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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) TEKNIK INFORMATIKA Jurnal Civics: Media Kajian Kewarganegaraan JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mantik Penusa MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Varian Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Abdi Masyarakat Indonesia Indo-MathEdu Intellectuals Journal Abdi Widya: Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Jurnal Ilmiah Pengabdian dan Inovasi INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT
Anthony Anggrawan
Bumigora University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Public Health Social Sciences Other

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Perancangan 3D Voxel Modeling Budaya Peresean dalam Addon Minecraft sebagai Media Edukasi Digital Rahman, Taufik; Anggrawan, Anthony; Hasbullah, Hasbullah
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 1 (2026): Februari - April
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i1.7013

Abstract

Penelitian ini bertujuan untuk merancang dan mengembangkan 3D voxel modeling budaya Peresean Sasak dalam bentuk addon Minecraft sebagai media edukasi digital berbasis game. Latar belakang penelitian ini didasarkan pada rendahnya minat generasi muda terhadap budaya lokal serta perlunya inovasi media pembelajaran yang interaktif dan kontekstual. Penelitian ini menggunakan metode perancangan dengan pendekatan kualitatif deskriptif. Proses pengembangan dilakukan melalui tahapan Design Thinking yang meliputi emphatize, define, ideate, prototype, dan test. Teknik pengumpulan data dilakukan melalui wawancara, observasi, dan dokumentasi untuk memperoleh pemahaman mendalam mengenai nilai budaya Peresean serta kebutuhan pengguna. Proses modeling dilakukan menggunakan aplikasi Blockbench untuk menghasilkan model karakter pepadu, penjalin, serta atribut budaya seperti sapuq, kain songket, rotan, dan tameng dalam format voxel yang kompatibel dengan sistem Minecraft. Hasil penelitian menunjukkan bahwa elemen budaya Peresean dapat diadaptasi ke dalam bentuk visual berbasis blok tanpa menghilangkan identitas dan makna simboliknya. Penyederhanaan bentuk akibat keterbatasan sistem voxel tetap mampu mempertahankan karakteristik budaya secara visual. Selain itu, hasil uji coba menunjukkan bahwa media berbasis game interaktif memiliki potensi meningkatkan ketertarikan generasi muda terhadap budaya lokal. Dengan demikian, pengembangan addon Minecraft berbasis budaya Peresean dapat menjadi alternatif media edukasi digital dalam mendukung pelestarian budaya Sasak.
Enhancing support vector machine performance using particle swarm optimization for sentiment analysis Christofer Satria; Anthony Anggrawan; Peter Wijaya Sugijanto; Husain Husain; I Nyoman Yoga Sumadewa; Victoria Cynthia Rebecca
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i2.pp523-534

Abstract

Recently, social media has established itself as a leading platform in various sectors. Meanwhile, text extraction and sentiment analysis classification have attracted significant attention in research. Regrettably, traditional sentiment analysis often falls short of accurately capturing sentiment nuances. At the same time, machine learning has enabled more effective sentiment analysis, data mining, and classification, as well as the development of models that incorporate artificial intelligence. Therefore, the purpose of this study is to optimize sentiment analysis of public opinion in social media regarding Grand Prix motorcycle racing (MotoGP) and World Superbike (WSBK) events using machine learning and an optimized machine learning method. This study applies the support vector machine (SVM) machine learning method and enhances its performance through optimization by integrating it with the particle swarm optimization (PSO) algorithm. This study found that the SVM method achieved 80.15% accuracy, 75.63% recall, and 76.89% F1-score. In contrast, the SVM method combined with PSO achieves accuracies of 81.82%, 79.9%, and 79.62% for recall, precision, and F1-score, respectively, in classifying the sentiment of sporting events. The implications suggest that applying Hybrid SVM with PSO significantly enhances classification accuracy in sentiment analysis.
Pembuatan Animasi Game Budaya Sasak Sebagai Media Pembelajaran Muatan Lokal Anthony Anggrawan; Hasbullah Hasbullah; I Gde Ary Putra Waisnawa; Christofer Satria
Jurnal SASAK : Desain Visual dan Komunikasi Vol. 7 No. 2 (2025): SASAK
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/sasak.v7i2.5302

Abstract

Seiring dengan pesatnya perkembangan teknologi digital, media game banyak dimanfaatkan sebagai sarana edukasi, termasuk dalam pembelajaran muatan lokal di sekolah dasar. Tujuan dari penelitian ini adalah merancang game digital platform bertema eksplorasi budaya Sasak sebagai media pembelajaran muatan lokal bagi peserta didik sekolah dasar. Game ini dikembangkan untuk memfasilitasi pemahaman budaya lokal secara interaktif dan menyenangkan. Metode yang digunakan dalam penelitian ini adalah pendekatan Design Thinking, yang mencakup tahapan perumusan ide cerita, penyusunan skenario, pembuatan storyboard, pemodelan dan animasi 3D dengan perangkat lunak 3ds Max, serta pengeditan video dan sound rendering melalui Wondershare Filmora. Hasil penelitian ini adalah seluruh fitur game berfungsi optimal berdasarkan uji coba black box kepada 30 responden, di mana 86,7% menyatakan antarmuka mudah digunakan dan 83,3% menunjukkan peningkatan ketertarikan terhadap budaya Sasak. Kesimpulan penelitian ini adalah media game edukasi berbasis digital berpotensi besar sebagai sarana pembelajaran muatan lokal yang menarik, interaktif, dan efektif dalam memperkenalkan budaya daerah kepada siswa sekolah dasar.     
A Locally Grounded Retrieval-Augmented LLM-Based Chatbot for Bilingual Stunting Prevention Consultation among Health Cadres in Indonesia Tanwir, Tanwir; Hidjah, Khasnur; Susilowati, Dyah; Anggrawan, Anthony; Sulistianingsih, Neny
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5459

Abstract

Stunting remains a major public health challenge in Indonesia, affecting 21.6% of children under five nationally and 18.34% in Nusa Tenggara Barat (NTB), which strains the capacity of health cadres to deliver timely and accurate nutrition education. This study aims to develop a consultation chatbot by integrating Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to provide context-aware stunting prevention guidance. A total of 45 journal articles and 7 books were curated to construct 7,642 question–answer pairs using a RAG-based pipeline. Text preprocessing involved segmentation, embedding, and Byte Pair Encoding tokenization, followed by fine-tuning a LLaMA 3 model on an NVIDIA L4 GPU. Model performance was evaluated using ROUGE and BERTScore metrics, complemented by a small pilot usability assessment. The RAG-integrated model achieved a ROUGE-1 score of 81.03% and a BERTScore F1 of 93.48%, consistently outperforming baseline models. These findings demonstrate the potential of RAG-enhanced LLMs to support scalable and accessible health informatics solutions for empowering health cadres in resource-limited and rural settings.
Digital citizenship for sustainable development goals: A character-based approach in civic education Suriadi Ardiansyah; Anthony Anggrawan; Dadang Iskandar
Jurnal Civics: Media Kajian Kewarganegaraan Vol. 23 No. 1 (2026)
Publisher : Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jc.v23i1.88912

Abstract

This paper explores the transformative role of character-based digital citizenship in redefining civic education in the digital era. Amid the rapid flow of information and the growing negative impact of technology on national values, civic education must be adaptive and relevant. The study highlights the strategic contribution of digital citizenship to achieving the Sustainable Development Goals, in particular Target 4.7, which emphasises education for sustainable development and global citizenship, and Target 16, which supports peaceful and inclusive societies and access to justice. This research applies a descriptive qualitative approach, using a systematic literature review strategy combined with thematic analysis, to critically and in-depth explore relevant scientific literature. These findings suggest that integrating character education into digital citizenship fosters ethical awareness, responsible digital behaviour, and democratic participation. This synergy strengthens civic competence and promotes inclusive, peaceful, and sustainable educational settings. The study provides strategic insights for educators, curriculum developers, and policymakers, proposing new conceptual models that align digital citizenship with global citizenship goals and advance 21st-century citizenship learning.
Menggugah Kreativitas Siswa Sekolah Menengah Kejuruan Negeri 1 Sikur dalam Menciptakan Identitas Brand Produk Makanan yang Ikonik Hasbullah Hasbullah; Anthony Anggrawan; Christofer Satria; I Nyoman Yoga Sumadewa; Elyakim Nova Supriyedi Patty; Ni Gusti Ayu Dasriani
ADMA : Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol. 7 No. 1 (2026): ADMA: Jurnal Pengabdian dan Pemberdayaan Mayarakat
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/adma.v7i1.6259

Abstract

Persaingan industri makanan yang semakin kompetitif menuntut pelaku usaha, termasuk lulusan SMK, tidak hanya memiliki keahlian dalam mengolah rasa, tetapi juga dalam membangun identitas visual yang kuat. Faktanya, banyak produk makanan hasil praktik siswa SMK kelas XII yang memiliki kualitas rasa tinggi namun belum didukung oleh branding dan desain kemasan yang mampu menarik minat pasar. Kegiatan pengabdian masyarakat ini bertujuan untuk menggugah kreativitas siswa SMK dalam menciptakan identitas brand produk makanan yang ikonik dan kompetitif melalui pendampingan intensif. Program ini dilaksanakan menggunakan metode workshop interaktif dan pendampingan praktis. Tahapan kegiatan meliputi: 1) Pemberian materi tentang fundamental branding dan psikologi warna; 2) Praktik desain kemasan menggunakan aplikasi digital sederhana; dan 3) Evaluasi hasil karya melalui sesi presentasi produk. Peserta kegiatan adalah siswa kelas XII SMK Negeri 1 Sikur Lombok Timur yang sedang mempersiapkan tugas akhir atau proyek kewirausahaan. Melalui pendampingan ini, siswa berhasil menciptakan prototipe desain kemasan yang lebih modern, dan memenuhi standar pelabelan produk makanan. Kreativitas siswa terlihat pada kemampuan dalam memadukan kearifan lokal produk dengan estetika visual kekinian yang disukai generasi Z. Kesimpulan kegiatan ini berhasil memberikan bekal keterampilan praktis bagi siswa SMK untuk menjadi wirausahawan muda yang siap bersaing di pasar digital melalui kekuatan visual produk yang ikonik.
Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Hairani; Anthony Anggrawan; Muhammad Ridho Akbar; Khasnur Hidjah; Muhammad Innuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6623

Abstract

Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a top destination. The rapid growth of tourism in Lombok requires a deep understanding of tourists' perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths and weaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore, this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using Chi-Square and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtained from Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism.
Development of highway vehicle detection using background subtraction and Haar cascade methods Ni Gusti Ayu Dasriani; Anthony Anggrawan; Khasnur Hidjah; Christofer Satria; I Nyoman Yoga Sumadewa
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp1004-1015

Abstract

Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Model Deteksi Serangan Jaringan Menggunakan Machine Learning Dengan Teknik Ensemble Learning Lauw, christopher Michael; Advaita Hary, Adex; Anggrawan, Anthony; Syahrir, Moch.; Sulistianingsih, Neny
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3369

Abstract

Stock is one of the most popular investment instruments due to its potential to generate substantial returns. However, the high volatility of stock prices requires investors to employ accurate prediction models to support investment decision-making. This study aims to compare the performance of the Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods in predicting the stock price of PT Gudang Garam Tbk using historical data enriched with technical indicators. The study adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The prediction models were developed using historical stock price data enriched with technical indicators and evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The experimental results demonstrate that the ANN model outperformed the SVR model, achieving an MSE of 2923.86, RMSE of 1709.93, MAE of 1294.76, MAPE of 8.38%, and an R² of 0.68, while the SVR model obtained an MSE of 5211.06, RMSE of 2284.57, MAE of 2126.84, MAPE of 12.73%, and an R² of 0.42. Furthermore, the 240-day forecasting results indicate that the ANN model projects an upward (bullish) trend, whereas the SVR model predicts a relatively stable (sideways) trend. These findings indicate that the Artificial Neural Network (ANN) is more effective than Support Vector Regression (SVR) for predicting the stock price of PT Gudang Garam Tbk, as it produces lower prediction errors and demonstrates superior predictive performance. Keyword: Stock Price Prediction, Artificial Neural Network, Support Vector Regression, CRISP-DM. Abstrak Saham merupakan salah satu instrumen investasi yang banyak diminati karena berpotensi memberikan keuntungan yang tinggi. Namun, tingginya volatilitas harga saham menyebabkan investor memerlukan model prediksi yang akurat sebagai dasar pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan kinerja metode Artificial Neural Network (ANN) dan Support Vector Regression (SVR) dalam memprediksi harga saham PT Gudang Garam Tbk menggunakan data historis yang diperkaya dengan indikator teknikal. Penelitian ini menerapkan metodologi Cross-Industry Standard Process for Data Mining (CRISP-DM) yang meliputi tahapan business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Model dibangun menggunakan data historis harga saham yang diperkaya dengan indikator teknikal, kemudian dievaluasi menggunakan metrik Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model ANN memberikan performa yang lebih baik dibandingkan SVR dengan nilai MSE sebesar 2923,86, RMSE sebesar 1709,93, MAE sebesar 1294,76, MAPE sebesar 8,38%, dan R² sebesar 0,68, sedangkan model SVR memperoleh nilai MSE sebesar 5211,06, RMSE sebesar 2284,57, MAE sebesar 2126,84, MAPE sebesar 12,73%, dan R² sebesar 0,42. Pada prediksi jangka panjang selama 240 hari, model ANN memproyeksikan tren harga yang meningkat (bullish), sedangkan model SVR menghasilkan tren yang relatif stabil (sideways). Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode Artificial Neural Network (ANN) lebih efektif dibandingkan Support Vector Regression (SVR) dalam memprediksi harga saham PT Gudang Garam Tbk karena mampu menghasilkan tingkat kesalahan yang lebih rendah dan kemampuan prediksi yang lebih baik. Kata kunci: Prediksi harga saham; Artificial Neural Network; Support Vector Regression; CRISP-DM.
Co-Authors Abdillah, Mokhammad Nurkholis Abdul Rahim Advaita Hary, Adex Ahmat Adil Alfilail, Nur Anggriani, Rini Aprilia Dwi Dayani Ariq, Tomy Ayu Dasriani, Ni Gusti Azhar, Raisul Azhari Azhari Bidari Andaru Widhi Candra, M. Ade Canggih Wahyu Rinaldi Cecep Kusmana christofer satria Christofer Satria Christofer Satria Dadang Iskandar, Dadang Dadang Priyanto Dadang Pyanto Dafa Awanta Dayani, Aprilia Dwi Dedi Aprianto Dewa Ayu Oki Astarini Diah Supatmiwati Dian Syafitri Chani Saputri Didiharyono, D. Donny Kurniawan Dwi Kurnianingsih Dyah Susilowati Dyah Susilowati Efrizoni, Lusiana Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Erwin Suhendra Fadiel Rahmad Hidayat Fadli, M. Najmul Hairani Hairani Haryono Haryono Hasbullah Hasbullah Hasbullah Hasbullah Hasbullah Hasbullah Hasbullah Helna Wardhana Hengki Tamando Sihotang Herawati, Baiq Candra Hilda Hastuti Huda, Dias Nabila Husain Husain I Gde Ary Putra Waisnawa I Nyoman Yoga Sumadewa I Nyoman Yoga Sumadewa Ikang Murapi Irwan Cahyadi Jean Suciasti Gunawan Junendri Ardian Kamil, Wahyu Katarina Katarina Khairan marzuki Khasnur Hidjah Khasnur Hidjah Kurniadin Abd Latif Lalau Ganda Rady Putra Lalu Ganda Rady Putra Lanang Sakti lauw, Christopher Michael Lauw, christopher Michael Lutfie, Muhammad Hilal Mumtaz M. Ade Candra M. Thontowi Jauhari Mardedi, Lalu Zazuli Azhar Maulana, Rahmat Mayadi Mayadi Mayadi Mayadi Miswaty, Titik Ceriyani Muhammad Innuddin Muhammad Ridho Akbar Muhammad Rosikhu MUHAMMAD TAJUDDIN Muhammad Zaki Pahrul Hadi Muhammad Zulfikri Muhsin, Lalu Busyairi Neny Sulistianingsih Ni Gusti Ayu Dasriani Nurhidayati, Maulida Nurul Azmi Nurul Hidayah Peter Wijaya Sugijanto Peter Wijaya Sugijanto Primajati, Gilang Purnama, Baiq Kartika Putu Tisna Putra Qudsi, Jihadil R. Ayu Ida Aryani Raden Bagus Faizal Irani Sidharta Rahmat Maulana Rahmawati, Lela Rahmiati, Baiq Fitria Rini Anggriani Rini Anggriani Riosatria, Riosatria Santoso, Heroe Sarjon Defit Satuang Satuang Sirojul Hadi Siti Soraya Sri Astuti Iriyani SUBUDIARTHA, I NYOMAN Sugijanto Sulistianingsih, Neny Sunardy Kasim Supriantono, Herman Suriadi Ardiansyah Sutarman Syahrir, Moch. Syamsurrijal Syamsurrijal Tanwir, Tanwir Tomi Tri Sujaka Triwijoyo, Bambang Krismono v, Sovian Veithzal Rivai Zainal Victoria Cynthia Rebecca Wayan Canny Naktiany Wenny Wijaya Wiya Suktiningsih Zulkipli Zulkipli