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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 394 Documents
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CRITICAL SUCCESS FACTORS OF AGILE SOFTWARE DEVELOPMENT IN WATERFALL PROJECT: A CASE STUDY APPROACH Indra Bayu; Teguh Raharjo; Bob Hardian Syahbuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5524

Abstract

The evolution of software development methodologies has seen Agile rise in response to the limitations of traditional approaches like Waterfall, characterized by its iterative, collaborative, and adaptable nature. However, integrating Agile within the rigid, structured frameworks of organizations accustomed to Waterfall presents significant challenges. This study addresses how to effectively combine these methodologies to mitigate conflicts and achieve successful project outcomes by identifying and analyzing the Critical Success Factors (CSFs) that enable a harmonious integration of Agile into Waterfall environments. Conducted at PT ABC, a firm balancing formal client interactions and contract creation with internal adoption of Scrum, this research uses the Analytic Hierarchy Process (AHP) to systematically prioritize CSFs through literature review, questionnaire development, data collection, and pairwise comparison analysis. The findings reveal that "Communication and Team Environment" is the most influential factor, with a priority vector weight of 0.178, followed by "Project Management and Strategy," "Leadership and Management Support," and "User and Customer Engagement." These factors are pivotal in achieving a balance between control and flexibility in software development projects. The study's implications for PT ABC and other organizations, especially those handling multiple projects and requiring on-site presence while managing other projects, demonstrate how to leverage the strengths of both methodologies for optimal project outcomes. This research provides a model for other organizations striving for similar integrative efforts, showcasing practical strategies to enhance project flexibility and coordination.
A SYSTEMATIC LITERATURE REVIEW ON ROLE OF PROJECT MANAGEMENT IN DIGITAL FORENSICS INVESTIGATION Panji Zulfikar Sidik; Teguh Raharjo
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5527

Abstract

The landscape of digital forensics has evolved significantly with the advent of sophisticated cybercrimes and the proliferation of digital devices. Digital forensics is a rapidly evolving discipline, characterized by unique challenges such as rapidly changing technology, large volumes of data, and stringent legal requirements. Effective project management in this context is crucial to ensure that investigations are conducted efficiently, accurately, and in compliance with legal standards. This systematic literature review aims to comprehensively analyze the role of project management practices in optimizing digital forensics investigations. Using established search protocols and selection criteria, we identified and analyzed relevant studies published between 2016 until 2023 that explored the application of project management methodologies, challenges, and best practices within the context of digital investigations. By applying effective project management strategies, investigators can ensure efficient, accurate, and legally sound digital investigations, ultimately contributing to successful criminal prosecutions and civil litigation outcomes.
APPLICATION OF OWASP ZAP FRAMEWORK FOR SECURITY ANALYSIS OF LMS USING PENTEST METHOD Rusydi Umar; Imam Riadi; Sonny Abriantoro Wicaksono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5534

Abstract

Learning Management System (LMS) is an application currently popular for online learning. The presence of LMS offers better prospects for the world of education, where its highly efficient use allows learning anywhere and anytime through the internet or other computer media. This study focuses on analyzing the security of the Learning Management System (LMS) on the domain e-learning.ibm.ac.id using the Pentest method with the Owasp Zap Framework. Security is a crucial step that needs to be considered by IBM Bekasi in protecting data and information from hacker threats. In this study, the method used is Pentest. Pentest is a series of methods used to test the security of a system by conducting literature studies, searching for data information, and domain information, followed by testing using Owasp Zap to find security-related vulnerabilities. The results of the testing using the Pentest method involve several stages of testing and scanning. The first step is checking domain information using Whois Lookup tools and then scanning using ZenMap on e-learning.ibm.ac.id. In this domain information search, the domain status serverTransferProhibited and clientTransferProhibited was found. The next stage is Vulnerability Analysis, where scanning is performed on the domain e-learning.ibm.ac.id using Owasp Zap tools. Based on the results from Owasp Zap scan, 16 vulnerabilities were found, with the breakdown being 2 high risk, 3 medium risk, 6 low risk, and 5 informational. In the exploitation stage using SQLMap, errors were found in the tested parameters, preventing injection.
A COMPARATIVE EVALUATING NUMERICAL MEASURE VARIATIONS IN K-MEDOIDS CLUSTERING FOR EFFECTIVE DATA GROUPING Relita Buaton; Solikhun Solikhun
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5545

Abstract

The K-Medoids Clustering algorithm is a frequently employed technique among researchers for data categorization. The primary difficulty addressed in this investigation pertains to the extent of optimality achieved when varying distance computation methodologies are applied within the framework of K-Medoids Clustering. This study is primarily concerned with the application of K-Medoids Clustering, employing a multitude of distance calculation methods, specifically those involving numerical metrics. The aim is to undertake a comparative analysis of Davies-Bouldin Index (DBI) values in order to ascertain the most productive distance calculation technique. In this research, the distance calculation methodologies include Manhattan Distance, Jaccard Similarity, Dynamic Time Warping Distance, Cosine Similarity, Chebyshev Distance, Canberra Distance and Euclidean Distance. The dataset consists of sales data from Devi Cosmetics, covering the period between January and April 2022 and comprising 56 distinct sales items. The research provides an exhaustive evaluation of numerical metrics concerning the K-Medoids Clustering algorithm. The findings indicate that the optimal clustering is achieved using the Chebyshev distance, resulting in 9 clusters with a DBI value of 166.632. The study's contribution is that it can improve more optimal data grouping to help make decisions correctly.
ENHANCING UNDERWATER IMAGE QUALITY: EVALUATING COMBINATIVE APPROACHES FOR EFFECTIVE IN SEAGRASS BED ECOSYSTEM Sri Dianing Asri; Indra Jaya; Agus Buono; Sony Hartono Wijaya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5566

Abstract

The Complex underwater characteristics, challenges for image processing tasks. These images often have poor visibility due to low contrast, light scattering and various types of interference. There is a lack of exploration into the effectiveness of existing underwater image enhancement methods, particularly in the context of seagrass ecosystems, allows for further investigation. This study aims to explore and evaluate the effectiveness of various methods in underwater image enhancement, including Colour Balanced, CLAHE, and Unsharp Masking and their combinations, starting with converting video data from UTS devices into two-dimensional images. Furthermore, the quality of images taken from underwater cameras placed in a complex and wild seagrass meadow environment was improved using the proposed method, and the quality was evaluated by the SSIM value. The results show that the CLAHE method has the highest average SSIM value of 0.898. Meanwhile, the combined Color Balanced-CLAHE method achieved an SSIM value of 0.683 in a separate evaluation. This combination is an innovative approach to address complex underwater image quality problems, providing a more specific and adaptive solution. Overall, the proposed method is able to improve the visual quality of images on aspects such as clarity, color, and visibility of objects in the image
SENTIMENT ANALYSIS ON RENEWABLE ENERGY ELECTRIC USING SUPPORT VECTOR MACHINE (SVM) BASED OPTIMIZATION Pungkas Subarkah; Bagus Adhi Kusuma; Primandani Arsi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5575

Abstract

Government policy regarding the discourse on the use of renewable energy in electricity, this discourse is widely discussed in the community, especially on social media twitter. The public's response to the implementation of the use of renewable energy varies, there are positive, negative and neutral responses to this government policy. Sentiment analysis is part of Machine Learning which aims to identify responses in the form of text. The data used in this study amounted to 1,367 tweets. The purpose of this study is to determine the sentiment analysis of government discourse related to the use of renewable energy using an optimisation-based Support Vector Machine (SVM) algorithm approach. This research involves several stages including data collection, data pre-processing, experiments and modelling and evaluation. The data is divided into 3 classes, 120 positive, 1221 neutral and 26 negative. In this research, there are five optimisation models used namely Forward Selection, Backward Elimination, Optimised Selection, Bagging and AdaBoost. The results obtained are the use of Optimised Selection (OS) optimisation with the Support Vector Machine (SVM) algorithm obtained an increase in accuracy from 93% to 96%. The increase in the use of SVM using selection optimization obtained the highest increase, because other optimization techniques only reached 1% and 2% of the original results using the SVM algorithm, namely the accuracy value of 93% to 96% (high accuracy). From the research that has been done, it is certainly important to understand public sentiment towards renewable energy policies, especially renewable energy electricity, the hope is that this research will become a reference for the government.
EXPLORING AGILE EFFORT ESTIMATION ISSUES: A SYSTEMATIC LITERATURE REVIEW Tetti Sinaga; Teguh Raharjo; Ni Wayan Trisnawaty
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5597

Abstract

Effort estimation is crucial in software development, especially in Agile projects. The 2020 Standish Group survey found that only 31% of software projects success. The success of a software development project depends on the accuracy of effort estimation. This research aims to analyze studies related to effort estimation methods in Agile software development to identify related issues. A systematic literature review by Kitchenham was conducted across Emerald, Science Direct, Scopus, SpringerLink, and IEEE databases and identified 239 relevant studies from 2018 and 2023, ultimately focusing on 40 studies about effort estimation challenges in Agile software development. The research revealed 59 issues related to various estimation methods. The main challenge in effort estimation for Agile software development is team experience and limited knowledge about the domain, which results in inaccurate estimation result. Requirements’ details, tasks complexity, and lack of data will complicate problem-solving and the prediction of the duration of completion. Reliance on expert judgment will increase the risk of bias and inaccuracy in estimates. These challenges increase the likelihood of project failure due to a mismatch between initial planning and reality as development progresses.
COMPARISON OF PROFILE MATCHING AND MOORA METHODS IN DETERMINING LOAN ELIGIBILITY Wayan Eka Ariawan; Gede Indrawan; I Gede Aris Gunadi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5608

Abstract

The objective of this research is to analyze the comparison between the profileimatching method and MOORA in supporting decision-making for loan approvals at the Widya Dharma Student Cooperative (KOPMA). The criteria used in this research include basic salary, length of service, loan duration, membership status, loan amount, and number of dependents. These two methods are compared based on their accuracy levels. The accuracy levels are obtained through testing with the Mean Average Precision (MAP) technique, which measures the accuracy in ranking. The testing is conducted by comparing the ranking results from the method calculations with the rankings from the KOPMA chairman. The analysis results show that the Profile Matching method has a higher accuracy rate, which is 67.83%, compared to the MOORA method, which has an accuracy rate of 45.46%. Besides method testing, system testing was also conducted using the User Acceptance Test (UAT) technique. The UAT results indicate that the developed system aligns with the business processes in determining loan eligibility, the menu layout and contents within the system are well-organized, the system features function properly and are easy to understand, and the system meets expectations.
MULTICLASS CLASSIFICATION FOR STUNTING PREDICTION USING DEEP NEURAL NETWORKS Wulan Sri Lestari; Yuni Marlina Saragih; Caroline Caroline
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5636

Abstract

Stunting is a chronic nutritional issue that hinders child growth and leads to serious long-term health and developmental impacts, particularly in developing countries. Therefore, early and accurate prediction of stunting is crucial for implementing effective interventions. This research aims to develop a multiclass classification model based on Deep Neural Networks (DNNs) to predict stunting status. The model is trained using a comprehensive dataset that encompasses various health variables related to stunting. The research process includes data collection, data preprocessing, dataset splitting, and training and evaluation of the DNNs model. The model can classify stunting status into four categories: stunted, severely stunted, normal, and tall. Further analysis is conducted to evaluate the influence of various parameters on the model's performance, including dataset splitting ratios (80:20 and 70:30) and learning rates (0.001, 0.0001, and 0.00001). The results show that a learning rate of 0.0001 yields the highest prediction accuracy, at 93.64% and 93.83% for the two data-splitting schemes. This indicates that this learning rate has achieved an optimal balance between convergence speed and the model's generalization capability. Additionally, the developed DNNs model can identify complex patterns hidden within the data without being affected by noise. These findings confirm that appropriate parameter selection, particularly the dataset splitting ratio and learning rate, can significantly enhance the DNNs model's ability to identify complex data patterns.
CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X) Zahwa Dewi Artika; Erwin Budi Setiawan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 2 (2024): JITK Issue November 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i2.5640

Abstract

Along with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods.

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