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All Journal IPTEK The Journal for Technology and Science JSI: Jurnal Sistem Informasi (E-Journal) JURNAL TEKNOLOGI DAN OPEN SOURCE JSAI (Journal Scientific and Applied Informatics) Journal of Computing Theories and Applications DIGIMAS: Jurnal Transformasi Digital Masyarakat
Brian Rizqi Paradisiaca Darnoto
Universitas Jember
Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Library & Information Science Other

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A Systematic Comparison of Software Requirements Classification Fajar Baskoro; Rasi Aziizah Andrahsmara; Brian Rizqi Paradisiaca Darnoto; Yoga Ari Tofan
IPTEK The Journal for Technology and Science Vol 32, No 3 (2021)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v32i3.13005

Abstract

Software requirements specification (SRS) is an essential part of software development. SRS has two features: functional requirements (FR) and non-functional requirements (NFR). Functional requirements define the needs that are directly in contact with stakeholders. Non-functional requirements describe how the software provides the means to carry out functional requirements. Non-functional requirements are often mixed with functional requirements. This study compares four primarily used machine learning methods for classifying functional and non-functional requirements. The contribution of our research is to use the PROMISE and SecReq (ePurse) dataset, then classify them by comparing the FastText+SVM, FastText+CNN, SVM, and CNN classification methods. CNN outperformed other methods on both datasets. The accuracy obtained by CNN on the PROMISE dataset is 99% and on the Seqreq dataset is 94%.
Language-Similarity-Guided Transfer Fine-Tuning of Pre-trained Transformer Models for Sentiment Analysis Across 12 Indonesian Regional Languages Brian Rizqi Paradisiaca Darnoto; Dony Bahtera Firmawan
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.15975

Abstract

Sentiment analysis for Indonesian regional languages faces two persistent challenges: labeled training data is extremely limited for most regional varieties, and transformer models pre-trained on Bahasa Indonesia do not generalize reliably to languages with substantially different morphological structures. Prior work on the NusaX benchmark has primarily relied on direct fine-tuning, treating each regional language independently and without exploiting linguistic proximity between related languages as a transfer signal. This paper proposes Language-Similarity-Guided Transfer (LSGT), a sequential fine-tuning strategy that first adapts a pre-trained model to a pivot language selected using character trigram similarity, followed by fine-tuning on the target language. Four transformer models are evaluated across all 12 NusaX languages using the official train/validation/test splits: IndoBERT, NusaBERT, mBERT, and XLM-R. Performance is evaluated using four metrics: accuracy, macro F1, macro precision, and macro recall. Experimental results show that LSGT improves macro F1 in 44 of 48 model-language combinations, demonstrating that the fine-tuning strategy itself is a major factor in low-resource cross-lingual sentiment classification. XLM-R benefits most strongly from LSGT, achieving an average improvement of +0.137 macro F1 and a peak gain of +0.298 on Madurese. SHAP-based token attribution analysis further reveals that predictions rely heavily on named entities and domain-specific nouns rather than sentiment-bearing vocabulary, indicating a dataset-level bias inherited from the original SmSA corpus and propagated through the NusaX translation pipeline.
Integrasi Metode Pengambilan Keputusan Multi kriteria untuk Penilaian Lahan Tembakau: Studi Kasus Menggunakan SMART, TOPSIS, dan AHP Dony Bahtera Firmawan; Brian Rizqi Paradisiaca Darnoto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8482

Abstract

The results of decision making play an important role in achieving a goal in solving certain problems. Decision making process requires data or supporting evidence that can be used as a guide for the selection of solutions based on available alternatives, to produce choices that can increase productivity. Multi Criteria Decision Making (MCDM) method for the analysis of research data namely SMART, TOPSIS, and AHP. The three methods are tested, because each MCDM method has a different way of working or algorithm, so it is necessary to experiment with certain cases. This study aims to determine the performance of the SMART, TOPSIS, and AHP methods with a case study of selecting of tobacco land recommendations. The application of three MCDM methods for alternative analysts of prospective tobacco land based on testing to determine the accuracy of comparing the results/output of the system with expert recommendation solutions using a sample of 10 tobacco land that produce priority/ranking for tobacco land recommendations, shows that the performance of the three methods produces priority selection results different, with an accuracy of SMART 80
Optimalisasi Limbah Kulit Semangka sebagai Produk Manisan untuk Meningkatkan Nilai Tambah dan Kemandirian Ekonomi Keluarga Amalia, Karina Nine; Ferdiansyah Putra Manggala; Abdurohman; Ahmad Muzaki; Ahmad Rofiki; Brian Rizqi Paradisiaca Darnoto; Dony Bahtera Firmawan; Tri Agustina Nugrahani; Katarina Leba; Muhammad Andryan Wahyu Saputra
Jurnal Transformasi Digital Masyarakat (DIGIMAS) Vol. 2 No. 1 (2026): Vol. 2 No. 1 (2026): DIGIMAS: Jurnal Transformasi Digital Masyarakat
Publisher : Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/digimas.v2i1.60033

Abstract

Semangka merupakan salah satu komoditas hortikultura yang banyak dikonsumsi masyarakat karena rasanya yang manis, menyegarkan, serta memiliki kandungan air yang tinggi. Namun, pemanfaatan buah semangka umumnya hanya berfokus pada bagian daging buah, sedangkan kulit semangka masih dianggap sebagai limbah rumah tangga yang belum memiliki nilai ekonomi optimal. Kondisi tersebut menunjukkan perlunya upaya pemberdayaan masyarakat melalui inovasi pengolahan limbah kulit semangka menjadi produk bernilai tambah. Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Kecamatan Mayang, Kabupaten Jember, dengan tujuan meningkatkan pengetahuan dan keterampilan masyarakat dalam mengolah kulit semangka menjadi produk manisan, sekaligus memberikan edukasi mengenai strategi pemasaran produk secara daring maupun luring. Metode pelaksanaan kegiatan meliputi tahap sosialisasi, penyuluhan, demonstrasi pembuatan manisan kulit semangka, serta pendampingan pemasaran produk. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta mengenai potensi pemanfaatan limbah kulit semangka dan kemampuan peserta dalam menghasilkan produk olahan yang layak konsumsi dan memiliki nilai jual. Selain itu, peserta juga memperoleh wawasan terkait pengemasan dan pemasaran digital sebagai upaya pengembangan usaha berbasis pangan lokal. Kegiatan ini diharapkan dapat menjadi alternatif pemberdayaan ekonomi masyarakat sekaligus mendukung pengurangan limbah pangan melalui pemanfaatan produk hortikultura secara berkelanjutan.
A Deep Learning-Based Sentiment Classification for Identifying Advertorial Content in Online News Brian Rizqi Paradisiaca Darnoto; Dony Bahtera Firmawan; Fahrobby Adnan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 1 (2025): Jurnal Teknologi dan Open Source, June 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i1.4450

Abstract

The rapid advancement of technology and the widespread use of the internet have brought significant positive and transformative impacts across various aspects of human life, including finance, healthcare, education, and the media industry. One notable consequence of information transparency is the vast availability and large-scale exchange of data. However, this also presents new challenges, particularly in the spread of misleading content such as disguised advertorials that resemble genuine news. This threatens the objectivity of the information received by the public. To address this issue, an automated solution is needed to identify the distinguishing characteristics of advertorials in online news content. This study proposes a deep learning approach using the Convolutional Neural Network (CNN) model to detect sentiment as an indicator of advertorial content. CNN is a widely used deep learning model for processing sequential and spatial data, capable of automatically learning features from text. The dataset comprises news articles categorized by advertorial traits, such as positive or neutral sentiment, persuasive language, and promotional content highlighting specific entities. The data undergo several processing stages, including text preprocessing, tokenization, padding, and CNN model training. Model performance is evaluated using accuracy, precision, recall, and F1-score. The experimental results show a validation accuracy of 84%, although overfitting issues were observed. Despite ongoing limitations, such as restricted data and suboptimal parameter tuning, the findings suggest that the CNN model has potential for automatically detecting advertorial content and can serve as a basis for future research using more advanced models and refined parameter adjustments.
Sistem Pendukung Keputusan Pemilihan Tanaman Pangan Berdasarkan Rotasi Tanam Pada Zona Agroekologi Alf3.1 Menggunakan Metode Simple Multi Attribute Rating Technique Brian Rizqi Paradisiaca Darnoto
JSI: Jurnal Sistem Informasi (E-Journal) Vol 17 No 2 (2025): JSI: Jurnal Sistem Informasi (E-Journal)
Publisher : Jurusan Sistem Informasi Fakultas Ilmu Komputer Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/jsi.v17i2.240

Abstract

Indonesia merupakan negara agraris dengan pertanian sebagai salah satu sektor ekonomi utama bagi masyarakat. Hal tersebut menunjukkan besarnya potensi sektor pertanian di Indonesia sebagai sektor dengan pertumbuhan tertinggi pada sisi produksi. Tanaman pangan seperti padi, jagung, dan aneka kacang memiliki peran penting karena digunakan sebagai makanan pokok masyarakat Indonesia.). Kementerian Pertanian yang tertuang dalam Rencana Strategis Kementerian Pertanian tahun 2020–2024, dengan target 65–80% pada tahun 2020 dan 80–95% pada tahun 2024. Budidaya tanaman pangan yang efektif dan efisien dibutuhkan untuk memaksimalkan produksi tanaman pangan. Salah satu metode budidaya tanaman yang umum digunakan petani Indonesia untuk meningkatkan produktifitas tanaman adalah rotasi tanaman. Rotasi tanaman merupakan sistem budidaya tanaman dengan menggilir atau menanam tanaman lebih dari satu jenis dalam waktu yang tidak bersamaan. Sistem yang dapat memberikan rekomendasi tanaman pangan berdasarkan rotasi tanam adalah sistem pendukung keputusan. Dalam penelitian ini menggunakan salah satu metode Simple Multi Attribute Rating Technique (SMART) yang akan di implementasikan ke dalam sistem yang dapat memberikan rekomendasi tanaman pangan berdasarkan suhu, curah hujan, tekstur tanah, kedalaman tanah, ph, bahaya erosi, drainase dan rotasi tanam. Berdasarkan hasil pembahasan memiliki presentase sebesar 90% sesuai dengan perhitungan sistem, perhitungan pakar dan buku.
Cross-Domain Faithfulness Evaluation of SHAP and Attention-Based Explanations in Transformer NLP Models Dony Bahtera Firmawan; Brian Rizqi Paradisiaca Darnoto
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.16258

Abstract

Transformer-based models such as BERT, RoBERTa, DistilBERT, and DeBERTa have achieved remarkable performance across a wide range of natural language processing (NLP) tasks. However, their decision-making processes remain difficult to interpret, particularly in high-risk applications such as hate speech detection, where unreliable explanations may undermine model transparency, trust, and accountability. This study investigates whether explainability methods remain faithful and stable under domain shift in transformer-based text classification. Four transformer architectures were fine-tuned and evaluated on two linguistically distinct datasets: IMDb Movie Reviews and Hate Speech Offensive. Model performance and explanation quality were assessed using classification accuracy, macro F1-score, top-k token-removal faithfulness analysis, and cross-domain Spearman rank correlation. Experimental results show that DeBERTa achieved the highest classification performance, reaching accuracies of 95.6% on IMDb and 91.3% on Hate Speech. Across all evaluated models and datasets, SHAP consistently produced higher faithfulness scores than attention-based explanations. Cross-domain analysis further revealed reduced agreement between SHAP and attention-based explanations under domain shift, indicating lower explanation consistency across linguistically distinct domains. Qualitative error analysis further showed that implicit sentiment, sarcasm, and domain-specific slang remain major sources of prediction errors. Overall, the results demonstrate that superior predictive performance does not necessarily correspond to higher explanation faithfulness or stronger cross-domain stability. These findings highlight the importance of jointly evaluating predictive performance, explanation faithfulness, and explanation robustness when developing trustworthy transformer-based NLP systems.
AgriMisinfo-ID: A Multi-Platform Dataset and Ensemble Transformer-Based Detection System for Agricultural Misinformation in Indonesian Social Media Brian Rizqi Paradisiaca Darnoto; Nelly Oktavia Adiwijaya; Dony Bahtera Firmawan; Fadhel Akhmad Hizham; Nandini Putri Hanifa Jannah; Talitha Puspitasari; Fabyan Yastika Permana
Journal of Computing Theories and Applications Vol. 4 No. 1 (2026): JCTA 4(1) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17025

Abstract

Agricultural misinformation on social media poses risks to Indonesian food security and farmer livelihoods. False claims about fertilizers, pesticides, crop varieties, and farming practices can spread rapidly across social media platforms such as Instagram, YouTube, and Facebook, as well as through publicly available datasets, potentially influencing agricultural decisions and outcomes. This paper introduces AgriMisinfo-ID, a bilingual, multi-platform dataset for agricultural misinformation detection, containing 5,288 labeled samples collected from social media and supplementary public datasets across Indonesian agricultural contexts. A hybrid detection system is proposed that combines an ensemble of fine-tuned Transformer models, IndoBERT and XLM-RoBERTa, with a Knowledge Verification module that cross-references agricultural claims against Wikidata and Wikipedia. Training uses Focal Loss to address class imbalance, together with GPT-4o-mini-based paraphrase augmentation for minority classes. Across three random seeds, the weighted ensemble achieves an F1-Macro of 0.5870 ± 0.0028 and an accuracy of 0.8027 ± 0.0039 on the test set, outperforming the individual models, a TF-IDF/SVM baseline, and an equal-weight ensemble in terms of F1-Macro. The Knowledge Verification module provides evidence-based verdicts that can support the inspection and auditability of model decisions. This work provides a reproducible benchmark for agricultural misinformation research in bilingual, low-resource settings.
Co-Authors Abdurohman Ahmad Muzaki Ahmad Rofiki Amalia, Karina Nine Baskoro, Fajar Dony Bahtera Firmawan Fabyan Yastika Permana Fadhel Akhmad Hizham Fahrobby Adnan Ferdiansyah Putra Manggala Katarina Leba Muhammad Andryan Wahyu Saputra Nandini Putri Hanifa Jannah Oktavia, Nelly Rasi Aziizah Andrahsmara Talitha Puspitasari Tri Agustina Nugrahani Yoga Ari Tofan