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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pengabdian Masyarakat Waradin Jurnal Sains Student Research Jurnal of Information Technology and Society (JITS) Jurnal Ilmiah Nusantara Journal of Sharia Economy and Islamic Tourism HUMAN: Journal of Social Humanities and Science Journal of Informatics and Vocational Education
Zikri Wahyuzi
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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The Role of Artificial Intelligence in Transforming Smart Tourism: Enhancing Customer Experience and Service Personalization Sidiq, Zaini Fajar; Z, Sahman; Wahyuzi, Zikri; Ifada, Zuriyat
Journal of Sharia Economy and Islamic Tourism Vol 5, No 2 (2025): Maret
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jseit.v5i2.30705

Abstract

Abstract: This research aims to examine the role of artificial intelligence (AI) in transforming smart tourism, with a focus on improving traveler experience and personalizing services. A Systematic Literature Review approach was used to analyze literature published in the last 10 years from reputable indexers such as Dimensions, DOAJ and Scopus. The results show that technologies such as AI, virtual reality (VR), and augmented reality (AR) have great potential to revolutionize the tourism industry. These technologies enable personalization of services, more efficient management of tourist flows, as well as the provision of real-time relevant information that can improve tourist experience and environmental sustainability. Despite progress in the application of these technologies, research gaps still exist, particularly in developing smart tourism models that integrate local and cultural values, as well as the application of technologies that support ecosystem preservation. In Indonesia, the development of smart tourism models that take into account the richness of local culture and history needs to be further encouraged through further research to create an inclusive and sustainable approach. Therefore, there is a need to develop a smart tourism model that integrates ecosystem sustainability, cultural heritage preservation, and technological solutions to address ethical issues related to the use of travelers' personal data.Abstrak: Penelitian ini bertujuan untuk mengkaji peran kecerdasan buatan (AI) dalam mentransformasi pariwisata cerdas, dengan fokus pada peningkatan pengalaman wisatawan dan personalisasi layanan. Pendekatan Systematic Literature Review digunakan untuk menganalisis literatur yang dipublikasikan dalam 10 tahun terakhir dari pengindeks bereputasi seperti Dimensions, DOAJ dan Scopus. Hasil penelitian menunjukkan bahwa teknologi seperti AI, realitas virtual (VR), dan augmented reality (AR) memiliki potensi besar untuk merevolusi industri pariwisata. Teknologi ini memungkinkan personalisasi layanan, pengelolaan aliran wisatawan yang lebih efisien, serta penyediaan informasi relevan secara real-time yang dapat meningkatkan pengalaman wisatawan dan keberlanjutan lingkungan. Meskipun ada kemajuan dalam penerapan teknologi ini, kesenjangan riset masih ada, khususnya dalam mengembangkan model pariwisata cerdas yang mengintegrasikan nilai lokal dan budaya, serta penerapan teknologi yang mendukung pelestarian ekosistem. Di Indonesia, pengembangan model pariwisata cerdas yang memperhitungkan kekayaan budaya dan sejarah lokal perlu lebih didorong melalui riset lebih lanjut untuk menciptakan pendekatan yang inklusif dan berkelanjutan. Oleh karena itu, perlu ada pengembangan model pariwisata cerdas yang mengintegrasikan keberlanjutan ekosistem, pelestarian warisan budaya, dan solusi teknologi untuk mengatasi masalah etika terkait penggunaan data pribadi wisatawan.
PENGENALAN POLA KASUS POTENSI BANJIR DI PANGKALPINANG DENGAN ALGORITMA RANDOM FOREST DAN XGBOOST MENGGUNAKAN GOOGLE EARTH ENGINE Randi Atul Aufa; Eka Altiarika; Arvi Pramudyantoro; Yudistira Bagus Pratama; Zikri Wahyuzi
Jurnal Teknologi Informasi dan Masyarakat Vol 3 No 1 (2025): Journal of Information Technology and Society (JITS)
Publisher : Universitas Muhammadiyah Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35438/jits.v3i1.1415

Abstract

Banjir merupakan bencana yang sering terjadi di Kota Pangkalpinang dan menimbulkan dampak sosial serta ekonomi yang signifikan. Setiap tahun, curah hujan tinggi dan elevasi wilayah yang rendah menyebabkan genangan air di berbagai titik, mengganggu aktivitas masyarakat dan infrastruktur kota. Oleh karena itu, diperlukan sistem peringatan dini yang efektif berbasis teknologi untuk mengenali pola potensi banjir secara akurat dan cepat. Penelitian ini bertujuan untuk mengenali pola kasus banjir dengan menggabungkan data historis lingkungan dan iklim menggunakan algoritma pembelajaran mesin melalui platform Google Earth Engine (GEE). Metode yang digunakan dalam penelitian ini meliputi pengumpulan data spasial dan klimatologis dari GEE, seperti curah hujan, kelembapan tanah, suhu permukaan, tutupan lahan, dan elevasi. Data selanjutnya diproses menggunakan Google Colab, termasuk tahapan preprocessing dan feature engineering. Algoritma Random Forest dan XGBoost digunakan dalam pendekatan ensemble learning dengan metode soft voting. Data dibagi menjadi 80% untuk pelatihan dan 20% untuk pengujian. Evaluasi model dilakukan menggunakan metrik Accuracy, Precision, Recall, dan F1 Score. Hasil penelitian menunjukkan bahwa model memiliki performa tinggi dengan Accuracy 0.98, Precision 0.97, Recall 0.99 dan F1 Score 0.98. Prediksi potensi banjir tahun 2025–2030 menunjukkan tren perubahan jumlah titik banjir dengan probabilitas tinggi setiap tahunnya. Visualisasi pengenalan pola potensi banjir dalam bentuk peta interaktif di GEE mempermudah analisis spasial dan mendukung pengambilan keputusan mitigasi. Penelitian ini diharapkan dapat menjadi solusi praktis dalam peringatan dini dan strategi adaptasi terhadap bencana banjir.
Perbandingan Sentimen Komentar Youtube pada Video Promosi Bisnis Kuliner di Bangka Belitung Menggunakan Algoritma Machine Learning Al Ahfaz Reza Ramdani; Yudistira Bagus Pratama; Arvi Pramudyantoro; Eka Altiarika; Zikri Wahyuzi
Jurnal Teknologi Informasi dan Masyarakat Vol 3 No 1 (2025): Journal of Information Technology and Society (JITS)
Publisher : Universitas Muhammadiyah Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35438/jits.v3i1.1416

Abstract

Penelitian ini membandingkan sentimen komentar youtube pada video promosi bisnis kuliner di Bangka Belitung menggunakan algoritma machine learning. Penelitian ini bertujuan untuk memahami persepsi masyarakat terhadap bisnis kuliner lokal yang tersebar di platform youtube, serta mengetahui algoritma mana yang lebih efektif dalam mengklasifikasikan sentimen komentar. Metode yang digunakan adalah analisis sentimen dengan pendekatan kualitatif. Algoritma machine learning yang digunakan adalah Support Vector Machine (SVM) dan Naïve Bayes. Data diperoleh melalui proses web scraping terhadap 27 video kuliner khas Bangka Belitung, seperti lempah kuning, mie Koba, otak-otak, dan martabak Bangka, yang kemudian dikumpulkan menjadi 13.692 komentar. Komentar-komentar tersebut diproses melalui tahapan preprocessing, seperti case folding, penghapusan simbol dan angka, tokenisasi, stopword removal, serta stemming. Setelah itu, dilakukan pelabelan sentimen secara manual dan otomatis untuk mengklasifikasikan komentar ke dalam kategori positif, negatif, dan netral. Model klasifikasi kemudian dibangun menggunakan algoritma SVM dan Naïve Bayes, dan dilakukan evaluasi menggunakan metrik akurasi, presisi, recall, dan f1-score. Hasil evaluasi menunjukkan bahwa SVM memiliki akurasi lebih tinggi (86.55%) dibandingkan Naïve Bayes (84.63%). Hasil penelitian menunjukkan bahwa mayoritas komentar memiliki sentimen netral, dengan sedikit komentar positif dan negatif. Penelitian ini memberikan wawasan tentang sentimen masyarakat terhadap bisnis kuliner di Bangka Belitung, yang dapat bermanfaat bagi pelaku bisnis kuliner dalam meningkatkan strategi pemasaran dan pelayanan mereka.
Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning Wahyuzi, Zikri; Ahmad Luthfi; Dhomas Hatta Fudholi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i1.5530

Abstract

This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such as weather conditions. Using a data set that encompasses electrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and feature engineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The study acknowledges the limitations of the data set, particularly when comparing electricity usage during work hours and outside working hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for broader applicability. The ultimate goal is to develop a predictive model that serves as a valuable tool for improving energy management in smart office settings, optimizing electricity usage, and contributing to long-term firm profitability.
Pemanfaatan Teknologi Digital dan Kecerdasan Buatan dalam Menunjang Ekonomi Desa Melalui UMKM Irwan, Andesta Granitio; Wahyuzi, Zikri; Dalimunthe, Nurzaidah Putri; Martahayu, Vika; Juliansyah, Ari
Jurnal Pengabdian Masyarakat Waradin Vol. 5 No. 3 (2025): September : Jurnal Pengabdian Masyarakat Waradin
Publisher : Sekolah Tinggi Ilmu Ekonomi Pariwisata Indonesia Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56910/wrd.v5i3.655

Abstract

The village economy supported by MSMEs is very important for the progress of a village, especially in the islands, but limited access provides minimal opportunities for MSMEs to develop. Kumbung Village, located in South Bangka Regency, is one of the villages that has the above problems, therefore in increasing the competence of MSMEs, it is necessary to socialise the potential and use of digital technology, especially the use of intelligence that is relatively easy and inexpensive to use. Community service activities are carried out at the Kumbung Village Office with the target of village officials and the community, especially technology-savvy teenagers. The community service programme focused on the use of artificial intelligence in designing strategies to improve the quality of MSMEs and explore the potential of the village that can support the economy of residents. In the service programme, the practice of using Gemini.Ai was carried out to find the potential of MSMEs, making logos as product branding, and marketing strategies. The results obtained were the addition of skills of socialisation participants related to the use of Gemini.Ai in providing ideas and strategies as well as branding village MSME products with positive results and participants understood the advantages and disadvantages in the Gemini.Ai application in providing assistance for the marketing process of MSME products.
PERBANDINGAN PERFORMA ALGORITMA NAIVE BAYES DAN SVM UNTUK ANALISIS SENTIMEN KOMENTAR YOUTUBE TERHADAP INDUSTRI ESPORTS DI INDONESIA Tito Dian Permana; Yudistira Bagus Pratama; Zikri Wahyuzi; Eka Altiarika; Arvi Pramudyantoro
JURNAL ILMIAH NUSANTARA Vol. 2 No. 6 (2025): Jurnal Ilmiah Nusantara
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jinu.v2i6.6753

Abstract

The esports industry in Indonesia is rapidly growing and gaining significant attention on social media, particularly YouTube, where comments reflect public perceptions. This study compares the performance of Naive Bayes and Support Vector Machine (SVM) in classifying sentiments from YouTube comments and explores key themes using Latent Dirichlet Allocation (LDA). Data were collected via the YouTube Data API v3, labeled with TextBlob and manually verified into positive, negative, and neutral categories. After preprocessing and TF-IDF representation, class imbalance was handled with SMOTE, and models were trained and evaluated using accuracy, precision, recall, F1-score, and confusion matrix. Results indicate that Naive Bayes achieved 73.85% accuracy with an F1-score of 0.71, while SVM slightly outperformed with 73.97% accuracy and the same F1-score. SVM showed better consistency in classifying negative and neutral comments, whereas Naive Bayes was more effective for positive ones. LDA revealed dominant discussion topics such as appreciation, enthusiasm, community interaction, criticism, and support for esports development. These findings highlight SVM’s superior overall performance and the value of LDA in uncovering public discourse, providing both academic contribution and practical insights for the esports industry in understanding public sentiment.
Identifikasi Pola Perubahan Tutupan Lahan (Land Cover) Akibat Penggunaan Lahan (Land Use) Menggunakan Algoritma Random Forest Di Kabupaten Bangka Tengah Ari Ardiansyah; Yudistira Bagus Pratama; Zikri Wahyuzi; Arvi Pramudyantoro; Andesta Granitio Irwan
JOURNAL SAINS STUDENT RESEARCH Vol. 3 No. 6 (2025): Jurnal Sains Student Research (JSSR) Desember
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jssr.v3i6.7072

Abstract

Central Bangka Regency has been facing growing environmental pressures resulting from the expansion of oil palm plantations, mining operations, and accelerated urban development. These activities have caused considerable changes in land cover, posing a threat to the sustainability of local ecosystems. This study aims to examine land cover dynamics between 2019 and 2022 and to forecast future conditions for 2030 as a basis for sustainable spatial planning. Sentinel-2A satellite imagery was processed using the Google Earth Engine(GEE) platform, employing the Random Forest(RF) algorithm to classify land cover into five categories: forest, water, built-up, oil palm plantations, and barren. Model validation through the Overall Accuracy metric demonstrated strong classification performance, reaching 0.90297 in 2019 and 0.90849 in 2022. The analysis showed a 21.63% reduction in forest area, alongside significant increases in oil palm and built-up land. The projection for 2030 suggests that forest cover may decline to just 3.35% of the total area, with oil palm plantations and built-up land becoming dominant. These results emphasize the necessity of implementing sustainable land-use management strategies to maintain a balance between economic growth and environmental conservation in Central Bangka Regency.
Analisis Sentimen terhadap Kasus Korupsi Timah di Kepulauan Bangka Belitung menggunakan Algoritma Indobert dan Bidirectional LSTM Sevtian, Andre; Pratama, Yudistira Bagus; Wahyuzi, Zikri
HUMAN: Journal of Social Humanities and Science Vol. 3 No. 1 (2025): HUMAN: Journal of Social Humanities and Science, July 2025
Publisher : ASIAN PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58738/human.v3i1.1116

Abstract

Kasus korupsi timah di Kepulauan Bangka Belitung menjadi sorotan publik karena dampaknya terhadap lingkungan, perekonomian daerah, serta kepercayaan masyarakat terhadap institusi negara. Komentar publik yang tersebar di platform YouTube menjadi sumber data potensial untuk dianalisis guna memahami kecenderungan sentimen masyarakat. Oleh karena itu, penelitian ini bertujuan untuk melakukan analisis sentimen terhadap kasus tersebut dengan menggunakan algoritma IndoBERT dan Bidirectional LSTM. Tahapan penelitian menggunakan metode CRISP-DM yang mencakup business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Data dikumpulkan melalui YouTube Data API, kemudian diberi label sentimen menggunakan pendekatan hybrid, yaitu pelabelan otomatis dengan model pretrained IndoBERT serta verifikasi manual. Dua algoritma utama yang digunakan untuk mengklasifikasikan sentimen adalah IndoBERT dan Bidirectional LSTM, dengan evaluasi performa berdasarkan metrik accuracy, precision, recall, F1-score, dan AUC menggunakan skema Stratified K-Fold Cross Validation. Hasil evaluasi menunjukkan bahwa IndoBERT unggul dalam klasifikasi sentimen dengan rata-rata akurasi validasi sebesar 96,67% dan nilai F1-score sebesar 90,62%. Model ini mengungguli Bidirectional LSTM yang mencatat akurasi sebesar 95,60% dan F1-score sebesar 88,11%. Berdasarkan hasil tersebut, IndoBERT dipilih untuk diimplementasikan ke dalam sistem analisis sentimen berbasis web menggunakan framework Streamlit. Sistem ini mendukung masukan berupa URL video YouTube atau tema tertentu, serta mampu mengekstrak komentar, mengklasifikasikan sentimen, dan menyajikan visualisasi hasil secara otomatis. Dengan demikian, dapat disimpulkan bahwa IndoBERT lebih efektif dalam menganalisis sentimen publik terkait kasus korupsi timah di Kepulauan Bangka Belitung.
Classification of Batik Cual Bangka Belitung Based on Deep Learning: YOLOv11 Approach Andika Saputra; Zikri Wahyuzi; Yudistira Bagus Pratama
Journal of Informatics and Vocational Education Vol. 9 No. 1 (2026): Journal of Informatics and Vocational Education - March
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i1.3208

Abstract

The rapid growth of artificial intelligence, particularly deep learning, has enabled significant advancements in computer vision and automated image recognition. However, the application of these technologies to traditional cultural artifacts remains limited, especially within the domain of Indonesian textile heritage. Batik Cual Bangka Belitung, which features intricate ornamentation and visually similar motifs, presents unique classification challenges that conventional Convolutional Neural Network (CNN) models struggle to address effectively. To overcome these limitations, this study introduces an automatic motif classification system using the YOLOv11 architecture, a state-of-the-art object detection model capable of identifying and distinguishing fine-grained visual patterns. The research follows a systematic pipeline consisting of dataset collection, curation, manual motif labeling, image preprocessing, model configuration, training, and testing. A curated dataset of Batik Cual images was augmented and divided into training, validation, and testing subsets to ensure robust evaluation. Experimental results demonstrate strong model performance, achieving a precision of 0.934, recall of 0.808, mAP50 of 0.950, and mAP50–95 of 0.8172. These findings confirm that YOLOv11 can accurately detect motif regions and classify them under varying structural and textural conditions. The study contributes not only a reliable technical framework for recognizing Batik Cual motifs, but also supports digital preservation efforts and future cultural computing applications.
Co-Authors Ahmad Luthfi Al Ahfaz Reza Ramdani Andesta Granitio Irwan Andika Saputra Ari Ardiansyah Arvi Pramudyantoro Dalimunthe, Nurzaidah Putri Dhomas Hatta Fudholi Eka Altiarika Ifada, Zuriyat Juliansyah, Ari Randi Atul Aufa Sevtian, Andre Sidiq, Zaini Fajar Tito Dian Permana Vika Martahayu Yudistira Bagus Pratama Z, Sahman