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Jurnal Tekinkom (Teknik Informasi dan Komputer)
Published by Politeknik Bisnis Indonesia
ISSN : 26211556     EISSN : 26213079     DOI : https://doi.org/10.37600/tekinkom
Core Subject : Science,
Computer Science & IT
Jurnal TEKINKOM merupakan jurnal yang dimaksudkan sebagai media terbitan kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai isu Ilmu - ilmu komputer dan sistem informasi, seperti : Pemrograman Jaringan, Jaringan Komputer, Teknik Komputer, Ilmu Komputer/Informatika, Sistem Informasi, dan Multi Disiplin Penunjang Domain Penelitian Komputasi, Sistem dan Teknologi Informasi dan Komunikasi, dan lain-lain yang terkait. Artikel ilmiah dimaksud berupa kajian teori (theoritical review) dan kajian empiris dari ilmu terkait, yang dapat dipertanggungjawabkan serta disebarluaskan secara nasional maupun internasional.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 60 Documents
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Search results for , issue "Vol 7 No 2 (2024)" : 60 Documents clear
SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN PEGAWAI BIRO AKADEMIK MENGGUNAKAN MOOSRA Pertiwi, Nur Fajar Kurnia; Munthe, Ibnu Rasyid; Sihombing, Volvo
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1538

Abstract

This study aims to design and develop a decision support system (DSS) for the employee selection process at the academic bureau of Labuhan Batu University using the MOOSRA method. The main issue faced is the use of subjective and non-standardized traditional selection methods, which can reduce the accuracy and efficiency in selecting the right candidates. The method applied in this study is MOOSRA, which can process various selection criteria such as educational qualifications, work experience, information technology skills, communication skills, and discipline. The results of the calculation of values ​​and rankings indicated that alternative A4 was the best candidate, followed by A9 and A1. The results of the study indicate that the use of the MOOSRA method in the decision support system can provide more objective and efficient recommendations in the employee selection process at the academic bureau of Labuhan Batu University.
IMPLEMENTASI SISTEM ANTRIAN ONLINE PADA DUKCAPIL KLATEN MENGGUNAKAN METODE USER CENTERED DESIGN (UCD) Raharjo, Agus Budi; Aji, Adam Sekti
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1748

Abstract

The Population and Civil Registration Office (Dukcapil) has an important role in providing public services related to the processing of population documents. In Klaten Regency, people often face problems in taking care of legal identity documents at the Dukcapil, such as long and disorganized queues. The queuing system that is still done manually is considered less efficient and prone to fraud. The community also has difficulty in monitoring the order of the queue, so they have to wait a long time without certainty when they will be served. To overcome these problems, an integrated and computerized online queue information system is needed. This research aims to design and build an online queue information system at the Klaten Regency Dukcapil using the User Centered Design method. This system can provide better, efficient, and transparent services to the community. System development uses the Kotlin programming language and MySQL database. Black box testing is also carried out to find out the errors that exist in the system. Through the online queuing system, people can take queue numbers online and monitor the queue sequence in real time, thus providing convenience and comfort in accessing Dukcapil services. Thus, the quality of public services, especially in the processing of population documents, can be improved.
PERBANDINGAN PENERAPAN ALGORITMA K-MEANS DAN FUZZY C-MEANS DALAM ANALISIS CLUSTERING TERHADAP PERGERAKAN HARGA HISTORIS SAHAM BANK RAKYAT INDONESIA Purba, Winda Nia; Hartanto, Ricky
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1214

Abstract

This study aims to analyze and compare the application of K-Means and Fuzzy C-Means algorithms for clustering historical stock price movements of Bank Rakyat Indonesia (BRI). Clustering is a method that groups data based on similarities, crucial in stock data analysis to aid more precise investment decision-making. The K-Means algorithm deterministically assigns each data point to a single cluster, while Fuzzy C-Means allows partial membership across multiple clusters, offering greater flexibility. The research findings indicate that the K-Means algorithm forms three primary clusters with a Silhouette Score of 0.4667, which defines clusters more clearly than Fuzzy C-Means, which has a score of 0.4199. The clusters produced by K-Means provide better-defined separations among stocks with medium, high, and low prices, based on price movements and transaction volume. In contrast, Fuzzy C-Means, despite its ability to handle overlapping data, results in less clearly defined clusters compared to K-Means. Based on these results, the K-Means algorithm is deemed more effective for clustering analysis in the context of BRI stocks. This research is expected to contribute to the development of more comprehensive stock movement analysis models and support investors in making better-informed investment decisions.
IMPLEMENTASI METODE RANDOM FOREST UNTUK KLASIFIKASI PENJUALAN PRODUK SABUN PALING LARIS Pratiwi, Galuh Eka; Nugroho, Adi
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1610

Abstract

This research aims to analyze soap product sales data in a supermarket to understand sales patterns and categorize products based on their sales levels. Considering the multitude of soap products, the supermarket finds it difficult to conduct research on the best-selling soap products. The data used includes 4,694 soap product sales data from January 2022 to December 2023, with variables such as type, brand, price, and quantity sold. In this study, the Random Forest method is used to classify soap products into four categories: not popular, less popular, popular, and most popular. The process of data analysis and processing was carried out using Google Colaboratory with the Python programming language. Based on the evaluation results, the produced model has an accuracy of 94.6%, which indicates that this method is effective in classifying products based on sales levels. The results of this research are expected to help supermarkets optimize inventory management and design more targeted marketing strategies to increase profits. With this classification, supermarkets can focus more on products that have the potential to contribute greater profits.
OPTIMASI PROSES SELEKSI PEGAWAI MENGGUNAKAN SISTEM PENDUKUNG KEPUTUSAN METODE COPRAS Andriyani, Wahyu Fitri; Sihombing, Volvo; Juledi, Angga Putra
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1545

Abstract

This study aims to develop a decision support system (DSS) based on the COPRAS method to help simplify the employee selection process at SPX Express, Rokan Hilir. The challenges faced in the selection process are subjectivity in assessment and non-integration of data, which results in less than optimal selection decisions. The COPRAS method is used to evaluate candidates based on predetermined criteria, such as Personality Tests, Health Tests, Knowledge Tests, Age, and Work Experience. This study involves the stages of determining the weight of the criteria, collecting data, and processing data using the COPRAS method to produce candidate rankings. The results showed that the candidates with the highest rankings were CP_02, CP_01, and CP_05. This DSS system can improve efficiency and objectivity in employee selection decision making.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI PEMASARAN LAPTOP BEKAS MENGGUNAKAN METODE ARAS Purba, Mila Hanim; Sihombing, Volvo; Irmayani, Deci
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1542

Abstract

This study aims to develop a decision support system (DSS) based on the Additive Ratio Assessment (ARAS) method to help used laptop entrepreneurs in Bagan Sinembah District in determining the optimal marketing location. The problems faced include the selection of business locations that are often done based on intuition without considering data and strategic factors, such as market demand, operational costs, infrastructure, market competition, and economic growth potential. The research method involves identifying the main criteria, collecting alternative location data, normalizing data using the ARAS method, and calculating the utility value for each alternative. The results of the study showed that the three best alternatives for marketing locations were A1 in the first position with a utility value of 0.86507, A4 in the second position with a value of 0.79749, and A6 in the third position with a value of 0.78166. Based on the results of the study, it shows that the ARAS method is effective in dealing with multi-criteria problems, providing recommendations for marketing locations with the highest utility value, which is considered the best choice.
SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KINERJA APARATUR DESA DENGAN METODE TOPSIS Tiara, Dewi; Munthe, Ibnu Rasyid; Sihombing, Volvo
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1543

Abstract

This study aims to develop a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate the performance of village officials more objectively, quickly, and accurately at the Bagan Sari Village Office, South Labuhan Batu Regency. The main problem faced is that the assessment of the performance of officials is still carried out manually, which has the potential to produce subjective and inconsistent data. This study uses a quantitative approach with the following stages: determining criteria and weights, collecting data, and processing data using the TOPSIS method. The criteria used include discipline, attendance, cooperation, and loyalty. The results of the study indicate that the TOPSIS-based system can produce village official performance ratings with high accuracy, minimize bias, and accelerate the decision-making process. This system is expected to be able to provide strategic guidance for the Village Head in improving the quality of service and motivating officials to improve their performance. The implementation of this technology is also a strategic step in modernizing village governance.
ANALISIS PERUBAHAAN PERMINTAAN TRANSAKSI UNTUK MENINGKATKAN KEPUASAAN PELANGGAN SHOPEE DENGAN ALGORITMA FUZZY C MEANS Tajrin, Tajrin; Hasugian, Debi Maria; Nasution, Olyfia Akbar
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1530

Abstract

This study uses Fuzzy C-Means to analyze Shopee customer demand and transaction success, aiming to improve customer satisfaction by understanding shopping patterns and purchase conversion rates. With the rapid growth of internet usage and e-commerce, consumer behavior analysis has become crucial for improving customer satisfaction. This study utilizes the Fuzzy C-Means algorithm to cluster data based on attributes such as location, product price, sales volume, and customer ratings. The Fuzzy C-Means algorithm allows handling ambiguous data and identifying significant patterns in transactions and customer satisfaction. The study results indicate that the algorithm successfully grouped the data into three main clusters: the first cluster has an average price of Rp 120,000, an average sales volume of 5,000 units, and an average rating of 4.8; the second cluster has an average price of Rp 140,000, an average sales volume of 3,000 units, and an average rating of 4.7; the third cluster has an average price of Rp 130,000, an average sales volume of 4,000 units, and an average rating of 4.9. This research provides valuable insights for e-commerce companies to design more effective marketing strategies and improve service quality based on the analysis of demand changes and transaction conversion effectiveness.
PEMODELAN INSPEKSI PAINTING DEFECT PADA MOBIL MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) Ramadhan, Muchamad Fachrul; Fauzi, Ahmad; Wahiddin, Deden; Rohana, Tatang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1519

Abstract

Quality control is an important process carried out at the last stage of the production process, this activity is carried out by checking a product. Painting defects on cars are a problem that must be considered in the car production process at car companies. The perfection of a product is important to increase the level of customer satisfaction. These checking activities are still carried out manually with human power, which can still cause defective products to be missed in a production process that occurs as a result of human error. The use of artificial intelligence can be used to detect image and video objects, used to overcome the problem of human error in carrying out checks. Convolutional Neural Networks (CNN) is an algorithm that can be used in product defect inspection, image recognition, and image classification. The study focuses on modeling the inspection and detection of painting defects in cars using CNN, emphasizing the importance of quality control in ensuring product quality. The CNN model is trained with image data of normal car paint and defective car paint, and evaluated using a confusion matrix for optimal parameters. The results show quite high accuracy in detecting car paint defects of 98% with the help of the ResNet50 transfer learning CNN architecture.
ANALISIS SENTIMEN ULASAN APLIKASI ”ACCESS BY KAI” MENGGUNAKAN ALGORITMA MACHINE LEARNING Nugroho, Moh Andi Setyo; Susilo, Dahlan; Retnoningsih, Dwi
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1854

Abstract

The trains have become one of the most popular modes of transportation in Indonesia. In line with technological advancements, PT KAI launched an application called "Access by KAI," which has received various ratings on the Google Play Store. This research aims to analyze user sentiment towards the "Access by KAI" application. The study employs the SEMMA methodology (Sample, Explore, Modify, Model, and Assess) as a framework, utilizing a combination of machine learning algorithms and lexicon-based approaches. Data collection was conducted through web scraping user reviews from the Google Play Store from January to June 2024, resulting in 9,124 reviews. The best model for sentiment classification in this study is Logistic Regression, achieving an accuracy of 84%, followed by the Random Forest algorithm with an accuracy of 78%, and Naive Bayes with an accuracy of 73%. The results indicate that negative sentiment predominates, suggesting that the "Access by KAI" application requires improvements, particularly in areas that have generated user complaints. Words such as "difficult," "login," "wrong," "payment," and "failed" reflect user frustration related to the login and payment processes, leading to user dissatisfaction. This research is expected to provide insights into user opinions regarding the "Access by KAI" application.

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