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Photovoltaic Fault Detection in Remote Areas Using Fuzzy-Based Multiple Linear Regression (FMLR)

International Journal of Advanced computer science and Applications 2025年第4期16卷 243-249页

作者： Ardianto, Feby Ermatita, Ermatita Sofijan, Armin Doctoral Program in Engineering Science Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia Faculty of Engineering Universitas Sriwijaya Palembang Indonesia

This research focused on developing and implementing a fault detection model for photovoltaic (PV) systems in remote areas, utilizing a Fuzzy-Based Multiple Linear Regression (FMLR) approach. The study aimed to address the challenges of monitoring PV systems in locations with limited access to conventional power grids and technical resources. The fault detection system integrated environmental parameters such as solar radiation, temperature, wind speed, and rainfall, alongside PV system parameters like panel voltage, current, battery voltage, and inverter performance. Data collection and preprocessing were conducted over a specified period to identify operational patterns under both normal and faulty conditions, ensuring data accuracy through cleaning, normalization, and categorization. The research was conducted in Pandan Arang Village, Kandis District, Ogan Ilir Regency, South Sumatera, Indonesia, contributing to the improvement of reliability and sustainability of renewable energy sources in isolated communities. The total number of data points for 276 rows with 6 attributes each was 1656 records. The MLR model was developed to predict the output power of the PV system, while fuzzy logic was employed to handle uncertainties in the data, offering a more flexible and adaptive decision-making process. The system applied fuzzy rules to determine the charging status (P3), categorizing it into Optimal Charging, Adjusted Charging, Charging Delay, or Fault Alert. The model was tested with real-time data, and its performance was validated through comparison with manual inspections. The results showed that the FMLR-based fault detection system effectively identified faults and optimized the performance of the PV system, making it suitable for remote areas in South Sumatera. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Multiple linear regression

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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2025 International Conference on Advancement in Data science, E-learning and Information System, ICADEIS 2025

作者： Abidin, Zaenal Junaidi, Akmal Wamiliana Lumbanraja, Favorisen Rosyking Kurniasari, Dian Borman, Rohmat Indra Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Universitas Lampung Department of Computer Science Bandar Lampung Indonesia Universitas Lampung Department of Mathematics Bandar Lampung Indonesia Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandar Lampung Indonesia

ISBN: (纸本)9798331513320

Stemming, an essential procedure in natural language processing (NLP), diminishes words to their base forms, facilitating tasks such as information retrieval and sentiment analysis. Although stemming techniques for highresource languages are well-developed, numerous low-resource languages, including dialect of Tulang Bawang, suffer from inadequate solutions owing to a scarcity of linguistic data and resources. Existing systems, including rule-based stemmers, have demonstrated efficacy in processing low-resource languages such as Indonesian and Javanese by utilizing established morphological rules. Nonetheless, these methods encounter considerable obstacles, such as restricted adaptability, inability to accommodate unusual root structures, and excessive dependence on fixed rules that might result in over- or understemming. Rule-based methodologies frequently misidentify roots when faced with intricate affixes or unconventional word forms. We introduce an improved rule-based Tulang Bawang Stemmer aimed at overcoming these constraints by enhancing current linguistic rules and integrating new patterns specific to the language's morphology. Assessed on 500 test samples and 200 independent test samples, our improved stemmer attained gold standard evaluation metrics of 96.2% and 94%, respectively, surpassing prior implementations in both precision and generalization. The findings demonstrate the potential of enhanced rule-based techniques to improving NLP for lowresource languages. Improved stemming performance enables better downstream applications, promotes more efficient text analysis, and advances research in underrepresented languages. © 2025 IEEE.

关键词： Linguistics

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia computer science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, computer science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Rule-Based Dialect of Tulang Bawang Stemmer

Rule-Based Dialect of Tulang Bawang Stemmer

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Advancement in Data science, E-learning and Information Systems (ICADEIS), International Conference on

作者： Zaenal Abidin Akmal Junaidi Wamiliana Favorisen Rosyking Lumbanraja Dian Kurniasari Rohmat Indra Borman Doctoral Program of Mathematics and Natural Sciences Universitas Lampung Bandar Lampung Indonesia Department of Computer Science Universitas Lampung Bandar Lampung Indonesia Department of Mathematics Universitas Lampung Bandar Lampung Indonesia Faculty of Engineering and Computer Science Universitas Teknokrat Indonesia Bandar Lampung Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

关键词： Measurement Sentiment analysis Text analysis Electronic learning Morphology Linguistics Data science Information retrieval Machine translation Information systems

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Evaluating natural language processing derived linguistic features associated with current suicidal ideation, past attempts, and future suicidal behavior

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Journal of Psychiatric Research 2025年 187卷 25-33页

作者： McBride, Lauren Badal, Varsha D. Harvey, Philip D. Pinkham, Amy Aich, Ankit Parde, Natalie Depp, Colin San Diego State University/University of California San Diego Joint Doctoral Program in Clinical Psychology San Diego CA United States Department of Psychiatry University of California San Diego La Jolla CA United States Department of Psychiatry University of Miami Miller School of Medicine Miami FL United States Research Service Bruce W Cater Miami VA Healthcare System Miami FL United States School of Behavioral and Brain Sciences The University of Texas at Dallas Richardson TX United States National Institute on Drug Abuse National Institutes of Health Baltimore MD United States University of Pennsylvania Philadelphia PA United States Department of Computer Science University of Illinois Chicago Chicago IL United States

Background: People with psychosis have a higher suicide risk than the general population. Natural language processing (NLP) has been used to understand communication in psychosis and suicide risk prediction, but not to predict future suicidal behavior in people with psychosis. We utilized NLP-derived linguistic features from a dyadic task among people with psychotic disorders to predict current suicide ideation, past suicide attempts, and future suicidal behavior. Methods: N = 112 adults with psychotic disorders completed the Columbia-Suicide Severity Rating Scale at baseline and one-year follow-up to capture lifetime suicide attempts, current suicidal ideation, and suicidal behavior during the follow-up period. At baseline, participants completed a dyadic role-play task called the Social Skills Performance Assessment. Lexical features, lexical diversity, and sentiment features were extracted from task transcripts. Models for each outcome were generated using a 70 %–30 % train-test split with MLPRegressor. SHapley Additive exPlanations (SHAP) was utilized for feature analysis. Results: A total of 42.9 % of participants had baseline suicidal ideation, 67.9 % had at least one past suicide attempt, and 13.3 % had at least one reported new suicidal behavior at one-year follow-up. The models had strong predictive performance for past attempts (F1 = 0.75) and current ideation (F1 = 0.74–0.79), with future suicide behavior models showing the strongest predictive performance (F1 = 0.86–0.93). The top features varied across suicide ideation, past attempts, and future behavior. Discussion: NLP-derived features from a dyadic task were associated with high predictive accuracy for future suicidal behavior. Pending replication, these findings suggest that NLP-derived linguistic features from dyadic tasks could contribute to understanding suicide risk among people with psychosis. © 2025 Elsevier Ltd

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Mining Opinion Based on Tweets about Student Exchange with Tweepy and TextBlob 9

Mining Opinion Based on Tweets about Student Exchange with T...

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9th International Conference on Information Technology, computer and Electrical Engineering, ICITACEE 2022

作者： Octaria, Orissa Manongga, Danny Iriani, Ade Purnomo, Hindriyanto Dwi Setyawan, Iwan Universitas Kristen Satya Wacana Computer Science Doctoral Program Palembang Indonesia Universitas Kristen Satya Wacana Computer Science Doctoral Program Salatiga Indonesia

ISBN: (数字)9781665471503

ISBN: (纸本)9781665471480

To find out people's opinions about student exchange, this research was made to find out by taking a data set from Twitter with a machine learning calculation within the form of Tweepy and after that handling it with TextBlob. TextBlob is one of the libraries in python 2 and python 3 which is used to process textual data. After getting the results, it is expected to use the data to help the community know what student exchange is and its benefits. Social media (Facebook, Instagram, Twitter, etc.) these days can be utilized for analysing the objects, e.g. political sees, items, administrations, etc. Social media is widely used at this time and we choose twitter because twitter is a social media application that is popular in the world and indeed a lot of research using twitter. Twitter had become the third-highest social organization in January 2009 from its previous position of twenty-two. Currently, more than 500 million selected clients in 2012, generating more than 340 million tweets. The conclusion is that there are still many people who do not know what student exchange is as much as 22% and 54% consider student exchange to be negative because they do not know the benefits of student exchange. Positive knowledge about student exchange is only 24%, this will support further research of creating a knowledge management system on student exchange. So hopefully that many people are interested in joining the student exchange program. © 2022 IEEE.

关键词： Python

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Construction of a Self-Balancing Cube With Embedded Reaction Wheel Drive and Preliminary Investigation of Simple Harmonic Motion Patterns 4

Construction of a Self-Balancing Cube With Embedded Reaction...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Siregar, Baihaqi Sitompul, Opim Salim Budiman, Mohammad Andri Fahmi Universitas Sumatera Utara Computer Science Doctoral Program Indonesia

ISBN: (纸本)9798350376111

The use of reaction wheels to help maintain the position of objects in balance remains an interesting topic to discuss in research. The harmonic motion resulting from the continuous rotation of the reaction wheel is read by the installed sensor, so that acceleration and angular velocity values are obtained on the X -axis, Y -axis and Z -axis. The self-balancing cube was built to be used as a vehicle for the reaction wheel to be embedded. The phenomenon that occurred during the experiment showed that there was a certain pattern formed as a result of the self-balancing cube swinging to maintain its balance. © 2024 IEEE.

关键词： Mechatronics

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Evaluating masked self-supervised learning frameworks for 3D dental model segmentation tasks

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Scientific Reports 2025年第1期15卷 1-15页

作者： Lucas Krenmayr Reinhold von Schwerin Daniel Schaudt Pascal Riedel Alexander Hafner Marc Geserick Cooperative Doctoral Program for Data Science and Analytics University of Ulm 89081 Ulm Germany Department of Computer Science University of Applied Sciences 89081 Ulm Germany smyl tp GmbH 89073 Ulm Germany

The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, especially in the medical domain. Masked self-supervised learning has shown great promise in overcoming the challenge of data scarcity. However, its effectiveness has not been well explored in the 3D domain, particularly on dental models. In this work, we investigate the applicability of the four recently published masked self-supervised learning frameworks-Point-BERT, Point-MAE, Point-GPT, and Point-M2AE-for improving downstream tasks such as tooth and brace segmentation. These frameworks were pre-trained on a proprietary dataset of over 4000 unlabeled 3D dental models and fine-tuned using the publicly available Teeth3DS dataset for tooth segmentation and a self-constructed braces segmentation dataset. Through a set of experiments we demonstrate that pre-training can enhance the performance of downstream tasks, especially when training data is scarce or imbalanced—a critical factor for clinical usability. Our results show that the benefits are most noticeable when training data is limited but diminish as more labeled data becomes available, providing insights into when and how this technique should be applied to maximize its effectiveness.

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Optimization of the Use of Artificial Neural Network Models for Accuracy Data Measurement Palm Oil Production Prediction Rate

Optimization of the Use of Artificial Neural Network Models ...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Setiawan, Iwan R. Fanani, Ahmad Z. Surname, Given Name Purwanto, Purwanto Dian Nuswantoro University Doctoral Program in Computer Science Semarang Indonesia

ISBN: (纸本)9798350320954

Predictions are necessary in modern times if the amount of production, profit and loss analysis, and development must be calculated on a specific scale. The average prediction only determines a certain period using data series, so it will be very helpful on certain production scales, especially palm oil production as a commodity with a very large amount of investment. Palm oil production is Indonesia's production potential, which requires a prediction pattern for a certain period. The autoregressive integrated moving average (ARIMA) model is used to calculate production rates by performing predictive data analysis of palm oil production development over a period of time. To prove the level of foresight in the prediction process, the next step is to calculate the level of foresight of the prediction process error through the Mean Square Error (MSE) model. To get the accuracy value, the Artificial Neural Network (ANN) algorithm approach is carried out using the MATLAB tool. There are several ways to test the level of accuracy;the results are compared with the calculation results using MS Excel as well as the Statistical Product and Service Solution (SPSS) tool. The calculated accuracy of SPSS (0.015) and MS Excel (0.0201) are known, while the ANN algorithm is more observant with a value of 0.0055. © 2023 IEEE.

关键词： Forecasting

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