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All Journal TEKNIK INFORMATIKA Jurnal Sains dan Teknologi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal FATEKSA : Jurnal Teknologi dan Rekayasa Jurnal Teknologi Terpadu Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro Tematik : Jurnal Teknologi Informasi Komunikasi JNANALOKA Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Scientific Research, Education, and Technology Jurnal Honai
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Study Literature Study on Predicting Gold Prices using Machine Learning Sri Wahyuningsih; Kusrini; Hanafi
DIELEKTRIKA Vol 10 No 2 (2023): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v10i2.335

Abstract

Gold is a metal that is believed to be able maintain prices and can be a medium of exchange that has different forms and functions. In addition, gold can be used as a long-term investment tool at various prices. Price variations can be influenced by many factors, so predicting gold prices can minimize risk. Gold price predictions are not only interesting for collectors but also interesting for analysts to discuss. Machine learning is one method that is often used to predict gold prices. In this study will discuss the study of literature based on the methods and results of previous literature. The purpose of this study is to determine the best performance of the methods that have been used and can be used as a reference in predicting gold prices. The purpose of writing this journal is to provide an overview as well the benefits of applying data mining techniques to machine learning. These benefits include development better understanding of machine learning, as well as improved decision making and technological innovation.
Pengaruh Keseimbangan Data terhadap Akurasi Model Support Vector Machine pada Data Set Donor Darah Widyanto, Agung; Kusrini; Kusnawi
Jurnal Teknologi Terpadu Vol 9 No 2 (2023): Desember, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v9i2.771

Abstract

In classification, unbalanced data is expected. Unbalanced data has an inequality ratio between the majority and minority classes. Models trained with unbalanced data tend to predict the minority class as the majority class. This study aims to determine the effect of data balance on the accuracy of the Support Vector Machine (SVM) classification model. The data set used is the blood donor data set downloaded from the repository belonging to the University of California, Irvine (UCI). The Waikato Environment for Knowledge Analysis (WEKA) tool was chosen to present the results of training development and model testing. The research framework scheme is used as a reference for knowledge flow. In scenario 1, data pre-processing includes handling missing values using mean-impulse and normalizing MinMax scaling. With a data set that has an inequality ratio of 1:3, the SVM classifier gets an accuracy performance of 76.7%. In scenario 2, post-pre-processing is done by balancing the data using the Synthetic Minority Oversampling Technique (SMOTE). SVM classifier gets 69.8% accuracy performance. Model performance is evaluated using confusion metrics. The gap in recall values for each class is very high in scenario 1 (2.8% and 99.8%). Things are different in scenario 2 (75.6% and 64%). The test results of 748 samples obtained an accuracy of 76.7% for the scenario-1 model and 93.2% for the scenario-2 model. This proves that the balance of data influences the accuracy of the SVM classification model.
PREDIKSI KEBAKARAN HUTAN MENGGUNAKAN ALGORITMA NAIVE BAYES DAN KNN Ahsan, Muhammad Salimy; Zakaria, Zakaria; Hadi, Zulpan; Kurni, Samuel Everth Andrias; Kusrini
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11609

Abstract

Forest fires are one of the disasters that cause problems for the environment. Forest fires can cause damage and threats, not only to forest resources but also to the entire ecosystem, both fauna and plants that can damage biodiversity and the environment of an area and can endanger human life. The source of forest fires was initially thought to come from a dry and hot environment, but in some cases, forest fires are triggered by human activities in clearing land for agriculture or other purposes. One of the factors that influence the spread of forest fires is several variables combined with humidity levels, wind speed, and rainfall. In this study, researchers used machine learning algorithms KNN and Naïve Bayes to predict forest fires and compare the results of the performance levels of each method used. The results obtained indicate that the naive Bayes method has an accuracy value of 53.33% and K-NN has an accuracy value of 62.66%
Chicken Disease Classification Based on Inception V3 Algorithm for Data Imbalance Ahsan, Muhammad Salimy; Kusrini; Dhani Ariatmanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12737

Abstract

In order to supply the world's protein needs, one of the most crucial industries is the poultry business. The problem that often occurs in chicken farms is disease, and this can have a significant impact on the farm. The availability of large enough amounts of data makes it possible to carry out the process of monitoring chicken diseases using deep learning technology for the classification of chicken diseases. With the availability of large enough data, the dataset has a variety of features that cause problems with data clutter. To overcome the problem of data conflict, an oversampling technique is used to increase the sample data from the minority class so that it has the same value as the other majority classes, and the Inception-V3 algorithm is used to classify chicken diseases based on fecal images. The total number of data used was 8067, which were broken down into the following four categories: Healthy, Salmonella, Coccidiosis, and Newcastle disease. Data balancing was done using oversampling to get the total data to 10500 before the evaluation process was started. The data was distributed by splitting it by 80% of the data will be used for training, 10% for data validation, and 10% for testing. The results of the test, which employed Inception V3 without oversampling, produced the highest possible score of 94.05%.
Impact of Data Augmentation Techniques on the Implementation of a Combination Model of Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) for the Detection of Diseases in Rice Plants FIRDAUS, MOHAMAD; KUSRINI; M. Rudyanto Arief
Journal of Scientific Research, Education, and Technology (JSRET) Vol. 2 No. 2 (2023): Vol. 2 No. 2 2023
Publisher : Kirana Publisher (KNPub)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58526/jsret.v2i2.94

Abstract

Detection of disease in rice plants is important to avoid damage and reduce yields. In this study, the influence of data augmentation techniques on the application of a combination model of Convolutional Neural Network (CNN) and Multilayer Perceptron (MLP) was carried out to detect diseases in rice plants. This study uses a dataset of rice images for disease detection which contains 6034 images of rice with five categories, namely Bacterial Leaf Bligh, Blast, Brown Spot, normal, and tungro. This dataset is divided into three parts, namely training data, validation data and test data. The data augmentation techniques used are rotation, brightness and zoom on rice images. The combination model of CNN and MLP is built using the Python programming language and the TensorFlow deep learning framework. In measuring the success rate of the built model, it can be measured using the accuracy, precision and recall values obtained in the model test. Several scenarios were carried out to produce the best model, namely the use of data augmentation techniques, the number of layers and the number of iterations (epochs). From the experiments that have been carried out which have been tested with data as many as 25 digital images, the best model is obtained with an testing accuracy of 92%, 94% precision and 92% recall. This model applies a random zoom augmentation technique with a value of 0.5 – 1.0, CNN+MLP with 3 layers and a dataset ratio of 80:20 and an epoch early stop, This result has increased by more than 10%.
Penerapan Algoritma Naïve Bayes Untuk Klasifikasi Status Gizi Stunting Pada Balita Maula Hidayat, Fajar; Kusrini; Ainul Yaqin
DIELEKTRIKA Vol 11 No 2 (2024): DIELEKTRIKA
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/dielektrika.v11i2.384

Abstract

Child stunting is a major public health concern in Indonesia. This study uses the Naïve Bayes classification algorithm to assess the nutritional condition of stunted children based on demographic and anthropometric characteristics. The information used comes from the Toddler Weighing Month (Bulan Penimbangan Balita - BPB) in Majalengka Regency. Data type conversion, separating data into training and testing sets, and data normalization are all examples of preprocessing steps. The model's evaluation results reveal an accuracy of 94.65%, with precision and recall for each category of stunted nutritional status. This study makes a substantial contribution to early diagnosis and mitigation of stunting in Indonesia, as well as providing the framework for future development of more powerful predictive models.
Peningkatan kinerja arsitektur ResNet50 untuk Menangani Masalah Overfitting dalam Klasifikasi Penyakit Kulit Handoko Adji Pangestu; Kusrini
TEMATIK Vol 11 No 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1876

Abstract

Skin diseases are a significant global health issue, affecting millions of people worldwide. Deep learning, particularly with the transfer learning approach, has shown great potential in improving the diagnosis of skin diseases. This study aims to evaluate various techniques in the context of skin disease classification using transfer learning, focusing on the utilization of the ResNet50 architecture. The steps include data preprocessing, model design with variations in dense layers, fine-tuning, and dropout, as well as model performance evaluation. The results indicate that adding dense layers and fine-tuning significantly improve classification accuracy. Models without additional dense layers achieved an accuracy of around 90%, while fine-tuned models achieved an accuracy of about 94%, and models with added dense layers and fine-tuning achieved an accuracy of about 92%. Overall, adding dense layers and fine-tuning are effective strategies for enhancing the performance of skin disease classification models.
Analisa Performa Convolutional Neural Network dalam Klasifikasi Citra Apel dengan Data Augmentasi Dzalfa Tsalsabila Rhamadiyanti; Kusrini
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 5 No. 1 (2024): Agustus 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v5i1.2023

Abstract

Augmentation is creating new samples from an original dataset by applying small random transformations to the original dataset but retaining its labels. This research applies Data Augmentation to the Convolutional Neural Network model for apple image classification. The apple images used are Braeburn apples which have orange to red skin with a yellow background, Crimson Snow apples which have red skin, and Pink Lady apples with bright pink skin and yellow and green hues. There are 675 apple images used, divided into three classes, each with 225 photos. Four augmentation techniques are applied, namely flipping, cropping, rotation, and noise injection. This research carried out six scenarios, namely without augmentation, using each augmentation technique separately and combining two augmentation techniques, which produced the highest accuracy values. From the six scenarios, it was found that the augmentation technique that produced the best accuracy value was noise injection, namely 98.82%, followed by flipping with an accuracy of 72.78%, then rotation with an accuracy value of 68.64% and an augmentation technique that produced an accuracy value. The lowest is cropping, namely 67.46%. The two best augmentation techniques, noise injection, and flipping, were combined and produced an accuracy value of 84.02%. The accuracy value obtained by this combination could be more optimal due to the effect of noise injection, which can erase consistent changes in orientation from flipping. This needs to be improved so that the model can learn consistent features. It is hoped that future research can maximize the effectiveness of augmentation techniques by choosing augmentation techniques that complement each other and suit the characteristics of the data being processed
Optimasi Model Support Vector Machine dengan Particle Swarm Optimization untuk Mendeteksi Serangan Injeksi SQL (Studi Kasus : PT. Naisha Inspirasi Muslimah) Kusrini; Arnap, Adam
JNANALOKA Vol. 05 No. 02 September Tahun 2024
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2024.v5-no02-%p

Abstract

PT. Naisha Inspirasi Muslimah merupakan perusahaan yang mengoperasikan toko online untuk penjualan produk tekstil dan pernah mengalami kehilangan data akibat serangan injeksi SQL. Untuk meningkatkan manajemen keamanan komputer, PT. Naisha melakukan tata kelola teknologi informasi dengan menggunakan ISACA Design Toolkit COBIT 2019. Penelitian ini melakukan studi literatur mengenai deteksi injeksi SQL dan menemukan bahwa beberapa penelitian sebelumnya menggunakan model Support Vector Machine (SVM) yang dioptimasi dengan Particle Swarm Optimization (PSO). Pada penelitian terdahulu, optimasi model SVM dengan PSO dapat meingkatkan nilai performa model. Penelitian ini akan menggunakan model SVM yang akan dioptimasi dengan PSO untuk menangani dataset berupa kueri-kueri SQL.  Proses vektorisasi dengan Term Frequency - Inverse Document Frequency (TF-IDF) juga diterapkan untuk memberikan bobot pada setiap token. Hasil penelitian menunjukkan bahwa nilai optimal yang ditemukan adalah Best C sebesar 593,0497396215296 dan Best gamma sebesar 0,07795813722739078. Dengan parameter tersebut, model SVM+PSO berhasil mencapai akurasi sebesar 0,99 dan F1 Score sebesar 0,99, yang secara signifikan lebih tinggi dibandingkan dengan model SVM biasa yang hanya mencapai akurasi 0,79 dan F1 Score 0,73. Hasil ini menunjukkan bahwa optimasi SVM dengan PSO secara substansial meningkatkan kinerja model SVM dalam mendeteksi injeksi SQL, sehingga dapat menjadi solusi yang efektif untuk meningkatkan keamanan sistem informasi di PT. Naisha Inspirasi Muslimah.
IMPLEMENTASI ARTIFICIAL NEURAL NETWORK PADA KASUS REGRESI LINEAR BERGANDA UNTUK MEMPREDIKSI JUMLAH PAKAN AYAM PETELUR Ali Asgar Zainal Abidin; Kusrini; Ferry Wahyu Wibowo
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 9 No 2 (2024): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v9i2.50836

Abstract

Produksi telur ayam petelur adalah bagian penting dalam industri peternakan dan berperan besar dalam memenuhi kebutuhan masyarakat akan telur sebagai sumber makanan. Penelitian ini menggunakan algoritma Jaringan Saraf Tiruan (JST), yang sering digunakan untuk memprediksi data, untuk melakukan prediksi jumlah pakan yang dibutuhkan oleh ayam petelur. Penelitian ini bukan tentang hasil prediksi konkret, tetapi lebih tentang evaluasi kemampuan algoritma JST dalam melakukan prediksi berdasarkan dataset yang diperoleh dari sumber Kaggle.Dalam penelitian ini, berbagai model arsitektur jaringan neural telah dieksplorasi, termasuk model dengan struktur 5-30-1, 5-40-1, 5-50-1, dan 5-60-1. Setiap model telah dilatih dan diuji, dan hasilnya menunjukkan bahwa model arsitektur terbaik adalah yang memiliki struktur 5-40-1, dengan tingkat kinerja (MAPE) terendah sekitar 4.04 dan RMSE sebesar 6.71, yang merupakan tingkat kesalahan terendah dibandingkan dengan enam model lainnya. Ini mengindikasikan bahwa model tersebut dapat digunakan dengan baik untuk melakukan prediksi jumlah pakan yang dibutuhkan oleh ayam petelur.
Co-Authors Agustina Rahmawati Agustina, Nova Ahmad Almas'ud ZD Ahsan, Muhammad Salimy Ali Asgar Zainal Abidin Anggit Dwi Hartanto Anggit Hartanto Arief Setyanto Arli Aditya Parikesit Arnap, Adam DHANI ARIATMANTO Dzalfa Tsalsabila Rhamadiyanti Ema Utami Ferry Wahyu Wibowo Hanafi Handoko Adji Pangestu Kurni, Samuel Everth Andrias Kusnawi Kusnawi M. Rudyanto Arief Maula Hidayat, Fajar Mawene, Brenstein Mohamad Firdaus, Mohamad Rosadi, Ilham Aziz Sari, Yunita Sartika Sri Wahyuningsih Tamaulina Br Sembiring Tolla, Yefta TONNY HIDAYAT Widyanto, Agung Yanuargi, Bayu Zakaria Zakaria Zulkarnain, Jefri Zulpan Hadi