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All Journal Jurnal Sistem dan Informatika ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Multimedia JPIn : Jurnal Pendidik Indonesia Jurnal Bumigora Information Technology (BITe) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Abdi Cendekia: Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Ilmiah Pengabdian dan Inovasi Seminar Ilmiah Nasional Teknologi, Sains, dan Sosial Humaniora (SINTESA) Explore
Mardedi, Lalu Zazuli Azhar
Universitas Bumigora
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Sentiment Classification of Football Supporters Using NusaBERT Embeddings, BiLSTM, and BiGRU Methods Guntara, Muhammad; Gusti Ayu Diah Gita Kartika Santi, I; Hairani, Hairani; Lalu Zazuli Azhar Mardedi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6151

Abstract

Class imbalance and the use of non-standard language in football supporters’ opinions on social media constitute major obstacles to producing accurate sentiment classification for evaluating federation performance. This study aims to identify the most effective bidirectional recurrent architecture for capturing public opinion after applying data balancing techniques. Using a primary dataset of 1,039 instances (604 positive and 435 negative samples), the proposed method integrates a pre-trained NusaBERT model with hybrid Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) layers. To address data imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to the training data, with dataset partitioning using a stratified split ratio of 70:30. The results indicate that the NusaBERT-BiLSTM model achieves the best performance, with a testing accuracy of 70.83% and an F1-score of 0.6990, outperforming the BiGRU variant, which attains an accuracy of only 64.74%. Furthermore, NusaBERT-BiLSTM demonstrates greater reliability in detecting negative sentiment, achieving a recall value of 0.6336 compared to 0.4504 for BiGRU. In conclusion, combining NusaBERT's semantic strength with SMOTE-based balancing and BiLSTM layers significantly enhances the model’s sensitivity to minority opinions without causing data leakage. This study contributes a more objective classification model for national team management to accurately map public criticism and aspirations on social media.
Workshop dan Pelatihan Aplikasi Naskah Lontar Sasak sebagai Upaya Digitalisasi dan Pelestarian Budaya Lokal Sunardy Kasim; Lalu Zazuli Azhar Mardedi; Khairan Marzuki; Syamsurrijal Syamsurrijal; Diah Supatmiwati; Abdul Muhid
Abdi Cendekia : Jurnal Pengabdian Masyarakat Vol 5 No 2 (2026): Juni
Publisher : Yayasan Zia Salsabila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61253/abdicendekia.v5i2.569

Abstract

Kegiatan workshop dan pelatihan aplikasi naskah lontar Sasak merupakan upaya strategis dalam meningkatkan literasi budaya dan kemampuan pemanfaatan teknologi digital pada masyarakat. Lontar Sasak sebagai warisan budaya lokal mengandung nilai-nilai adat, sejarah, dan pengetahuan tradisional yang perlu dilestarikan melalui pendekatan yang adaptif terhadap perkembangan zaman. Oleh karena itu, kegiatan pengabdian ini difokuskan pada implementasi workshop dan pelatihan penggunaan aplikasi naskah lontar sebagai media pembelajaran dan pelestarian budaya. Program ini dilaksanakan oleh Bale Agung Ajar Wali bekerja sama dengan dosen Universitas Bumigora, serta didukung oleh Program Dana Indonesiana dari Kementerian Kebudayaan Republik Indonesia. Metode pelaksanaan meliputi sosialisasi, pelatihan teknis penggunaan aplikasi, praktik langsung oleh peserta, serta diskusi mengenai nilai-nilai budaya yang terkandung dalam naskah lontar. Hasil kegiatan menunjukkan bahwa workshop dan pelatihan mampu meningkatkan pemahaman peserta terhadap isi naskah lontar serta keterampilan dalam mengoperasikan aplikasi, termasuk dalam mengakses, membaca, dan menelusuri informasi dalam naskah digital. Selain itu, kegiatan ini juga mendorong peningkatan kesadaran peserta terhadap pentingnya pelestarian budaya lokal melalui pemanfaatan teknologi. Dengan demikian, workshop dan pelatihan aplikasi naskah lontar Sasak berperan sebagai model pengabdian yang efektif dalam mengintegrasikan literasi budaya dan literasi digital berbasis komunitas.
The Implementation of CIRC Strategy in Writing National and International Journal Articles Syamsurrijal; Dedy Febry Rachman; Lalu Zazuli Azhar Mardedi; Muhammad Fathoni; Ni Putu Sinta Dewi; Muhammad Zaki Pahrul Hadi
Jurnal Ilmiah Pengabdian dan Inovasi Vol. 2 No. 2 (2023): Jurnal Ilmiah Pengabdian dan Inovasi (Desember)
Publisher : Insan Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57248/jilpi.v2i2.339

Abstract

Buruknya dan rendahnya kemampuan menulis mahasiswa dan guru di Indonesia perlu mendapat perhatian serius dari pemerintah. Sebagai seorang pendidik dan calon pendidik, mereka harus dibekali kemampuan menulis yang baik dan benar. Pembelajaran berbasis pengalaman hidup menantang guru dan mahasiswa untuk memperoleh keterampilan yang diperlukan dalam memecahkan permasalahan kehidupan yang semakin kompleks. Pemilihan program Collaborative Integrated Reading and Writing (CIRC) didasarkan pada pertimbangan bahwa lahirnya sebuah ide memerlukan rangsangan. Untuk itu dilakukan pelatihan penulisan karya ilmiah standar nasional dan internasional. Tujuan pengabdian ini adalah menguji kefektifan penerapan tehnik CIRC dalam penulisan karya ilmiah berstandar nasional dan internasional. Metode pelaksanaan yaitu dengan membagi peserta menjadi beberapa kelompok, kemudian melakukan asesmen dan terakhir mengidentifikasi keberhasilan metode atautehnik CIRC dalam penulisan karya ilmiah. Hasil penelitian menunjukkan bahwa dengan tehnik CIRC kemampuan menulis siswa dan guru mengalami perubahan yang cukup baik, sebanyak 17 dari 37 siswa dan guru (45,95%) mampu merumuskan masalah dengan benar.  Ada 86,5% siswa dan guru yang sudah baik dan rapi dalam hal mekanik penulisan dan sistematika penulisan. Dari segi pengorganisasian ide, meskipun belum optimal, dapat dikatakan bahwa siswa dan guru sudah banyak mengalami peningkatan dibandingkan dengan siklus sebelumnya.
Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems Moch. Syahrir; Lalu Zazuli Azhar Mardedi
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 13 No. 2 (2023): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v13i2.52-67

Abstract

The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.
Co-Authors Abdul Muhid Apriani Apriani Ariyanto Ariyanto Baiq Chandra Herawati Dedi Aprianto Dedy Febry Rachman Diah Supatmiwati Dyah Susilowati Eria Raudhatul Jannah Fadli, M. Najmul Guntara, Muhammad Gusti Ayu Diah Gita Kartika Santi, I Guterres, Juvinal Ximenes Hairani Hairani HARYONO I Nyoman Miyarta Yasa I Putu Hariyadi Indah Puji Lestari Kartarina, Kartarina Khairan marzuki Lalu Ganda Rady Putra Miftahul Jihad Miftahul Madani Muhammad Fathoni Muhammad Haris Nasri Muhammad Idham Kholid Muhammad Innuddin Muhammad Ridho Hansyah Muhammad Zaki Pahrul Hadi Ni Putu Sinta Dewi Pahrul Irfan Rahmawati, Lela Regina Aprilia Riberu Riska Haerani Sasih Gunalan Setiawan, Pandu Dwi Sunardy Kasim Sutarman Sutarman Syahrir, Moch. Syamsurrijal Syamsurrijal Usman Ibnu Affan Wira Hendri