Journal of Informatics, Electrical and Electronics Engineering
Fokus kajian Journal of Informatics, Electrical and Electronics Engineering, yaitu: 1. Control System, 2. Artificial Intelligence, 3. Informatics Engineering, 4. Electronics, 5. Advanced energy material, 6. Automatic power control, 7. Battery technology, 8. Distributed generation, 9. Distribution system, 10. Electric power generation, 11. Electric vehicle, 12. Electrical machine, 13. Energy optimization, 14. Energy conversion, 15. Energy efficiency, 16. Energy exploitation, 17. Energy exploration, 18. Energy management, 19. Energy mitigation, 20. Energy storage, 21. Energy system, 22. Fault diagnostics, 23. Green energy, 24. Green technology, 25. High voltage, 26. Insulation technology, 27. Intelligent power optimization, 28. Monitoring operation, 29. Motor drives, 30. Natural energy source, 31. Power control, 32. Power data transaction, 33. Power economic, 34. Power electronics, 35. Power engineering, 36. Power generation, 37. Power optimization, 38. Power quality, 39. Power system analysis, 40. Power system information, 41. Power system optimization, 42. Protection system, 43. Renewable energy, 44. SCADA, 45. Security operation, 46. Smart grid, 47. Stability system, 48. Storage system, and 49. Transmission system
Articles
72 Documents
<![CDATA[MCDM Using Multi-Attribute Utility Theory and PIPRECIA in Customer Loan Eligibility Recommendations ]]>
<![CDATA[Setiawansyah]]>;
<![CDATA[Sintaro, Sanriomi]]>;
<![CDATA[Aldino, Ahmad Ari]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 2 (2023): Desember 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i2.1628
<![CDATA[Customer loan eligibility recommendations are an important step in the risk and financial assessment process. To ensure business continuity and customer satisfaction, it is important for financial institutions to consider several key factors. Problems in determining lending include various aspects that include credit risk that is difficult to assess appropriately, insufficient information about potential borrowers, potential fraud that can harm lenders. Regardless, financial institutions must ensure sound risk management and comply with applicable regulations and compliance standards to minimize potential losses and maintain fairness in decision-making related to lending. Cooperatives act as institutions that provide access to loans with more flexible terms than conventional financial institutions. The loan application process involves evaluating the customer's eligibility, which includes an analysis of financial condition, credit history, and repayment capacity. The contribution of this study provides a recommendation for savings and loan cooperatives in lending to customers using a decision support system model. The purpose of this study is to evaluate and compare customer loan feasibility using a combination of Multi-Criteria Decision Making (MCDM) methods, namely Multi-Attribute Utility Theory (MAUT) and Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA). This study aims to determine the ability of both methods to provide a comprehensive understanding of loan feasibility by considering a number of relevant criteria. The results of the customer loan eligibility rating recommend rank 1 with a final value of 0.5127 obtained by Customer K, rank 2 with a final value of 0.432 obtained by Customer I, and rank 3 with a final value of 0.3559 obtained by Customer F.]]>
<![CDATA[Analisis Penerapan Data Mining Terhadap Kasus Positif Covid-19 Menggunakan Metode K-Means Clustering ]]>
<![CDATA[Azhari, Ridhan]]>;
<![CDATA[Hartama, Dedy]]>;
<![CDATA[Lubis, Muhammad Ridwan]]>;
<![CDATA[Nasution, Della Fatricia]]>;
<![CDATA[Windarto, Agus Perdana]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 3 No. 2 (2023): Desember 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v3i2.1760
<![CDATA[This study has problems such as the absence of the use of the K-means clustering algorithm for data on positive COVID-19 cases in the Indonesian province. The purpose of this study is to apply the K-means clustering method in finding the closest distance to produce the lowest and highest clusters of data on positive COVID-19 cases in the Indonesian province. K-means is one of the algorithms in the non-hierarchical Clustering technique that tries to partition the existing data in the form of one or more clusters. The results obtained from the k-means clustering method produced 2 clusters, namely the lowest cluster C1 = 30 items while the highest cluster C2 = 4 items. This research can be used as a reference and can be further developed with other clustering methods or algorithms such as k-medoid in order to get a comparison of results and steps to use algorithms related to clustering.]]>
<![CDATA[Analisis Kepuasan Mahasiswa Pekanbaru Pada Aplikasi Flip dengan Metode End User Computing Satisfaction (EUCS) ]]>
<![CDATA[Anggi Widya Atma Nugraha]]>;
<![CDATA[Inggih Permana]]>;
<![CDATA[Febi Nur Salisah]]>;
<![CDATA[Tengku Khairil Ahsyar]]>;
<![CDATA[M. Afdal]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v4i4.2439
<![CDATA[A Flip is a Financial Technology (fintech) company providing admin fee-free money transfer services that has been used by more than 10 million users. Along with technological developments in the financial sector, Flip must be able to compete and survive against similar service providers. Efforts that can be made to compete include measuring satisfaction levels in using Flip. The purpose of this study is to assess the level of satisfaction of Flip users so that the results of this research can be used to provide recommendations for evaluating the Flip information system. In conducting satisfaction level analysis, the End User Computing Satisfaction (EUCS) approach can be applied. EUCS is able to evaluate usage satisfaction in using information systems in the areas of content, accuracy, format, ease of use, and timeliness based on information system usage experience. The research was conducted with sample data from university student users of the Flip application in Pekanbaru City. Based on the test results, the highest result with a percentage value of 80% in the Very Satisfied category was observed in the Ease of Use variable from the Likert scale results. The average satisfaction level of Flip application users was 77% in the Satisfied category. The Classical Assumption Test results showed that in the normality test, the testing was normal, and in the multicollinearity testing, it was found that multicollinearity did not occur in the test results. In the Multiple Linear Regression Test, the variable equation result obtained was Y = 0.158 + 0.114X1 + 0.031X2 + 0.054X3 + 0.111X4 + 0.001X5. Based on the Coefficient of Determination Test results, it was found that the content variable, accuracy variable, format variable, ease of use variable, and timeliness variable were able to explain their relationship to the dependent variable and showed an influence of 53%.]]>
<![CDATA[Kombinasi Metode High Pass Filtering dan Contrast Stretching Dalam Peningkatan Detail dan Kontras Citra ]]>
<![CDATA[-, Nurhidayati]]>;
<![CDATA[Lailan Sofinah Harahap]]>;
<![CDATA[Juwita Sari]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v4i4.2460
<![CDATA[Digital images have many functions in fields such as medicine, remote sensing, security, and social media, where cinematography and visual delivery are very complex. However, images often have problems with noise, blur, and low contrast. Furthermore, in an underwater image, another challenge arises because light is absorbed and scattered unevenly, which makes the image look blurry and dark. This study aims to explain the improvement of the digital image processing process in two ways, namely through High Pass Filtering and Contrast Stretching. Details and edges of the image that you want to focus on will be added with High Pass Filtering which is done through 2D FFT. While the name of the second method, namely Contrast Stretching, means clarifying the differences in objects and backgrounds by widening the range of pixel intensity. The trial was carried out on a grayscale image measuring 200x200 pixels in JPG format. The processes carried out include conversion to grayscale, high frequency, application of Low Pass Filter, and contrast stretching. Based on the processing results, the increase in image sharpness reached 35% and contrast 42% when compared to unprocessed images. These figures will then be very helpful in visual analysis and interpretation]]>
<![CDATA[Pemanfaatan Teknik Web Scraping Untuk Estimasi Tarif dan Sarana Transportasi Bus Jakarta-Malang Pada Traveloka ]]>
<![CDATA[Anjani, Dewi]]>;
<![CDATA[Novianti, Desi]]>;
<![CDATA[Bachtiar, Yogi]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v4i4.2461
<![CDATA[The development of land transportation technology in Indonesia has significantly affected people's mobility. In the context of the world of land transportation, data is a valuable asset that can provide deep insights into various aspects of land transportation, from travel route planning to traffic management. By utilizing data effectively, we can build a more efficient, safe, and sustainable transportation system. Data is an important component in supporting information. Nowadays, there is a lot of data on a website that can provide us with information and insight or views on an event. Information on a website is not only about one or two pieces of information but can also be a collection of data that is collected and requires further analysis. One way to collect data and information from a website is with web scraping techniques. Researchers want to apply this web scraping technique to analyze the estimation of fares and transportation facilities for the Jakarta-Malang bus. The goal is to find out the ideal fare and transportation advice as a support for a mature travel plan. The result If we want to travel from Jakarta to Malang using land transportation, there are 14 PO. Buses are available as options with various classes and departure times. Bus fares offered vary from Rp. 325,500 to Rp. 700,000.]]>
<![CDATA[Pengukuran Retensi Pelanggan Insyira Oleh-Oleh Berdasarkan Analisis Sentimen Pengguna Instagram ]]>
<![CDATA[Fiki]]>;
<![CDATA[Inggih Permana]]>;
<![CDATA[Febi Nur Salisah]]>;
<![CDATA[Eki Saputra]]>;
<![CDATA[Arif Marsal]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v4i4.2473
<![CDATA[Instagram as a social media platform has opened new opportunities for businesses to market their products creatively and efficiently. Through interactive features such as the comments section, users can express their opinions about the products or services offered. These comments contain sentiments that can be analyzed to understand customer perceptions. This study aims to measure customer retention using sentiment analysis of Instagram user comments. The comment data was collected using web scraping techniques from the Instagram page, followed by labeling using a lexicon-based approach and sentiment classification into positive, negative, and neutral categories through sentiment analysis. This analysis is linked to the concept of customer retention, which is an important strategy for maintaining long-term relationships with consumers. Furthermore, the results of customer retention analysis in this study show that positive sentiment has a retention rate of 53.4% (303 out of 567 comments), neutral sentiment 6.9% (45 out of 650 comments), and negative sentiment 15.1% (22 out of 146 comments). Overall, 370 out of 1,363 comments, or 27.1%, were categorized as contributing to retention. In terms of the proportion of sentiment contributing to total retention, positive comments dominate with 81.9% (303 out of 370). These findings suggest that although neutral comments are the most frequent, positive sentiment contributes the most to customer retention. This indicates that positive sentiment is a strong predictor of customer loyalty, highlighting the importance for companies to foster positive experiences through quality products, reliable services, and active engagement on social media. Insyira is capable of maintaining customer retention, especially from those who express positive sentiment, which reflects satisfaction with its products, services, and interactions on social media]]>
<![CDATA[Implementasi Naïve Bayes untuk Memprediksi Tingkat Kunjungan Pelanggan Menggunakan Algoritma Naïve Bayes ]]>
<![CDATA[Nazwa Adelia Putri]]>;
<![CDATA[Zihan Maharani]]>;
<![CDATA[Ilona Dwi Shelvani]]>;
<![CDATA[Harly Okprana]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v5i1.2474
<![CDATA[This study aims to implement the Naive Bayes algorithm in predicting customer visit rates at Kyemoona Kitchen by utilizing available historical data. With the development of digital technology, data analysis has become an important aspect in supporting business decision making. However, manual analysis of complex and diverse data can be challenging. Therefore, a machine learning-based approach, specifically Naive Bayes, is used to explore patterns in big data and generate accurate predictions. In this study, the data collected includes variables such as visit time, promotion type, weather conditions, holidays, and other factors. The Naive Bayes model achieved an accuracy of 85.6%, with other evaluation metrics such as precision of 82.4%, recall of 84.2%, and F1-score of 83.3%. The results show that this algorithm can identify significant factors, such as promotions and weather conditions, that affect customer visits. This study not only provides practical insights for Kyemoona Kitchen in planning data-driven operational strategies, but also aims to inspire other small and medium-sized enterprises (SMEs) to adopt similar analytical technologies. However, this study has limitations, such as dependence on data quality, which can affect the accuracy of the model. Therefore, it is recommended that future research combine Naive Bayes with other algorithms and use larger datasets for more reliable results.]]>
<![CDATA[Penerapan Metode ARAS Dalam Pemilihan Objek Wisata yang Terbaik ]]>
<![CDATA[Thamriansyah]]>;
<![CDATA[Muhammad Al Farid]]>;
<![CDATA[Jesdyka Calvin Samuel Purba]]>;
<![CDATA[Safira Izzati]]>;
<![CDATA[Rizki Alfadilah Nasution]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v5i1.2475
<![CDATA[Abstract?Tourism is an important sector in the economy of Central Tapanuli Regency, with various tourist attractions that have great potential to be developed as leading destinations. The existence of diverse tourist attractions, ranging from beaches, tropical forests, waterfalls, to lakes, makes this region rich in natural potential that can attract local and foreign tourists. This study aims to determine the best tourist attractions in the Tapanuli Tengah Regency area using the ARAS (Additive Ratio Assessment) method. This method is used because it is able to assess various alternatives objectively based on certain criteria, such as Natural Attraction, Ease of Access, Supporting Facilities, Environmental Sustainability, and Economic Impact on the local community. The assessment process was carried out by calculating the normalization and integration values of each criterion, which were then used to determine the final ranking of each tourist attraction. The ARAS method was used to calculate the normalization and integrated values of each predetermined criterion, thereby providing an objective ranking for each tourist attraction. The analysis results show that Pandan Beach ranks first with the highest optimal value of 1, followed by Sibolga Tropical Forest (0.9659), Sibolangit Waterfall (0.9175), and Linting Lake (0.9114). Meanwhile, Samosir Island ranks last with a score of 0.8558, indicating the need for improvement in several aspects]]>
<![CDATA[Perancangan Sistem Manajemen Apotek: Integrasi Data Obat dan Penjualan Secara Digital ]]>
<![CDATA[Tyo, Rahardian Prasetyo]]>;
<![CDATA[Arisantoso]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v5i1.2531
<![CDATA[The manual management of drug data and sales transactions in pharmacies often leads to several issues, including recording errors, service delays, and difficulties in generating reports. This study aims to design a web-based pharmacy management system that integrates drug inventory and sales data into a single platform to enhance operational efficiency and information accuracy. The system was developed using the Waterfall method, encompassing the stages of requirement analysis, system design, implementation, and testing. The application was built as a web-based system and evaluated using black-box testing to ensure that all features functioned according to the specified requirements. The testing results indicated that the system achieved a transaction recording accuracy rate of 98%, accelerated data entry processes by 65% compared to manual methods, and maintained stability across all test scenarios. These findings suggest that the proposed system effectively improves pharmacy operations by making them more structured, faster, and less error-prone, while contributing to the digitalization of pharmaceutical services.]]>
<![CDATA[Efektivitas Pelatihan Awal Berbasis Domain Spesifik Legal-BERT Untuk Natural Language Processing Hukum: Replikasi Dan Perluasan Studi Casehold ]]>
<![CDATA[Zakiri, Hasani]]>;
<![CDATA[Alva Hendi Muhammad]]>;
<![CDATA[Asro Nasiri]]>
<![CDATA[ Journal of Informatics, Electrical and Electronics Engineering Vol. 5 No. 1 (2025): September 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jieee.v5i1.2610
<![CDATA[Abstract?The emergence of domain-specific language models has demonstrated significant potential across various specialized fields. However, their effectiveness in legal natural language processing (NLP) remains underexplored, particularly given the unique challenges posed by legal text complexity and specialized terminology. Legal NLP has practical applications such as automated legal precedent search and court decision analysis that can accelerate legal research from weeks to hours. This study evaluates the CaseHOLD dataset to provide comprehensive empirical validation of domain-specific pretraining benefits for legal NLP tasks with focus on data efficiency and context complexity analysis. We conducted systematic experiments using the CaseHOLD dataset containing 53,000 legal multiple-choice questions. We compared four models: BiLSTM, BERT-base, Legal-BERT, and RoBERTa across varying data volumes (1%, 10%, 50%, 100%) and context complexity levels. Paired t-tests with 10-fold cross-validation and Bonferroni correction ensure robust methodology that guarantees finding reliability. Legal-BERT achieved the highest macro-F1 score of 69.5% (95% CI: [68.0, 71.0]), demonstrating a statistically significant improvement of 7.2 percentage points over BERT-base (62.3%, p < 0.001, Cohen's d= 1.23). RoBERTa showed competitive performance at 68.9%, nearly matching Legal-BERT. The most substantial improvements occurred under limited data conditions with 16.6% improvement at 1% training data. Context complexity analysis revealed an inverted-U pattern with optimal performance on 41-60 word texts. The introduced Domain Specificity Score (DS-score) showed strong positive correlation (r = 0.73, p < 0.001) with pretraining effectiveness, explaining 53.3% of performance improvement variance. These findings provide empirical evidence that domain-specific pretraining offers significant advantages for legal NLP tasks, particularly under data-constrained conditions and moderate-high context complexity. The key distinction of this research is the development of a predictive DS-score framework enabling benefit estimation before implementation, unlike previous studies that only evaluated post-hoc performance. The results have practical implications for developing legal NLP systems in resource-limited environments and provide optimal implementation guidance for Legal-BERT.]]>
