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All Journal Informatik : Jurnal Ilmu Komputer Brilliance: Research of Artificial Intelligence Applied Information Technology and Computer Science (AICOMS)
Rivaldo, Mario
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Analisis Sentimen Terhadap Aplikasi Mitra Darat Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbor Wijaya, Ananda; Rivaldo, Mario; Rizky Pribadi, Muhammad
Applied Information Technology and Computer Science (AICOMS) Vol 3 No 1 (2024)
Publisher : Pengelola Jurnal Politeknik Negeri Ketapang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58466/aicoms.v3i1.1542

Abstract

The transportation industry is now an important element as the times develop, especially for today's young generation. Mitra Darat itself is also one of these industries. An application that allows users to easily find out the bus departure schedule that they will take anywhere and anytime on their mobile device. Reviews are definitely given for every app available both positive and negative. With this, we are trying to conduct sentiment analysis research for the Mitra Darat application through reviewing comments from the Google Play Store so that we can identify sentiments related to the use of the Mitra Darat application, as well as provide valuable insights to land transportation service providers to understand user views and improve user services. from the results of our sentiment analysis. The algorithms we use are KNN and NBC. These two algorithms are commonly used by many people because of their expertise in classifying sentiment analysis data and are also popular among researchers. Based on our test results, it can be concluded that our sentiment analysis model designed using the NB algorithm displays higher accuracy performance than KNN. The accuracy of the NB model reached 99.28%, while KNN achieved an accuracy of 80%. This shows that the naïve Bayes algorithm is more suitable to obtain maximum accuracy compared to using k-nearest neighbors.
Analisis Sentimen Terhadap Aplikasi Mitra Darat Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbor Wijaya, Ananda; Rivaldo, Mario; Pribadi, Muhammad Rizky
Informatik : Jurnal Ilmu Komputer Vol 20 No 3 (2024): Desember 2024
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v20i3.7967

Abstract

Industri transportasi sekarang menjadi elemen penting seiring dengan berkembangnya jaman terutama untuk generasi muda sekarang. Mitra Darat sendiri juga salah satu dari industri tersebut. Aplikasi yang memungkinkan untuk pengguna dengan mudah mengetahui jadwal keberangkatan bus yang akan mereka tumpangi dimana pun dan kapan pun di perangkat seluler mereka. Ulasan pasti diberikan untuk setiap aplikasi yang tersedia baik positif dan negatif. Dengan ini, kami mencoba melakukan penelitian analisis sentimen untuk aplikasi Mitra Darat melalui ulasan komentar dari google play store agar kami dapat mengidentifikasi sentimen yang terkait dengan penggunaan aplikasi Mitra Darat, serta memberikan wawasan beharga kepada penyedia layanan transportasi darat untuk memahami pandangan pengguna dan meningkatkan pelayanan pengguna dari hasil analisis sentimen kami. Algoritma yang digunakan kami ialah KNN dan NBC. Kedua algoritma ini sudah umum digunakan oleh banyak orang karena keahlian dalam mengklasifikasi data analisis sentimen dan juga popular di kalangan peneliti. Bedasarkan hasil pengujian kami bisa disimpulkan untuk model analisis sentimen kami yang dirancang menggunakan algoritma NB menampilkan performa akurasi lebih tinggi dibandingkan KNN. Akurasi model NB mencapai 99,28%, sedangkan KNN mendapatkan akurasi sebesar 80%. Ini menunjukkan bahwa algoritma naïve bayes lebih cocok digunakan untuk mendapatkan keakuratan yang maksimal dibandingkan menggunakan k-nearest neighbor.
Performance Comparison of EfficientNetB0 in Potato Leaf Disease Classification with Adam and SGD Rivaldo, Mario; Udjulawa, Daniel
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7482

Abstract

Potatoes (Solanum tuberosum L.) are an important food commodity for global food security, but they are highly susceptible to leaf diseases that reduce yield and tuber quality. This study aims to classify potato leaf diseases using the EfficientNetB0 architecture with two optimizers, Adam and SGD, and applying data augmentation techniques such as rotation, flipping, and cropping. The dataset consists of 3076 images divided into seven categories: Bacteria, Fungi, Healthy, Nematodes, Pests, Phytophthora, and Viruses. The results show that the Adam optimizer with a learning rate of 0.001, a batch size of 16, and 100 epochs provides the best performance. The training accuracy reached 92.10%, validation 81.49%, and testing 78.14%. The model precision was 0.7982, recall was 0.7536, and the F1 score was 0.7671. Meanwhile, the SGD optimizer produced a test accuracy of 79.55%, with precision of 0.7752, recall of 0.7781, and an F1 score of 0.7715. Although Adam's accuracy is higher, SGD shows better stability in preventing overfitting. This study confirms that data augmentation plays an important role in improving model performance, although the challenge of overfitting still needs to be addressed. Further studies are expected to optimize hyperparameters and explore other model architectures to improve the accuracy and efficiency of potato leaf disease classification.
Co-Authors Daniel Udjulawa Muhammad Rizky Pribadi Wijaya, Ananda