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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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ANALISIS PREDIKSI PENJUALAN TOKO FURNITUR DENGAN METODE LONG SHORT-TERM MEMORY (LSTM) Gunawan, Ricky; Dimiliu, M Bairaja; Valerine, Karen; Tamba, Saut Parsaoran
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.v7i2.1511

Abstract

This study aims to analyze and predict furniture store sales using the Long Short-Term Memory (LSTM) method, focusing on time series datasets from 2014 to 2017. The LSTM method was chosen because of its ability to handle remote dependencies in time series data, which is relevant in understanding furniture sales patterns and trends for strategic planning. The research stages include literature study, library research, field research, data acquisition, and data preprocessing using Python and Google Colab. Exploratory analysis of the data was carried out to understand the characteristics of the dataset, followed by the development of the LSTM model, data normalization, and model evaluation with RMSE, MAE, and MAPE metrics. The evaluation results show that the LSTM model produces RMSE of 39.27%, MAE of 32.74%, and MAPE of 42.28%. Nonetheless, there is potential to improve accuracy by integrating more variables, exploring different LSTM architectures, and utilizing regularization techniques. This research is expected to contribute to improving the effectiveness of furniture sales management strategies and inspire further development in the application of ESG in business prediction.
ANALISA KLASIFIKASI LOYALITAS PELANGGAN MENGGUNAKAN ALGORITMA NAÏVE BAYES Hayuningtyas, Ratih Yulia
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.v7i2.1725

Abstract

Customers are one of the important assets in business ventures, every company competes to attract customers by various means of promoting the products they sell. It turns out by focusing on the product it cannot attract customers, therefore the company changed its method become customer-oriented. Looking for information about products in high demand by customers can attract customers to remain loyal to the products sell. To find out whether customers are loyal or not from each visit, you can use a classification algorithm, namely Naïve Bayes. The Naïve Bayes algorithm is one of the best algorithms for classification because it can help find hidden data models during data analysis. In this research, we try to analyze customer data who buy Starbucks to find out which customers are very loyal to buy Strabucks products using the Naive Bayes algorithm. This algorithm groups loyal and non-loyal customer data, then separates it into test data and training data. From this data Testing will be carried out using the Naive Bayes algorithm to determine loyal customers. The results this research have an accuracy value of 87%, a precision value of 90% and a recall of 95%, which means this classification has good performance
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PENGGAJIAN KARYAWAN LEMBAGA PERKREDITAN DESA DENGAN METODE PROTOTYPE Sekartini, Made; Wijaya, I Nyoman Yudi Anggara; Kusuma, Ni Putu Noviyanti
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.v7i2.1477

Abstract

The financial department of the Tanjung Bungkak Traditional Village Credit Institution (LPD) has problems in the employee payroll process. The problem faced by the finance department is that employee payroll is still using Microsoft Excel, where employee salary calculations are still using Excel formulas and the data is not stored in a database so that searching for employee salary data takes a relatively long time. Payslip recording is still retyped using Microsoft Word so that when printing a payslip you are required to open the payroll report that has been created in Microsoft Excel. This is less effective and efficient, because errors can occur in making employee pay slips and result in frequent delays in salary reporting. This research aims to carry out Analysis and Design of a Payroll Cycle Accounting Information System in order to make it easier to computerize the payroll of Tanjung Bungkak Traditional Village LPD employees. The method used is the Prototype Model. The results of the system design that has been created produce a structured and systematic system to increase productivity and reduce errors in the application of appropriate information technology in business operations and can provide the information needed by the parties involved in the Tanjung Bungkak Traditional Village LPD. The interview results show the analysis and design of a payroll information system that has been created according to the needs of each section.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN FITNESS CENTRE TERBAIK MENGGUNAKAN METODE COMPLEX PROPORTIONAL ASSESSMENT Aurianda, Rieke; 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.v7i2.1547

Abstract

This study aims to design and implement a Decision Support System (DSS) to help the people of Bagan Batu Regency in choosing the best fitness center. The increasing awareness of a healthy lifestyle has triggered the development of the fitness industry, but the many choices often make it difficult for people to determine the right choice. The Complex Proportional Assessment (COPRAS) method is used in this study because of its ability to evaluate various alternatives based on benefit and cost criteria. Five criteria are considered, namely sports facilities, instructor quality, operating hours, membership fees, and location. Data were collected from several fitness centers in Bagan Batu and processed through a normalization and weighting process. Based on the results of data processing using this method, recommendations for the best fitness centers were obtained, namely FC_04, FC_08, and FC_07. The results of this study indicate that the COPRAS method can simplify and accelerate the provision of recommendations for selecting the best fitness location in Bagan Batu Regency.
SISTEM PENDUKUNG KEPUTUSAN PRIORITAS PESERTA PERTUKARAN MAHASISWA MENGGUNAKAN METODE WASPAS Alam, Dewi Pathimah; 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.v7i2.1540

Abstract

This study aims to develop a Decision Support System (DSS) based on the WASPAS (Weighted Aggregated Sum Product Assessment) method to assist Labuhan Batu University in selecting the best students to participate in the student exchange program. Manual selection which is often subjective and prone to bias is a problem that needs to be overcome. The WASPAS method was chosen because of its ability to combine weighted addition and weighted multiplication, resulting in a more comprehensive and objective evaluation. This study includes the stages of problem identification, student data collection, determination of selection criteria, and data processing using the WASPAS method. The criteria used include English language skills, leadership, knowledge, GPA, and achievement. The results of data processing using the method obtained an alternative with the Highest Value, namely A7 with a final result of 122.86582. The results of the study showed that the application of the WASPAS method resulted in a more transparent and objective evaluation compared to the manual selection method. By using this system, it is hoped that the selection process for student exchange program participants will be more efficient and on target.
PENGEMBANGAN AUGMENTED REALITY UNTUK APLIKASI PENGENALAN RUMAH ADAT NUSANTARA DENGAN METODE MDLC Suartama, I Komang; Astawa, Ni Luh Putu Ning Septyarini Putri; Juliharta, I Gede Putu Krisna
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.v7i2.1587

Abstract

This research developed an educational media application based on Augmented Reality (AR) called "AR Rumah Adat Nusantara," aimed at introducing traditional houses of Indonesia to students. The primary issue addressed is the need to preserve local culture, which is increasingly being eroded by modernization. This application is expected to enhance students' understanding of Indonesia's cultural heritage. The development method used is the Multimedia Development Life Cycle (MDLC), and the study employs a mixed-method approach combining quantitative and qualitative data. Data collection techniques include interviews, observations, and literature review. The application was tested using black box testing to ensure all functions work as intended. The study participants consisted of educators and fourth-grade students from a non-formal educational institution in Gianyar Regency, Bali. The results indicate that the application was successfully developed with 100% functionality as confirmed through testing. The study concludes that AR technology has significant potential in enhancing the learning process, particularly in introducing and preserving local culture. The "AR Rumah Adat Nusantara" application is expected to be an effective and engaging learning tool, helping students to learn about and appreciate Indonesia's rich cultural heritage.
PENERAPAN METODE ALGORITMA C4.5 DAN NAIVE BAYES UNTUK PREDIKSI PENYAKIT JANTUNG Sihotang, Putri Anasia; Sitanggang, Delima
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.v7i2.1535

Abstract

This study aims to compare the C4.5 Decision Tree and Naive Bayes algorithms in predicting heart disease to determine the most efficient algorithm. Heart disease is one of the leading causes of global mortality, including in Indonesia, due to vascular damage that disrupts the optimal functioning of the heart. The dataset used comes from the UCI Machine Learning Repository and the Kaggle website's "Heart Failure Prediction," totaling 918 records with 11 clinical attributes and 1 label. Data processing was conducted using Google Colab with the Python programming language. The results show that the C4.5 algorithm achieved an accuracy of 95.18% after feature selection using Particle Swarm Optimization (PSO), while without feature selection, it achieved an accuracy of 81%, precision of 83%, recall of 74%, F1-score of 78%, and an AUC value of 81%. Meanwhile, the Naive Bayes algorithm achieved a maximum accuracy of 90.87% without feature selection and performed best with an accuracy of 84%, precision of 83%, recall of 80%, F1-score of 81%, and an AUC value of 94%. These findings indicate that the Naive Bayes algorithm outperformed the C4.5 algorithm in several evaluation parameters.
DETEKSI PENYAKIT RUMPUT LAUT DENGAN RESIDUAL NEURAL NETWORK Nurlinda, Nurlinda; Hasmin, Erfan; Jufri, Jufri
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.v7i2.1621

Abstract

This research aims to detect seaweed diseases using the Residual Neural Network (ResNet) deep learning model. Seaweed, or Thallus, is a crucial fishery commodity in Indonesia, but it is often threatened by diseases such as Ice-ice and Bulu Kucing, which are challenging to distinguish visually. The dataset used in this study consists of images of healthy and diseased seaweed, which undergo preprocessing steps like resizing, augmentation, and data splitting. The ResNet model is trained on this processed data and evaluated using a Confusion Matrix, achieving an accuracy of 96.78% and a validation accuracy of 99.68%. These results demonstrate that ResNet has significant potential in detecting seaweed diseases, which can contribute to increasing productivity and improving the welfare of seaweed farmers.
PENGARUH SMOTE TERHADAP PERFORMA ALGORITMA RANDOM FOREST DAN ALGORITMA GRADIENT BOOSTING DALAM MEMPREDIKSI PENYAKIT STROKE Fadmadika, Fadilla; Handayani, Hanny Hikmayanti; Mudzakir, Tohirin Al; Indra, Jamaludin
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.v7i2.1575

Abstract

Stroke is a disease that can occur suddenly, causing progressive brain damage due to non-traumatic blood flow disruption in the brain. Common symptoms of stroke include numbness in the limbs and impaired communication. Stroke is the second leading cause of death in the world and the third leading cause of mental retardation globally. Predictive machine learning-based technology can help identify early symptoms of stroke for prevention and early intervention. This study aims to compare the performance of the Random Forest and Gradient Boosting algorithms in predicting stroke. By applying the SMOTE method to address class accuracy in the dataset, this study shows that the Random Forest model is superior, with an accuracy of 95.5%, a precision of 78.8%, a recall of 93.1%, and an f1-score of 84.2%. In conclusion, the Random Forest algorithm performs better than Gradient Boosting in predicting stroke, showing significant potential in assisting early detection and medical decision making.
IMPLEMENTASI ALGORITMA FORWARD CHAINING PADA SISTEM PAKAR DIAGNOSA HAMA DAN PENYAKIT TANAMAN PISANG goda, Karina Dhena; Lea, Victoria Coo; Ule, Yuliana
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.v7i2.1683

Abstract

The management of banana pest and disease outbreaks in Ngada Regency since 2022 has been hindered by farmers' lack of knowledge about early detection of symptoms and the limited availability of agricultural extension workers, which accelerates the spread and increases losses in agricultural land. This study aims to develop a web-based expert system to assist farmers in diagnosing banana pests and diseases quickly and accurately. The research methods involved data collection through field observations, interviews with farmers and agricultural experts, and literature studies from relevant references. The system design employs the waterfall development model, which includes requirements analysis, system design, implementation, and testing. The knowledge base of the system is designed using the forward chaining algorithm with 9 types of diseases and 40 symptoms. Implementation results indicate that the system was successfully tested using the black-box method with a 100% success rate, while the usability and responsiveness aspects scored 98% based on user evaluations. In conclusion, the forward chaining algorithm serves as an effective methode to support the diagnosis of banana pests and diseases and to enhance farmers' knowledge, thereby reducing losses caused by pest and disease attacks.

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