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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Information Systems and Informatics The Indonesian Journal of Computer Science Jurnal Pengabdian Masyarakat (J-AbMas)
Prastya, Ifnu Wisma Dwi
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Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Klasifikasi Dana Hibah Usaha Mikro Kecil dan Menengah dengan Metode Naïve Bayes Sanjaya, Ucta Pradema; Pribadi , Teguh; Prastya, Ifnu Wisma Dwi
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i3.3099

Abstract

Pandemi COVID-19 yang melanda membuat pengusaha mengalami melambatnya perekonomian. Untuk menstimulus perekonomian serta memberikan ketahanan terhadap pengusaha maka pemerintah memberikan dana hibah untuk pengusaha Usaha Micro Kecil dan Menengah. Pemberian dana hibah untuk Usaha Micro Kecil dan Menengah terkadang terdapat masalah dalam pembagiaanya. Dikarenakan terdapat permasalahan tersebut maka perlu adanya model data mining dalam menangani masalah terserbut. Data mining bentuk disiplin ilmu yang memiliki 5 peran antar lain metode klasifikasi. Pada metode klasifikasi yang mengunakan peluang yang ciri perhitungannya adalah metode naïve bayes. Metode naïve bayes sudah banyak digunakan untuk mengklasifikasikan beberapa penelitian terkait dengan ekonomi, kesehatan, dan lain sebagainya. Dari pengunaan naïve bayes, maka akan di evaluasi dengan X-Cross validation/ K-Fold validation. Dari perbandingan pengunaan fold validation maka nilai akurasi terbesar terdapat pada nilai 3 fold validation dengan nilai akurasi sebesar 95,96% dan untuk nilai recall pada percobaan metode fold validation semuanya mendapatkan nilai 100%. Nilai presisi paling tinggi pada percobaan 3 fold validation yaitu sebesar 87,96%.
Pelatihan Pembuatan Teh Bunga Telang sebagai Upaya Peningkatan Keterampilan dan Ekonomi Kreatif Ibu PKK Desa Tapelan Bojonegoro Aziz, Suudin; Prastya, Ifnu Wisma Dwi
Jurnal Pengabdian Masyarakat Vol. 1 No. 3 (2025): Jurnal Pengabdian Masyarakat (J-AbMas)
Publisher : CV. Dalle’ Deceng Abeeayla

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69623/j-abmas.v1i3.192

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan dalam bentuk pelatihan pembuatan teh bunga telang bagi ibu-ibu PKK Desa Tapelan, Kecamatan Ngraho, Kabupaten Bojonegoro. Tujuan kegiatan adalah meningkatkan pengetahuan dan keterampilan peserta dalam mengolah bunga telang (Clitoria ternatea) menjadi produk minuman herbal yang bernilai jual, sekaligus mendorong pengembangan ekonomi kreatif berbasis potensi lokal. Metode kegiatan meliputi tahap persiapan, penyampaian materi, demonstrasi, praktik langsung, serta evaluasi. Hasil pelatihan menunjukkan bahwa peserta mampu memahami proses pengolahan bunga telang, mulai dari pemilihan bahan, pengeringan, penyeduhan, hingga pengemasan sederhana. Antusiasme dan partisipasi aktif peserta menjadi indikasi keberhasilan kegiatan, sekaligus membuka peluang untuk pengembangan usaha rumah tangga berbasis teh telang. Dengan demikian, pelatihan ini memberikan manfaat nyata bagi peningkatan keterampilan masyarakat serta berpotensi mendukung pemberdayaan ekonomi keluarga di Desa Tapelan.
Indonesian Gold Price Forecasting Using Simple and Stacked LSTM with Expanding Window Lambang, Rahmat Tegar Patriot Hari; Prastya, Ifnu Wisma Dwi; Barata, Mula Agung Barata
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12148

Abstract

This study investigates the performance of two deep learning architectures, namely Simple LSTM and Stacked LSTM, for Indonesian gold price forecasting, with a particular focus on evaluating the effect of optimizer selection and learning rate configurations. An experimental framework is implemented using daily Indonesian gold price data from 2021 to 2024. Model performance is assessed using five-fold expanding window time series cross-validation to ensure robustness and avoid data leakage. Four adaptive training optimizers (Adam, Nadam, Adamax, and RMSprop) are evaluated across three learning-rate settings as part of a systematic sensitivity analysis of training hyperparameters. The results indicate that the Simple LSTM consistently outperforms the Stacked LSTM. The best performance is achieved by the Simple LSTM using the Adam optimizer with a learning rate of 0.01, yielding an RMSE of 9.235, MAE of 7.060, and MAPE of 0.71%. These findings demonstrate that simpler architectures combined with appropriate training configurations can provide superior forecasting accuracy for volatile financial time series.
Sentiment Analysis of the Free Nutritious Meal Program (MBG) on Social Media X (Twitter) Using K-Nearest Neighbor and Artificial Neural Network Hakim, Fernanda Amri; Prastya, Ifnu Wisma Dwi; Budiani, Jauhara Rana
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12205

Abstract

The Free Nutritious Meal Program (Makan Bergizi Gratis/MBG) is a national policy initiated by the Indonesian government to improve public nutritional status, particularly among children and vulnerable groups. Since its implementation, the program has generated extensive public discussion on social media, reflecting diverse opinions, support, and criticism. This study aims to analyze public sentiment toward the MBG program on social media X (Twitter) using machine learning-based text classification methods. A total of 9,038 Indonesian-language tweets were collected and processed through text preprocessing, semi-automatic sentiment labeling with manual validation, and feature extraction using the Term Frequency–Inverse Document Frequency (TF–IDF) method. Sentiments were classified into three categories: positive, neutral, and negative. The performance of K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), and ANN with class balancing using Synthetic Minority Over-Sampling Technique (ANN + SMOTE) was evaluated using accuracy, precision, recall, and F1-score metrics supported by confusion matrix analysis. The results indicate that the ANN + SMOTE model achieved the highest performance with an accuracy of 93.58%, outperforming ANN (92.59%) and KNN (86.28%). The sentiment distribution indicates that public opinion toward the MBG program is predominantly neutral (52.1%), followed by positive (40.0%) and negative (7.9%) sentiments. These findings suggest that while the MBG program is generally well received, negative sentiments provide important feedback related to program implementation and governance.
Optimization of Sleep Disorder Classification Using ANN with Multi-Method Feature Selection Kharisma, Devi Nova; Prastya, Ifnu Wisma Dwi; Saida, Ita Aristia
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1473

Abstract

Sleep disorders are health problems that can affect quality of life and have the potential to increase the risk of various chronic diseases. Therefore, a computational approach is needed to accurately and efficiently classify sleep disorders. The ANN model used has a two-layer hidden architecture with 128 and 64 neurons, respectively, and uses the ReLU activation function, equipped with a dropout layer to reduce overfitting. Three neurons with a softmax activation function make up the output layer, which produces probabilities for every class. To improve model performance, three feature selection methods were compared, namely Chi-Square, Information Gain, and Pearson Correlation. The test results showed that the ANN model without feature selection produced an accuracy of 89.3%. After feature selection, the model's performance improved significantly. The Chi-Square method produced 8 selected features with the highest accuracy of 97.3%, followed by Information Gain with 5 features and an accuracy of 97.3%, and Pearson Correlation with 3 features and an accuracy of 88.0%. The results of this study demonstrate that selecting appropriate features can significantly enhance an ANN's ability to categorize sleep problems. The proposed approach is expected to be a reference in the development of a more accurate sleep disorder diagnostic aid system.
Co-Authors Barata, Mula Agung Barata Hakim, Fernanda Amri Ita Aristia Sa'ida Jauhara Rana Budiani, Jauhara Rana Kharisma, Devi Nova Lambang, Rahmat Tegar Patriot Hari Pribadi , Teguh Sanjaya, Ucta Pradema suudin aziz