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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Articles 394 Documents
LONG SHORT TERM MEMORY APPROACH FOR SHORELINE CHANGE PREDICTION ON ERETAN BEACH Iryanto Iryanto; Ari Satrio; Ahmad Lubis Ghozali; Eka Ismantohadi; ZK Abdurahman Baizal; Putu Harry Gunawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4139

Abstract

Eretan Beach is one of the beaches in Indramayu and has a reasonably severe abrasion rate from year to year. The Eretan coastline always experiences significant changes due to erosion every year. Therefore, it is necessary to study changes in the coastline at Eretan beach. This study obtained coastline data from the Google Earth engine using CoastSat, a python-based open-source toolkit, from 1992 – 2022. The open-source geographic information system software used to process the data is the Quantum Geographic Information System. This study aims to analyze the Long Short-term Memory (LSTM) algorithm in predicting shoreline changes at Eretan Beach. The eight optimizer functions in the LSTM are used with nine different scenarios to analyze the algorithm's performance. The results of this study show that RMSProp has the best performance compared to other optimizers. The RMSE and MAPE values on the RMSProp are 35.06258 and 2.2923 on the training data and 9.2457 and 1.06786 on the test data. In addition, from the predictions for the next ten years at transect point 251, it was found that there would be an increase in the coastline.
MEASUREMENT OF EMPLOYEE INFORMATION SECURITY AWARENESS: CASE STUDY AT FINANCIAL INSTITUTION Friendly Nur Shakti; Achmad Nizar Hidayanto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4163

Abstract

The lack of awareness regarding information security among employees in financial institutions can have detrimental impacts on both customers and the institution itself, both financially and in terms of trust. Therefore, this research aims to assess the information security awareness at PT XYZ, a financial institution, in order to identify the existing level of awareness, which will be used to provide recommendations. The method applied in this research is quantitative, using a questionnaire as a tool for data collection and distribution with a voluntary sampling technique among PT XYZ employees concerning their awareness of information security. The assessment of information security awareness covers 8 specific aspects, with 7 of them drawing sources from HAIS-Q and another 1 referring to the KAMI Index, using the Analytic Hierarchy Process (AHP) for weighting each area. The total number of respondents participating in this research is 52. The research results affirm that PT XYZ employees have a positive awareness of information security, indicating that there are no urgent actions needed at present. However, there are specific areas with potential for improvement, hence recommendations are provided to enhance and sustain information security awareness among employees.
WEB-BASED INFORMATION SYSTEM PREDICTION OF VEHICLE THEFT VULNERABILITY IN JAYAPURA USING REGRESSION ANALYSIS Fegie Yoanti Wattimena; Johan Minggus Loly; Halomoan Edy Manurung
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4194

Abstract

Vehicle theft in Jayapura Regency is quite high and there is no application to assist the police in making estimates or predictions of the number of theft cases that will occur in the next year. In 2022, cases of theft in Jayapura district will start to increase. to make these predictions the authors designed and built a system that can predict the number of these cases in building this application the authors use the Regression Analysis method this process can help the police predict the number of cases in the coming year. The development method used is SDLC, linear regression analysis and using the PHP programming language, the database uses MYSQL, Sublime Text. This research was conducted because there was no system that could assist the staff of the Resort Police (Polres) of Jayapura Regency. From this research, a system for predicting the level of vulnerability to motorized vehicle theft has been successfully built at the Jayapura District Police with data processed for attendance data using face region, reporting data using barcodes, queue data using counters and digital archive data helping the police store important documents.
PREDICTION OF BIODIESEL FUEL PRICES USING MULTIPLE LINEAR REGRESSION ALGORITHMS Deny Haryadi; Dewi Marini Umi Atmaja; Adi Kuncoro
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4381

Abstract

Biodiesel is a fuel derived from palm oil and a type of fuel that is an alternative to renewable energy, can be renewed and has the potential to become a substitute for fossil sources that are used non-stop. The utilize of biodiesel can be an arrangement for Indonesia to diminish reliance on imported diesel fuel since biodiesel does not contain sulfur and is demonstrated to be ecologically inviting. The price of biodiesel-type biofuels can increase, decrease, or remain constant due to factors that influence it, including the price of biodiesel competitors, palm oil, and world crude oil. For this reason, it is necessary to have a method that can predict the price of biodiesel-type fuel so that in the future, the price of biodiesel-type biofuel does not decrease or become unable to compete with its competitors. Prediction of biodiesel fuel prices can be done by implementing a multiple linear regression algorithm, one of the data mining algorithms. RMSE results obtained in this study were 0.003 with a standard deviation of +/- 0.000 so it can be concluded that this algorithm is quite accurate in predicting the price of biodiesel-type biofuels. A comparison of the results of manual calculations with the implementation of RapidMiner in the study obtained the same results because there was a causal relationship between attributes. The use of the multiple linear regression algorithm in this research is useful in planning the right strategy and making decisions to maintain biodiesel market price stability in the future.
DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK Odi Nurdiawan; Heliyanti Susana; Ahmad Faqih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4575

Abstract

Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.
THE EFFECT OF IMMERSIVE TECHNOLOGY ON ENHANCING STUDENT LEARNING: A SYSTEMATIC LITERATURE REVIEW Kharisma Alivia Nastiti; Harry Budi Santoso; Kasiyah Junus; Mubarik Ahmad; Endina Putri Purwandari
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.4775

Abstract

Education is one of the fundamental factors influencing national development. Educators are required to constantly explore the newest and creative strategies to deliver topics to students and promote students’ engagement in the learning process. One technology that has numerous potentials to facilitate the education process is immersive technology. Immersive technology has the potential to improve student achievement by enabling better learning outcomes. This study aims (1) to analyze the trends and impact of immersive technology usage in education; (2) to identify immersive technology that supports students’ learning processes in the pandemic era; and (3) to identify the usage of learning theories in educational application development that implement immersive technology to enhance students’ learning outcomes. This study examined seventeen selected studies after adopting the selection process of the systematic literature review process by Kitchenham. The selected studies were published between 2018 and 2022 by Emerald Insight, IEEE Xplore, and Scopus. There are three kinds of immersive technology identified in this study: virtual reality, augmented reality, and mixed reality. The study showed the nine types of students’ learning outcomes, and the results showed that using immersive technology significantly improved student learning compared to traditional methods. Furthermore, thirteen learning theories were adopted as the basis for developing educational applications. Future research directions are also suggested to continue developing promising and limited new technologies to enhance the variety of education-oriented applications. Immersive technologies designed for learning are still on a long journey, including considerations of readiness and potential effects that may arise for users such as privacy and security concern.
INTEGRATION OF BLOCKCHAIN TECHNOLOGY IN DIGITAL LIBRARIES: A SOFTWARE ENGINEERING DESIGN Arie Gunawan; Munir Munir; Yudi Wibisono; Chairul Furqon
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5010

Abstract

This research aims to design software engineering that integrates blockchain technology in digital libraries to improve system security and reliability. This integration is expected to overcome challenges related to data security, service reliability, and efficiency in digital library management. The research methodology involves collecting data through literature, expert interviews, and observations, on the implementation of blockchain technology in digital libraries, then analyzing data to support data design such as etherum, smart contracts, address, node.js, solidity, metamask, and sublime text, then using the Agile Extreme Programming (XP) method for software development. The research results include the design of a decentralized blockchain architecture, the use of smart contracts, and the application of cryptographic techniques to enhance security. Immutability testing in the context of blockchain involves verifying data consistency, validating the process of adding data, testing the ability to delete data, testing against attacks, and activities on immutable data. These tests were conducted using the Truffle framework. The results show that the system is able to maintain data integrity well.
A SYSTEMATIC LITERATURE REVIEW: RECURSIVE FEATURE ELIMINATION ALGORITHMS Arif Mudi Priyatno; Triyanna Widiyaningtyas
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5015

Abstract

Recursive feature elimination (RFE) is a feature selection algorithm that works by gradually eliminating unimportant features. RFE has become a popular method for feature selection in various machine learning applications, such as classification and prediction. However, there is no systematic literature review (SLR) that discusses recursive feature elimination algorithms. This article conducts a SLR on RFE algorithms. The goal is to provide an overview of the current state of the RFE algorithm. This SLR uses IEEE Xplore, ScienceDirect, Springer, and Scopus (publish and publish) databases from 2018 to 2023. This SLR received 76 relevant papers with 49% standard RFEs, 43% strategy RFEs, and 8% modified RFEs. Research using RFE continues to increase every year, from 2018 to 2023. The feature selection method used simultaneously or for comparison is based on a filter approach, namely Pearson correlation, and an embedded approach, namely random forest. The most widely used machine learning algorithms are support vector machines and random forests, with 19.5% and 16.7%, respectively. Strategy RFE and modified RFE can be referred to as hybrid RFEs. Based on relevant papers, it is found that the RFE strategy is broadly divided into two categories: using RFE after other feature selection methods and using RFE simultaneously with other methods. Modification of the RFE is done by modifying the flow of the RFE. The modification process is divided into two categories: before the process of calculating the smallest weight criteria and after calculating the smallest weight criteria. Calculating the smallest weight criteria in this RFE modification is still a challenge at this time to obtain optimal results.
APPLICATION OF GROUP DECISION MAKING IN DETERMINING CULINARY TOURISM WITH TOPSIS AND BORDA METHODS Wd. Shaqina Rafa Naura; St. Hajrah Mansyur; Purnawansyah Purnawansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5017

Abstract

Makassar City is one of the destination cities for traveling. Makassar City offers a variety of interesting tours, one of which is culinary tourism. The determination of the best culinary tourism is based on the criteria set by the Makassar City Tourism Office. In managing culinary destinations, tourists are often faced with many choices, so they are confused about choosing the most attractive culinary destinations. This research uses the TOPSIS and BORDA methods. The TOPSIS method is used in determining culinary tourism alternatives based on criteria that become recommendations and the BORDA method is used in determining the selected alternatives based on several DMs who evaluate alternatives. The main objective of this research is to apply group decision making in selecting the best culinary tourism destinations in Makassar City based on group preferences and related criteria with TOPSIS and BORDA methods. This research has conducted 5 iterations involving 4 DMs from the Makassar City Tourism Office. Based on the results of the interview, 8 criteria and 35 alternatives were obtained. Where the Coto Nusantara alternative is ranked the highest with a value of 109,949. While Sop Saudara Irian is ranked last with a value of 62,896. The general benefit of this research is the application of group decision making in determining culinary tourism with the TOPSIS and BORDA methods can produce more objective and representative decision results. This can increase tourist satisfaction in determining culinary tourism.
AN INNOVATIVE LEARNING ENVIRONMENT: G-MOOC 4D TO ENHANCE VISUAL IMPAIRMENTS LEARNING MOTIVATION Rujianto Eko Saputro; Berlilana Berlilana; Wiga Maulana Baihaqi; Sarmini Sarmini; Yuli Purwati; Fandy Setyo Utomo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5037

Abstract

The proliferation of visual impairment among school-age children in Indonesia has prompted the need for specialized online learning solutions. The G-MOOC 4D platform, a novel Learning Management System (LMS), is designed to address this need by leveraging gamification and artificial intelligence to enhance accessibility for visually impaired users. This study reports on the development and testing of two AI models within the G-MOOC 4D framework: a facial recognition model for secure user authentication and a voice command model for interactive learning. User Acceptance Testing (UAT), conducted with expert users, namely teachers at a special needs school, showed high approval rates for the platform's features. The results show that all metrics, accuracy, precision, and recall reach their optimal values at a distance of 40 cm for face detection. The respective metric scores at that distance, precision: 100%, accuracy: 98%, and recall: 97%. Additionally, the voice command functionality tested achieved a 100% recognition rate, reflecting the platform’s potential to significantly ease the learning process for visually impaired students. The findings underscore the importance of integrating assistive technologies into educational platforms to ensure all students have equal access to learning opportunities.