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All Journal Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT
Saputra, Bagus Hendra
Unknown Affiliation
Computer Science & IT Library & Information Science Mathematics

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Evaluation of ARIMA model performance in projecting future sales: case study on electronic products Saputra, Bagus Hendra
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 5 (2024): November : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.993.pp329-337

Abstract

The sales performance of electronic products is significantly affected by a variety of internal and external factors, necessitating precise forecasting models to aid strategic decision-making. This research investigates the effectiveness of ARIMA models in predicting future sales, focusing on a case study involving electronic products. The study utilizes monthly sales data obtained from company records and industry databases. The methodology includes assessing data stationarity through the Augmented Dickey-Fuller (ADF) test, applying differencing when required, and determining ARIMA parameters using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) analyses. The findings reveal that ARIMA models effectively capture seasonal variations and trend patterns. Their performance is assessed using metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). This study highlights the need to incorporate external factors into prediction models to enhance accuracy and recommends exploring alternative approaches that can better adapt to dynamic market conditions.
Digitalization of guest record management through a web-based information system to support security and efficiency Mawadah, Helvina Salsabila; Prabukusumo, M Azhar; Saputra, Bagus Hendra
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.297

Abstract

Digitization of visitor registration improves efficiency and security in visit management in many institutions. This project seeks to create a web-based information system that replaces manual registration techniques to reduce the possibility of data loss and inaccuracy of registration, while increasing the efficiency and accuracy of guest information processing. The study used a software development methodology that leverages the Rapid Application Development (RAD) concept, which facilitates rapid and adaptive system development. The novelty of this system lies in its integration of automated blacklist detection and personalized notification workflows, features that are not commonly found in prior visitor registration systems. The system was designed and implemented specifically within an institutional environment but has the potential to be generalized and adapted for diverse organizational contexts, including government offices, corporate facilities, and educational institutions. The study results show that the web-based information system improves efficiency in visitor registration through automated verification, real-time monitoring, and notification integration, thereby facilitating safer and more organized guest management. This solution aims to enable institutions to improve their visitor reception processes, strengthen workplace security, and facilitate digital transformation in visit management.
Comparative study of machine learning algorithms for predicting drug induced autoimmunity using molecular descriptors Delfiero, Yusuf Rio; Hidayati, Ajeng; Saputra, Bagus Hendra
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.436

Abstract

Drug induced autoimmunity (DIA) poses significant challenges in pharmaceutical development due to its complex immunological mechanisms and delayed clinical manifestations. This study proposes a comparative evaluation of three ensemble machine learning models CatBoost, XGBoost, and Gradient Boosting for predicting DIA using molecular descriptors. A curated dataset of drug compounds with known autoimmune outcomes was analyzed through a systematic workflow incorporating preprocessing, stratified sampling, and model evaluation using accuracy, F1 score, and ROC AUC. Results indicate that CatBoost achieved the highest ROC AUC, while XGBoost demonstrated superior balance between precision and recall, as reflected by its F1 score. Feature importance analysis using SHAP highlighted key molecular properties such as SlogP_VSA10 and fr_NH2 as major contributors to prediction outcomes. The study provides a reproducible and interpretable framework for early toxicity screening, offering valuable insights for data driven decision making in drug safety assessment.
Co-Authors Delfiero, Yusuf Rio Hidayati, Ajeng Mawadah, Helvina Salsabila Prabukusumo, M Azhar