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<![CDATA[Penerapan Metode Rapid Application Development Dalam Pengembangan Aplikasi Persediaan Material Panel Listrik Berbasis Web ]]>
<![CDATA[Azis, Munawar Abdul]]>;
<![CDATA[Wahyudi, Mochamad]]>;
<![CDATA[Aryanti, Riska]]>
<![CDATA[ Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 4 No. 2 (2023): November 2023 ]]>
Publisher : <![CDATA[LPPM Universitas Bina Sarana Informatika ]]>
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DOI: 10.31294/reputasi.v4i2.2496
<![CDATA[PT Indomitra Global is a company engaged in electrical contracting services and provides various types of electrical panels needed by clients. Electrical panels require many important materials, for example mcb, sockets, cables, and many other important components. The material inventory system carried out at PT Indomitra Global still uses a manual method in the material inventory system. This process has several obstacles, namely not having a centralized database that makes material inventory data vulnerable to loss and there are often differences in the suitability of the amount of material in the warehouse with the amount in Microsoft Excel, because data management is still not easy enough and due to human error or input errors. On the basis of this problem, a web-based material inventory application was made using the Rapid Application Development (RAD) method. The material inventory system produced in this study is able to handle material data management which previously was still not easy enough to do, such as searching for data, managing incoming and outgoing material transaction data and making it easier to generate incoming and outgoing material reports based on time periods]]>
<![CDATA[Analisis Sentimen Pemanfaatan Artificial Intelligence di Dunia Pendidikan Menggunakan SVM Berbasis Particle Swarm Optimization ]]>
<![CDATA[Saepudin, Atang]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Fitriani, Eka]]>;
<![CDATA[Royadi, Royadi]]>;
<![CDATA[Ardiansyah, Dian]]>
<![CDATA[ Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024 ]]>
Publisher : <![CDATA[LPPM Universitas Bina Sarana Informatika ]]>
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DOI: 10.31294/coscience.v4i1.2921
<![CDATA[The utilization of Artificial Intelligence (AI) in the field of education in Indonesia has witnessed significant developments in recent years. The advancements in AI technology have opened up new opportunities to enhance the quality of education, and address various challenges faced by the Indonesian education system. This has naturally sparked diverse opinions and comments from the public, particularly on the social media platform X/Twitter. This research focuses on sentiment analysis of reviews expressed on the X/Twitter social media platform. The primary goal of this study is to develop an effective sentiment analysis method by leveraging the Support Vector Machine (SVM) algorithm optimized with Particle Swarm Optimization (PSO) for feature selection. In this research, user reviews from X/Twitter were collected and analyzed to identify positive or negative sentiments within the context of each comment. The SVM algorithm was used to classify sentiments based on similarity to comments with known sentiments. Feature Selection PSO was employed to optimize the parameters within SVM to enhance sentiment analysis accuracy. The results of sentiment analysis on comments or tweets on the X/Twitter social media platform using both SVM and PSO-based SVM algorithms indicated that the PSO-based SVM algorithm achieved a higher accuracy. The SVM algorithm with feature selection PSO produced accuracy 89.50%, precision 86.98%, recall 93.00%, and AUC 0.964. Meanwhile, the SVM algorithm had accuracy 87.50%, precision 85.46%, recall 90.50%, and AUC 0.956. This demonstrates that the use of feature selection PSO in the SVM algorithm is capable of improving the accuracy of the results.]]>
<![CDATA[PENERAPAN SISTEM INFORMASI AKADEMIK BERBASIS WEB MENGGUNAKAN METODE RAPID APPLICATION DEVELOPMENT ]]>
<![CDATA[Fitriani, Eka]]>;
<![CDATA[Royadi, Royadi]]>;
<![CDATA[Ardiansyah, Dian]]>;
<![CDATA[Saepudin, Atang]]>;
<![CDATA[Aryanti, Riska]]>
<![CDATA[ Journal of Information System, Applied, Management, Accounting and Research Vol 8 No 4 (2024): JISAMAR (September-November 2024) ]]>
Publisher : <![CDATA[Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta ]]>
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DOI: 10.52362/jisamar.v8i4.1551
<![CDATA[The world of technology is advancing rapidly in all fields every day, including education. Systems that support information delivery are now presented in applications or web platforms. SMK Negeri Pertanian requires an adequate information system to convey academic information to students and teachers and to manage student data at the school. To address this issue, it is necessary to implement a web-based academic information system at SMK Negeri Pertanian using the Rapid Application Development (RAD) method for system development. The Rapid Application Development (RAD) method was chosen because it emphasizes speed and flexibility, allowing the application to be completed more quickly. The developed academic information system will manage and display information such as teacher data, student data, subject data, grades, teaching schedules, and other academic-related information. The result of this implementation is an effective and efficient web-based information system for delivering academic information to students and teachers.]]>
<![CDATA[Optimalisasi Random Forest dan Support Vector Machine dengan Hyperparameter GridSearchCV untuk Analisis Sentimen Ulasan PrimaKu ]]>
<![CDATA[Titik Misriati]]>;
<![CDATA[Riska Aryanti]]>
<![CDATA[ Journal of Information System Research (JOSH) Vol 5 No 4 (2024): Juli 2024 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josh.v5i4.5347
<![CDATA[PrimaKu App has been a pioneer in the field of digital health since 2017. Through this application, parents can regularly and continuously monitor their children’s health and development. PrimaKu also has a formal alliance with the Indonesian Pediatric Association (IDAI) to promote child health in Indonesia. This application can be downloaded through the Google Play Store. Google Play Store has a feature that allows users to review the app before downloading. Sentiment analysis is used to distinguish between positive and negative reviews by users who have provided reviews so that an evaluation of the services provided can be made. This research aims to conduct sentiment analysis of user reviews of the PrimaKu application using Random Forest (RF) and Support Vector Machine (SVM) algorithms with TF-IDF weighting. Optimization was performed using hyperparameters to improve the performance of the Random Forest and SVM algorithms. The data used consisted of the 2,293 most relevant reviews collected from the Google Play Store. The most effective models for the Random Forest and Support Vector Machine were selected by adjusting the hyperparameters using GridSearch CV. The results of this study show that Random Forest has a higher success rate in classifying PrimaKu user review data, with an accuracy of 89%, precision of 88%, recall of 81%, and F1-Score of 85%.]]>
<![CDATA[IMPLEMENTASI MODEL WATERFALL PADA SISTEM INFORMASI OPERASIONAL CAR BOOKING ]]>
<![CDATA[Royadi, Royadi]]>;
<![CDATA[Ardiansyah, Dian]]>;
<![CDATA[Saepudin, Atang]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Fitriani, Eka]]>
<![CDATA[ JURSIMA Vol 10 No 3 (2022): Jursima Vol.10 No.3 ]]>
Publisher : <![CDATA[INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL ]]>
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DOI: 10.47024/js.v10i3.492
<![CDATA[Pada era globalisasi saat ini yang sangat dibutuhkan yaitu teknologi yang guna untuk menghasilkan informasi yang akurat dan cepat. Pihak perusahaan sering terjadi permasalahan terutama pihak pengelola kendaraan yaitu kesulitan dalam pendataan kendaraan operasional perusahaan karena masih menggunakan sistem yang manual dan memudahkan saat pembuatan laporan yang akan disampaikan pada pimpinan. Dengan masalah tersebut, maka dibuatkan suatu rancangan sistem informasi berbasis web yang dapat menangani masalah penggunaan kendaraan operasional perusahaan dengan tujuan pengolahan data kendaraan dapat lebih rapih dan tersimpan pada suatu database. Aplikasi Perancangan Operasional Pemesanan Mobil berbasis web ini menggunakan Framework laravel 7.0 dan pemrograman bahasa menggunakan PHP 7, HTML, Bootstrap 5 dan Javascript. Data dasar yang digunakan adalah Sqlservel 2012. Metode pengembangan perangkat lunak yang digunakan yaitu model waterfall. Hasil dari sistem yang dibuat sangat memudahkan pengelola kendaraan operasional dalam proses pendataan penggunaan kendaraan operasional perusahaan menjadi lebih efektif dan efisien dibandingkan dengan proses yang masih dilakukan secara manual sehingga dalam proses pembuatan laporan juga lebih mudah dan optimal.Kata Kunci: Sistem Informasi, Pemesanan Mobil, Laravel]]>
<![CDATA[Analisis Sentimen Pengguna Terhadap Aplikasi Indodana Di Google Play Store Menggunakan Metode Naive Bayes Classifier ]]>
<![CDATA[Rifqi Rizaldi]]>;
<![CDATA[Aryanti, Riska]]>
<![CDATA[ Journal of Informatics Management and Information Technology Vol. 4 No. 3 (2024): July 2024 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jimat.v4i3.400
<![CDATA[This research aims to evaluate user responses to the Indodana: Paylater & Pinjaman application through sentiment analysis using the naive bayes algorithm. Online lending apps such as Indodana have changed the way individuals access finance by providing a quick and easy process. However, the user's decision to choose a legal app and pay attention to the transparency of fees and loan terms is crucial. With more than ten million downloads and two million reviews, it is important to understand user sentiment so that developers can improve services and maintain public trust. A sentiment analysis method using multinominal naive bayes was used with two labelling approaches inset lexicon and rating. The evaluation was conducted on 500 Indodana: Paylater & Pinjaman reviews, dividing the data into training and testing and using TF-IDF features. The results show that inset lexicon labelling achieved 86% accuracy, whereas rating-based labelling achieved 87% accuracy. These results provide an in-depth view of user responses, aiding in the identification of factors that influence positive or negative perceptions of the app. As such, this research is important for guiding the development of safe, reliable, and compliant online lending applications, as well as for improving overall user satisfaction]]>
<![CDATA[Optimisasi Model Deep Learning untuk Deteksi Penyakit Daun Tebu dengan Fine-Tuning MobileNetV2 ]]>
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Agustiani, Sarifah]]>;
<![CDATA[Wildah, Siti Khotimatul]]>;
<![CDATA[Arifin, Yosep Tajul]]>;
<![CDATA[Marlina, Siti]]>;
<![CDATA[Misriati, Titik]]>
<![CDATA[ Journal of Informatics Management and Information Technology Vol. 4 No. 4 (2024): October 2024 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/jimat.v4i4.411
<![CDATA[Sugarcane leaf diseases are a serious threat in sugarcane farming because they can significantly reduce productivity and can cause major losses in yields if not detected early. Therefore, fast and accurate disease management is needed to prevent further losses. This study aims to develop a deep learning model based on MobileNetV2 with fine-tuning techniques to effectively detect sugarcane leaf diseases. Fine-tuning is a method used to adjust the parameters of a pre-trained model on a more specific target dataset. The dataset contains images of sugarcane leaves that have been classified per class based on the type of disease. In this study, fine-tuning was performed on the MobileNetV2 architecture that had been previously trained using the sugarcane leaf dataset. The fine-tuning process was carried out by rearranging the top few layers of MobileNetV2 and adding a special classification layer to predict the class of sugarcane leaf diseases. The model was trained through two stages: initial training to obtain a baseline performance and fine-tuning by opening several layers of MobileNetV2. In the initial evaluation, the model achieved a validation accuracy of 93.12%. After fine-tuning, the accuracy increased to 95.01%, indicating that this technique was able to significantly improve disease detection capabilities. The results of this study provide important contributions in the field of agriculture, especially in supporting the sustainability of sugarcane production through artificial intelligence-based technology. The implementation of the proposed model is expected to help farmers detect diseases more quickly and take timely preventive measures, thereby reducing losses.]]>
<![CDATA[Klasifikasi Multi Label untuk Deteksi Keseimbangan Emosi Pengguna Media Sosial Menggunakan K-Fold Cross Validation ]]>
<![CDATA[Misriati, Titik]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Sagiyanto, Asriyani]]>;
<![CDATA[Fachri, Muhamad]]>;
<![CDATA[Ramadhani, Arya]]>
<![CDATA[ Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josh.v6i1.6033
<![CDATA[Social media has grown in popularity, with millions of people using it to engage with and share information worldwide. Social media, in addition to serving as a communication tool, are crucial for expressing the emotions and feelings of users. The widespread use of social media has had a significant impact on people's emotions. In particular, negative emotions are frequently experienced and can have a significant impact on mental health. This study aimed to analyze multiple classification models to discover the optimal model for detecting emotional balance among social media users. The classification models utilized in this study include the K-Nearest Neighbor, Random Forest, Support Vector Machine, Decision Tree, and AdaBoost to identify the best classification model capable of detecting the emotional balance of social media users. Several classification models are applied and compared with the aim of evaluating model performance. This research project employed K-fold cross-validation to evaluate the categorization model by comparing various k values. The Random Forest algorithm achieved the greatest accuracy of 99.90% at a K-Fold cross validation value of 10 and an Area Under the Curve (AUC) value of 100%. Thus, this study successfully found a reliable model for accurately detecting emotions of social media users, which is expected to contribute to the development of mental well-being monitoring systems on social media platforms.]]>
<![CDATA[Selection of the Best E-Commerce Platform Based on User Ratings using a Combination Entropy and SAW Methods ]]>
<![CDATA[Ulum, Faruk]]>;
<![CDATA[Wang, Junhai]]>;
<![CDATA[Setiawansyah, Setiawansyah]]>;
<![CDATA[Aryanti, Riska]]>
<![CDATA[ Bulletin of Informatics and Data Science Vol 3, No 2 (2024): November 2024 ]]>
Publisher : <![CDATA[PDSI ]]>
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DOI: 10.61944/bids.v3i2.92
<![CDATA[Choosing the right e-commerce platform has a crucial role for consumers and business actors. For consumers, a reliable and user-friendly platform provides a safe, convenient, and efficient shopping experience. Considering various aspects of choosing the right e-commerce platform is a strategic investment that can provide long-term added value for all parties involved in the digital ecosystem. The purpose of this study is to identify and determine the best e-commerce platforms based on user experience and assessment with an objective and structured decision-making approach using a combination of Entropy and SAW methods. The results of the ranking of the best e-commerce platform selection determined through the combination of the Entropy and SAW methods, obtained that Shopee ranked first with the highest preference value of 0.9819, followed by Tokopedia in second place with a value of 0.973. Furthermore, Blibli is in third place with a score of 0.9401, followed by Lazada with a score of 0.9305, and the last is Bukalapak with a score of 0.9021. This research makes a significant contribution to multi-criteria decision-making by applying a combination of Entropy and SAW methods to evaluate and determine the best e-commerce platform based on user assessments. The results of this research can be used as a practical reference as a basis for strategic decision-making in choosing the e-commerce platform that best suits market needs]]>
<![CDATA[An Entropy-Assisted COBRA Framework to Support Complex Bounded Rationality in Employee Recruitment ]]>
<![CDATA[Oprasto, Raditya Rimbawan]]>;
<![CDATA[Wang, Junhai]]>;
<![CDATA[Pasaribu, A Ferico Octaviansyah]]>;
<![CDATA[Setiawansyah, Setiawansyah]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Sumanto]]>
<![CDATA[ Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/bulletincsr.v5i3.505
<![CDATA[In the employee recruitment process, decision-making often involves many criteria and relies on the subjective judgment of the decision-maker. The main problem lies in how to develop a decision support system that can overcome this complexity while maintaining rationality and objectivity. This study aims to apply a hybrid framework based on the entropy and COBRA methods to support objective decision-making in the employee recruitment process, and to overcome the limitations of subjectivity and bounded rationality in candidate selection with a structured data-driven approach. The entropy method is used to objectively determine the weight of criteria based on data variations, thereby helping to reduce subjectivity in decision-making and increase the rationality of COBRA analysis results. The results of the final calculation using the Entropy-COBRA method, were ranked nine candidates based on their final scores which reflected relative proximity to the ideal solution in the recruitment process. The candidate with the lowest score is considered to be the closest to the ideal solution and has the best overall performance. Raka employees ranked first with a final score of -0.0618, followed by Andra in second place with a score of -0.0597, and Fajar in third place with -0.0357. The results of the final score in the COBRA method with a lower score indicate that an alternative shows superior performance over the other. This framework makes a real contribution to data-driven decision-making for human resource management, particularly in the context of recruitment involving multiple criteria and alternatives.]]>
