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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 50 Documents
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Search results for , issue "Vol 8 No 1 (2025)" : 50 Documents clear
ANALISIS SENTIMEN TERHADAP ULASAN PENGGUNA APLIKASI DANA DI GOOGLE PLAYSTORE MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) Willyarnandi, Muchammad Chadavi; Huizen, Lenny Margaretta
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2253

Abstract

In today's era, technological advancements have significantly facilitated various aspects of life, including the financial sector through the emergence of financial technology (fintech). One of the widely used fintech services in Indonesia is the DANA digital wallet. The abundance of user reviews generated from the use of this application reflects the level of user satisfaction or dissatisfaction with the services provided. However, this data is generally unstructured text, making it difficult to analyze manually. Therefore, an automatic analysis method is needed to categorize the sentiments contained in these reviews. Support Vector Machine (SVM) is one of the algorithms that can be used for sentiment classification, although its effectiveness in analyzing reviews of the DANA application still requires further investigation.This study aims to analyze sentiment in user reviews of the DANA application obtained from the Google Play Store using the SVM algorithm. A total of 2,000 reviews were collected through scraping and processed through several stages, including data cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Text features were extracted using the Term Frequency-Inverse Document Frequency (TF-IDF) method before classification with SVM. SVM was chosen due to its ability to handle high-dimensional data and its strong performance in text classification tasks. The results indicate that SVM is capable of classifying sentiment with high accuracy, achieving around 90% or more, along with high precision, recall, and F1-score values. These findings are expected to help application developers understand user needs and complaints to enhance the quality of DANA’s services.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN MAHASISWA PKL TERBAIK MENGGUNAKAN METODE MOOSRA Purba, Arifin Tua; Manalu, Andi Setiadi; Sirait, Erwin
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2137

Abstract

Politeknik Bisnis Indonesia is a vocational higher education institution committed to producing graduates who are not only academically competent but also equipped with practical skills required in the workforce. One of the essential programs in its curriculum is the Internship (PKL), designed to allow students to apply their knowledge in real-world work environments. However, the selection of the best internship students has been conducted manually, leading to inefficiencies and potential subjectivity in the evaluation process. This study aims to design a Decision Support System (DSS) using the MOOSRA (Multi-Objective Optimization on the Basis of Simple Ratio Analysis) method to support a more objective and systematic selection process. The evaluation involves five main criteria: discipline, teamwork, skill, work quality, and attendance, with six student candidates as alternatives. The research stages include problem identification, criteria and weight determination, data collection, data processing with the MOOSRA method, system design, and system testing. The results show that the MOOSRA method effectively ranks the students, with student A4 selected as the best internship participant with the highest Yi score of 6.12347. This research demonstrates that the MOOSRA method can significantly improve decision-making accuracy and fairness in multi-criteria selection processes.
PERBANDINGAN ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR MACHINES DALAM MEMPREDIKSI TINGKAT RISIKO SERANGAN JANTUNG BERDASARKAN KEBIASAAN MEROKOK Harmaja, Okta Jaya; Fernando, Fernando; Melati, Melati
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1807

Abstract

Heart disease remains a major global health challenge, with smoking behavior identified as one of the most significant modifiable risk factors. This study aims to compare the performance of two machine learning algorithms—Random Forest and Support Vector Machine (SVM)—in predicting heart attack risk levels based on smoking habits and biometric indicators. Using a dataset of 3,901 subjects obtained from Kaggle, data preprocessing and feature engineering were conducted to optimize model accuracy. The SVM algorithm achieved an accuracy of 92.43%, with its best performance observed in the medium-risk category (precision: 0.95, recall: 0.97, F1-score: 0.96), although performance declined in low and high-risk categories. In contrast, the Random Forest algorithm demonstrated superior results, reaching 99.91% accuracy with perfect precision, recall, and F1-scores (1.00) across all risk categories. The findings indicate that Random Forest not only provides more consistent and accurate predictions but also minimizes classification errors effectively. This research suggests that Random Forest is a more reliable and robust approach than SVM for integrating into intelligent health information systems to support early detection and prevention strategies for heart disease, especially among individuals with active smoking behavior.
ANALISIS METODE CLASSIFICATION MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DALAM MENENTUKAN KUALITAS JERUK POMELO Tajrin, Tajrin; Thuan, Steven
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1970

Abstract

Pomelo (Citrus maxima), one of Indonesia's native citrus fruits, possesses high economic value and is widely cultivated across various regions in diverse varieties such as Bali Merah, Cikoneng, Nambangan, Raja, Ratu, and Pangkep. Despite its potential, quality assessment of pomelo fruits is still mostly conducted manually based on physical characteristics, which may lead to subjective and inconsistent results. This study aims to develop a more objective and efficient method by utilizing the K-Nearest Neighbor (K-NN) classification algorithm within a data mining framework. Six key features were used as classification variables: peel pigmentation, surface smoothness, fruit softness, weight, skin thickness, and overall quality. The research used a dataset collected from the North Sumatra Plantation Office over the past five years (2020–2024), which was processed and analyzed using the Orange application. Evaluation of the classification model achieved promising results, with an accuracy of 86.0%, F1-score of 0.860, precision of 0.861, recall of 0.860, AUC of 0.834, and MCC of 0.720. Additionally, predictions on new data samples confirmed the model’s ability to classify high-quality pomelo fruits effectively. These findings highlight the effectiveness of K-NN as a decision-support tool for improving fruit quality assessment processes and support the integration of data mining in smart agriculture practices.
ANALISIS SENTIMEN TERHADAP MOBIL LISTRIK MENGGUNAKAN METODE BERT DAN NER Purba, Windania; Panjaitan, Syahdani; Dahlim, Alvin; Ambarita, Bless Alget; Zendrato, Febriaman
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1984

Abstract

Air pollution from fossil fuel-powered vehicles poses serious health risks. To reduce greenhouse gas emissions, electric vehicles (EVs) have emerged as a greener alternative. However, EV adoption in Indonesia still struggles, mainly due to low public acceptance. This study analyzes Indonesian public sentiment toward EVs and the key factors influencing it, using Natural Language Processing (NLP) with BERT for sentiment classification and Named Entity Recognition (NER) for identifying important entities. The BERT model performed well, with 71.71% accuracy, 83.56% precision, 71.71% recall, 75.59% F1-score, and a misclassification error of 28.29%, outperforming Naïve Bayes and LSTM. Sentiment analysis found that 48.45% of the public expressed negative sentiment, 30.60% neutral, and only 20.95% positive. NER identified influential factors including public events, opinions, company reputation, product quality, pricing, and location.These findings offer important insights for policymakers and industry players in designing strategies to boost EV adoption in Indonesia.
INTEGRASI LARAVEL DAN METODE SUS DALAM PENGEMBANGAN SISTEM PEMESANAN MAKANAN BERBASIS WEB Widjanarko, Albertus Raditya Danang
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2322

Abstract

The advance of information technology has significantly influenced digital transformation in across numerous industries including the food service sector. This research aims at the designing and evaluating a web-based food ordering information system. The system was developed by using Laravel framework and follows the Model-View-Controler (MVC) architectural pattern, thereby facilitating modularity and efficient data management. The development process had been done through methodical stages: requirements analysis, system design using Unified Modelling Language (UML), implementation of the web-based application, and system testing. Evaluation was undertaken using functional testing (black box) and usability assessment conducted using the System Usability Scale (SUS) methodology. The user feedback was gathered through questionnaires distributed after the system was launched online. The results of the black box testing confirmed that all system functionalities performed in accordance with the stipulated specifications. The usability evaluation produced a SUS score of 71.01, which is classified as acceptable. This indicates that the system meets established usability standards and can be adopted by users with a satisfactory level of confidence and ease. This research aims to contribute to the development of digital service platforms in the food service industry, with an emphasis on enhancing user experience.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN COOK’S ASSISTANT TERBAIK MENGGUNAKAN METODE ARAS Siregar, Victor Marudut Mulia; Sugara, Heru; Saragih, Roy Sahputra
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2134

Abstract

Selecting the right Cook’s Assistant is a critical factor in ensuring smooth restaurant operations, including at Lucky Seafood Restaurant in Pematang Siantar. The existing recruitment process often lacks objectivity and consistency, highlighting the need for a technology-based solution to improve decision-making accuracy and efficiency. This study aims to develop a Decision Support System (DSS) using the Additive Ratio Assessment (ARAS) method to assist management in selecting the most suitable candidate. The system evaluates five criteria: work experience, communication skills, and work attitude (benefit criteria), along with adaptation time and error rate (cost criteria). The ARAS method involves normalization, weighting, and the calculation of utility and utility degree values. The results indicate that among eight candidates, AC_06 ranks highest with a utility score of 1.51518, followed by AC_04 and AC_08. The implementation of this system has proven effective in reducing subjectivity, expediting the selection process, and increasing the accuracy of identifying the best candidate aligned with the restaurant’s operational needs.
MONITORING PERTUMBUHAN FISIK BALITA UNTUK PENDETEKSIAN DINI STUNTING ANAK BERBASIS MOBILE Rahayu, Ratna Rahmawati; Wulandari, Sekar; Wahyudi, Flagon Eka; Ramadhan, Muhammad Raffa
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2065

Abstract

Stunting is a nutritional deficiency that results in impaired growth and development in children. In Indonesia, the prevalence of stunting has emerged as a pressing public health concern. The government has implemented a series of measures to address the issue of stunting. However, the prevalence of stunting in 2023 showed only a marginal decrease of 0.1% compared to the previous year. The determination of an individual's nutritional status is contingent upon the utilization of the Standard Antropometry Table, which is predicated on the calculation of Z-Score limits. These limits are determined by the age, weight, and height of the subject. In order to prevent stunting, it is essential to conduct a timely assessment of the nutritional status of children. The objective of this study is to facilitate the detection of nutritional status using a system that incorporates a nutritional status assessment for children. This system is designed to detect stunting using a mobile interface, with a user interface based on XML and Android Studio. The developed system is capable of displaying the nutritional status of the child, the presence of stunting, and the child's growth patterns.
INTEGRASI ALGORITMA K-NEAREST NEIGHBORS DAN DECISION TREE UNTUK MEMPREDIKSI HIPERTENSI Aksha, Muhammad Iqbal Al; Yenni, Helda; Erlinda, Susi; Susanti, Susanti
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2306

Abstract

Hypertension is a prevalent health condition and a major risk factor for cardiovascular diseases. Early detection and management are essential to prevent complications. This study aims to optimize the accuracy and stability of hypertension risk prediction by applying a stacked ensemble technique that combines multiple base classifiers—K-Nearest Neighbors (KNN) and Decision Tree (DT)—with Logistic Regression as the meta-learner. The dataset used was imbalanced, thus requiring class balancing with the Synthetic Minority Over-sampling Technique (SMOTE), along with data preprocessing and scaling. The study applies a quantitative approach to train and evaluate models using Python. Results demonstrate that the stacked ensemble model achieves superior performance compared to individual classifiers, with a maximum accuracy of 74.52%. These findings indicate that the combination of different classifiers through ensemble stacking enhances the reliability and predictive capability of hypertension detection models. The approach offers potential value for improving early diagnosis and supporting clinical decision-making.
ANALISIS PENGARUH OVERSAMPLING SMOTE CREDIT CARD FRAUD DETECTION METODE FITUR FORWARD SELECTION Wijaya, Louise Ernest; Fancella, Shevira; Sihombing, Oloan
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.1971

Abstract

Credit card is one of the legal payment methods that is still widely used by the society. People use credit cards to buy various needs both in terms of food, clothing and food. Credit cards also present many dis-count and vouchers that can attract more and more users every day. But in the increasingly crowded use of credit cards, there are also cyber threats that are also growing rapidly along with the times. One of them is fraud to obtain data containing credit card information of an individual. To prevent/lower the risk of Cyber Crime, a credit card fraud detection method is needed. This research focuses on the influence of SMOTE oversampling and Forward Selection feature in the performance of a system used.

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