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All Journal JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah MEA (Manajemen, Ekonomi, dan Akuntansi) Riwayat: Educational Journal of History and Humanities
Budiyanto, Very
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Social Sciences

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PENGARUH KEBIJAKAN MONETER TERHADAP PERTUMBUHAN EKONOMI Budiyanto, Very; Wibowo, Wisnu

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Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (74.802 KB) | DOI: 10.31955/mea.v5i1.876

Abstract

This study investigates the effect of monetary policy on economic growth in Indonesia. Gross Domestic Product (GDP) is used as the dependent variable on the explanatory variables of monetary policy: inflation, money supply (M2), exchange rates and interest rates”.Time series data are from 1986 to 2019. This study adopts the Ordinary Least Squared (OLS) technique.”The results showed that inflation has a significant and negative effect on economic growth in Indonesia, the money supply (M2) and the exchange rate are significant variables affecting economic growth in Indonesia”. Meanwhile, interest rates do not have a significant and negative effect on economic growth in Indonesia
OPTIMASI MODEL DENGAN ALGORITMA SUPPORT VECTOR REGRESSOR MENGGUNAKAN GRID SEARCH PADA PENILAIAN ESSAI OTOMATIS Radiatul Kamila, Ahya; Budiyanto, Very
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13704

Abstract

Proses evaluasi siswa adalah bagian penting dalam pendidikan untuk mengukur pemahaman dan kemajuan siswa secara menyeluruh. Evaluasi yang efektif harus bersifat komprehensif dan adil, salah satunya melalui soal esai yang memungkinkan siswa menunjukkan kemampuan berpikir kritis dan analitis. Namun, penilaian esai menghadapi tantangan seperti subjektivitas, waktu koreksi yang lebih lama, dan konsistensi penilaian. Untuk mengatasi masalah ini, digunakan penilaian esai otomatis dengan algoritma machine learning. Agar model machine learning dapat bekerja dengan baik, diperlukan data berkualitas dan pendekatan yang tepat. Penelitian ini berfokus pada optimasi algoritma Support Vector Regressor (SVR) untuk meningkatkan performa model. Optimasi dilakukan dengan penyesuaian pada tahap preprocessing dan modeling. Pada preprocessing, dibuat fitur baru, sementara pada modeling dilakukan perbandingan sebelum dan setelah tuning hyperparameter menggunakan Grid Search. Hasil penelitian menunjukkan bahwa optimasi SVR dengan Grid Search berhasil meningkatkan performa model, terbukti dari penurunan nilai MSE dari 0.0393 menjadi 0.0325. Selain itu, waktu komputasi sebelum dan setelah penggunaan Grid Search juga dibandingkan, yaitu 148ms dan 502.2ms. Meskipun Grid Search memerlukan waktu lebih lama, penurunan error yang signifikan sebanding dengan waktu tambahan tersebut.
Exploring the Effectiveness of Bi-LSTM in Detecting Indonesian-Language Hoax News Kamila, Ahya Radiatul; Budiyanto, Very; Surianto, Surianto
Riwayat: Educational Journal of History and Humanities Vol 8, No 3 (2025): July, Social Studies, Educational Research and Humanities Research.
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i3.48627

Abstract

This study aims to develop and evaluate a hoax detection model based on Bidirectional Long Short-Term Memory (Bi-LSTM) using a Semi-Supervised Learning approach. In the context of the increasing spread of false information on online platforms, the model is designed to automatically classify news articles as hoaxes or non-hoaxes, even when labeled data is limited. The initial model was trained on a labeled minor dataset and then used to predict labels for an unlabeled major dataset. After combining both datasets, a retraining process was conducted to improve the models generalization to various linguistic styles and sentence structures. Evaluation results show that the model achieved an accuracy of 84%, recall of 76.9%, precision of 70%, and an F1-score of 73.3%. These findings demonstrate that the semi-supervised approach, which combines labeled and unlabeled data, can significantly enhance model performance in hoax detection tasks. This study contributes to the development of an effective and adaptable automated hoax detection system that addresses linguistic challenges in online news texts.
Evaluation of Keyword Extraction using YAKE and KeyBERT in Text Preprocessing for Hoax News Detection Based on Bi-LSTM Kamila, Ahya Radiatul; Derhass, Gerry Hudera; Surianto, Surianto; Budiyanto, Very
Riwayat: Educational Journal of History and Humanities Vol 8, No 3 (2025): July, Social Studies, Educational Research and Humanities Research.
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/jr.v8i3.48626

Abstract

The spread of hoaxes through social media presents a significant challenge to the accuracy of public information. Automated detection based on natural language processing (NLP) offers a potential solution to this issue. This study investigates the impact of keyword extraction methods on the performance of hoax classification using the Bidirectional Long Short-Term Memory (Bi-LSTM) architecture. Two methods are evaluated: YAKE, which relies on statistical features, and KeyBERT, which utilizes semantic representations from the BERT transformer model. The IDNHoaxCorpus, an Indonesian-language dataset, serves as the experimental basis, undergoing preprocessing, keyword extraction, and model training stages. Evaluation metrics include accuracy, precision, recall, F1-score, and processing time. Results show that KeyBERT achieves higher accuracy and F1-score (82.56% and 73.30%, respectively) compared to YAKE (80.07% and 71.11%), but at the cost of significantly longer processing time (360 seconds vs. 13 seconds). These findings highlight a notable trade-off between accuracy and computational efficiency, which should be considered based on application requirements such as real-time systems or batch processing. This study underscores the importance of selecting appropriate feature extraction strategies in text-based hoax detection systems.
Co-Authors Derhass, Gerry Hudera Kamila, Ahya Radiatul Radiatul Kamila, Ahya Surianto Surianto Wibowo, Wisnu