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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo

Larissa Navia Rani

Universitas Putra Indonesia YPTK Padang

Author-ID : 2405040

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Articles

Hybrid DSS for recommendations of halal culinary tourism West Sumatra Mardison Mardison; Agung Ramadhanu; Larissa Navia Rani; Sofika Enggari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp273-283

Decision support system (DSS) is a system that design to support managers in deciding on multiple criteria and multiple attributes. This study combines two methods in the DSS, that are analytical hierarchy process (AHP) method and simple additive weighting (SAW) method. This combination of two DSS method named hybrid DSS. The AHP method is using to find the weighting or priorities of criteria in DSS and then the value will use by SAW method using to find the decision. The decision of this DSS is the recommendation of halal culinary tourism in West Sumatra Indonesia. The purpose of this study is to provide updates from previous studies, related to adding indicators of halal culinary tourism and other information updates. The number of potential culinary tourism attractions and tourism, the problems that exist in the real field, is still lack of culinary information in West Sumatra. As a result, many tourists find it difficult to find the best and economical culinary. The SAW and AHP methods become the hybrid DSS method that will be able to classify and provide information on halal tourism in West Sumatra that is precise, accurate, consistent, and validated.

Alliance Rules- based Algorithm on Detecting Duplicate Entry Email Arif Hanafi; Sulaiman Harun; Sofika Enggari; Larissa Navia Rani
Journal of Computer Scine and Information Technology Volume 7 Issue 3 (2021): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v7i2.7

The way that email has extraordinary significance in present day business communication is certain. Consistently, a bulk of emails is sent from organizations to clients and suppliers, from representatives to their managers and starting with one colleague then onto the next. In this way there is vast of email in data warehouse. Data cleaning is an activity performed on the data sets of data warehouse to upgrade and keep up the quality and consistency of the data. This paper underlines the issues related with dirty data, detection of duplicatein email column. The paper identifies the strategy of data cleaning from adifferent point of view. It provides an algorithm to the discovery of error and duplicates entries in the data sets of existing data warehouse. The paper characterizes the alliance rules based on the concept of mathematical association rules to determine the duplicate entries in email column in data sets.

Sistem Pendukung Keputusan Pemilihan Kualitas Kedelai Sebagai Bahan Baku Tahu Menggunakan Metode TOPSIS Larissa Navia Rani
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 | Full PDF (237.748 KB)

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.

Rancang Bangun Sistem Informasi Pendaftaran Murid Baru secara Online pada Purwacaraka Padang Larissa Navia Rani; Desva Willton
Jurnal KomtekInfo Vol. 6 No. 1 (2019): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

The rapid development of information technology today, triggering all activities in all fields of using technology in carrying out its operational activities without exception Purwacaraka Padang in registering new students. This new online student registration information system is used to make it easier for prospective students to register without having to come directly to Purwacaraka Padang. In addition, with this application the activities carried out can be displayed in this application and provide the latest information to prospective students and the community. The design of this system uses the UML model (Unified Model Language) which can provide an overview of how the program runs and the interaction between actors and systems. In addition, the use of the PHP programming language that is supported by MySQL databases also makes this system an useful online information system.

(Implementation of the Electre (Elimination Et Choix Traduisan La Realite) Method in a Healthy Food Menu Decision Support System for Toddlers in the Sasak Area Health Center Pasisie Using the Php And Databse Mysql Programming Language) Mardison; Syafrika Deni Rizki; Larissa Navia Rani; Agung Ramadhanu; Repelita Witri
Jurnal KomtekInfo Vol. 7 No. 1 (2020): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Healthy food menu for toddlers is selected based on criteria that have been determined by Puskesmas Sasak Ranah Pasisie. The selection of healthy food menus for toddlers is carried out by the institution as the selection of healthy foods for toddlers. The selection of toddler food menus still uses traditional methods that have not been systemized. With these problems, a decision support system is applied using the La Raelite Elimination Et Choix method. This method is able to give subjective and inuitive considerations of the criteria factors that are considered important influences on alternative choices. Decision Support System is a computer technology solution that can be used to support decision making that is complex in problem solving in an organization, in order to be able to determine the selection of the best competitive and superior healthy food menu from the many toddler food menus then the process of selecting a healthy toddler food menu that determines the optimal alternative is fast and efficient.

Sistem Perpustakaan Buku Digital Berbasis Website dan Aplikasi Telegram Menggunakan QR Code Larissa Navia Rani; Dicky Wiransyah; Halifia Hendri
Jurnal KomtekInfo Vol. 9 No. 2 (2022): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

In general, information and communication technology or ICT (Information and Communication Technology) has become an inseparable part of global life. In the global era, the website aims to convey information digitally, such as the creation of a digital book library website for the need for information technology in disseminating information. The main problem faced by the Indonesian people, especially in the field of education during the COVID-19 pandemic, is the low level of quality of human resources. One of the efforts to improve the quality of resources for children, millennials, and even adults is the development of interest in reading books and the habit of reading books. The library is expected to be a center for the development of interest in reading books and the habit of reading books. During the COVID-19 pandemic, of course, libraries will be closed to prevent crowds and prevent the spread of COVID-19. Based on this problem, interest in reading books is getting lower, and to find books on the internet, on average, they are paid and difficult to access. To overcome these problems, new innovations and developments are needed from classic libraries to digital libraries by collaborating with computerized technology and information technology so that the resulting information can be presented quickly, precisely, efficiently and free of charge.

Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; 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

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.

Automated Pixel-Level Concrete Defect Detection using U-Net Architecture: A Comparative Study with Clustering-Based Segmentation Halifia Hendri; Larissa Navia Rani; Sofika Enggari; Agung Ramadhanu; Febri Hadi
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

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.

Co-Authors Arif Hanafi Desva Willton Dicky Wiransyah Febri Hadi Halifia Hendri Mardison Mardison Mardison Ramadhanu, Agung - Repelita Witri Sofika Enggari Sulaiman Harun Syafrika Deni Rizki Yuhandri Yuhandri

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