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All Journal JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Sains Student Research Jurnal of Information Technology and Society (JITS) Jurnal Ilmiah Nusantara JURNAL AKADEMIK EKONOMI DAN MANAJEMEN Riau Jurnal Teknik Informatika Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Journal of Informatics and Vocational Education
Arvi Pramudyantoro
Universitas Muhammadiyah Bangka Belitung
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Social Sciences Other

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APPLYING USER-CENTERED DESIGN FOR MOBILE APPLICATIONS INTERFACE DESIGN Fidya Farasalsabila; Verra Budhi Lestari; Jangkung Tri Nugroho; Arvi Pramudyantoro; Ema Utami
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 1 (2023): Desember 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2619

Abstract

Abstract: The Healthy Food Supplier App, which introduces groundbreaking innovations to increase public access to and delivery of healthy food, has emerged as a new phenomenon in the food sector. This research intends to examine and describe how the use of healthy food suppliers contributes to improving public health by making healthy food accessible to obtain and deliver it quickly. The development of mobile applications with the aim of providing healthy food and ingredients for the community is the subject of this research. The aim of this app is to provide original responses to issues related to raising awareness of quality of life and the value of nutritious food in everyday life. The User Centered Design (UCD) method needs to be applied to categorize various user information needs and package them into a mobile application design model. The results of the application design showed that the interface design for the pioneer food and health food raw material application was successfully created using the implementation of the UCD method with a number of revisions that have been updated since the evaluation as one of the feature developments in the application. The food recording feature and personalized recommendations implemented in this mobile application provide users with awareness and guidance in adopting healthier eating patterns.            Keywords: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design  Abstrak: Aplikasi Pemasok Makanan Sehat yang memperkenalkan inovasi terobosan untuk meningkatkan akses publik ke dan pengiriman makanan sehat, telah muncul sebagai fenomena baru di sektor makanan. Penelitian ini bermaksud untuk mengkaji dan mendeskripsikan bagaimana penggunaan pemasok makanan sehat berkontribusi dalam peningkatan kesehatan masyarakat dengan membuat makanan sehat dapat diakses untuk mendapatkan dan mengantarkannya dengan cepat. Pengembangan aplikasi mobile dengan tujuan menyediakan makanan dan bahan makanan sehat bagi masyarakat menjadi pokok bahasan penelitian ini. Tujuan dari aplikasi ini adalah untuk memberikan tanggapan orisinal terhadap masalah yang terkait dengan peningkatan kesadaran kualitas hidup dan nilai makanan bergizi dalam kehidupan sehari-hari. Metode User Centered Design (UCD) perlu diterapkan untuk mengkategorikan berbagai kebutuhan informasi pengguna dan mengemasnya ke dalam model desain aplikasi mobile. Hasil perancangan aplikasi didapatkan bahwa desain antarmuka aplikasi pelopor makanan dan bahan baku makanan kesehatan berhasil dibuat dengan menggunakan implementasi metode UCD dengan sejumlah revisi yang sudah diperbarui sejak evaluasi sebagai salah satu pengembangan fitur pada aplikasi. Fitur pencatatan makanan dan rekomendasi personal yang diterapkan pada aplikasi mobile ini memberikan pengguna kesadaran dan panduan dalam mengadopsi pola makan yang lebih sehat. Kata Kunci: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design
PENGGABUNGAN K-NEAREST NEIGHBORS DAN LIGHTGBM UNTUK PREDIKSI DIABETES PADA DATASET PIMA INDIANS: MENGGUNAKAN PENDEKATAN EXPLORATORY DATA ANALYSIS Pramudyantoro, Arvi; Utami, Ema; Ariatmanto, Dhani
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.4966

Abstract

Diabetes Melitus merupakan masalah kesehatan yang signifikan di seluruh dunia. Dengan menggabungkan algoritma K-Nearest Neighbors (KNN) dan Light Gradient Boosting Machine (LightGBM),penelitian ini menyajikan pendekatan baru untuk meningkatkan prediksi diabetes. Kumpulan data Indian Pima, yang terkenal dengan intrik dan signifikansinya dalam penelitian diabetes, menjadi subjek penelitian ini. Untuk menyelidiki pola dan hubungan dalam data, penelitian ini menggunakan analisis data eksploratif, atau EDA. Pra-pemrosesan data yang komprehensif, yang mencakup pengkodean, normalisasi, dan penanganan nilai yang hilang, adalah yang berikutnya. Karena KNN dan LightGBM cocok dengan fitur kumpulan data ini, maka keduanya dipilih. Performa model dioptimalkan melalui penggunaan teknik pengoptimalan seperti Pencarian Acak dan Pencarian Grid untuk mengubah hyperparameter. Metrik seperti skor F1, kurva ROC, analisis presisi-recall, dan akurasi-presisi digunakan untuk menilai model. Hasilnya menunjukkan peningkatan signifikan dalam keakuratan prediksi diabetes, yang menunjukkan bahwa penggunaan LightGBM bersama dengan KNN dan EDA secara hati-hati dapat meningkatkan akurasi prediksi. Khususnya bila dipertimbangkan dalam konteks data kesehatan yang rumit, temuan ini secara signifikan memajukan deteksi penyakit kronis. Menggunakan kumpulan data Pima Indians, algoritma KNN dan LightGBM bekerja sama untuk mencapai akurasi tertinggi sebesar 90,6%.
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.
APPLYING USER-CENTERED DESIGN FOR MOBILE APPLICATIONS INTERFACE DESIGN Farasalsabila, Fidya; Lestari, Verra Budhi; Nugroho, Jangkung Tri; Pramudyantoro, Arvi; Utami, Ema
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 1 (2023): Desember 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2619

Abstract

Abstract: The Healthy Food Supplier App, which introduces groundbreaking innovations to increase public access to and delivery of healthy food, has emerged as a new phenomenon in the food sector. This research intends to examine and describe how the use of healthy food suppliers contributes to improving public health by making healthy food accessible to obtain and deliver it quickly. The development of mobile applications with the aim of providing healthy food and ingredients for the community is the subject of this research. The aim of this app is to provide original responses to issues related to raising awareness of quality of life and the value of nutritious food in everyday life. The User Centered Design (UCD) method needs to be applied to categorize various user information needs and package them into a mobile application design model. The results of the application design showed that the interface design for the pioneer food and health food raw material application was successfully created using the implementation of the UCD method with a number of revisions that have been updated since the evaluation as one of the feature developments in the application. The food recording feature and personalized recommendations implemented in this mobile application provide users with awareness and guidance in adopting healthier eating patterns.            Keywords: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design  Abstrak: Aplikasi Pemasok Makanan Sehat yang memperkenalkan inovasi terobosan untuk meningkatkan akses publik ke dan pengiriman makanan sehat, telah muncul sebagai fenomena baru di sektor makanan. Penelitian ini bermaksud untuk mengkaji dan mendeskripsikan bagaimana penggunaan pemasok makanan sehat berkontribusi dalam peningkatan kesehatan masyarakat dengan membuat makanan sehat dapat diakses untuk mendapatkan dan mengantarkannya dengan cepat. Pengembangan aplikasi mobile dengan tujuan menyediakan makanan dan bahan makanan sehat bagi masyarakat menjadi pokok bahasan penelitian ini. Tujuan dari aplikasi ini adalah untuk memberikan tanggapan orisinal terhadap masalah yang terkait dengan peningkatan kesadaran kualitas hidup dan nilai makanan bergizi dalam kehidupan sehari-hari. Metode User Centered Design (UCD) perlu diterapkan untuk mengkategorikan berbagai kebutuhan informasi pengguna dan mengemasnya ke dalam model desain aplikasi mobile. Hasil perancangan aplikasi didapatkan bahwa desain antarmuka aplikasi pelopor makanan dan bahan baku makanan kesehatan berhasil dibuat dengan menggunakan implementasi metode UCD dengan sejumlah revisi yang sudah diperbarui sejak evaluasi sebagai salah satu pengembangan fitur pada aplikasi. Fitur pencatatan makanan dan rekomendasi personal yang diterapkan pada aplikasi mobile ini memberikan pengguna kesadaran dan panduan dalam mengadopsi pola makan yang lebih sehat. Kata Kunci: Design Interface; Mobile Applications; Requirements Engineering; User-Centered Design
Pengembangan Virtual Assistant menggunakan Teknologi NLP dengan Metode Algoritma Machine Learning untuk Layanan Informasi Akademik di SMA Negeri 1 Parittiga Berbasis Web Yuniarni Yuniarni; Yudistira Bagus Pratama; Arvi Pramudyantoro
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 3 No. 5 (2025): Oktober: Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v3i5.1152

Abstract

This study aims to develop a web-based Virtual Assistant to improve the efficiency of academic information services at SMA Negeri 1 Parittiga. The research was motivated by the delays and inaccuracies in information delivery caused by the manual system still used in the school. The system development was carried out using the Research and Development approach with the Waterfall model, which includes the stages of needs analysis, design, implementation, and evaluation. The main technologies used are Natural Language Processing (NLP) and the Long Short-Term Memory (LSTM) machine learning algorithm, which allow the assistant to understand and respond to user questions in natural language in a contextual way. The system architecture uses Flask as the backend, Vue.js as the frontend, and Laravel for administrative data management. The testing results show that the system has an accuracy level of 88.4% in providing correct answers and a user satisfaction level of 92%, surpassing the target success rate of 80%. These findings prove that integrating NLP and LSTM can enhance the system's ability to understand conversational context and speed up the distribution of academic information. The study concludes that a web-based Virtual Assistant is an effective solution for the digitalization of school information services and has the potential to support the implementation of artificial intelligence technology in secondary education in Indonesia.
ANALISIS DATA PELANGGAN DENGAN ALGORITMA K-MEANS UNTUK PENINGKATAN PENJUALAN LAYANAN ICONNET DI BANGKA BELITUNG Muhamad Mustaqim; Yudistira Bagus Pratama; Arvi Pramudyantoro
JURNAL AKADEMIK EKONOMI DAN MANAJEMEN Vol. 2 No. 4 (2025): JURNAL AKADEMIK EKONOMI DAN MANAJEMEN 
Publisher : CV. KAMPUS AKADEMIK PUBLISING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jaem.v2i4.7106

Abstract

Sales increase is an essential factor for telecommunication service providers, including ICONNET, a subsidiary of PLN, amid intense market competition. Companies face the challenge of designing effective marketing strategies without structured customer data analysis. This study aims to apply the K-Means Machine Learning algorithm to analyze and cluster ICONNET customer data in Bangka Belitung, with the expected results supporting strategic sales increase decisions. The methodology employed is Data Mining with the CRISP-DM framework, where the modeling process implements the K-Means algorithm. The determination of the optimal number of clusters (K) was consistently performed using the Elbow Method and Silhouette Score, yielding an optimal value of K=2. The clustering results successfully divided customers into two main groups: Cluster 0, dominated by users of low-value packages (Package 1 and 2), and Cluster 1, consisting of users of higher-value packages (specifically Package 5). This segmentation provides a basis for ICONNET to formulate differentiated service strategies and targeted marketing offers tailored to the characteristics and preferences of each customer segment, which directly supports operational efficiency and long-term business growth.
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.
Generation of Batik Cual Bangka Belitung Motif Variations Using Stable Diffusion Models Rakha Piadika; Zikri Wahyuzi; Arvi Pramudyantoro
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.v9i1.3215

Abstract

Batik Cual Bangka Belitung is a visual cultural heritage characterized by repetitive motifs, fine line details, and high ornament complexity, presenting significant challenges for digital exploration without compromising its visual identity. This study aims to generate variations of Batik Cual motifs using a prompt-guided image-to-image (img2img) approach based on the Stable Diffusion model with a single-reference image. Furthermore, the research analyzes the influence of the strength parameter on the delicate balance between structural similarity and generative visual variation. The dataset consists of 13 independently collected Batik Cual motifs, pre-processed through RGB conversion and standardized to a resolution of 512×512 pixels. Controlled experiments were conducted using varied strength values of 0.4, 0.6, and 0.8, while maintaining other parameters constant. Quantitative evaluation utilized the Structural Similarity Index Measure (SSIM) to assess structural integrity and CLIP similarity to measure semantic alignment between the prompt and output image. The results indicate that increasing strength consistently decreases SSIM values, signifying greater structural deviation from the reference image, whereas CLIP similarity remains relatively stable across configurations. Quantitatively, a strength of 0.4 offers the optimal combination of structural similarity and semantic suitability. However, qualitative assessments reveal that for certain motifs, a strength of 0.6 produces a more balanced variation between pattern innovation and motif character preservation. These findings confirm a measurable trade-off between identity preservation and generative exploration, demonstrating the potential of Stable Diffusion as a controlled method for developing Batik Cual digital assets.
Pendeteksi Penyakit Daun Kentang Menggunakan Algoritma Convolutional Neural Network (CNN) Arvi Pramudyantoro; Muhamad Kurniawan; Hendi Hendra Bayu
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4740

Abstract

Potato leaf disease is one of the main problems in potato cultivation because it can reduce plant quality, decrease crop yield, and cause economic losses for farmers. Manual disease detection still has limitations because it depends on farmers’ experience and is prone to errors, especially when disease symptoms have similar visual characteristics. This study aims to apply the Convolutional Neural Network (CNN) algorithm to predict potato leaf diseases based on digital images. The dataset used in this study was obtained from Kaggle and consisted of 1,500 potato leaf images divided into three classes: healthy leaves, early blight, and late blight. The research stages included dataset collection, data splitting into training, testing, and validation data, CNN modeling using Jupyter Notebook, model training with 50 epochs, model evaluation using a Confusion Matrix, and model implementation into a web-based system using Flask. The test results show that the CNN model was able to classify potato leaf diseases with an accuracy of 97%. These results indicate that CNN is effective in recognizing visual patterns in potato leaf images, such as color changes, spots, and leaf damage. This study is expected to serve as a basis for developing an early detection system for potato leaf diseases that is faster, more accurate, and easier for farmers to use.
Early Fire Detection Using IoT and Deep Learning Syandhu Dea Fermanda; Zikri Wahyuzi; Arvi Pramudyantoro
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
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.v9i3.3515

Abstract

Fire incidents in strategic facilities such as weapon storage rooms can cause severe damage, threaten personnel safety, and disrupt operational readiness. Conventional fire detection systems generally rely on smoke or temperature sensors, which often respond only after hazardous conditions reach a certain threshold. Therefore, this study proposes an early fire prevention system based on Internet of Things (IoT) and deep learning using CCTV cameras. The system was developed using the System Development Life Cycle (SDLC) with the Waterfall model. The object detection model employed YOLOv8 and was trained on a laptop before being deployed to a Raspberry Pi 5 as the real-time processing unit. The implemented hardware consisted of a Raspberry Pi 5, a Logitech webcam, monitor, keyboard, and mouse. Testing was conducted in a room measuring 6 m × 4 m × 3.5 m. The developed system successfully detected four object classes, namely fire, smoke, cigarette, and person. The implemented logic mechanism classified fire detection as a fire incident, while simultaneous cigarette and smoke detection was categorised as smoking activity with potential fire risk. In addition, the system successfully sent automatic warning notifications through Telegram, enabling faster response without continuous manual monitoring. The results indicate that combining YOLOv8, Raspberry Pi 5, and IoT communication can provide an effective, practical, and low-cost intelligent fire prevention solution for indoor strategic facilities.
Co-Authors A. Resti Supraeni resti Al Ahfaz Reza Ramdani Andesta Granitio Irwan Andesta Granitio Irwan Ari Ardiansyah Ari Juliansyah Dalimunthe, Nurzaidah Putri DHANI ARIATMANTO Eka Altiarika Eka Altiarika Ema Utami Fidya Farasalsabila Hendi Hendra Bayu Jangkung Tri Nugroho Muhamad Kurniawan Muhamad Mustaqim Nugroho, Jangkung Tri Rakha Piadika Randi Atul Aufa Syandhu Dea Fermanda Tito Dian Permana Verra Budhi Lestari Vika Martahayu Wenni Siswanti Yudistira Bagus Pratama Zikri Wahyuzi Zikri Wahyuzi