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All Journal Proceeding International Conference on Information Technology and Business Bulletin of Computer Science and Electrical Engineering (BCSEE) Journal of Comprehensive Science
Nisar, Nisar
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Sistem Pakar Mendiagnosis Penyakit Pada Ayam Broiler Menggunakan Metode Forward Chaining Hendra Setiawan, Muhammad; Nisar, Nisar
Journal of Comprehensive Science Vol. 4 No. 5 (2025): Journal of Comprehensive Science (JCS)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v4i5.3154

Abstract

Industri peternakan ayam broiler di Indonesia menghadapi tantangan besar dalam mendeteksi dan menangani penyakit secara dini untuk mencegah kerugian finansial dan meningkatkan produktivitas. Penelitian ini mengembangkan sistem pakar berbasis kecerdasan buatan dengan metode forward chaining yang dirancang untuk membantu peternak dalam mendiagnosis penyakit ayam broiler secara cepat dan akurat. Metode kualitatif dengan pendekatan deskriptif digunakan untuk memahami kondisi peternakan di PT. Rama Jaya, Lampung, melalui observasi langsung, wawancara, dan studi literatur. Sistem pakar yang dihasilkan berupa website interaktif dengan fitur diagnosis berbasis gejala ayam, manajemen data penyakit, dan relasi gejala yang terintegrasi. Hasil implementasi menunjukkan peningkatan akurasi diagnosis hingga 90%, pengurangan waktu pengambilan keputusan, penghematan biaya operasional, serta peningkatan kesejahteraan ternak. Sistem ini berpotensi mendukung keberlanjutan dan modernisasi peternakan ayam broiler di Indonesia. Penelitian merekomendasikan kolaborasi dengan ahli veteriner dalam pengembangan lebih lanjut, penyusunan basis pengetahuan dinamis, serta pelatihan pengguna agar sistem lebih optimal dan dapat diterima oleh peternak dengan berbagai tingkat kemampuan teknologi. Implementasi sistem pakar ini diharapkan menjadi solusi efektif untuk mendukung pengelolaan kesehatan ternak yang lebih baik dan efisien.
Implementasi Deep Learning Menggunakan Arsitektur Vgg-19 Untuk Deteksi Penyakit Pada Tebu Berdasarkan Citra Daun Berbasis Website Wijaya Samdoria, Samodra; Nisar, Nisar
Journal of Comprehensive Science Vol. 4 No. 5 (2025): Journal of Comprehensive Science (JCS)
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/jcs.v4i5.3155

Abstract

Tanaman tebu merupakan komoditas strategis dalam industri gula nasional, namun produktivitasnya sering terhambat oleh serangan penyakit daun yang sulit terdeteksi secara dini. Deteksi manual oleh petani atau tenaga ahli memerlukan waktu dan biaya yang tidak sedikit, serta rentan terhadap kesalahan manusia. Penelitian ini bertujuan untuk mengembangkan sistem deteksi penyakit pada daun tebu berbasis website menggunakan pendekatan deep learning dengan arsitektur VGG-19. Metode yang digunakan meliputi pengumpulan dataset citra daun tebu yang terklasifikasi dalam kondisi sehat dan berbagai jenis penyakit, dilanjutkan dengan preprocessing citra, pelatihan model menggunakan VGG-19, serta integrasi model ke dalam antarmuka website untuk penggunaan praktis oleh pengguna. Hasil penelitian menunjukkan bahwa model VGG-19 yang diimplementasikan berhasil mencapai tingkat akurasi sebesar 92,3% dalam mendeteksi dan mengklasifikasikan kondisi daun tebu berdasarkan citra yang diunggah melalui platform. Sistem ini diharapkan dapat menjadi solusi inovatif dalam mendukung proses pemantauan kesehatan tanaman secara real-time dan efisien, serta berkontribusi pada peningkatan produktivitas pertanian tebu di Indonesia.
Enhancing Rose Leaf Disease Detection Accuracy Using Optimized CNN Parameters Nabilah, Latifah; Nisar, Nisar; Amnah, Amnah; Arfida, Septilia
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1184

Abstract

The CNN model developed in this study demonstrated remarkable performance, achieving an outstanding validation accuracy of 99.96%. Through experimentation, it was found that employing the RMSprop optimizer with a learning rate of 0.001 yielded superior results compared to the Adam optimizer utilized in previous iterations. Additionally, increasing the number of epochs from 10 to 20 resulted in a significant enhancement in accuracy, highlighting the importance of iterative training for model refinement. Moreover, the implementation of Early Stopping proved to be a valuable technique, effectively conserving training time by halting the training process once optimal accuracy levels were reached. These findings underscore the efficacy of various optimization strategies in bolstering the performance of CNN models for rose leaf disease detection. The achieved accuracy rates signify a substantial advancement in disease detection technology, holding promise for enhancing agricultural productivity and ensuring plant quality. This research contributes valuable insights into the optimization of CNN parameters, paving the way for further advancements in automated disease detection systems in the field of agriculture.
Comparison of Tomato Leaf Disease Detection Using Transfer Learning Architecture with the VGG19 Method Amelia, Indah; Nisar, Nisar
Bulletin of Computer Science and Electrical Engineering Vol. 4 No. 2 (2023): December 2023 - Bulletin of Computer Science and Electrical Engineering
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/bcsee.v4i2.1185

Abstract

Diseases in plants are often detrimental to agriculture, can be seen manually and require a very long time, which can lead to possible errors in disease detection. Detecting diseases in plants early can overcome these problems and reduce the risk of reduced crop production. The aim of this research is to make a comparison of quickly and accurately detecting tomato leaf diseases compared to previous researchers who used Deep Learning applications. Which can be applied effectively for image classification using the VGG19 method. The implementation of this model uses a dataset containing 2,694 images, including 3 different types of diseases. That the conclusion of this research is the fastest and most accurate way to detect tomato leaf diseases. To prove this research, results and necessary data will be presented in this paper. The accuracy obtained on the VGG-19 architecture was 91.85% with the best increase in accuracy compared to the previous journal which only produced 87% accuracy.
The Application Of The Convolution Neural Network Method Uses A Webcam To Analyze The Facial Expressions Of Problematic Students In The Counseling Guidance Unit (Case Study At SMAN 1 Penengahan Lampung Selatan) Pratama, Rendi; Kurniawan, Rio; Rosandi, Triowali; Nisar, Nisar
Prosiding International conference on Information Technology and Business (ICITB) 2023: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 9
Publisher : Proceeding International Conference on Information Technology and Business

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Guidance and Counseling is a service provided to students to help them develop their potential optimally. Detecting students' facial expressions in the counseling room plays a crucial role in assisting counselors in understanding the emotional state of students who may need help, such as depression, anxiety, or stress, as they often find it difficult to express their feelings verbally. Therefore, this research will focus on 7 types of facial expressions: Anger, Disgust, Fear, Happiness, Neutral, Sadness, and Surprise. To classify these facial expressions, a Convolutional Neural Network (CNN) technique will be used, which identifies objects based on color and contours in an image. The aim of this research is to create a CNN model that can detect students' facial expressions during counseling sessions. In this study, the machine learning life cycle method is also employed as a stage in building the CNN model, starting with data collection with a total of 618 images, data cleaning, labeling the data, splitting the data into training and testing data with an 80% training data and 20% testing data ratio, creating the CNN architecture, training and evaluating the created model, and finally implementing it using a webcam. The results of this research show that the model achieved an accuracy of 33%. However, the facial expression detection features using the CNN model successfully detected students' facial expressions despite having a low prediction accuracy rate. Keywords— Convolutional Neural Network, Facial Expression Detection, Guidance Counseling, Machine Learning, Webcam
Co-Authors Amelia, Indah amnah amnah Emilia Mustary Hendra Setiawan, Muhammad M. Yogi Riyantama Isjoni Mahyuddin Mahyuddin Nabilah, Latifah pratama, rendi Rio Kurniawan Rosandi, Triowali septilia arfida Wijaya Samdoria, Samodra