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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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Perbandingan Algoritma Naïve Bayes dan TextBlob Untuk Mendapatkan Analisis Sentimen Masyarakat Pada Sosial Media Giesta Rahguna Putri; Muhammad Akbar Maulana; Samsul Bahri
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.815

Abstract

Media sosial Twitter adalah platform yang populer di Indonesia untuk berkomunikasi dan mendapatkan informasi dengan cepat. Hal ini memungkinkan masyarakat dengan mudah mengungkapkan opini dan sentimen mereka. Penelitian ini berfokus pada perbandingan algoritma TextBlob dan Naïve Bayes dalam menganalisis sentimen masyarakat. Temuan menunjukkan bahwa TextBlob mengklasifikasikan sebagian besar tweet sebagai positif, sementara Naïve Bayes menunjukkan kecenderungan yang serupa dengan akurasi sebesar 78,18%. Dari analisis TextBlob, sekitar 50,98% komentar menunjukkan sentimen positif, 16,01% negatif, dan 33,33% netral. Dengan menggunakan kedua algoritma ini, penelitian berhasil mengidentifikasi sentimen masyarakat dengan akurasi yang baik, menunjukkan distribusi yang jelas antara sentimen positif, netral, dan negatif.
The Smart Door Lock Using Face Recognition Access Based on Internet Of Things (IoT) Farrel Laogi Murjitama; Hafidz Nur Raihan; Rangga Prasetya Adiwijaya; Desi Fitriani Ramadan; Bagas Imanuel Pasaribu; Bintang A. Silalahi; Nada Nadiefah Tasman; Syafira Audri Dwijayanti; Ummu Putri Salsabila Panjaitan; Yudhi S. Purwanto
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.816

Abstract

Security is one of the basic things that humans need. In relation to a house or room, the focus of security is on the door lock. Various types of door locks have been created, ranging from conventional ones with physical keys, to modern types. This modern type of door lock is also made with various ways to open it. Some use a series of codes (keypad), some use card sensors, fingerprint sensors, to the use of face recognition technology. Several door lock technologies with face recognition have also been created, but they are still expensive. The other problem is that those devices are not equipped with some fail-safe mechanisms, in case there are troubles with the device. This smart door lock is made using face recognition technology based on the Internet of Things. This lock is equipped with an ESP32cam camera integrated in the ESP8266MOD module that can recognize faces that have been registered in the database on the website. In addition, the door is also equipped with a push button to open the door from the inside, and a button as a backup if there is a malfunction of the face recognition feature. The device test indicates no apparent issues and operates smoothly. The accuracy test for the camera yields positive outcomes, reaching up to 100% in normal lighting conditions, and dropping to around 60-80% in blur condition. Accuracy is further compromised, potentially dropping in dim light that the images are only reached 40-60% for clear images and 20% in blurry images.
Adopsi Gamifikasi Pada Mobile Learning Menggunakan Extended Technology Acceptance Model (TAM) Febriane Devi Rahmawati; Edwin Pramana; Hartarto Junaedi
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.826

Abstract

Pembelajaran konvensional saat ini mulai bergeser dengan pembelajaran digital atau mobile learning karena dianggap lebih efektif dan interaktif. Gamification memiliki potensi yang besar dalam pembelajaran sebagai pembelajaran yang mengadaptasi permainan (game based learning). Tujuan penelitian ini adalah untuk mengidentifikasi dan mengetahui faktor-faktor yang mempengaruhi seseorang untuk mengadopsi gamifikasi pada mobile learning. Selain itu analisis pada faktor moderasi juga diteliti. Penelitian ini dilakukan pada mahasiswa di perguruan tinggi yang pernah menggunakan gamification pada mobile learning dengan rentang usia 17-25 tahun dengan jumlah responden pada penelitian ini adalah 402 responden. Pada tahap awal penelitian dilakukan pengembangan model teoritis dan penyusunan kuisioner, kemudian prosedur selanjutnya melakukan pemrosesan data dimulai dengan faktor analisis, uji validitas, dan uji reliabilitas. Selanjutnya dilakukan penggambaran model penelitian dengan AMOS dan dilakukan analisis SEM dari model TAM yang diberikan sehingga mendapatkan nilai standardize dan nilai magnitude of effect. Hasil dari penelitian ini terdapat 9 hipotesis yang diterima dan 1 hipotesis yang ditolak. Hipotesis yang ditolak adalah Social Influence terhadap Perceived Usefulness. Dalam pengujian efek moderasi, hasil nilai Pairwise Parameter Comparisons menunjukkan bahwa usia memberikan efek moderasi Perceived Ease of Use, Social Influence dan Perceived Usefulness terhadap hubungannya dengan Intention to Use.
Implementation of Classification Algorithm for Sentiment Analysis: Measuring App User Satisfaction Rizki Aulia Putra; Rice Novita; Tengku Khairil Ahsyar; Zarnelly
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.827

Abstract

Google Play Store is the official app store for Android devices from Google that offers rating and review features. This feature on the platform is a source of data for sentiment analysis in research on app user satisfaction. The purpose of this study is to provide an overview of app user satisfaction and evaluate the accuracy of the algorithms used. The algorithms compared include Support Vector Machine (SVM), namely linear, rbf, sigmoid, and polynomial kernels with Naïve Bayes Classifier (NBC). The key variables analyzed include perceived usefulness, perceived ease of use, relia-bility, responsiveness, and website design. The results showed that the SVM algorithm with a linear kernel achieved the highest accuracy of 95.23% compared to the NBC algorithm of 91.43%. For other accuracy results, rbf kernel 94.35%, sigmoid kernel 95.19% and polynomial kernel 93.31%. In addition, the results of sentiment analysis on application user satis-faction revealed that 75% of users were dissatisfied, with the service indicator having the highest number of negative sen-timents. These findings suggest that sentiment analysis can be an effective tool for companies to measure and improve user satisfaction. In addition, these results can also be a useful reference for new users in assessing apps before using them.
Facial Expression Recognition to Detect Student Engagement in Online Lectures Joko Siswantoro; Januar Rahmadiarto; Mohammad Farid Naufal
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.853

Abstract

In synchronous online lectures, the lecturers often provide the lecture material directly through video conference technology. On the other hand, there are many students who do not pay attention to the lecturers when they are participating in online lectures. As a consequence, in this research, an application was developed to assist lecturers in gathering data regarding the degree to which students who participate in online lectures pay attention to the presented information. The application employed a convolutional neural network (CNN) model to recognize each student's facial expressions and place them into one of two classes: either engaged or disengaged. The captured student facial image was preprocessed to facilitate the classification process. The preprocessing stage consisted of image conversion to gray scale, face detection using the Haar-Cascade Classifier model, and a median filter to reduce noise. In the process of designing a CNN model, three different hyperparameter tuning scenarios were implemented. These tuning scenarios aimed to obtain the best possible CNN model by determining which CNN model hyperparameters were the most optimal. The results of the experiments indicate that the CNN model from the second scenario has the highest level of accuracy in terms of recognizing facial expressions, coming in at 86%. The results of this research have been tested to measure the level of student participation in online lectures. The trial results show that the proposed application can help lecturers evaluate student engagement during online lectures.
Redesigning User Interface of Datascripmall Mobile Apps Using User Centered Design Method Nicholas Hiu; Yana Erlyana
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.854

Abstract

The rapid growth of the e-commerce industry, driven by technological advancements and increased internet access, has intensified competition for attracting and retaining customers. In Indonesia, the shift from desktop computers to smartphones has made mobile commerce (m-commerce) increasingly dominant. PT Datascrip, a leading Indonesian company, launched Datascripmall, a B2C and B2B e-commerce marketplace, in August 2020. Despite initial success, a decline in mobile app users indicated a need for an improved user interface (UI) and user experience (UX). This research underscores the urgent need to redesign the Datascripmall mobile app's UI using the User-Centered Design (UCD) methodology, focusing on user needs and preferences. The study employed questionnaires to identify the need for clearer explanations and a more consistent interface. Adding smart features and shortcuts for experienced users was found to boost efficiency and satisfaction. Interviews with the Datascripmall manager confirmed the necessity of a UI/UX redesign to enhance mobile app user numbers. The UCD process involved understanding the context of use, specifying user requirements, designing solutions, and evaluating them against these requirements. The study highlights the benefits of a redesigned UI/UX, enhancing the user experience with greater intuitiveness and engagement. Both qualitative and quantitative data support recommendations for creating a user-friendly interface and increasing overall user engagement. The result of this redesign is a prototype framework developed using Figma, which encompasses page structure, features, and content, providing a comprehensive view of the Datascripmall application UI design. This redesign aims to enhance user satisfaction and increase user numbers, leading to a more comfortable and engaging shopping experience.
Comparison of Extreme Learning Machine Methods and Support Vector Regression for Predicting Bank Share Prices in Indonesia Williem Kevin Setiadi; Vincentius Riandaru Prasetyo; Fitri Dwi Kartikasari
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.856

Abstract

Investing is the practice of postponing current consumption to obtain more significant value in the future. One profitable form of investment is stock investment, where investors buy company shares to benefit from appreciation in share value or dividend payments. Before investing in shares, investors need to pay attention to movements in the Composite Stock Price Index (IHSG), which reflects the performance of the Indonesian stock market. The Indonesian Stock Exchange (BEI) recorded around 740 companies listed in 2021. The BEI also compiled the LQ45 list of 45 stocks with the largest market capitalization, including the four largest banks in Indonesia. However, investing in bank shares only sometimes produces profits due to share price fluctuations. Stock price analysis and price movement predictions are important steps before investing. Extreme Learning Machine (ELM) and Support Vector Regression (SVR) methods are techniques used to predict time series data. This research compares the performance of the two methods in predicting stock prices of the big 4 Indonesian banks. The dataset used in this research comes from the Yahoo Finance site, which was taken since the market crash recovery period due to the Covid-19 pandemic. Based on the evaluation conducted, both the ELM and SVR methods are effective for predicting the share prices of the big four Indonesian banks. In terms of accuracy, the SVR method outperforms the ELM method due to its superior MAPE value. However, when considering computing time, the ELM method is more efficient than the SVR method.
Innovative Approach of 2D Platformer Mobile Game Development “Super Journey” Kelvin Ferdinand; Kevin Jonathan JM; Darius Andana Haris
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.857

Abstract

This study investigates the design and development process of “Super Journey”, a 2D platformer mobile game aimed at enhancing user engagement and satisfaction through innovative game mechanics and design. Utilizing the Agile methodology, the development stages included conceptualization, design, implementation using the Unity game engine, and iterative testing and evaluation based on user feedback. This development process involved crafting a detailed game design document, creating initial sketches and prototypes, and integrating graphical elements, animations, and game mechanics. The game features 3 levels with simple controls, visually appealing pixel art, and progressively challenging levels. A survey conducted with 20 participants revealed high overall satisfaction (4.15 out of 5), with particular praise for level design (4.25) and game mechanics (4.2). Feedback indicated areas for improvement, such as balancing difficulty levels and incorporating more diverse obstacles and enemies. The findings underscore the importance of agile, user-centered design in game development and provide insights for future iterations to further enhance the gaming experience. “Super Journey” exemplifies the effective integration of classic platformer elements with modern innovations, highlighting its potential in the competitive mobile gaming market. The results of this research are expected to serve as a reference and inspiration for other game developers to create superior products by combining innovative technology and thoughtful design.
Forecasting Model of Export and Import Value of Oil and Gas Using Gated Recurrent Unit Method Ilham Adji Saputra; Anik Vega Vitianingsih; Yudi Kristyawan; Anastasia Lidya Maukar; Jack Febrian Rusdi
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.861

Abstract

Indonesia’s natural resources are abundant, including oil and gas. It is one of the countries active in international trade, including exports and imports. Oil and gas exports are a significant source of income for the country, encouraging economic growth. Oil and gas imports are very important to meet domestic energy needs, which continue to increase in demand. Increasing oil and gas imports can increase the trade balance, which can affect the country’s economic stability if the value of imports exceeds the value of exports. Forecasting is a solution to overcome these problems by forecasting the value of oil and gas exports and imports. The gated recurrent unit (GRU) method is used for forecasting in this study because it has a simple computation and fairly high accuracy. The dataset used is monthly time series data from 1993 to 2023 from the website of the Badan Pusat Statistik (BPS). The MAPE results on the GRU model forecast the value of oil and gas exports and imports at 12.19% and 14.30%, respectively. The best average forecasting of export and import values obtained a MAPE of 13.38%.
Algoritma Machine Learning Dalam Melakukan Prediksi Pemilihan Konfigurasi Kapal Tunda di Pelabuhan Tanjung Priok Budi Tri Yulianto; Raden Muhammad Atok
Teknika Vol 13 No 2 (2024): Juli 2024
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.v13i2.862

Abstract

Pengoperasian pelabuhan secara dasar meliputi berbagai kegiatan pelayanan, salah satu proses pelayanan kegiatan di pelabuhan yaitu pelayanan pemanduan dan penundaan kapal. Salah satu langkah yang dibutuhkan dalam proses penetapan kapal tunda dalam pelayanan pemanduan dan penundaan kapal yaitu pemilihan konfigurasi Kapal Tunda. Penelitian ini menguji performa klasifikasi Support Vector Machine (SVM) dan Naïve Bayes Classifier (NBC) pada data Laporan Harian Gerakan Kapal (LHGK) di Pelabuhan Tanjung Priok selama periode 2021 untuk proses pemodelan dan evaluasi. Penelitian ini bertujuan untuk membuat modelan prediksi dalam penentuan konfigurasi Kapal Tunda, evaluasi hasil model prediksi untuk memilih konfigurasi kapal tunda di Pelabuhan Tanjung Priok. Dengan menerapkan model klasifikasi NBC dan SVM yang ditingkatkan dengan kernel Linier dan RBF, termasuk juga pemilihan fitur baik untuk SVM dan Naïve Bayes. Hasil uji perbandingan model prediksi antara SVM dan NBC menujukan bahwa klasifikasi SVM memberikan hasil yang paling optimal, yaitu menggunakan kernel linier pada nilai C=10, diperoleh akurasi sebesar 84,7%, recall sebesar 84,7%, F1-score sebesar 88,7%, dan akurasi sebesar 88,7%. Penelitian ini dimasa yang akan datang dapat dimanfaatkan dalam proses pengambilan keputusan dalam menentukan susunan konfigurasi Kapal Tunda oleh petugas pelabuhan.

Page 23 of 28 | Total Record : 276

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