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All Journal International Journal of Artificial Intelligence Research Journal of Applied Data Sciences Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo
Rani, Larissa Navia
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Sistem Pendukung Keputusan Pemilihan Kualitas Kedelai Sebagai Bahan Baku Tahu Menggunakan Metode TOPSIS Rani, Larissa Navia
Jurnal Sains Informatika Terapan Vol. 1 No. 2 (2022): Jurnal Sains Informatika Terapan (Juni, 2022)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v1i2.59

Abstract

Soybeans are a plant that has a high level of protein, and soy sauce is also widely used by producers as raw ingredients for tofu, tempe, various, processed foods, and other beverages. the various types of soybeans make business perpetrators confused about determining the best quality of soybeans. therefore, to speed up the selection of the best-quality soy needed a decision support system. the method used in the support systems of this decision is the technique for order preference by similarity to the ideal solution (TOPSIS), which is chosen because this method is able to select the best alternatives from the existing alternatives. using this system is expected to be able to assist the business perpetrators in making decisions when choosing the type of soybeans that have the best quality.
Perancangan Sistem Customer Relationship Management (CRM) Berbasis Web pada Senyaman Resto & Coffee Rahim, Muhamad Aulia; M, Mardison; Jamhur, Annisak; Rani, Larissa Navia
Jurnal KomtekInfo Vol. 11 No. 4 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i4.615

Abstract

Marketing or promotional activities that are still less effective are a business obstacle, especially at Senyaman Resto & Coffee. This can be seen from the large amount of sales transaction data, sales transaction data is still recorded in books and records. Based on this, this research aims to design a Customer Relationship Management (CRM) system to increase promotional activities and improve services to Senyaman Resto & Coffee consumers. This CRM system design was built using PHP and MySql programming. CRM system design also adopts design tools using Unified Modeling Language (UML). Based on performance testing of the CRM system that has been designed, it appears that the system can provide features for creating sales and customer reports. These results had quite an impact on sales data processing which was built better than before. The contribution of this research also provides efficiency in the promotion and sales management processes that occur at Senyaman Resto & Coffee
Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth Rani, Larissa Navia; Defit, Sarjon; Muhammad, L. J.
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (379.161 KB) | DOI: 10.29099/ijair.v5i1.223

Abstract

The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes
Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation Hendri, Halifia; Rani, Larissa Navia; Enggari, Sofika; Ramadhanu, Agung; Hadi, Febri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1298

Abstract

Concrete surface defect detection is a critical aspect of maintaining the integrity and safety of infrastructure in civil engineering. Traditional manual inspection methods are time-consuming, prone to human subjectivity, and often limited by physical accessibility, necessitating the development of robust automated solutions. This paper presents an automated pixel-level concrete surface defect detection system utilizing the U-Net deep learning architecture. The primary contribution and novelty of our approach lie in optimizing the network's encoder-decoder structure with skip connections to effectively capture both broad contextual features and precise spatial localization. This overcomes the critical limitations of existing traditional methods, which frequently struggle with complex concrete background textures, inherent noise, and uneven illumination. To validate our approach, the proposed U-Net model is systematically compared against a widely used baseline method, K-Means clustering combined with Gray-Level Co-occurrence Matrix (GLCM) texture analysis. The evaluation was conducted using a comprehensive dataset consisting of 1000 high-resolution concrete images. Experimental results reveal that the deep learning architecture vastly outperforms the traditional baseline. Specifically, the U-Net model achieved an outstanding F1-Score of 92.47%, a precision of 93.18%, and a mean Intersection over Union (mIoU) of 86.55%. In stark contrast, the K-Means and GLCM approach only yielded an F1-Score of 69.83% and an mIoU of 54.21%. These quantitative findings demonstrate that the proposed U-Net-based system not only successfully minimizes false segmentations but also provides a highly reliable, efficient, and scalable computational framework. Ultimately, this research delivers a practical solution that can be seamlessly integrated into continuous automated structural health monitoring systems, paving the way for safer and more proactive civil infrastructure management.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Ramadhanu, Agung; Mardison, Mardison; Hendri, Halifia; Hadi, Febri; Rani, Larissa Navia; Yuhandri, Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Co-Authors Enggari, Sofika Febri Hadi Halifia Hendri Jamhur, Annisak M, Mardison Mardison Mardison Muhammad, L. J. Rahim, Muhamad Aulia Ramadhanu, Agung Sarjon Defit Yuhandri Yuhandri, Yuhandri