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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat PAKAR Pendidikan Journal of Applied Data Sciences Journal of Computer Scine and Information Technology Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo
Sofika Enggari
Universitas Putra Indonesia YPTK Padang
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Poor Family Classification Decision Support System using the Simple Additive Weighting (SAW) Method Lili Amareza Patriani; Sumijan; Sofika Enggari
Journal of Computer Scine and Information Technology Volume 9 Issue 3 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i3.83

Abstract

Poverty is a problem that continues to be the focus of attention for the government. Poverty has also caused people to be willing to sacrifice anything for their survival. To anticipate this problem, various policies have been adopted by the government to break the chain of poverty. One of them is providing assistance funds to poor families (PKH). This is felt directly by all levels of underprivileged society. One of the efforts of the Koto Ranah Tapan government to eradicate poverty that occurs in Koto Ranah Tapan is to follow the central government program, namely the launch of government financial assistance (PKH). These funds will be distributed to poor residents in Koto Ranah Tapan through the nagari guardian office in Koto Ranah Tapan. However, the distribution of aid funds to poor families is often not on target due to a large level of manual calculation error which makes the aid not on target and also the office of the nagari village of high cliff village has not been able to objectively determine the families who receive the aid. To help determine which families are worthy of receiving poor family assistance funds, a decision support system is needed. With this Decision Support System (DSS), it is hoped that the decision-making process can minimize the occurrence of wrong targets that often arise in the process of selecting poor families who wish to receive aid funds . In this calculation the author uses the Simple Additive Weighting (SAW) method, because this method is suitable for accurate calculations and is very helpful in calculating any data obtained. The results obtained were that Ade Irma Suryani got the highest score with a score of 10.8 and was ranked at the top (Best 1), so she could be considered the best recipient of aid funds.
Evaluation of New Employee Selection using the Multi Factor Evaluation Process Method Dian Marissa; Sofika Enggari; Dodi Guswandi
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.96

Abstract

The process of accepting and selecting prospective employees is the earliest process for a company to get quality employees that the company or agency needs. Companies must have criteria for the employees they want. On CV. Adtuil Photocopying in recruiting employees is still less efficient, namely prospective employees still send application files to the company or via expedition delivery. So HRD will have difficulty in selecting prospective employees because they have to record and double-check incoming application files as well as the process of determining the right criteria . Solutions used to overcome problems on CV. Adtuil uses a decision support system for selecting new employees, using the Multi Factor Evaluation Process (MFEP) method. This method is quantitative which uses a weighting system in decision making. Application design using the Vb programming language. Net and MySQL databases that can manage data quickly and accurately. The results of this research show that there were 3 employees who received 10 alternative data, namely A1, A5, A9 with scores > 75. After using this decision support system it can help CV. Adtuil Photocopy in determining employee acceptance precisely, quickly and accurately
Implementation of the Topsis and AHP Methods in the Decision Support System for Determining the Best Employees Yolan Ananda Putri; Sumijan; Sofika Enggari
Journal of Computer Scine and Information Technology Volume 10 Issue 2 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i2.103

Abstract

Every company or agency needs Human Resources (HR) in the form of employees who have competence and good performance. Employees are one of the most important assets owned by a company. The West Sumatra Province Transportation Service is the organizer of government affairs in the field of transportation or transportation policy for the West Sumatra Province region where the selection of the best employees is still not optimal using Microsoft Excel. The aim of designing a new system at the Provincial Transportation Service is to create optimization in the assessment of each employee to facilitate the recapitulation of employee data. The data is analyzed and processed according to the research framework, namely using a Decision Support System, especially the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) methods. In this research, 10 alternative employees were taken to be assessed. Based on formula calculations using the AHP method, it is used to determine the weighted value of each existing criterion, then the resulting values from the weighting are used to carry out rankings using the TOPSIS method. After carrying out calculations using these 2 methods, the result was that the best employee was alternative 9 in the name of Rusdi with a value of 0.9995. So with this calculation the results can show which employees have the right to be the best employees in that agency
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

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.
Co-Authors Arif Hanafi Dian Marissa Dodi Guswandi Febri Hadi Hafizul Fadli Halifia Hendri Husna Arsyah, Rahmatul Larissa Navia Rani Lili Amareza Patriani Lita Gusrian Mardison Mardison Muhammad Tajuddin Nabila Frisca Oktavia Ramadhanu, Agung - Rani Anggraini Sarjon Defit Sepsa Nur Rahman Silfia Andini Sulaiman Harun Sumijan Sumijan Sumijan Syafri Arlis Yolan Ananda Putri