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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Abdimasku : Jurnal Pengabdian Masyarakat Switch: Jurnal Sains dan Teknologi Informasi
Etika Kartikadarma
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Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Pendampingan Pembuatan Media Pembelajaran Berbasis Multimedia Bagi Guru SD Negeri Pedurungan Kidul 02 Semarang Danang Wahyu Utomo; Etika Kartikadarma; Erlin Dolphina; Defri Kurniawan; Purwanto Purwanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1802

Abstract

Media pembelajaran saat ini telah berkembang, salah satu contohnya adalah media pembelajaran digital atau biasa disebut literasi digital. Literasi digital dapat berupa teks, audio, atau video. Cara mendapatkannya dapat melalui berbagai sumber seperti media sosial dan halaman web. Keuntungan dari media pembelajaran digital adalah dapat meningkatkan kemampuan belajar siswa. Guru juga dapat menggunakan berbagai sumber seperti teks, gambar, audio dan video dalam materi pembelajaran. Maka program kemitraan Masyarakat (PKM) dari Udinus menawarkan pendampingan pembuatan media pembelajaran berbasis multimedia dengan Canva. Metode yang digunakan dalam program kemitraan Masyarakat adalah praktek dengan Canva. Dalam praktek tersebut, para guru diawali membuat slide presentasi kemudian diubah menjadi video pembelajaran dengan memanfaatkan asset yang disediakan oleh Canva.
Penggunaan Algoritma Naïve Bayes dengan Polarity Textblob untuk Analisis Sentimen pada Acara ASEAN CUP 2024 U-16 di Media Sosial Twitter Arya Erlangga; Yani Parti Astuti; Etika Kartikadarma; Sindhu Rakasiwi; Egia Rosi Subhiyakto
Switch : Jurnal Sains dan Teknologi Informasi Vol. 3 No. 1 (2025): Januari : Switch: Jurnal Sains dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/switch.v3i1.357

Abstract

Football is a popular sport in the world and is enjoyed by people of all ages. The Indonesia U-16 national team played in the ASEAN CUP 2024 event in this field. Twitter users gave their support through #timnasday during the event. This provided many forms of support for the Indonesian national team which made it difficult to identify positive, neutral, and negative sentiments. This requires the use of lexicon-based textblob to perform automatic labeling. In the labeling results using textblob from a total of 1138 user tweet data resulted in positive sentiment values of 50.9% or 579 positive data, neutral 33.7% or 384 neutral data, and negative 15.4% or 175 negative data. In the test results using one of the machine learning from the naïve bayes classifier, namely gaussian naïve bayes with the division of test data and training data of 0.3 and 0.7, the accuracy value is 98.53%
Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3 Udayanti, Erika; Etika Kartikadarma; Fahri Firdausillah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 3 (2024): June 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i3.5784

Abstract

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.
Co-Authors Arya Erlangga Defri Kurniawan Egia Rosi Subhiyakto Erlin Dolphina Fahri Firdausillah Purwanto Purwanto Sindhu Rakasiwi Udayanti, Erika Utomo, Danang Wahyu Yani Parti Astuti