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All Journal Jurnal Computech & Bisnis (e-Journal) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Techno Nusa Mandiri : Journal of Computing and Information Technology Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS) In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Journal of Social Work and Science Education
Venia Restreva Danestiara
Program Studi Informatika, Fakultas Teknologi Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Penerapan Metode K-Nearest Neighbor untuk Identifikasi Produk Nike Paling Laris Terjual Danestiara, Venia Restreva; Arifudin, M. Achya; Aprilla, Aura Tifa; Setiawan, Dani
In Search (Informatic, Science, Entrepreneur, Applied Art, Research, Humanism) Vol 23 No 2 (2024): In Search
Publisher : LPPM UNIBI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/insearch.v23i2.1129

Abstract

Industri ritel menghadapi persaingan yang semakin ketat, sehingga kemampuan untuk memprediksi produk terlaris menjadi faktor penting dalam memaksimalkan keuntungan. Nike, sebagai salah satu produsen sepatu olahraga terkemuka, mengalami penurunan signifikan selama pandemi COVID-19 dengan pendapatan turun sebesar 38% dan kerugian mencapai $790 juta pada tahun 2020. Oleh karena itu, prediksi akurat terhadap produk terlaris periode 2020-2021 menjadi krusial untuk memulihkan kinerja pasar global. Penelitian ini menerapkan algoritma K-Nearest Neighbors (KNN) untuk memprediksi produk Nike yang paling laris menggunakan dataset open-source dari Kaggle. Penelitian ini melibatkan tahapan preprocessing data, seperti feature selection, normalisasi, dan label encoding. Model KNN diimplementasikan dengan variasi nilai k untuk mengevaluasi pengaruhnya terhadap akurasi. Evaluasi model dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-Score. Hasil penelitian menunjukkan akurasi model mencapai 87% dengan keseimbangan baik antara precision (0.89) dan recall (0.87). "Women's Street Footwear" teridentifikasi sebagai produk terlaris dengan 57,737unit terjual. Model menunjukkan performa optimal dengan akurasi 75%. Penelitian ini memberikan kontribusi signifikan dalam pengambilan keputusan strategis untuk optimasi inventory dan strategi penjualan Nike.
OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING Alamsyah, Nur; Restreva Danestiara, Venia; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Hendra, Acep
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6507

Abstract

MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.
PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING Venia Restreva Danestiara; Marwondo Marwondo; Nayla Nurul Azkiya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5994

Abstract

The dengue virus experiences rapid mutation and genetic variability, posing challenges in developing effective antiviral therapies. This study explores the prediction of binding affinities between potential antiviral drug inhibitors and the NS2B-NS3 protease of the dengue virus using machine learning models. Molecular docking simulations were conducted with AutoDock Vina to generate interaction data between viral proteins and ligands. The generated datasets were used to train several machine learning models, including Random Forest Regressor (RF Regressor), Support Vector Regression (SVR), and Extreme Gradient Boosting Regressor (XGBoost Regressor). The RF Regressor model demonstrated the highest accuracy in predicting binding affinities, measured through Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient (R). However, the XGBoost Regressor and SVR models showed better generalization in practical scenarios. This study highlights the potential of machine learning to optimize the drug discovery process and provides significant insights into antiviral drug development for dengue fever.
Analisis Tren Laporan Keuangan dan Peramalan Pendapatan Agripreneurship di SMKN PP Lembang Danestiara, Venia; Nugraha, Arif Bakti; Pangestu, Idham Azis; Saputra, Naufal Fajar; Putra, Raka Deny Adi
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.140

Abstract

The activity "Trend Analysis for Financial Reports and Income Forecasting in the Agripreneurship Sector at the Negeri Lembang Agricultural Development Vocational School" aims to provide a basic understanding of digital technology that can be applied in the agribusiness sector, increase students' insight and readiness in facing challenges and opportunities in the Agripreneurship 4.0 era, provide practical skills in using technology in production, marketing and agribusiness management, and facilitate students in designing digital technology-based business strategies in agribusiness. Apart from that, the expected result after carrying out the seminar on Trend Analysis for Financial Reports and Income Forecasting in the Agripreneurship Sector is to provide a basic understanding of technology and be able to utilize tools such as SPSS and Google Sheet to increase productivity and efficiency in identifying challenges and opportunities in the agripreneurship sector for students at the Lembang State Agricultural Development Vocational School.
Approximate Bayesian Inference for Bayesian Confidence Quantification in DNA Sequence Classification Using Monte Carlo Dropout Approach Alamsyah, Nur; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Danestiara, Venia Restreva
Innovation in Research of Informatics (Innovatics) Vol 7, No 1 (2025): March 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i1.14349

Abstract

Splice junction classification in DNA sequences is critical for understanding genetic structures and processes, particularly the differentiation between exon-intron (EI), intron-exon (IE), and neither boundaries. Traditional neural network models achieve high accuracy but often lack the ability to quantify uncertainty, which is essential for reliability in sensitive applications such as bioinformatics. This study addresses this limitation by incorporating Bayesian confidence quantification into DNA sequence classification using the Monte Carlo Dropout (MCD) approach. A baseline neural network was first implemented as a reference, achieving a test accuracy of 95.61%. Subsequently, MCD was applied, which not only improved the test accuracy to 96.03% but also provided uncertainty estimation for each prediction by sampling multiple inferences. The uncertainty values enabled the identification of low-confidence predictions, enhancing the interpretability and reliability of the model. Experiments were conducted on a binary-encoded DNA sequence dataset, representing nucleotides (A, C, G, T) and their splice junctions. The results demonstrated that MCD is a robust approach for DNA sequence classification, offering both high predictive performance and actionable insights through uncertainty quantification. This research highlights the potential of Bayesian confidence quantification in genomic studies, particularly for tasks requiring high reliability and interpretability. The proposed approach bridges the gap between accurate predictions and the need for robust uncertainty estimation, contributing to advancements in bioinformatics and machine learning applications in genetic research.
A Metaheuristic Wrapper Approach to Feature Selection with Genetic Algorithm for Enhancing XGBoost Classification in Diabetes Prediction Alamsyah, Nur; Budiman; Danestiara, Venia Restreva; Yoga, Titan Parama; Nursyanti, Reni; Kaunang, Valencia
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2366

Abstract

This study addressed the problem of selecting the most relevant features for improving the accuracy of diabetes classification using health indicator data. The research focused on a binary classification task based on the Behavioral Risk Factor Surveillance System dataset, which comprised over seventy thousand records and twenty-one predictive features related to individual health behaviors and conditions. A metaheuristic wrapper approach was developed by integrating a Genetic Algorithm for feature selection with an XGBoost classifier to evaluate the predictive quality of each feature subset. The fitness function was defined as the average classification accuracy obtained through cross-validation. In addition to feature selection, hyperparameter optimization of the XGBoost model was carried out using a Bayesian-based search strategy to further enhance performance. The proposed method successfully identified a subset of fourteen optimal features that contributed most significantly to the prediction of diabetes. The final model, combining the selected features and optimized parameters, achieved an accuracy of 0.753, outperforming both the baseline models trained on all features and models using features selected through deterministic methods. These results confirmed the effectiveness of combining evolutionary feature selection with model tuning to build efficient and interpretable predictive models for medical data classification. This approach demonstrated a practical solution for managing high-dimensional data in the context of chronic disease prediction.
Information Systems Strategic Planning at one of the Vocational High Schools in Cimahi Akbar, Imannudin; Danestiara, Venia Restreva; Himmaniah, Syahira Putri
Jurnal Computech & Bisnis (e-journal) Vol. 18 No. 1 (2024): Jurnal Computech & Bisnis (e-Journal)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/d9g6r803

Abstract

Abstract A strategic plan is an organization's detailed and extensive strategy to effectively manage its resources and achieve its goals within a specified period. Effective implementation of an information system necessitates strategic planning inside an organization to capitalize on the advantages of the information system implementation fully. This also applies to a Vocational High School in Cimahi. The school encounters many obstacles, including managing school data and performance assessments and requiring a unified system across departments to facilitate the school's operational procedures. Strategic planning establishes specific objectives, such as integrating information systems in academic and non-academic operations, overseeing school data management, enhancing infrastructure management efficiency, and boosting competitiveness by developing a web-based school support system for information and promotional purposes. Aligning the information system's strategies with the school's business strategies will aid in accomplishing these objectives. Developing information system strategies necessitates alignment with the organization's existing business strategies. This research examines the strategic information system planning process, specifically employing the Ward-Peppard technique. This approach facilitates a more comprehensive comprehension of the firm before developing a strategic plan. It involves doing a SWOT analysis, Value Chain analysis, Porter's Five Forces analysis, and McFarlan's strategic grid analysis as recommendations for the proposed information system strategies.
Recognition and Prediction of Rice Variety–Climate Suitability Using YOLOv9 and Naïve Bayes in Agricultural Lands Marwondo, Marwondo; Danestiara, Venia Restreva; Badar, Arif Adnan; Ardiansyah, Fachrizal
Journal of Social Work and Science Education Vol. 7 No. 1 (2026): Journal of Social Work and Science Education
Publisher : Yayasan Sembilan Pemuda Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52690/jswse.v7i1.1390

Abstract

The suitability of rice varieties to agroclimatic conditions is a key factor in determining rice productivity in Indonesia. Climate variability and land limitations require a decision support system capable of assisting farmers in selecting rice varieties suitable for local environmental conditions. This study aims to develop an integrated artificial intelligence-based system that combines YOLOv9 for image-based rice variety recognition and Naïve Bayes for climate suitability prediction based on temperature and humidity parameters. Image data of five rice varieties Ciherang, Inpari 32, Inpari Nutrizinc, Mekongga, and Baroma were collected directly from agricultural fields in Bandung Regency and processed through annotation, augmentation, and model training stages. The YOLOv9 model performed well in distinguishing rice varieties with relatively similar morphological characteristics, with an mAP@50 value of 0.8932. Meanwhile, the Naïve Bayes model achieved 78% accuracy in predicting climate suitability based on altitude, temperature, and humidity, and produced predictions consistent with agronomic recommendations. Both models were then integrated into a Gradio-based interactive interface to facilitate use by non-technical users. The results indicate that this integrated approach has the potential to be an effective decision support system for assisting in the selection of rice varieties that are adaptive to microclimate conditions, thereby supporting more efficient and sustainable rice cultivation practices.
Co-Authors Abdillah, Muhammad Syaddad Acep Hendra Akbar, Imannudin Aliman Fauzi Aprilla, Aura Tifa Ardiansyah, Fachrizal Badar, Arif Adnan Budiman Dani Setiawan Dirham Triyadi Fadhilah, Agung Nur Graha Prakarsa Himmaniah, Syahira Putri Ikhsan Nur, Muhammad Kaunang, Valencia Marwondo Muhammad Achya Arifudin Nayla Nurul Azkiya Nugraha, Arif Bakti Nur Alamsyah Pangestu, Idham Azis Putra, Raka Deny Adi R. Yadi Rakhman Alamsyah Reni Nursyanti Saputra, Naufal Fajar Setiana, Elia Syahira Putri Himmaniah Taufik Hidayah Teofilus Hendrawan, Yesaya Titan Parama Yoga Valencia Claudia Jennifer Kaunang Wiyanudin