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
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<![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.]]>
<![CDATA[Penerapan Algoritma C4.5 untuk Klasifikasi Tingkat Kedisiplinan Siswa Sekolah Menengah ]]>
<![CDATA[selipuri]]>;
<![CDATA[Rosyana Fitria Purnomo]]>;
<![CDATA[Yodhi Yuniarthe]]>
<![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.v5i1.2630
<![CDATA[Abstract?This study aims to evaluate the performance of the Decision Tree algorithm based on the entropy criterion (C4.5) in classifying student eligibility by considering both academic and non-academic data. The dataset consists of 200 entries with nine attributes, including attendance percentage, number of lateness incidents, disciplinary violations, average academic scores, participation, study hours, and extracurricular activities. Data processing was carried out through several stages, namely cleaning, transformation, feature selection, training and testing data splitting, and model evaluation using a confusion matrix. The experimental results show that the proposed model achieved an accuracy of 87.5%, an average precision of 85.6%, an average recall of 84.2%, and an F1-Score of 84.8%. These findings confirm that the C4.5 algorithm can be effectively applied to support student performance classification with a fairly high level of reliability.]]>
<![CDATA[Analisis Sistem Informasi Keuangan Punia.Id Dengan Menggunakan Rapidminer ]]>
<![CDATA[Nasrudin, Ahmad Nasrudin]]>
<![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.v5i1.2691
<![CDATA[With the development of information technology, digital systems are increasingly being adopted by companies to improve efficiency and transparency in financial management. PT Kanzun Bahriyah Sentosa uses Punia.id as a financial information system to record and manage company transactions, including ship operations and fish sales. However, during implementation, several technical and non-technical obstacles were encountered, such as users' difficulty in understanding the Chart of Accounts, which affected the accuracy of financial reports. Therefore, an analysis of the effectiveness of using Punia.id in improving the efficiency of the company's financial system is needed. This study uses observation, interviews, documentation, and questionnaires with the Likert scale technique to measure the effectiveness and obstacles in using Punia.id. In addition, a problem-solving approach and linear regression analysis were used to evaluate the relationship between the level of difficulty in managing the Chart of Accounts and the level of errors in financial reporting. Based on the results of tests conducted on the Financial Information System using the Rapidminer method, it can be seen that the results are quite significant based on the tests that have been carried out. The independent variable (X), based on the correlation test results, has an R Square value of 0.42, which is 42%. With these results, the value can be calculated]]>
<![CDATA[Penerapan Model Transfer Learning Dalam Mendalami Penyakit Daun Jagung Menggunakan Arsitektur VGG19 ]]>
<![CDATA[Setiyo Adi Wibowo]]>;
<![CDATA[Rudi Kurniawan]]>;
<![CDATA[Budi Santoso]]>
<![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.v5i1.2702
<![CDATA[The process of processing corn leaf disease data using the VGG 19 architecture based on deep learning is to analyze corn leaf diseases that result in low yields. In describing the values to be managed in this study, a digital image dataset of corn leaf diseases consisting of 5 classes with 3923 images per class was used. The objectives of this study are to enable easy prediction of corn leaf disease and to treat the disease. It also aims to enable pattern recognition of corn leaf disease based on digital images using the VGG19 architecture model. The results of corn leaf disease classification obtained from the VGG19-based model show excellent performance in identifying various plant health conditions. With an overall accuracy of 97.96%, this model successfully distinguishes between five disease classes, namely Common Rust, Grey Leaf Spot, Healthy, Northern Leaf Blight, and Northern Leaf Spot. This figure reflects the effectiveness of the model in recognizing the distinctive visual patterns of each disease, which is very important for effective crop management.]]>
<![CDATA[Pengembangan Aplikasi Absensi Mobile Terintegrasi dengan Sistem Backoffice Berbasis Web Menggunakan Metode Pengembangan Perangkat Lunak Waterfall ]]>
<![CDATA[Nasya Putri Restyarna]]>;
<![CDATA[Andi Taufik]]>;
<![CDATA[Eko Setia Budi]]>
<![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.v5i1.2504
<![CDATA[Attendance systems play a crucial role in human resource management by serving as the foundation for accurate and efficient employee presence tracking. PT Urun Bangun Negeri previously used a manual attendance method that was prone to data errors, manipulation, and inefficiencies in reporting. This study aims to develop a digital attendance application based on Android that integrates with a web-based backoffice system. The software development method used is the Waterfall model, consisting of requirement analysis, system design, implementation, and testing phases. Kotlin was used to develop the mobile application, while Laravel was used for the web system. Key features include GPS-based location validation and selfie verification for attendance authenticity. System testing using Blackbox and User Acceptance Test (UAT) methods showed that the application performed well, with a success rate of more than 95% and a very high level of user satisfaction. These results demonstrate that the developed system effectively enhances efficiency, accuracy, and transparency in the company’s attendance process.]]>
<![CDATA[Klasifikasi Penerimaan Peserta Didik Baru Berdasarkan Sistem Zonasi Menggunakan Algoritma K-Nearest Neighbors ]]>
<![CDATA[Syah Fiqri, Muhammad]]>;
<![CDATA[Andi Taufik]]>
<![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.v5i1.2595
<![CDATA[The implementation of the zoning system in student admission (PPDB) often raises challenges in determining eligibility based on domicile, requiring a data-driven approach to support the selection process. This study aims to classify new students of SMPN 16 Bogor for the 2025 academic year using the K-Nearest Neighbors (KNN) algorithm. The dataset consists of 1,153 student records with attributes including longitude, latitude, distance from home to school, and zoning labels. Preprocessing involved data cleaning, label encoding, and feature standardization before splitting the data into 75% training and 25% testing sets. The optimal parameter was found at K=18 with a minimum error rate of 0.1591. Experimental results showed an accuracy of 97% for training data and 84% for testing data, indicating that the model performs reasonably well despite signs of overfitting. This research contributes by demonstrating that spatial attributes can be effectively integrated into zoning-based classification and provides a foundation for developing more objective and adaptive decision support systems in the context of student admissions.]]>
<![CDATA[Perancangan Website E-Commerce shop.ruanginterio.com dengan Integrasi Payment Gateway Paypal ]]>
<![CDATA[Monoarfa, Fachrin]]>;
<![CDATA[Setiyani, Hari]]>
<![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.v5i1.2606
<![CDATA[Digital transformation drives companies to adopt online-based sales systems. PT Ruang Inovasi Teknologi (RIT), which previously relied on conventional sales methods, requires an integrated e-commerce system to increase efficiency, expand market reach, and provide secure transactions. This study aims to design and develop an e-commerce website integrated with the PayPal payment gateway, including system requirements analysis, UI/UX design, and Laravel-based implementation. The method involves field studies, interviews with stakeholders, and a Minimum Viable Product (MVP) development approach. The outcome is an e-commerce platform (shop.ruanginterio.com) that supports automatic transactions and digital financial reporting, provides user-friendly checkout, and enables global market expansion.]]>
