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Jurnal Pilar Nusa Mandiri

pilar
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Published by STMIK Nusa Mandiri

ISSN : 19781946 EISSN : 25276514 DOI : -

Core Subject : Science,

Computer Science & IT

Jurnal Pilar merupakan jurnal ilmiah yang diterbitkan oleh program studi sistem informasi STMIK Nusa Mandiri. Jurnal ini berisi tentang karya ilmiah yang bertemakan: Rekayasa Perangkat Lunak, Sistem Pakar, Sistem Penunjang, Keputusan, Perancangan Sistem Informasi, Data Mining, Pengolahan Citra.

Arjuna Subject : -

Articles 395 Documents

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APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT Saputri, Daniati Uki Eka; Aziz, Faruq; Khasanah, Nurul; Hidayat, Taopik; Septian, Rendi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5532

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.

INTEGRATION OF TELEGRAM BOT AND UPTIME KUMA FOR WI-FI NETWORK MONITORING USING MIKROTIK Nurjanah, Siti; Sembiring, Falentino; Ayuningsih, Rieska Rahayu
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5535

Wi-Fi network monitoring is a crucial aspect in ensuring the availability and security of widely used internet services. This research confirms the use of Telegram bots for notification integration with the Uptime-Kuma monitoring program and proxy devices for Wi-Fi network monitoring. The main router and network controller in this study were Mikrotik devices, while Uptime Kuma was used as a monitoring tool to track the performance and availability of the Wi-Fi network. When important events are discovered on the network, network administrators can receive quick notifications through integration with Telegram bots. In the context of Wi-Fi networks, the Security Policy Development Life Cycle (SPDLC) method is used to design relevant and effective security policies. It includes the stages of planning, implementation, monitoring, evaluation and regular updating of security policies to maintain optimal levels of security. The results show that the integration of Telegram bots with the Kuma Uptime monitoring tool can improve network availability. This allows quick reaction to Uptime and Downtime reports in network conditions, which record the percentage of time network services are available. Thus, administrators do not need to wait for complaints from users if the connection is suddenly lost, because changes in connection status will automatically send a notification to Telegram.

GAME DEVELOPMENT PROJECT MANAGEMENT USING SCRUM FRAMEWORK: HYPERCASUAL GAME CASE STUDY 'RUSH RUNNER' Nurjaman, Moh.; Baturohmah, Habi; Warman, Cecep
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5555

The research aims to explore and implement an effective Project Management approach in Game Development using the Scrum Framework. A case study was conducted on the development of a hypercasual game titled "Rush Runner." With a focus on rapid iterations, continuous feedback, and adaptation to changes, the Scrum methodology was used to manage the game development workflow from planning to publication. Concrete steps were taken to adapt to user needs and evolving demands as an integral part of the development process. The research findings indicate that the implementation of Scrum had a positive impact on productivity, quality, and user satisfaction in hypercasual game development. Furthermore, the integration of Project Management and the Scrum approach optimized overall time, cost, and quality. These findings provide valuable insights for practitioners and researchers in the field of game development to understand the importance of an adaptive and responsive Project Management approach to market changes and user needs.

XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION Alamsyah, Nur; Budiman, Budiman; Yoga, Titan Parama; Alamsyah, R Yadi Rakhman
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5569

Climate change and increasing global temperatures have increased the frequency and intensity of forest fires, making fire risk evaluation increasingly important. This study aims to improve the accuracy of predicting forest fuel drought conditions (Drought Code) by using the XGBoost algorithm optimized with RandomizedSearchCV. The research methods include collecting data related to forest fires, preprocessing data to ensure quality and consistency, and using RandomizedSearchCV for XGBoost hyperparameter optimization. The results showed that the optimized XGBoost model resulted in a decrease in Mean Squared Error (MSE) and an increase in R-squared value compared to the default model. The optimized model achieved an MSE of 0.0210 and R2 of 0.9820 on the test data, indicating significantly improved prediction accuracy for forest fuel drought conditions. These findings emphasize the importance of hyperparameter optimization in improving the accuracy of predictive models for forest fire risk assessment.

PREDICTING GEN-Z PERSONALITY ON TWITTER BASED ON BIG FIVE MODEL WITH KNN AND SVM Darmawan, Aang Kisnu; Alfarisi, Salman; Hozairi, Hozairi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5580

Generation Z is a group that is very connected to digital technology, especially social media such as Twitter. Their widespread presence on these platforms creates a unique opportunity to understand their behavioural patterns and personalities. However, research on personality prediction on social media is still limited and focused on certain platforms or different age groups. Personality prediction can help to find out someone's personality by just looking at tweets on social media. This research aims at two things: first, to build a Gen-Z personality prediction model on Twitter based on the Big Five Personality Model with the K-Nearest Neighbor (KNN) algorithm and Support Vector Machine (SVM). Second, test and compare the performance of previously generated personality prediction models with various evaluation metrics. The research results show that the KNN algorithm has an accuracy rate of 0.73%, precision of 0.73%, recall of 0.73%, and score of 0.72%. Based on the test results, the SVM algorithm obtained the best accuracy, which received an accuracy of 0.78%, precision of 0.82%, recall of 0.78%, and F1-score of 0.78%. This research contributes in two ways: first, scientifically, by understanding Gen-Z personalities on Twitter, and second, by developing new prediction methods and insights into Gen-Z behaviour. Second, practically, by helping with communication and marketing strategies, product/service development and social interventions for Gen-Z.

EVALUATION OF IT GOVERNANCE USING COBIT 2019 ON REGIONAL ASSET MANAGEMENT AGENCY OF DKI JAKARTA Herliani, Meidina; Wella, Wella
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5629

An adequate level of IT availability can obtained by implementing IT Governance, which pay attention to all related issues service readiness, including services and resources. This research aimed to assess the IT Governance capability at the Regional Asset Management Agency (BPAD), a government institution responsible for asset management. The study specifically focused on issues related to data and information management, including leadership and risk management challenges. Using the COBIT 2019 Framework, data were collected through interviews and observations. The respondent of this research are 4 who work in the Data and Information Sub-Sector and one Head of Asset Administration. The findings revealed that the EDM 05 process achieved a capability level of 4, surpassing the organization's target. However, the APO 08 and APO 12 processes were rated at level 2, highlighting areas in need of improvement. The study provides recommendations to enhance BPAD's performance and optimize its business activities.

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS OF DIGITAL IDENTITY APPLICATION USERS Maulana Abrari, Muhammad Naufal; Abdulloh, Ferian Fauzi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5736

In the rapidly evolving digital era, the Population Identity Application (IKD) plays a crucial role in streamlining civil administration processes in Indonesia, allowing easier and faster access to population services. This study aims to explore the application of machine learning algorithms in analyzing user responses to the IKD application. Three popular algorithms: Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Naïve Bayes were chosen to classify sentiment from 1301 user reviews on the Google Play Store into positive and negative categories. After performing data preprocessing such as tokenization and stemming, hyperparameter optimization was conducted using GridSearchCV to enhance classification accuracy. The research results indicate that the SVM algorithm, optimized with hyperparameters, including the use of the rbf kernel and a parameter value of C = 1, achieved the highest accuracy of 85.60%, making it the most effective method for sentiment classification of the IKD application. These findings provide valuable insights for the government and developers in refining the features and performance of IKD, contributing to the efficiency and security of digital administration in Indonesia. Furthermore, this study opens opportunities for further development that is more responsive to user needs and expectations in the future.

CLASSIFICATION OF HEART DISEASE USING THE K-NEAREST NEIGHBOR ALGORITHM AND LOGISTIC REGRESSION Sugitha, I Kadek Agga; Triayudi, Agung; Handayani, Endah Tri Esti
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5742

Heart disease is a major cause of death in the world, including in Indonesia, with increasing rates and death rates that carry a huge burden on health and society. Lack of awareness of early signs contributes significantly to this challenge. This study aims to prevent heart disease through early diagnosis using K-Nearest Neighbor (K-NN) and Logistic Regression algorithms. The database, obtained from Kaggle.com, includes 15 clinical units for cardiac diagnosis. The test shows that the K-NN method with k = 3 achieves the highest performance on the experimental data (30%), with 90% precision, 93% precision, 87% recall, and 90% f1 - score. In comparison, Logistic Regression and sigmoid achieved 86% precision, 83% precision, 90% recall, and 86% f1-score on the same experimental data. These results show that K-Nearest Neighbor is better than Logistic Regression as a classification algorithm for heart disease database. Applying these findings to the web-based Streamlit system is expected to improve the efficiency and timeliness of heart disease screening.

DESIGNING AN ANDROID-BASED FUTSAL BOOKING APP USING FCFS AND MULTILEVEL FEEDBACK QUEUE ALGORITHMS Ardani, Rega Listya; Sani, Asrul
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5778

The improvement of data innovation has affected the advancement of human life, moving from ordinary strategies to present day computerized strategies. One application of data innovation in everyday life is online planning and booking. This investigate points to design an Android-based futsal field booking application utilizing the Primary Come To begin with Serve (FCFS) and Multilevel Input Line (MLFQ) calculations.This application is expected to energize clients in making futsal field reservations viably and effectively. The modify strategy utilized in this ask generally is Remarkable Programming (XP). The FCFS calculation was chosen for its straightforwardness in serving requests based on range arrange, though the MLFQ calculation endowments prioritization based on noteworthiness or specific criteria, engaging crucial bookings to be taken care of speedier. The comes approximately of this think approximately appear that the planned application capacities well concurring to client needs and gives ease inside the futsal field booking handle. By combining these two calculations, the application is expected to make a versatile and versatile reservation system, which isn't because it were compelling in directing booking lines but additionally sensible for all clients. The utilization of this application outlines exceptional potential in advancing the quality of futsal field booking organizations and can serve as a appear for making comparable applications in other ranges.

BERKAH JAYA ELECTRIC SHOP APPLICATION IS BASED ON ANDROID Andiani, Andiani; Nur, Siti Anzila
Jurnal Pilar Nusa Mandiri Vol. 20 No. 2 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i2.5890

Berkah Jaya Electric Shop is a shop engaged in the sale of various kinds of electrical equipment. Goods sold by the store such as cables, sockets, and switches. The process of selling goods at the store is only done offline, namely by means of buyers coming directly to the store location. The process of managing store goods is still done by manually recording. This certainly causes problems such as the obstruction of buyers by time, and inefficient management of stock items. With this research, a sales application will be created that can make it easier for buyers to order goods, make product complaints, get information on the goods needed, and assist sellers in managing stock items and reports. This application will be built using the Waterfall method, utilizing the PHP, Laravel, and Kotlin programming languages. Making this application makes it easier for buyers to place orders via smartphones and sellers can more easily manage goods.

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