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All Journal Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer
Sofinah Harahap, Lailan
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Analisis Jaringan Syaraf Tiruan Menggunakan Algoritma Hebb Rule Untuk Mengetahui Serta Diagnosa Penyakit pada Tanaman Cabai Rahma Nasution, Luftia; Sofinah Harahap, Lailan; Kartika Dewi, Aulia
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2322

Abstract

Chili is a plant that belongs to plants that come from the solanacae family group (a type of eggplant tribe). The presence of plant diseases in chili plants can cause a decrease in chili production and even the death of chili plants. One of the causes of plant diseases is pathogenic fungi that can exist in seeds, soil, or air, even in vector insects. Diseases of chili plants due to pathogenic fungi are often difficult to overcome. This can be caused by, among other things, the degree of malignancy or fungus, the susceptibility level of the chili plant, and the support of appropriate environmental factors. Therefore, it is necessary to recognize every chili plant disease due to many causes so that decisions can be made to prevent or manage it appropriately. Therefore, the method used in analyzing the diagnosis in chili plants is the hebb rule method algorithm where this method regulates the weight value where this weight will process the completion of the diagnosis.
Optimasi Penjadwalan Produksi dengan Jaringan Syaraf Tiruan dan Algoritma Genetika Siregar, Dzilhulaifa; Sofinah Harahap, Lailan; Fadlan Alamsyah, Muhammad
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2324

Abstract

Optimal production scheduling is essential to improve operational efficiency in the manufacturing industry. This study proposes a combination of Neural Networks (NN) and Genetic Algorithms (GA) to solve production scheduling problems. NN is used to predict processing time based on historical data, while GA optimizes the production sequence to minimize idle time and increase throughput. Simulation results show that this combined method provides a more efficient scheduling solution compared to conventional methods.
Penerapan Multi-Layer Perceptron untuk Prediksi Durasi Tidur Berdasarkan Faktor Kebiasaan Harian Risdi Aulia, Rafif; Hidayat Lubis, Fitra; Sofinah Harahap, Lailan
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2326

Abstract

This study applies a Multi Layer Perceptron (MLP), a type of Artificial Neural Network (ANN), to predict sleep duration based on daily habits, including screen time, exercise, and caffeine intake. The methodology involves data preprocessing, MLP architecture design, hyperparameter tuning using Grid Search, and model evaluation. The final model configuration includes two hidden layers with 10 neurons each, utilizing the tanh activation function and adam optimizer with a learning rate of 0.1. The model evaluation on test data shows promising accuracy, with a Mean Squared Error (MSE) of 0.065 and Mean Absolute Error (MAE) of 0.204. These results indicate that the MLP model effectively captures complex patterns in the dataset and provides accurate sleep duration predictions. However, certain samples showed significant prediction discrepancies, suggesting the potential influence of unobserved factors, such as health conditions or stress. Further research could improve model performance by including additional features or exploring alternative models like Random Forest or Gradient Boosting.
Implementasi Multilayer Perceptron untuk Klasifikasi Berita Hoax dalam Media Sosial Amanda, Hervilla; Faiza, Nayla; Sofinah Harahap, Lailan
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2336

Abstract

The very fast dissemination of information via social media in the current digital era has facilitated the spread of fake news or hoaxes. Hoax news is false information, often created deliberately to spread or manipulate public opinion. The spread of hoaxes on social media can have serious impacts, such as public unrest. Therefore, automatic detection of hoax news is very important to maintain the integrity of information circulating in society. This research aims to implement the Multilayer Perceptron (MLP) algorithm in classifying news as "hoax" or "not hoax". The MLP algorithm works by learning from training data containing labeled news text. Based on certain patterns and features, this model is expected to be able to detect whether a piece of news is a hoax or not. The implementation of Perceptron for hoax news classification aims to provide a system that can help social media users filter information, so that it can support a healthier and more trustworthy social media ecosystem. This research uses data collection methods from various social media and news sites, data preprocessing, MLP model formation, system implementation, and model evaluation. The implementation results show that the MLP model is able to classify hoax news with an accuracy of 63.1%. It is hoped that these findings can contribute to the development of accurate and efficient hoax detection technology.
Implementasi Jaringan Syaraf Tiruan (JST) untuk Mengenali Pola TandaTangan dengan Metode Backpropagation Farras Ashar, Suthan; Iqbal, Farhan; Sofinah Harahap, Lailan
Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research Vol. 2 No. 1 (2025): NOVEMBER 2024 - JANUARI 2025
Publisher : UNIVERSITAS SERAMBI MEKKAH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/mister.v2i1.2341

Abstract

Artificial Neural Networks (ANN) is a computer technology in the field of artificial intelligence that is able to understand complex data patterns. One of ANN's technological capabilities is being able to predict solutions based on training patterns provided during the system learning process. This study aims to apply the signature pattern by applying ANN using the Backpropagation method. Backpropagation method is one of the learning algorithms related to the preparation of weights based on the value of errors in learning. The image will be processed using the Backpropagation method which will be obtained by the introduction. The results introduce 50 signature data samples and 50 signature sample data. The test is carried out using 50 samples, where each sample will be requested once. From the results of the research that has been done it can be concluded that the results obtained from the parameters with a learning rate of 0.5, epoch 100, objectives 1e-5 and momentum 0.9 with the results of 68% system testing.
Evaluasi Kinerja Model RNN & LSTM untuk Prediksi Magnitude Gempa di Indonesia Fazira, Rara; Yudistira, Dimas; Sofinah Harahap, Lailan
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 6 (2024): Desember : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i6.498

Abstract

Indonesia di kawasan Cincin Api Pasifik, yang dikenal memiliki aktivitas seismik yang sangat tinggi dengan ribuan gempa bumi yang terjadi setiap tahunnya. Penelitian ini bertujuan untuk menganalisis kinerja Recurrent Neural Network (RNN) dan Long Short-Term Memory (LSTM) dalam memprediksi magnitudo gempa bumi menggunakan data historis yang diambil dari Kaggle. Data tersebut mencakup rentang waktu dari November 2008 hingga September 2022, yang telah melalui proses normalisasi serta perpecahan menjadi data pelatihan dan pengujian. Model evaluasi kinerja dilakukan dengan menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Pada uji coba pertama, LSTM menunjukkan performa terbaik dengan nilai MAE 0.6226 dan RMSE 0.7731 pada data pengujian, lebih baik dibandingkan RNN yang mencatatkan MAE 0.6271 dan RMSE 0.7831. Sebaliknya, pada uji coba kedua, RNN unggul dengan nilai MAE 0.5583 dan RMSE 0.7008, sementara LSTM memiliki MAE 0.5822 dan RMSE 0.7132. Hasil ini menunjukkan bahwa LSTM lebih cocok untuk menangani pola data temporal yang kompleks, sedangkan RNN lebih andal pada dataset dengan pola yang lebih sederhana. Penelitian ini diharapkan dapat menjadi pijakan dalam pengembangan sistem prediktif untuk mitigasi risiko bencana gempa bumi di Indonesia.
Co-Authors Amanda, Hervilla Fadlan Alamsyah, Muhammad Faiza, Nayla Farras Ashar, Suthan Fazira, Rara Hidayat Lubis, Fitra Iqbal, Farhan Kartika Dewi, Aulia Rahma Nasution, Luftia Risdi Aulia, Rafif Siregar, Dzilhulaifa Yudistira, Dimas