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Bulletin of Computer Science Research
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27743659     DOI : -
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Computer Science & IT
Bulletin of Computer Science Research covers the whole spectrum of Computer Science, which includes, but is not limited to : • Artificial Immune Systems, Ant Colonies, and Swarm Intelligence • Bayesian Networks and Probabilistic Reasoning • Biologically Inspired Intelligence • Brain-Computer Interfacing • Business Intelligence • Chaos theory and intelligent control systems • Clustering and Data Analysis • Complex Systems and Applications • Computational Intelligence and Soft Computing • Distributed Intelligent Systems • Database Management and Information Retrieval • Evolutionary computation and DNA/cellular/molecular computing • Expert Systems • Fault detection, Fault analysis, and Diagnostics • Fusion of Neural Networks and Fuzzy Systems • Green and Renewable Energy Systems • Human Interface, Human-Computer Interaction, Human Information Processing • Hybrid and Distributed Algorithms • High-Performance Computing • Information storage, security, integrity, privacy, and trust • Image and Speech Signal Processing • Knowledge-Based Systems, Knowledge Networks • Knowledge discovery and ontology engineering • Machine Learning, Reinforcement Learning • Networked Control Systems • Neural Networks and Applications • Natural Language Processing • Optimization and Decision Making • Pattern Classification, Recognition, speech recognition, and synthesis • Robotic Intelligence • Rough sets and granular computing • Robustness Analysis • Self-Organizing Systems • Social Intelligence • Soft computing in P2P, Grid, Cloud and Internet Computing Technologies • Support Vector Machines • Ubiquitous, grid and high-performance computing • Virtual Reality in Engineering Applications • Web and mobile Intelligence, and Big Data • Cryptography • Model and Simulation • Image Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 47 Documents
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Search results for , issue "Vol. 5 No. 5 (2025): August 2025" : 47 Documents clear
Optimalisasi Akurasi Prediksi Curah Hujan Bulanan Menggunakan Deep Learning Yafik, Muhammad Ikrom; Chairani, Chairani
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.v5i5.735

Abstract

The Province of Lampung exhibits high rainfall variability influenced by various atmospheric dynamics such as the Asian Monsoon, Australian Monsoon, El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD). Accurate rainfall prediction is crucial across multiple sectors, including agriculture, water resource management, and hydrometeorological disaster mitigation. However, prediction methods commonly used in the region are still dominated by statistical approaches or conventional machine learning techniques, which often struggle to capture long-term temporal patterns in rainfall data. On the other hand, deep learning technologies such as the Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) offer better capabilities in modeling time series data, yet no specific comparative evaluation has been conducted for rainfall prediction in the Lampung Province. Comparing these two methods is important because the architectural characteristics of RNN and GRU differ in handling long-term dependencies, and selecting the right model can directly impact prediction accuracy and the effectiveness of decision-making in affected sectors. This study aims to implement and compare the performance of RNN and GRU in predicting monthly rainfall in Lampung Province using data from 80 rain gauges distributed across 15 districts/cities over the period from January 1991 to February 2025. The results show that the RNN model outperforms the GRU model, with lower RMSE (115.61 vs. 119.50), smaller MAE (86.94 vs. 91.28), and higher R² (0.35 vs. 0.30). Predictions for the period from March 2025 to February 2026 reveal a clear seasonal pattern, with minimum rainfall occurring in August 2025 (peak dry season) and maximum rainfall in January 2026 (peak rainy season). This study demonstrates that RNN is more effective than GRU in capturing the temporal patterns of rainfall, making it more recommended for long-term prediction applications.
Implementasi Animasi 2D menggunakan Motion Graphic sebagai Media Informasi Palang Merah Indonesia Wulandari, Irma; Fananda, Ibrohim Yofid; Hasim, Jauari Akhmad Nur; Pramulen, Aji Sapta; Damastuti, Fardani Annisa; Zukhaha, Ashiliya Atsmara
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.v5i5.739

Abstract

The Indonesian Red Cross (PMI) has a significant responsibility in conveying humanitarian information to the public. However, the challenges of effectively communicating information to the broader community remain a primary concern, as conventional media often lack appeal and are difficult to comprehend fully. To address this issue, this study implements 2D animation based on motion graphics as a communication medium for PMI. The animation production process includes creating storyboards, developing visual illustrations, graphic processing, adding motion elements, voice narration, and audio-visual synchronization, resulting in a communicative and engaging medium. 2D animation was chosen because it can present messages with simple yet clear visuals, while motion graphics provide engaging motion dynamics that make information easier to understand and remember. The integration of both allows for the delivery of messages that are concise, interactive, and in line with the characteristics of digital media that are widely accessed by the public. Evaluation results show a significant increase in the level of understanding among respondents after watching the video, with post-test scores reaching 94.8% in the PMI member group and 91.2% in the general public group. These findings affirm that 2D animation media based on motion graphics is effective in enhancing the appeal, understanding, and effectiveness of PMI communication, thus it can be an innovative alternative strategy to expand the reach of humanitarian information.
Analisis Klasifikasi Kesiapan Digital Desa Menggunakan Decision Tree dan Pemetaan Spasial Fatimah Ahmad, Hafidlotul; Firdawanti, Aulia Rizki; Agustiani, Nur
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.v5i5.741

Abstract

Digital transformation at the village level is a strategic element in promoting equitable development and improving public service delivery. However, the level of digital readiness across regions remains uneven. This study aims to classify the digital readiness of villages in West Java Province by utilizing data from Open Data Jabar (opendata.jabarprov.go.id) related to the number of digital villages, internet access, and village development strata. A Decision Tree classification algorithm was employed to categorize regions into two readiness classes: high and low. The modeling results indicate that the number of self-reliant (mandiri) villages and the percentage of villages with internet access are the most influential variables in the classification. Although internet infrastructure is available in most areas, it does not always correspond to the level of village digitalization. Districts with high internet access but a low number of self-reliant villages are still classified as having low readiness. The model achieved an accuracy of 83%, although its performance in identifying the high readiness class was limited due to class imbalance in the dataset. Spatial visualization was also used to highlight regional disparities in digital readiness. This study provides an early contribution to digital readiness mapping of villages using a machine learning approach in Indonesia.
Penerapan Algoritma BM25 dalam Pencarian Lowongan Pekerjaan pada Website Job Portal Kheng, Tek; Asri, Jefry Sunupurwa; Wahyu, Sawali; Yulhendri, Yulhendri
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.v5i5.760

Abstract

The development of the digital era has grown rapidly all the time which has significantly changed the job search process for job applicants, making Online Job Portals one of the main places in human resource recruitment activities, however, the effectiveness of Job Portals Job search still has fundamental weaknesses such as the job search technology used still uses simple string matching which can cause less relevant search results and reduce the quality of user experience in applying for jobs. This study was conducted to improve the quality of job vacancy search results on Job Portal A Career by applying the Okapi BM25 algorithm. This research method uses a Rapid Application Development (RAD) development approach, such as designing a client server architecture with Next.js as the frontend, ASP.NET Core as the backend and PostgreSQL as the main database. The BM25 algorithm is integrated directly into the database using the VectorChord BM25 extension to calculate the search relevance score with the user inputted query. In testing with the query “accelist the quality support career IT need”, the system displays 800 of 1,011 documents (79.13%) with a non-zero relevance score. Furthermore, evaluation through User Acceptance Testing (UAT) showed a user satisfaction rate of 91.2%, confirming that BM25 is capable of displaying the most relevant results at the top of the rankings and supporting the effectiveness of the search system. The results of this study can be concluded that the BM25 algorithm is a more effective and efficient search solution with high scalability potential for application to other web-based job search systems.
Penerapan Algoritma Naive Bayes Untuk Sistem Klasifikasi Status Gizi Bayi Balita Abas, Mohamad Ilyas; Lamusu, Rizal; Pranata, Widya Eka; Syahrial, Syahrial; Ibrahim, Irawan; Hasyim, Wahyudin; Kiayi, Verliana
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.v5i5.508

Abstract

Infants and toddlers are in a critical period of rapid growth and development, often referred to as the "golden age." During this stage, regular nutritional assessments are essential to monitor health status and detect potential nutritional problems early. This study aims to classify the nutritional status of infants and toddlers using the Naïve Bayes algorithm, a probabilistic classification method based on Bayes' theorem with a strong assumption of attribute independence. The main attributes used in the classification system include age, weight, and height. The dataset consists of 700 records of infants and toddlers collected from previous observations. The results show that the Naïve Bayes algorithm can be effectively implemented for nutritional status classification, achieving a system accuracy of 88.14%. This indicates that the method performs well and has the potential to be utilized in decision support systems for child health monitoring.
Sistem Pendukung Keputusan Pemilihan Perangkat Umum Persmabimed Berbasis Web dengan Metode Profile Matching Setiawan, Abi; Reza Nur Afdal; Shabrina Prabudi; Debi Yandra Niskah
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.v5i5.531

Abstract

Persmabimed is an organization under Universitas Negeri Medan that serves as a platform for students receiving the KIP-K scholarship and plays an important role in managing member activities and information. However, the selection process for general board members such as the chairman, secretary, and treasurer is still conducted manually and tends to be subjective, potentially leading to a mismatch between candidates and their assigned roles. This study aims to design and implement a web-based decision support system using the Profile Matching method to assist in the selection process of Persmabimed board members. The method is chosen for its ability to compare candidate profiles with ideal profiles based on GAP values and criterion weights. Data was collected through observation, interviews with organization administrators, and literature studies. The selection process involves calculating core and secondary factors, followed by ranking based on a weighted combination of hard and soft skills. The system’s results demonstrate that the Profile Matching method can produce objective and accurate decisions, selecting Irvan Affandi as chairman, Dinda Rizky Fadilah as secretary, and Kiki Ratna Sari as treasurer. The system was developed using PHP and Bootstrap to ensure accessibility and streamline the selection process. This research improves efficiency and fairness in organizational decision-making and can be applied to similar organizations in the future.
User Interface Design for Doctor Reservation Website using Design Thinking Method Puspa Diah Narendri, Azelia; Cahya Wardhana, Ariq
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.v5i5.534

Abstract

In the current era of digital transformation, the rapid integration of healthcare services is crucial to meet patient expectations and improve service delivery. Klinik Putri faces challenges such as long queues and difficulties in booking doctor appointments, which negatively impact patient satisfaction. This study aims to design a user-friendly, web-based doctor reservation system using the Design Thinking methodology, which consists of five stages: empathize, define, ideate, prototype, and test. Usability testing was conducted using the System Usability Scale (SUS), a standardized tool for evaluating system usability. The results showed an average SUS score of 83, placing the system in the “acceptable” category, Grade B, and receiving an “excellent” rating according to the Adjective Rating scale. These findings demonstrate that the proposed website design effectively addresses user needs, enhances the user experience, and contributes to improving the efficiency of healthcare services at Klinik Putri.
Business Intelligence untuk Validasi Desain Karakter Berbasis Budaya pada Game Aventala: “The Lost Tribe” Damastuti, Fardani Annisa; Yoda, Sevtian Bintang; Revindasari, Fony; Kusdianta, Naufal Airlangga
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.v5i5.563

Abstract

This study focuses on culture-driven character design for Aventala: The Lost Tribe by transforming Indonesian endemic animals and cultural elements into humanoid forms documented in a character sheet. The objective is to formulate and validate a culture-driven character design pipeline via: (i) a personification sheet that maps physiology–fantasy–psychology–sociology, (ii) scene-based moodboards to align tone and persona, and (iii) a user study employing a six-indicator 5-point Likert instrument (mythology, culture, fantasy, naming, traits, and sheet readability) analyzed in a Business Intelligence dashboard. The method combines narrative comprehension (DRTA), qualitative data curation, sheet construction, moodboard development, and an online survey with the target audience. Results show a moodboard satisfaction level of 85.24% and character acceptance ranging from 83% to 86%, indicating coherence across cultural representation, fantasy elements, naming, and traits. These findings suggest the proposed pipeline is effective for evidence-based design, and the personification sheet serves as a practical cross-team artifact to guide iteration decisions.
Analisis Sentimen Publik terhadap ‘Save Raja Ampat’ di Media Sosial Menggunakan Model IndoBERT Eko Putro, Dimas; Juarsa, Doris; Putra Hermana, BP; Bagastian, Bagastian; Sulistiani, Heni
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.v5i5.621

Abstract

The "Save Raja Ampat" campaign has emerged as a significant environmental issue that has garnered widespread public attention on social media platforms, particularly TikTok and YouTube. Videos tagged with #SaveRajaAmpat have sparked various public responses, ranging from full support to criticism of natural resource exploitation. This phenomenon highlights the importance of understanding public sentiment as an indicator of the campaign's effectiveness. This study aims to analyze public sentiment toward the campaign using a language modeling approach based on artificial intelligence, namely IndoBERT. The data were obtained from user comments on TikTok videos promoting the “Save Raja Ampat” campaign, totaling 10,000 comments. The analysis process involved several stages, including data preprocessing, sentiment labeling (positive, negative, neutral), and the training and evaluation of the IndoBERT model. Preliminary results indicate that the majority of public sentiment toward the campaign is positive, with the model achieving an accuracy rate of 71% in sentiment classification. This study contributes to understanding public perception of environmental issues and demonstrates the effectiveness of using the IndoBERT model in the context of social media.
Klasifikasi Tingkat Risiko Gempa di Indonesia Menggunakan Pola Spasial dan Temporal Berbasis Decision Tree Prasetio, Mugi; Sulistiani, Heni; Inonu, Onassis Yusuf; Magda, Kardita; Santosa, Budi
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.v5i5.624

Abstract

Indonesia is an area that is very vulnerable to earthquakes due to its location in the meeting zone of active tectonic plates. This study aims to classify the level of earthquake risk based on spatial and temporal patterns using the Decision Tree method as a solution in predicting potential earthquake hazards. The data used is earthquake data in Indonesia from 2015 to 2023 obtained from public datasets, including location information (latitude and longitude), event time (year and month), and earthquake magnitude. Earthquakes are categorized into three risk classes: Low (M < 4.0), Medium (4.0 ? M < 6.0), and High (M ? 6.0). The Decision Tree model was successfully built with an average accuracy of 88% on the test data. The results show that earthquakes mostly occur in active subduction zones such as the Sunda Subduction Zone (Sumatra and Java), Banda Arc (Nusa Tenggara, Maluku, Seram), Sulawesi, and Papua. Temporal analysis also shows fluctuations in the number of earthquakes by year and season, with increased activity in certain months. The spatial visualization reinforces the finding that the eastern region of Indonesia is more seismically active than the western region. This research proves that machine learning approaches can be used to support earthquake disaster mitigation through historical data-based risk identification.

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