Articles
<![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[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[Implementasi Sistem Informasi Inventory Barang di Sekolah Berbasis Website Menggunakan Metode Rapid Application Development ]]>
<![CDATA[Royadi, Royadi]]>;
<![CDATA[Ardiansyah, Dian]]>;
<![CDATA[Saepudin, Atang]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Fitriani, Eka]]>
<![CDATA[ DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 6, No 1: JUNI 2025 ]]>
Publisher : <![CDATA[Universitas Dharmawangsa ]]>
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DOI: 10.46576/device.v6i1.6433
<![CDATA[Pengelolaan data inventaris barang di lingkungan sekolah masih banyak dilakukan secara manual, sehingga rentan terhadap kesalahan pencatatan, kehilangan data, dan sulitnya akses informasi. Penelitian ini bertujuan untuk mengimplementasikan sistem informasi inventory berbasis website sebagai solusi digital yang dapat meningkatkan efisiensi dan akurasi dalam pengelolaan inventaris. Perkembangan teknologi informasi yang pesat ini dengan penerapan sistem informasi berbasis web yang dapat digunakan untuk mengelola data inventaris secara terstrutur dan rapi. Metode Rapid Application Development (RAD) digunakan dalam pengembangan sistem ini karena mampu mempercepat proses pembuatan aplikasi melalui tahapan prototyping dan keterlibatan aktif pengguna. Sistem yang dihasilkan memungkinkan pencatatan, pemantauan, dan pelaporan data inventaris secara real-time dan terstruktur. Hasil implementasi menunjukkan bahwa sistem dapat mempercepat alur kerja, memudahkan akses informasi, serta meningkatkan transparansi dan akuntabilitas. Dengan demikian, penggunaan metode Rapid Application Development (RAD) dalam pengembangan sistem inventory berbasis web terbukti efektif dalam memenuhi kebutuhan manajemen inventaris di sekolah secara lebih modern dan efisien.]]>
<![CDATA[Peningkatan Kemampuan Digital Masyarakat Melalui Pelatihan Website E-Commerce Berbasis AI ]]>
<![CDATA[Sarifah Agustiani]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Wahyuni, Tri]]>;
<![CDATA[Saepudin, Atang]]>;
<![CDATA[Haliza Ramadhanti, Pristya]]>;
<![CDATA[Roy Prasetya, Andreas]]>
<![CDATA[ Darma Abdi Karya Vol. 4 No. 1 (2025): Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat ]]>
Publisher : <![CDATA[LPPM POLITEKNIK LP3I ]]>
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DOI: 10.38204/darmaabdikarya.v4i1.2338
<![CDATA[Kemampuan masyarakat dalam mengadopsi teknologi digital merupakan faktor krusial dalam mendukung pengembangan usaha lokal. Warga RT.010 Kelurahan Tegal Parang, Jakarta Selatan, yang mayoritas berprofesi sebagai pelaku usaha mikro seperti pengrajin, pengemudi ojek online, dan sopir taksi, masih menghadapi kendala signifikan dalam memanfaatkan teknologi digital, khususnya di bidang e-commerce dan kecerdasan buatan (AI). Rendahnya literasi digital dan keterbatasan akses terhadap pelatihan teknologi menjadi hambatan utama dalam memperluas jangkauan usaha mereka di era digital. Untuk mengatasi hal tersebut, dilakukan kegiatan pelatihan pembuatan website berbasis AI yang bertujuan meningkatkan kemampuan digital warga. Selain mengenalkan konsep dasar e-commerce dan AI, kegiatan ini membimbing peserta dalam membangun website usaha secara instan menggunakan platform ZipWP AI Website Builder tanpa memerlukan keterampilan pemrograman. Hasil pelatihan menunjukkan adanya peningkatan pemahaman dan keterampilan digital warga serta tumbuhnya semangat untuk mengelola usaha secara daring. Pelatihan ini juga berkontribusi dalam pembentukan ekosistem digital komunitas yang mendukung inklusi teknologi secara berkelanjutan. Dengan adanya kegiatan ini, diharapkan warga menjadi lebih siap menghadapi tantangan ekonomi digital sekaligus memperkuat ketahanan dan kemandirian komunitas dalam menghadapi era Revolusi Industri 4.0.]]>
<![CDATA[Combination of Response to Criteria Weighting Method and Multi-Attribute Utility Theory in the Decision Support System for the Best Supplier Selection ]]>
<![CDATA[Ulum, Faruk]]>;
<![CDATA[Wang, Junhai]]>;
<![CDATA[Megawaty, Dyah Ayu]]>;
<![CDATA[Sulistiyawati, Ari]]>;
<![CDATA[Aryanti, Riska]]>;
<![CDATA[Sumanto, Sumanto]]>;
<![CDATA[Setiawansyah, Setiawansyah]]>
<![CDATA[ J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology ]]>
Publisher : <![CDATA[LPPM STIKI MALANG ]]>
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DOI: 10.32664/j-intech.v13i01.1810
<![CDATA[Choosing the right supplier is a strategic factor in supporting operational efficiency and a company's competitive advantage. This process requires a decision support system that is able to assess various alternatives objectively and in a structured manner. This study aims to develop a decision support system in the selection of the best supplier by combining the Response to Criteria Weighting (RECA) and Multi-Attribute Utility Theory (MAUT) methods. The RECA method is used to objectively determine the weight of each criterion based on the variation of data between alternatives, so as to reduce subjectivity in the weighting process. Meanwhile, the MAUT method functions to calculate the total utility value of each supplier based on the normalization value and weight that has been obtained. The results of the RECA method show the objective weight of each criterion, which is then used in the MAUT calculation process. The results of the analysis, obtained in the best supplier selection based on the total score of each candidate, it can be seen that PT Global Niaga Mandiri ranks first with the highest score of 0.6512, this shows that this company is the best choice in the supplier selection process. In second place is UD Anugrah Bersama with a score of 0.399, followed by PT Indo Logistik Prima in third place with a score of 0.3451. The combination of the RECA and MAUT methods has been proven to be able to produce accurate, rational, and accountable decisions. This system provides a measurable approach in filtering supplier alternatives efficiently and is relevant to be applied to various other multi-criteria decision-making contexts.]]>
<![CDATA[Sistem Informasi Pencatatan Permintaan Dan Pengeluaran ATK Pada Balai Pengamanan Alat Fasilitas Kesehatan Jakarta ]]>
<![CDATA[Masngud]]>;
<![CDATA[Aryanti, Riska]]>
<![CDATA[ Media Teknologi dan Informatika Vol. 2 No. 1 (2025): Juni ]]>
Publisher : <![CDATA[Universitas Bina Sarana Informatika ]]>
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DOI: 10.31294/pynjvn06
<![CDATA[Peralatan perkantoran atau sering disebut dengan Alat Tulis Kantor (ATK) dan Barang Habis Pakai (BHP) merupakan suatu kebutuhan yang harus dipenuhi dan diperhatikan penggunaanya pada suatu instansi seperti pada kantor Balai Pengamanan Fasilitas Kesehatan Jakarta. Saat ini BPAFK Jakarta masih menggunakan sistem manual dengan Microsoft Excel dan pencatatan menggunkan formulir kertas untuk mengelola data ATK/BHP mulai dari pendataan alat yang masuk hingga pendistribusian kepada setiap divisi pegawai. Hal ini menyebabkan beberapa masalah signifikan, seperti risiko terjadinya kesalahan dalam pengetikan dan data yang tidak akurat, keterlambatan dalam pembuatan laporan, serta kurangnya efisiensi dalam proses administrative. Oleh karena itu kantor BPAFK Jakarta memerlukan sebuah sistem yang dapat mendukung proses pengolahan data Pencatatan Permintaan dan Pengeluaran ATK/BHP tersebut. Sistem yang akan dikembangkan diharapkan akan dapat membantu dalam membuat laporan Pencatatan Permintaan dan Pengeluaran ATK/BHP. Metode pengembangan sistem yang dipilih dalam penelitian ini adalah model pengembangan sistem Rapid Application Development (RAD). Desain sistem dalam penelitian ini menggunakan Unified Modelling Language (UML). Proses pembuatan program (coding) menggunakan bahasa pemrograman PHP dengan HTML dan MySQL untuk pembuatan database. Pendekatan kasus uji dalam penelitian ini menggunakan pengujian Black Box. Hasil pengujian yang telah dilakukan menunjukkan bahwa semua fungsi yang dimiliki sistem telah berjalan sesuai dengan fungsinya.]]>
