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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 407 Documents
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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.
IMPLEMENTASI SISTEM INFORMASI REKAM MEDIS WEB UNTUK PRAKTIK MANDIRI BIDAN MENGGUNAKAN METODE SCRUM Fadillah, Muhammad Adzka Fardhan; Sutarman, Sutarman
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.v7i2.1708

Abstract

This study aims to design and develop a web-based medical record information system for the Independent Midwifery Practice (PMB) Euis Tita Kartika, in order to improve the efficiency of patient data recording and medical records. The system is built using the Agile methodology, specifically the Scrum framework, which enables iterative system development and responsiveness to changing needs. This study identifies various issues in manual medical record keeping, such as recording errors, inaccurate data, and the risk of data loss. The results of the study show that the developed medical record information system can increase data retrieval speed, reduce recording errors, and facilitate medical data access for staff and midwives. The system includes features such as multi-user login, patient data management, medical record keeping, and user data management, which can be accessed by various roles according to needs. The implementation of this system is expected to support the improvement of healthcare services at PMB Euis Tita Kartika and minimize issues related to manual medical record keeping.
ANALISIS KINERJA METODE K-NEAREST NEIGHBORS PADA DATA JOB FAIR Purnamawati, Annida; Winnarto, Monikka Nur; Mailasari, Mely
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.v7i2.1617

Abstract

This study examines the performance of the K-Nearest Neighbors (KNN) method for classifying applicant data in data-driven job fair activities. The challenges faced include managing large volumes of applicant data and identifying optimal parameters for classification. The study uses a dataset containing 20,000 entries from Kaggle, with attributes such as skills, work experience, and completed projects. After data preprocessing, experiments were conducted using the KNN method with the Euclidean Distance algorithm, within a range of k values from 3 to 9. The results show that k = 3 provides the best performance with an accuracy of 65.00%, precision of 63.78%, recall of 71.88%, and an F1-score of 67.64%. The conclusion indicates that smaller k values capture local patterns better, while larger k values tend to reduce performance. This research contributes to the development of data-driven recruitment systems by enhancing the efficiency and accuracy of applicant selection. Further studies are recommended to explore additional optimization methods and feature combinations to improve classification accuracy.
IMPLEMENTASI METODE RANDOM FOREST UNTUK MEMPREDIKSI PENJUALAN PRODUK Barus, Ertina Sabarita; Darmanto, Darmanto
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.v7i2.1510

Abstract

This study aims to predict product sales at CV Pelumas Murni Keluarga using the Random Forest method to overcome sales fluctuations that impact stock management and production planning. Uncertainty in sales forecasting can cause excess or shortage of stock, thus hampering the company's growth. This method was chosen because of its advantages in handling complex data and producing accurate predictions. The study was conducted quantitatively through observation and collection of automotive lubricant sales data from January to June 2023. Data was analyzed using the Google Colab application to implement the Random Forest model. The process involves data preprocessing, model building, and evaluation using out-of-bag data. The results of the study show that the Random Forest method is able to significantly increase the accuracy of sales predictions, providing a stronger foundation in developing sales strategies and inventory management. Thus, this study is expected to help CV Pelumas Murni Keluarga in optimizing operational efficiency and increasing profitability.

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