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All Journal Abdimasku : Jurnal Pengabdian Masyarakat Journal of Computing Theories and Applications
Aris Marjuni
Universitas Dian Nuswantoro
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Health Professions Industrial & Manufacturing Engineering Social Sciences

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Pelatihan Peningkatan Ketrampilan Desain dan Layout Buku Elektronik bagi Guru dan Dosen Aris Marjuni; Daurat Sinaga; Edi Sugiarto; Nova Rijati
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1812

Abstract

Buku, merupakan salah satu bentuk media pembelajaran yang digunakan untuk menyampaikan bahan pembelajaran kepada peserta didik. Profesi guru dan dosen mengharuskan untuk selalu menyusun dan memperbaharui bahan pembelajaran sesuai jenjang dan obyek pembelajarannya. Tugas pembuatan bahan pembelajaran tersebut pada era digital saat ini telah dimudahkan melalui pembuatan buku elektronik atau e-book. Berkembangnya e-book memungkinkan pendidik untuk berimprovisasi menyajikan bahan pembelajaran yang uptodate dan menarik. Penyusun e-book dapat memasukkan konten-konten grafis maupun tekstual yang lebih menarik berbiaya murah dengan teknik desain grafis. Namun demikian, tidak semua pendidik memiliki kemampuan dalam membuat e-book yang menarik. Kegiatan peningkatan ketrampilan teknik desain dan layout pembuatan buku elektronik bagi guru dan dosen ini bertujuan untuk menambah wawasan, pengetahuan, dan ketrampilan teknik desain dan layout pembuatan e-book, terutama bagi guru dan dosen yang belum memiliki pengalaman dalam bidang desain grafis. Pelatihan dilaksanakan secara daring dan diikuti oleh 137 peserta guru dan dosen dari sekolah dan perguruan tinggi. Hasil pelatihan menunjukkan bahwa 93% peserta menyatakan bertambah pengetahuan dan ketrampilan dalam hal teknik desain dan layout pembuatan e-book berdasarkan tugas yang diberikan dari pelatihan.
Attention-Augmented GRU for Stock Forecasting: A Trade-Off Between Directional Accuracy and Price Prediction Error R. Daniel Hartanto; Guruh Fajar Shidik; Farrikh Alzami; Ahmad Zainul Fanani; Aris Marjuni; Abdul Syukur
Journal of Computing Theories and Applications Vol. 3 No. 4 (2026): JCTA 3(4) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.15863

Abstract

Attention mechanisms have been widely incorporated into recurrent neural network architectures for financial time series forecasting, with most prior work reporting improvements in price-level error metrics. This study revisits that claim through a controlled empirical comparison of four deep learning architectures on nearly two decades of Telkom Indonesia (TLKM) closing price data from the Indonesia Stock Exchange (IDX). The models evaluated are a three-layer Gated Recurrent Unit (GRU) baseline, a comparable Long Short-Term Memory (LSTM) network, a Bahdanau end-attention GRU (Attn-GRU-V2), and a multi-head self-attention GRU hybrid (Attn-GRU-V3). Each architecture is trained over 30 independent runs with distinct random seeds, and performance is reported as 95% confidence intervals derived from the t-distribution. Statistical comparisons employ the Wilcoxon signed-rank test, a nonparametric paired test appropriate given the confirmed non-normality of residuals. The main finding is a consistent trade-off: the plain GRU achieves the lowest RMSE (94.02 ± 1.22 IDR) across all 30 runs, while Attn-GRU-V2 achieves the highest directional accuracy (45.91 ± 0.09%), surpassing GRU in every independent run. Bahdanau attention weights are nearly uniform across the 30-day lookback window (coefficient of variation: 3.21%), indicating that the mechanism cannot identify selectively informative timesteps in this univariate price series. This finding is consistent with the weak-form Efficient Market Hypothesis for the Indonesian market. An ablation study reveals that a 20-day lookback window maximizes directional accuracy (47.72 ± 0.21%) for the Attn-GRU-V2 model. These results suggest that Bahdanau end-attention consistently and significantly improves directional accuracy relative to a plain GRU baseline, providing an architecturally attributable advantage for direction-based applications, even when absolute price-level error is not reduced. The directional accuracy values remaining below 50% across all models are consistent with a weak-form efficiency characterization of the Indonesian market.
Co-Authors Abdul Syukur Ahmad Zainul Fanani Alzami, Farrikh Edi Sugiarto Guruh Fajar Shidik Nova Rijati R. Daniel Hartanto Sinaga, Daurat