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All Journal Foristek JUSIFO : Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING CCIT (Creative Communication and Innovative Technology) Journal Jurnal Teknik Informatika (JUTIF) The Indonesian Journal of Computer Science
Rinianty, Rinianty
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Optimization of Inventory Management with QR Code Integration and Sequential Search Algorithm: A Case Study in a Regional Revenue Office Fajar, Moh; Azhar, Ryfial; Anshori, Yusuf; Laila, Rahma; ., Rinianty; Lapatta, Nouval Trezandy
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8919

Abstract

Inventory management at a government office was previously conducted manually, leading to issues such as data inaccuracies, delays in item searches, and low work efficiency. This study develops a web-based inventory management system integrated with QR Code technology and a sequential search algorithm to address these challenges. The system was developed using the prototyping method, with iterative design based on user feedback until the final version met the office's operational needs. Key features of the system include digital inventory recording, item tracking using QR Codes, and real-time information access through a web-based interface. The system was tested in two stages: simulation and direct implementation in a real-world environment, involving 10 respondents to evaluate effectiveness and usability. The test results showed a 95% improvement in data recording accuracy, a 60% reduction in item search time, and an average user satisfaction score of 77.25 based on the System Usability Scale (SUS). This research successfully improved inventory management efficiency and demonstrated the system’s potential for adoption by other similar organizations, with modular adjustments tailored to their needs.
Sentiment Analysis for the 2024 DKI Jakarta Gubernatorial Election Using a Support Vector Machine Approach Mariani, Mariani; Angreni, Dwi Shinta; Nur, Sri Khaerawati; Rinianty, Rinianty; Jayanto, Deni Luvi
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9260

Abstract

This study analyzes public sentiment regarding candidates in the 2024 DKI Jakarta Gubernatorial Election utilizing a Support Vector Machine (SVM) approach. Recognizing the pivotal role of social media, particularly Twitter, in shaping public opinion, the research addresses the challenges of processing large volumes of unstructured data. Through systematic data preprocessing and feature extraction, the SVM model was applied, achieving a sentiment classification accuracy of 70%. The analysis revealed a distribution of sentiments where 36.1% of comments were positive, 33.4% negative, and 30.5% neutral. These findings illustrate the complexities of public discourse surrounding key political events, highlighting the model's efficacy and the nuances of sentiment detection. Moreover, discussions on model limitations elucidate areas for enhancement, suggesting future avenues including the adoption of more sophisticated algorithms and improved data processing techniques. This research contributes to the understanding of voter sentiment dynamics in a significant electoral context, providing insights that may assist campaign strategies and political analyses in Indonesia.
Implementation of Collaborative Filtering in the Salted Fish Recommendation Process Rizky, Moh Taufiq; Rinianty, Rinianty; Nugraha, Deny Wiria; Amriana, Amriana; Lapatta, Nouval Trezandy
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11576

Abstract

The development of e-commerce in the current era has been so rapid that buying and selling transactions are carried out online through various media, including websites and applications. With so many products available in the application, users often feel confused when choosing the product they want to buy, so it takes a long time to choose a product to avoid regret after purchasing it. In this study, a web-based recommendation system was created for the process of recommending salted fish with the aim of making it easier for customers to choose the type of salted fish. The Collaborative Filtering method was used, employing Pearson Correlation as a tool to calculate the similarity value between users, then using Weighted Sum to calculate the prediction value. Collaborative Filtering often experiences the cold start problem, where the system has difficulty providing recommendations to users who do not yet have a transaction history. Therefore, the author proposes a popularity-based strategy as a measure to overcome this problem. Based on testing, the author obtained results of MAE = 0.63 and RMSE = 0.81 based on train-test split results with a data distribution of 80:20, 80% of the dataset for training and 20% of the dataset for testing with an accuracy of 70-80%, indicating that this system works well. This system has been tested using the Blackbox method.
Co-Authors Afiah, Nurul Anggun Amriana Amriana Andi Hendra Bagaskara, Febrian Chrissma Chairunnisa Ar. Lamasitudju Deni Luvi Jayanto Deny Wiria Nugraha Dwi Shinta Angreni Fajar, Moh Fitri, Siti Fitriana Kartika, Rina Laila, Rahma Lamasitudju, Chairunnisa Mariani Mariani Nisa, Chairunisa Nouval Trezandy Lapatta Rahmah Laila Rasmita, Hajra Rizka Ardiansyah Rizky, Moh Taufiq Ryfial Azhar, Ryfial Septiano Anggun Pratama Siti Fitriana Sri Khaerawati Nur Sukardi Sukardi Syahrullah Syahrullah Wirdayanti Wongkar, Noel Marcell Jonathan Yanti, Wirda Yusuf Anshori