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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 13 Documents
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Search results for , issue "Vol 6, No 3: November 2025" : 13 Documents clear
The smart e-bike ecosystem integrates internet of things and artificial intelligence Sutikno, Tole; Purnama, Hendril Satrian
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p307-314

Abstract

The smart e-bike ecosystem, a combination of internet of things (IoT) and artificial intelligence (AI), has transformed urban mobility. This study aims to shed light on the transformative potential of the smart e-bike ecosystem in the context of urban transportation solutions. It includes real-time navigation, crash detection, and a smart electric drive to encourage sustainable practices and reduce reliance on traditional vehicles. The use of smart locks and parking beacon systems creates a safe and efficient urban infrastructure, encouraging e-bike use. This approach reduces traffic congestion and carbon emissions. IoT frameworks in smart e-bikes improve the user experience and contribute to urban mobility solutions. Real-time monitoring of critical parameters, such as battery levels, speed, and maintenance requirements, keeps riders informed and safe at all times. IoT-enabled features, such as navigation assistance, shorten travel times and improve the efficiency of urban transportation systems. The evolution of smart e-bikes is consistent with the anticipated improvements of 6G networks, which promise to transform communication infrastructures. AI-powered features such as real-time navigation and crash detection make rides safer. The use of smart electric drives and cloud server technology promotes a data-driven approach to transportation. Future research and development should look into the use of advanced localization techniques to improve user experience while addressing accuracy and energy consumption issues.
A dual-model machine learning approach to medicare fraud detection: combining unsupervised anomaly detection with supervised learning Arockiasamy, Jesu Marcus Immanuvel; Bhoopathi, Gowrishankar
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p245-252

Abstract

Medicare fraud, costing $54.35 billion in improper payments in 2024, undermines U.S. healthcare by draining resources meant for vulnerable populations. Traditional detection methods struggle with reactive designs, high false positives, and reliance on scarce labeled data, exacerbated by a 0.017% fraud prevalence. This paper proposes a dual-model machine learning framework to tackle these challenges. Unsupervised anomaly detection uses cluster-based local outlier factor (CBLOF) and empirical cumulative outlier detection (ECOD) to identify novel fraud patterns across 37 million records. These findings are validated by the list of excluded individuals/entities (LEIE). Supervised classification, with C4.5 decision trees and logistic regression, refines these anomalies using an 80:20 balanced dataset, reducing false positives by 63%. Key innovations include hybrid sampling to address class imbalance, LEIE integration for labeled validation, and parallelized processing of 2.1 million claims hourly. Achieving an area under the curve (AUC), a measure of model accuracy, of 88.3%, this approach outperforms single-model systems by 24%, blending exploratory detection with actionable precision. This scalable, interpretable framework potentially advances fraud detection, safeguarding public funds and Medicare’s integrity with a practical, adaptable solution for evolving threats.
Optimizing energy distribution efficiency in wireless sensor networks using the hybrid LEACH-DECAR algorithm Muntashir, Muhammad Abyan Nizar; Sulistyawan, Vera Noviana; Hudallah, Noor
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p262-273

Abstract

Wireless sensor network (WSN) is a network system consisting of various supporting components that integrate information to the base station. In its operation, delivery is greatly influenced by energy usage because limited battery supply causes variability in energy consumption on node activity factors, communication distance, and environmental conditions. So, in order to increase performance and energy efficiency, a routing protocol is required by selecting the best path through cluster head. The technique of determining the cluster head (CH) based on energy is used to avoid irregularity (randomness). In this study, the hybrid routing protocol selects CH based on the remaining energy, considering distance, coverage radius, and energy metrics. The system test evaluation compares the implementation of low-energy adaptive clustering hierarchy (LEACH) and hybrid LEACH- Distributed, energy and coverage-aware routing (DECAR). The results of 300 rounds show that the hybrid achieves a packet delivery ratio close to 100% and a throughput of 78.22 Kbps, while LEACH achieves a packet delivery ratio of 92.18% and a throughput of 247.15 Kbps. The average energy consumption of LEACH is 99.27%, while the hybrid shows much greater efficiency at 30.55%. This study emphasizes the significance of maintaining equilibrium performance and energy consumption in the development of future routing protocols.
Hybrid feature fusion from multiple CNN models with bayesian-optimized machine learning classifiers Rismawati, Dewi; Surono, Sugiyarto; Thobirin, Aris
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p315-325

Abstract

Information technology advancements have created big data, necessitating efficient techniques to retrieve helpful information. With its capacity to recognize and categorize patterns in data, especially the growing amount of picture data, deep learning is becoming a viable option. This research aims to develop a medical image classification model using chest X-Ray with four classes, namely Covid-19, Pneumonia, Tuberculosis, and Normal. The proposed method combines the advantages of deep learning and machine learning. Three pre-trained CNN models, VGG16, DenseNet201, and InceptionV3, extract features from images. The features generated from each model are fused to enhance the relevant information. Furthermore, principal component analysis (PCA) was applied to reduce the dimensionality of the features, and Bayesian optimization was used to optimize the hyperparameters of the machine learning algorithms support vector machine (SVM), decision tree (DT), and k-nearest neighbors (k-NN). The resulting classification model was evaluated based on accuracy, precision, recall, and F1-score. The results showed that FF-SVM, which is the proposed model, achieved an accuracy of 98.79% with precision, recall, and F1-score of 98.85%, 98.82%, and 98.84%, respectively. In conclusion, fusing feature extraction from multiple CNN models improved the classification accuracy of each machine-learning model. It provided reliable and accurate predictions for lung image diagnosis using chest X-Ray.
Javanese and Sundanese speech recognition using Whisper Raharjo, Alim; Zahra, Amalia
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p253-261

Abstract

Automatic speech recognition (ASR) technology is essential for advancing human-computer interaction, particularly in a linguistically diverse country like Indonesia, where approximately 700 native languages are spoken, including widely used languages like Javanese and Sundanese. This study leverages the pre-trained Whisper Small model an end‑to‑end transformer pretrained on 680,000 hours of multilingual speech, fine tuning it specifically to improve ASR performance for these low resource languages. The primary goal is to increase transcription accuracy and reliability for Javanese and Sundanese, which have historically had limited ASR resources. Approximately 100 hours of speech from OpenSLR were selected, covering both reading and conversational prompts, the data exhibited dialectal variation, ambient noise, and incomplete demographic metadata, necessitating normalization and fixed‑length padding. with model evaluation based on the word error rate (WER) metric. Unlike approaches that combine separate acoustic encoders with external language models, Whisper unified architecture streamlines adaptation for low‑resource settings. Evaluated on held‑out test sets, the fine‑tuned models achieved Word Error Rates of 14.97% for Javanese and 2.03% for Sundanese, substantially outperforming baseline systems. These results demonstrate Whisper effectiveness in low‑resource ASR and highlight its potential to enhance transcription accuracy, support language preservation, and broaden digital access for underrepresented speech communities. 
Decision making with analytical hierarchy process algorithm and prototype model for exemplary teachers Sumardiono, Sumardiono; Ismail, Norhafizah; Priyadi, Wiwit; Riyanto, Agus; Rusmana, Indra Martha
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p225-234

Abstract

The selection process for exemplary teachers in vocational schools in Bekasi City has so far been carried out subjectively without a structured system, relying on internal meetings and daily notes, thus causing problems of transparency, accuracy, and efficiency. To overcome this, this study developed an online decision support system (DSS) that makes use of the analytical hierarchy process (AHP) algorithm to create an objective and measurable selection method based on five criteria: discipline, travel costs, personality, teaching administration, and learning achievement. Quantitative methods were applied by collecting data through questionnaires and observations, while the system prototype was designed through the stages of problem analysis, design, implementation, and evaluation. The AHP algorithm was used to process the decision matrix, benefit-cost-based normalization, weighting, and pairwise comparisons, with a consistency test (CR =0.044) ensuring the reliability of the results. This system successfully identified Didi Saputra, S.Pdi., as the best exemplary teacher with the highest preference value (0.92), while providing a significant impact in the form of increased accuracy (reducing subjective bias), transparency (clear ranking reports), and efficiency (faster selection process). The research findings demonstrate the effectiveness of AHP as a structured solution for exemplary teacher selection, with potential for adoption by other educational institutions and sustainability through a web-based system.
Mediterranean and northern european archaeology: a computational comparison Kchan, Hamza; Noor, Saira
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p326-334

Abstract

Despite the proliferation of computational tools in archaeology, few studies systematically compare their regional adaptations or explore the epistemological assumptions guiding their application. This paper addresses four critical research gaps: (i) the lack of comparative regional analysis between the Mediterranean and Northern Europe in computational archaeology, (ii) the insufficient integration of philosophical and epistemological frameworks in predictive modeling, (iii) the underexplored application of artificial intelligence (AI) and network theory in spatial analysis, and (iv) the limited interdisciplinary synthesis of biological, geospatial, and digital data. By examining representative case studies from both regions, we highlight the methodological innovations, theoretical orientations, and institutional dynamics that shape regional practices. The study underscores the necessity of integrating computational methods with interpretive depth and interdisciplinary collaboration to foster a more reflective and inclusive digital archaeology. 
Optimizing diplomatic indexing: full-parameter vs low-rank adaptation for multi-label classification of diplomatic cables Nurlaila, Dela; Girsang, Abba Suganda
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p274-282

Abstract

Accurate classification of diplomatic cables is crucial for Mission’s evaluation and policy formulation. However, these documents often cover multiple topics, hence a multi-label classification approach is necessary. This research explores the application of pre-trained language models (CahyaBERT, IndoBERT, and MBERT) for multi-label classification of diplomatic cable executive summaries, which align with the diplomatic representation index. The study compares full-parameter fine-tuning and low-rank adaptation (LoRA) techniques using cables from 2022-2023. Results demonstrate that Indonesian-specific models, particularly the IndoBERT, outperform multilingual models in classification accuracy. While LoRA showed slightly lower performance than full fine-tuning, it significantly reduced GPU memory usage by 48% and training time by 69.7%. These findings highlight LoRA’s potential for resource-constrained diplomatic institutions, advancing natural language processing in diplomacy and offering pathways for efficient, real-time multi-label classification to enhance diplomatic mission evaluation.
Implementation of IoT-based water quality monitoring instruments in cantang grouper cultivation ponds Kusuma, Hollanda Arief; Alajuri, M Hasbi sidqi; Anggarudin, Anggarudin; Setyono, Dwi Eny Djoko; Irawan, Henky
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p235-244

Abstract

Grouper fish farming in Indonesia has great potential, but water quality management remains a challenge. Manual monitoring at hatchery D-Marine aquaculture struggles to detect sudden changes, risking mass mortality. This study developed an IoT-based water quality monitoring system using an ESP32 microcontroller, DS18B20 temperature sensors, pH sensors, dissolved oxygen (DO) sensors, a micro-SD card, an organic light emitting diode (OLED) display, and the Ubidots platform. The methodology involved device design, sensor calibration, and field testing. Calibration showed sensor accuracy above 90%. Field tests recorded water temperatures of 26.84 °C (tank 1) and 27.74 °C (tank 2), with pH values of 6.73 and 6.87, which did not meet Indonesian national standard (SNI) standards. Data transmission to Ubidots had a 95% packet delivery ratio (PDR) for device 1 and 97% for device 2. The system successfully provided real-time water quality data, supporting effective farm management. However, improvements to the dissolved oxygen sensor and an automatic control system are needed for better stability and efficiency.
A machine learning approach for early prediction of mental health crises Chigagure, Hassan; Sakala, Lucy Charity
Computer Science and Information Technologies Vol 6, No 3: November 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v6i3.p335-345

Abstract

The global mental health crisis, intensified by the COVID-19 pandemic, placed unprecedented strain on healthcare systems and highlighted the urgent need for proactive crisis prevention strategies. This study investigated the effectiveness of various machine learning (ML) models in predicting mental health crises within 28 days post-hospitalization, leveraging an eight-year longitudinal dataset. Multiple data preprocessing techniques, including feature selection (EFSA, RFECV), imputation, and class imbalance handling (SMOTE, Tomek links), were systematically applied to enhance model performance. Six traditional classifiers—logistic regression, support vector machine, k-nearest neighbors, naive Bayes, XGBoost, and AdaBoost—were evaluated alongside ensemble learning (EL) methods (bagging, boosting, stacking). Performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used for comprehensive assessment. Results demonstrated that ensemble methods, particularly boosting and bagging, consistently achieved high predictive accuracy (up to 93%), with XGBoost and AdaBoost emerging as top performers. Feature selection and class imbalance techniques further improved model robustness and generalizability. The findings underscored the potential of ML-driven approaches for early identification of at-risk patients, enabling more effective resource allocation and timely interventions in mental health care. Recommendations for integrating these predictive tools into clinical workflows were discussed to support data-driven decision-making.

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