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Teknika
Published by Institut Informatika Indonesia Surabaya
ISSN : 25498037     EISSN : 25498045     DOI : https://doi.org/10.34148/teknika
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
Teknika is a peer-reviewed journal dedicated to disseminate research articles in Information and Communication Technology (ICT) area. Researchers, lecturers, students, or practitioners are welcomed to submit paper which has topic below: Computer Networks Computer Security Artificial Intelligence Machine Learning Human Computer Interaction Computer Vision Virtual/Augmented Reality Digital Image Processing Data Mining Web Mining Computer Architecture Software Engineering Decision Support System Information System Audit Business Information System Datawarehouse & OLAP And any other topics relevant with Information and Communication Technology (ICT) area
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 276 Documents
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Application of the Simple Additive Weighting Method in the Selection Process for Recipients of the 1000 Anak Negeri Scholarship at Nusa Putra University Hidayat, Rahmat; Triyono, Gandung; Oktiara, Dara Putri
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1194

Abstract

The 1000 Anak Negeri Scholarship at Nusa Putra University supports outstanding students from underprivileged families, yet its manual selection process is inefficient, subjective, and lacks transparency, leading to delays and potential misjudgments. This study aims to develop a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to enhance the accuracy, efficiency, and fairness of scholarship selection. The system evaluates applicants based on eight criteria: poverty status, parental occupation, income, family dependents, parental status, academic achievement, non-academic achievement, and Quran recitation ability, with assigned weights ensuring objective ranking. The SAW method normalizes decision matrices and calculates final scores to determine recipients, significantly improving efficiency and transparency compared to manual selection. The top-ranked recipient achieved a final score of 0.7765, followed by scores of 0.743, 0.625, 0.6105, and 0.584, demonstrating a more structured and reliable selection process. The automated approach reduces processing time, minimizes human errors, and ensures systematic selection based on predefined criteria. This research confirms that the SAW method provides a more accurate and reliable decision-making process, making scholarship distribution fairer and more targeted. The implementation of this system at Nusa Putra University serves as a model for other educational institutions to optimize their scholarship selection processes, ensuring that financial aid reaches students who need it most while improving transparency, efficiency, and decision-making accuracy.
Tourism Bike Tracking Design with The LoRaWAN Network Approach to The Meikarta Central Park Area Pratama, Legenda Prameswono; Manfaluthy, Mauludi; Nugroho, Feri; Hapsari, Anindya Ananda; Koko; Al-Humairi, Safaa Najah Saud
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1196

Abstract

Technological developments in Indonesia are relatively rapid, and most people have become part of the Internet of Things era. There are a couple of new technologies in the Internet of Things, one of which uses long-range (LoRa) communication technology, a wireless delivery method using radio signals at a frequency of 433 MHz. Meikarta Central Park is a city park that provides tourist bicycle rentals. Problems often occur when visitors rent bicycles, resulting in tourist bicycles being lost or placed incorrectly. All these problems make it difficult for operators to find abandoned bicycle locations. This research develops a system to detect the location of Meikarta’s tourist bicycles using a GPS tracker with an Arduino microcontroller based on a long-range working principle. On the other hand, the location point can be seen on the map’s application so that the whereabouts of the bicycle can be known. The research results of the tracking from the tourist bicycle GPS tracker using LoRa were 5 test locations, the furthest range of 625 meters on the first tourist bicycle with an accuracy value of 11.2 meters. Meanwhile, the second tourist bike has the most extended range of 590 meters with an accuracy level of 4.4 meters.
Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods Fauzi, Ariq Ammar; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Wati, Seftin Fiti Ana
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1198

Abstract

TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application.  This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services.
Utilization of MLP and LSTM Methods in Hero Selection Recommendations for the Game of Mobile Legends: Bang Bang Yulianto, Masrizal Eka; Kristian, Yosi
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1201

Abstract

Mobile Legends is one of the popular MOBA games played in real-time. The game begins with each player selecting one hero in the draft pick phase. Choosing the right hero is very important because it can affect the chances of winning. This study uses datasets from rank mode matches conducted by streamers, top global heroes, and top leaderboards in Indonesia to compare the accuracy of the MLP and LSTM methods in recommending the fifth hero for one's team. The Concatenate Layer is used in model development. Modifying the dataset was also done by reducing the number of target classes and performing data augmentation to increase data variation. The results show that LSTM excels in top-1 recommendations with an accuracy of up to 59%. Meanwhile, MLP outperforms in top-3 and top-5 recommendations, indicating that this model is more flexible in providing multiple hero alternatives. The conclusion is that players can use the LSTM method if they only want to select the best single hero. However, if players prefer a broader range of hero recommendations, the MLP method is more suitable.
Implementation of Blockchain Technology for Image Plagiarism Detection Using DCT, AES128, and SHA-1 Algorithms Duran, Filbert; Leonardo; Shickhem; Yennimar
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1205

Abstract

Plagiarism encompasses the act of appropriating high-quality user-generated content as if it were one's own intellectual property. Image plagiarism can be conceptualized as a broader category that encompasses the challenges of detecting copied images. Identifying instances of plagiarism is of paramount importance not only for graphic designers, professional photographers, and bloggers but also for publishing entities and legal practitioners seeking to uncover unauthorized reproductions of their creations. In addressing this issue, the implementation of blockchain technology presents a viable solution. Fundamentally more than just a collection of interconnected blocks, blockchain is characterized by the systematic recording of digital signatures or hashes of each block. Blockchain is essentially more than just a collection of interconnected blocks; it is characterized by the systematic recording of a digital signature or hash of each block. To generate the hash, cryptographic methods can be applied. This study aims to develop a web-based application that is adept at detecting image plagiarism through the application of blockchain technology. Images submitted by users will undergo plagiarism detection by an application that uses blockchain methodology. This study applies the DCT method to extract features from images, then uses the AES-128 and SHA-1 methods to generate blockchain. The results of this study are in the form of a website that can be used to detect image plagiarism. From the results of the tests carried out, it was obtained that the combination of the DCT, AES-128 and SHA-1 methods can detect image plagiarism with an accuracy of 100%. This means that the combination of these methods can be applied to carry out the process of detecting image plagiarism with a very high level of accuracy.
Comparative Analysis of Naïve Bayes Algorithm Performance in English and Indonesian Text Sentiment Classification on Duolingo Application in Playstore Serlina, Andi; Rahim, Abdul; Arbansyah
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1207

Abstract

Text classification is an important topic in Natural Language Processing (NLP), especially when conducting research on user reviews on language learning apps such as Duolingo. This study compares the effectiveness of the Naïve Bayes algorithm in identifying sentiment in English and Indonesian reviews on the Duolingo app on Playstore. The approach includes data collection, text preparation (case folding, tokenization, stopword removal, and stemming), and Naïve Bayes algorithm evaluation for each dataset. Model performance was evaluated using accuracy, precision, recall, and F1-score. The Naïve Bayes method obtained 84% accuracy on the English dataset with a 90:10 data split and 67% accuracy on the Indonesian dataset with the same split ratio. The difference in the results obtained is due to several variables, including the use of informal language, slang, and more complicated word variants in Indonesian, which make proper classification more difficult for the model to achieve.

Page 28 of 28 | Total Record : 276

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