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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
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.
PENGGUNAAN METODE MABAC PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI MAGANG TERBAIK Safitri, Nina; Bangun, Budianto; 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.v7i2.1541

Abstract

This study aims to develop a Decision Support System (DSS) based on the Multiple Attribute Boundary Approximation Area Comparison (MABAC) method to assist Labuhan Batu University students in choosing the best internship location. The main problem faced is the difficulty in determining a strategic and quality internship location due to limited resources. This study uses a quantitative descriptive method involving five main criteria: Location Distance, Company Reputation, Facilities, Suitability of the Internship Program with the Curriculum, and Work Environment. Data were collected through literature, expert consultation, and primary and secondary data analysis. Based on the results of data processing using the MABAC method in this study, 3 best internship locations were obtained, namely locations A9, A4, and A3. The results of the analysis using the MABAC method show the system's ability to provide internship location recommendations based on objective multicriteria assessments. The resulting system can help make it easier for students to choose an internship location that suits their needs.
ANALISIS PREDIKSI HASIL PRODUKSI TANAMAN CABAI MENGGUNAKAN METODE MULTI LINIER REGRESI Sahputra, Sahputra; Sembiring, Delima Chrismas; Sipayung, Ivan Hasadaon; Barus, Ertina Sabarita
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.v7i2.1512

Abstract

This study aims to predict the yield of chili plants in Indonesia using multiple linear regression method. In this study, the variables analyzed include irrigation volume, temperature, soil moisture, soil pH, and plant growth parameters such as stems, branches, and leaves. Data were collected from 100 chili plant samples planted in Jatikusuma Village, Deli Serdang Regency, for 63 days. The method used for the analysis is multiple linear regression, which is applied to produce a prediction model of harvest yield. Multi linear regression method is used to perform forecasting with the development of the dependent variable (Y), namely the amount of production with independent variables consisting of x1 = plant growth rate, x2 = moisture, x3 = temperature, x4 = volume, x5 = soil pH, x6 = stem, x7 = branch, x8 = leaf. The results of the prediction analysis in this study obtained the intercept coefficient value is 153.94 from the total data of 100 samples, resulting in the level of fit of the multi linear regression model with an R2 score of 1.00 which shows the level of accuracy in a prediction of these results is very good.
SISTEM BERBASIS WEB DALAM MENDUKUNG PROSES MUSYAWARAH PERENCANAAN PEMBANGUNAN DESA Yanuarti, Elly; Irawan, Devi; Wahyuningsih, Delpiah; Perkasa, Eza Budi; Pambudi, Dita Heru
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.v7i2.1640

Abstract

The Village Development Planning Meeting (Musrenbangdes) is one of the annual activities carried out by Pagarawan Village. The current system has not yet digitized the administrative process of implementing musrengbangdes. The submission of activity proposals by community members is still done manually using handwriting on a piece of paper. All processes are still paper-based, making them less effective and efficient. In the current era, web technology should be utilized to improve services to the community. This research will develop a web-based system to accelerate the process of village development planning administration services. The analysis and design method used is object oriented method with UML diagram tools. The system development method uses Rapid Application Development (RAD). The results of system testing are 100% valid so that this system is declared feasible to implement. This web-based system was developed in accordance with the needs and provides easy access for the community in Pagarawan Village, accelerates the process of processing data for village development planning meetings (musrenbangdes), data is stored properly and neatly and makes it easier to search for data and information.
APLIKASI PEMESANAN PADA TOKO SABLON BAJU MENGGUNAKAN METODE RAPID APLICATION DEVELOPMENT Sugeng, Jonny Kurniawan; Ikrimach, Ikrimach
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.v7i2.1664

Abstract

In today's digital era, advancements in information technology have had a significant impact on various sectors, including the business world. "Sablon Yogyakarta," a store offering custom t-shirt printing services, faces challenges in accommodating its expanding customer base, especially in handling orders and managing customer data. The current manual ordering process has proven to be inefficient, prone to errors, and susceptible to delays in processing orders and providing timely confirmations. This inefficiency can affect customer satisfaction and limit the store's operational effectiveness. To address these issues, there is a pressing need for a web-based ordering system that allows customers to place custom t-shirt orders online, easily selecting designs, sizes, and printing preferences. This system will streamline the order process, reduce human error, and enable better management of order and customer data. Additionally, it will facilitate faster communication between the store and customers, enhancing overall service quality and ensuring smoother transaction processes.
IDENTIFIKASI POLA TIDUR GENERASI Z (GEN-Z) MENGGUNAKAN ALGORITMA KLUSTERISASI K-MEANS Annisa, Riski; Rahayuningsih, Panny Agustia; Anna, Anna; Hidayana, Reymond Syahputra; Ramadhani, Zulfikar Ismaya
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1740

Abstract

Generation Z faces great challenges in maintaining healthy sleep patterns due to lifestyle changes and high exposure to technology. This study aims to identify the sleep patterns of Generation Z as well as the main factors that affect their sleep quality using the K-Means algorithm. Data was collected from 300 participants through an online questionnaire that included variables such as sleep duration, difficulty sleeping, caffeine consumption, electronic device use, physical activity, and freshness after waking up. With a clustering approach, the results of the study showed that there were three main patterns: irregular sleep patterns (45%), healthy sleep patterns (35%), and poor sleep patterns (20%). The cluster with healthy sleep patterns had an average of 7-8 hours of sleep, high physical activity, and low caffeine consumption, while irregular sleep patterns were less dominated by the use of electronic devices before bedtime, high caffeine consumption, and low physical activity. These findings highlight the importance of lifestyle management in improving the sleep quality of Generation Z and provide a basis for the development of more effective interventions. This study concludes that data-based clustering is a useful method to understand the sleep patterns of a particular population in more depth.
PENERAPAN INTERNET OF THINGS (IOT) UNTUK OPTIMASI PEMELIHARAAN TANAMAN RUMPUT RAJA SECARA REAL-TIME Manalu, Andi Setiadi; Siregar, Victor Marudut Mulia; Sugara, Heru
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1709

Abstract

This study aims to optimize the maintenance of King Grass plants by utilizing Internet of Things (IoT) technology for real-time monitoring. King Grass is known as a source of livestock feed with high productivity, but its maintenance often faces challenges such as environmental fluctuations and inefficient irrigation patterns. This study uses an ESP32 microcontroller integrated with a DHT22 sensor, soil moisture sensor, and automatic water pump, and utilizes the Thingsboard platform to monitor data in real time. The data collected includes air temperature, air humidity, and soil moisture, which are displayed through interactive widgets. The system was tested by simulating various environmental conditions, showing success in automating irrigation based on soil moisture thresholds. The test results show that IoT technology is able to improve plant maintenance efficiency, reduce manual intervention, and ensure optimal conditions for plant growth. The use of the Thingsboard platform facilitates data monitoring and analysis for further planning. This study concludes that the application of IoT to King Grass maintenance provides an effective solution in increasing agricultural productivity through accurate and efficient monitoring and control.
PERAMALAN KEBUTUHAN STOK SEMBAKO MENGGUNAKAN METODE TREND MOMENT Prasetya, Rafid Artur; Irawan, Joseph Dedy; Orisa, Mira
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.v7i2.1812

Abstract

Forecasting stock requirements is vital for grocery businesses to optimize inventory management and meet market demand efficiently. This study addresses the challenges faced by Toko Amah, a grocery store relying on manual stock recording prone to errors, by developing a web-based forecasting system. Utilizing the Trend Moment method, the system analyzes historical sales data to predict future stock needs with greater accuracy. The study aims to enhance inventory decision-making processes, reduce human error, and improve overall business efficiency. The system was evaluated by comparing forecasted stock with actual inventory data, yielding a Mean Absolute Percentage Error (MAPE) of 20.89%. While the results indicate acceptable accuracy, further refinement is needed to minimize prediction errors. This study concludes that the Trend Moment method provides a practical solution for stock forecasting, supporting more systematic and reliable inventory management in grocery businesses.

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