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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Sains dan Teknologi Jurnal Teknologi Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Applied Information System and Management Jurnal Pengabdian Masyarakat AbdiMas JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal FATEKSA : Jurnal Teknologi dan Rekayasa JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Jurnal ilmiah teknologi informasi Asia Jurnal Sistem Informasi Komputer dan Teknologi Informasi
TONNY HIDAYAT
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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Modified Quantization Strategy for Enhanced Lossless RAW Audio Compression Tonny Hidayat; Hendra Kurniawan; Hastari Utama; Dewi Anisa Istiqomah; Joko Dwi Santoso
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 5 (2026): October 2026 (In progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i5.7512

Abstract

The amount of audio data required in today's world is rapidly expanding, posing a significant problem and challenge in terms of media storage and transfer. RAW audio data compression has grown in popularity due to its ability to lower file size without sacrificing quality. To anticipate the complexity of increasing the lossless audio compression ratio, this study proposes a lossless audio data compression algorithm that theoretically increases its efficiency ratio, thus providing multiple compression that combines linear prediction methods, integer wavelet transforms, and as a back-end process using Huffman code for binary output coefficients. The combined transformation produced employs a new prediction form and coefficient process, which is a novelty and the primary contribution of the coding of this algorithm structure. Based on the test results in many audio categories, the obtained results demonstrate that the proposed algorithm surpasses the performance of previous studies. The compression ratio increased from 2% to 5%. The algorithm discovered in this work is ideal for compressing audio with complex patterns and high irregularity using a quick compression procedure.
Comparison of Multilingual Model Sensitivity for Political Fact Verification with Integrated Multi-Evidence Nova Agustina; Kusrini Kusrini; Ema Utami; Tonny Hidayat
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1198

Abstract

Political news is frequently targeted by the dissemination of fake news on social media, which can influence public opinion and undermine trust in democratic processes. The main challenge in addressing this issue lies in the limited sensitivity of cross-lingual fact verification models in capturing semantic relationships between claims and evidence in long-text, multi-evidence settings. Existing approaches often struggle to assess the relevance and quality of evidence, resulting in suboptimal verification performance. This study compares three multilingual Large Language Models (LLMs), namely mBERT, XLM-R, and LaBSE, for political fact verification using an integrated multi-evidence approach. Experiments are conducted on the PolitiFact dataset, with performance evaluated using sensitivity, accuracy, precision, and F1-score metrics.The results indicate that mBERT achieves the highest overall sensitivity at 89.44%, followed by LaBSE at 81.81% and XLM-R at 78.81%. However, mBERT exhibits lower precision, whereas LaBSE provides a better balance between precision (87.02%) and accuracy (86.46%), resulting in an F1-score of 84.33%. XLM-R demonstrates lower sensitivity but maintains competitive precision (85.47%) and accuracy (84.60%), with an F1-score of 82.00%. Sensitivity analysis based on the number of evidence reveals distinct model behaviors, where mBERT performs optimally with six pieces of evidence, XLM-R is more effective under limited evidence conditions, and LaBSE shows a stable and increasing sensitivity trend as the amount of evidence increases, indicating robustness in multi-evidence scenarios. Further statistical analysis shows that XLM-R has the lowest performance variance, while LaBSE statistically outperforms mBERT in several evaluation aspects. Overall, LaBSE is recommended as the most balanced model for multi-evidence-based political fact verification.
Hybrid Residual SARIMAX-XGBoost for Provincial Rice Production Forecasting in Indonesia: A Systematic Literature Review and Conceptual Framework for Food Policy Decision Support Imannudin Akbar; Tonny Hidayat; Alva Hendi Muhammad; Emha Taufiq Luthfi
Applied Information System and Management (AISM) Vol. 9 No. 2 (2026): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v9i2.51470

Abstract

This study contributes a systematic literature review and conceptual framework for provincial rice production forecasting in Indonesia by integrating residual-based hybrid modelling, explainability, and food policy decision support. A total of 60 articles published between 2023 and 2026 were selected from 124 initial records identified from six academic search sources: ScienceDirect/Elsevier, IEEE Xplore, SpringerLink, MDPI journals, Scopus/Crossref tracing, and Google Scholar/manual tracking, using a PRISMA-style screening process. The review synthesizes evidence across rice production forecasting, crop-yield forecasting, hybrid machine learning, explainable artificial intelligence, and decision-support-oriented forecasting. The literature findings show that recent studies increasingly use agricultural statistics, climate variables, remote sensing, UAV imagery, crop-model outputs, machine learning, and deep learning. However, the reviewed studies rarely integrate province-aware determinants, residual-based model decomposition, time-aware validation, residual-level explainability, and DSS-ready policy outputs within a single framework. Based on this gap, the study proposes a conceptual Hybrid Residual SARIMAX-XGBoost framework. SARIMAX is positioned to model temporal, seasonal, autoregressive, and exogenous baseline patterns, while XGBoost learns nonlinear residual structures. SHAP is positioned as an explanation layer for interpreting forecast adjustments rather than as causal evidence. The proposed framework remains conceptual and requires future empirical validation using Indonesian provincial rice production data. This review provides theoretical contributions to hybrid forecasting literature and practical implications for information systems, decision support, and public-sector food policy planning.
Co-Authors AGUS PURWANTO Ainul Yaqin Akbar, Imannudin Alexander Phuk Tjilen Alfian, Wahyu Ali Mustopa, Ali Alif Syaiful Huda Alva Hendi Muhammad Alva Hendi Muhammad Andalantama, Yudikha Andi Sunyoto Arief Setyanto Aris Harwanto Arsad Asro Nasiri Bentar Candra P Dewi Anisa Istiqomah Dhani Ariatmanto Ema Utami Ema Utami Emha Taufiq Luthfi Firza Septian Hendra Kurniawan Ika Asti Astuti Ika Asti Astuti Ilmawati, Fahma Inti JOKO DWI SANTOSO Joko Dwi Santoso Kusrini Kusrini Kusrini Liana Trihardianingsih M. Ilham Arief Maie Istighosah Mawene, Brenstein Mohammad Rezza Pahlevi Muhammad Abdul Basit Muhammad Anwar Fauzi Mulyatun, Sri Nova Agustina Risna Sari Riswanto Riswanto Safril, Martinus Seniwati, Erni Setiana, Elia Setiawanto, Irwan TEGUH ARIFIANTO, TEGUH Theofanis Orphanoudakis Tutut Dwi Prihatin Utama, Hastari Yudha Randa Mad’hika Zulkarnain, Jefri