing systems, which allow analysis of changes in waiting time, queue length, server utilization, and service level based on variations in arrival (λ) and service (μ) parameters. The simulation was run using Python packages such as NumPy and SimPy to represent the arrival and service processes realistically. The results of the study show that an increase in the rate of customer arrivals significantly affects system performance, particularly in terms of an increase in average waiting time and queue length. In addition, adding more servers has been proven to reduce queue congestion and improve overall service quality. These findings are expected to serve as a basis for King Kuphi Cafe managers in making strategic decisions regarding the number of baristas and operational optimization to achieve more efficient service.
An Empirical Study of Temporal Graph Neural Networks for Dynamic Node Forecasting Ricky Maulana Fajri; Tasmi Tasmi; Ni Wayan Pricila Yuni Praditya
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i1.79

Abstract

Temporal graph modeling has become increasingly important for understanding and forecasting the dynamics of complex systems that evolve over time. One of the central challenges in temporal graph learning lies in identifying graph neural network (GNN) architectures that can effectively capture both spatial dependencies and temporal dynamics. This study presents a comprehensive benchmarking analysis of widely used GNN architectures, namely Graph Convolution Network (GCN), GraphSAGE, Graph Attention Network (GAT), Chebyshev Networks (ChebNet), and Simplified Graph Convolution Network (SGC), each integrated with recurrent mechanisms for temporal modeling. The evaluation is conducted on the WikiMaths dataset, a large-scale temporal graph dataset representing user visits of mathematics-related Wikipedia articles. Experimental results demonstrate that the choice of graph convolution operator significantly impacts temporal forecasting performance, with GraphSAGE and ChebNet consistently exhibiting superior performance compared to other architectures. This work provides empirical insights into the strengths and limitations of established temporal GNN models, contributing to a clearer understanding of their applicability in dynamic graph forecasting tasks.
Acquiring Knowledge from Data Analytics and Performance-Boosting on Multimedia Content Jed Wan; Hendi Sama; Muhamad Dody Firmansyah
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 1 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika.
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

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Abstract

In gaining meaningful and actionable insights from complex and diverse multimedia content, many studies have applied data analytics approaches—particularly data mining and machine learning—to uncover patterns, relationships, and hidden knowledge. This systematic literature review synthesizes 26 studies conducted over the past decade on acquiring knowledge from multimedia content using data analytics and performance-boosting techniques. Across domains such as social media, education, healthcare, e-commerce, and public safety, most works integrate text–image or audio–video pairs and increasingly adopt attention-based architectures and transformer models with early fusion strategies. To ensure comparability, each study’s evidence is recorded by considering the reported performance improvement over the authors’ baseline using the same dataset and evaluation metric. The most frequently used metrics include Accuracy, the F1-score (a harmonic mean of Precision and Recall), Precision, Recall, and the Area Under the Receiver Operating Characteristic Curve (AUC), which provides a threshold-independent measure of classification quality. The most common challenges identified include modality integration and alignment, data noise and quality, limitations of datasets and benchmarks, and domain shift, with fewer studies reporting class imbalance, computational cost, and interpretability or privacy issues. At the same time, promising opportunities emerge in the development of standardized multimodal benchmarks, efficient transformer-based and hybrid fusion pipelines, integration of external knowledge, domain-robust learning, and privacy-preserving explainable multimodal artificial intelligence. Overall, this review contributes a consolidated map of modalities, methods, and metrics, a performance-gain versus baseline table for quick comparability, a quantified challenge landscape, and a practical roadmap for guiding future research in multimodal sentiment analysis and related fields.

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