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All Journal Journal La Multiapp Indonesian Journal of Innovation Studies
Kumajas, Sondy C
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Web-Based Decision Support System for Determining the Best Employee Using the Simple Additive Weighting Method: Sistem Pendukung Keputusan Berbasis Web untuk Penentuan Karyawan Terbaik Menggunakan Metode Simple Additive Weighting Sorongan, Delgio Liem; Kainde, Quido C; Kumajas, Sondy C
Indonesian Journal of Innovation Studies Vol. 26 No. 4 (2025): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v26i4.1835

Abstract

Background: Employee performance assessment requires structured and transparent systems to support objective decision-making in modern organizations. Specific Background: PT. Mitra Jaya Samudera still employs manual assessment procedures, causing delays and subjective evaluation outcomes. Knowledge Gap: Existing studies seldom address decision support systems tailored to processing-division characteristics in fishery-based industries. Aims: This study develops a web-based decision support system using the Simple Additive Weighting method to determine the best employees in the Processing Division. Results: The system automates normalization, scoring, and ranking across five criteria—discipline, responsibility, productivity, cooperation, and standard operating procedure compliance. Black Box Testing shows a 100% functional success rate, while User Acceptance Testing reports 95% user satisfaction. Novelty: The system integrates division-specific criteria, interactive visualization, and a structured database architecture customized for operational workflows in fish processing environments. Implications: The system provides practical support for transparent evaluation, faster managerial decisions, and scalable implementation for broader human resource management practices. Highlights • Automated web-based system calculates employee ranking using Simple Additive Weighting. • Performance evaluation uses five structured and division-specific assessment criteria. • System achieved complete functional success and high user acceptance. Keywords Decision Support System, Simple Additive Weighting, Performance Appraisal, Web-Based System, Rapid Application Development
Leaf Type Recognition System Using Image Processing Method Using Convolutional Neural Network Algorithm Kolauw, Evan; Hasibuan, Alfiansyah; Kumajas, Sondy C
Journal La Multiapp Vol. 7 No. 2 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i2.3057

Abstract

A digital image-based leaf recognition system is one of the modern solutions in the fields of botany and agriculture to identify plants automatically. This study developed a leaf recognition system using image processing methods and Convolutional Neural Network (CNN) algorithms. CNN was chosen because of its ability to independently extract features through convolution layers, thus capturing important visual patterns such as shape, edges, textures, and leaf veins without requiring manual feature engineering processes. The research dataset consists of a collection of leaf images from several types of plants obtained through direct photo-taking and public dataset sources. Each image goes through a pre-processing stage, including cropping, resizing, image quality enhancement, and pixel normalization to ensure data consistency before entering the training stage. The CNN model is designed with several convolutional layers, pooling, activation functions, and fully connected layers to produce optimal classification performance. Model training is carried out by dividing training and testing data, as well as augmentation techniques to increase image variation. System performance is evaluated using accuracy, precision, recall, and confusion matrix. The test results show that the CNN model is able to recognize leaf types with a high level of accuracy and is stable under various test conditions, including variations in lighting and shooting angles. Overall, this study proves that CNN is an effective and reliable approach in building an automatic leaf recognition system. This system has the potential to be applied in the fields of precision agriculture, mobile application-based plant identification, and botanical research that require speed and accuracy in plant classification.
Co-Authors Hasibuan, Alfiansyah Kainde, Quido C Kolauw, Evan Sorongan, Delgio Liem