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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JITK (Jurnal Ilmu Pengetahuan dan Komputer) INTEK: Informatika dan Teknologi Informasi
Poningsih Poningsih
STIKOM Tunas Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Other

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Penerapan Arduino Uno Atmega 328P dalam Membangun Alat Penyemprot Cairan Pestisida Otomatis Radiftiya Indraswira; Poningsih Poningsih; Suhada Suhada; Indra Gunawan; Zulaini Masruro Nasution
INTEK : Jurnal Informatika dan Teknologi Informasi Vol. 4 No. 2 (2021)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/intek.v4i2.1677

Abstract

Pembudidayaan perkebunan kelapa sawit di Indonesia berkembang sangat pesat, baik itu milik individu maupun milik perusahaan. Namun, ada masalah yang mengambat dalam proses budidaya tanaman kelapa sawit tersebut. Salah satunya Penyemprotan tanaman kelapa sawit di perkebunan masih mengalami kendala, pekerja masih harus mengecek secara manual dengan mengunjungi lahan untuk melihat kondisi tanaman. Maka dari itu di buat alat untuk menyemprot pestisida secara otomatis. Dengan bantuan Arduino Uno dan sensor pendukung yaitu sensor Real Time Clock dan sensor Relay. Penggunan timer merupakan salah satu metode yang membantu pekerjaan menjadi lebih mudah dan mempersingkat waktu Tujuan dari penelitian ini merubah peran manusia untuk merawat tanaman kelapa sawit dalam membasmi gulma. Metode yang digunakan dalam penelitian ini yaitu metode observasi dengan pengamatan secara langsung terhadap objek yang diteliti kemudian dilakukan perancangan dan studi literature sebagai penunjang informasi yang mendukung penelitian ini. Hasil dari penelitian ini berupa alat penyemprot pestisida otomatis menggunakan timer yang dirancang menggunakan Arduino Uno Atmega 328P. Dengan adanya alat ini di harapkan dapat membantu pekerjaan menjadi lebih mudah dan efisien karena alat ini akan memompa pestisidah secara otomatis.
Reducing Overfitting in Neural Networks for Text Classification Using Kaggle's IMDB Movie Reviews Dataset Poningsih Poningsih; Agus Perdana Windarto; Putrama Alkhairi
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29509

Abstract

Overfitting presents a significant challenge in developing text classification models using neural networks, as it occurs when models learn too much from the training data, including noise and specific details, resulting in poor performance on new, unseen data. This study addresses this issue by exploring overfitting reduction techniques to enhance the generalization of neural networks in text classification tasks using the IMDB movie review dataset from Kaggle. The research aims to provide insights into effective methods to reduce overfitting, thereby improving the performance and reliability of text classification models in practical applications. The methodology involves developing two LSTM neural network models: a standard model without overfitting reduction techniques and an enhanced model incorporating dropout and early stopping. The IMDB dataset is preprocessed to convert reviews into sequences suitable for input into the LSTM models. Both models are trained, and their performances are compared using various metrics. The model without overfitting reduction techniques shows a test loss of 0.4724 and a test accuracy of 86.81%. Its precision, recall, and F1-score for classifying negative reviews are 0.91, 0.82, and 0.86, respectively, and for positive reviews are 0.84, 0.92, and 0.87. The enhanced model, incorporating dropout and early stopping, demonstrates improved performance with a lower test loss of 0.2807 and a higher test accuracy of 88.61%. For negative reviews, its precision, recall, and F1-score are 0.92, 0.84, and 0.88, and for positive reviews are 0.86, 0.93, and 0.89. Overall, the enhanced model achieves better metrics, with an accuracy of 89%, and macro and weighted averages for precision, recall, and F1-score all at 0.89. The applying overfitting reduction techniques significantly enhances the model's performance.
OPTIMIZATION OF THE INCEPTIONV3 ARCHITECTURE FOR POTATO LEAF DISEASE CLASSIFICATION Khairun Nisa Arifin Nur; Nazlina Izmi Addyna; Agus Perdana Windarto; Anjar Wanto; Poningsih Poningsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6554

Abstract

Potato leaf diseases can cause significant yield losses, making early detection crucial to prevent major damages. This study aims to optimize the Inception V3 architecture in a Convolutional Neural Network (CNN) for potato leaf disease classification by applying Fine Tuning Pre-Trained. This method leverages weights from a pre-trained model on a large-scale dataset, enhancing accuracy while reducing the risk of overfitting. The training process involves adjusting several final layers of Inception V3 to better adapt to specific features of potato leaf diseases. The results show that this approach improves classification performance, achieving an accuracy of 97.78%, precision of 98%, recall of 98%, and an F1-score of 98%. With better computational efficiency compared to previous architectures, this model is expected to be widely applicable in plant disease detection systems, particularly for farmers or institutions with limited resources.
Co-Authors Agus Perdana Windarto Indra Gunawan Khairun Nisa Arifin Nur Nazlina Izmi Addyna Putrama Alkhairi Radiftiya Indraswira Suhada Suhada Wanto, Anjar Zulaini Masruro Nasution