Jurnal Pilar Nusa Mandiri

EXPLORING THE ECONOMIC IMPACT OF SMART CITY INVESTMENT: A LITERATURE REVIEW

Widyantari Febiyanti, Apol Pribadi Subriadi — Mon, 23 Sep 2024 09:04:41 +0000

The current implementation of smart cities aims to enhance the quality of life for their communities. Smart city investments can be achieved through collaborative efforts between information and communication technology and human resources, transforming areas into sustainable cities. Many countries are becoming increasingly interested in smart city investments. However, it remains unclear how these investments can achieve their intended goals. The primary issue lies in the lack of effective methodologies to measure the economic impact of such investments. Many cities lack comprehensive assessment tools to gauge the economic impact of smart city implementations, making it difficult to determine whether these investments deliver the desired benefits. This article aims to provide references regarding the economic impacts of smart city investments and the frameworks that can be used to measure them. The methodology employed in this research is a literature review based on references published over the past five years. According to findings, smart city investments have been found to impact aspects such as e-commerce and e-business, the creation of environmentally friendly environments, cost savings and economic benefits, GDP growth, and increased income for regions/cities through effective smart city utilization. Several frameworks have been gathered to measure economic impact, such as Computable General Equilibrium (CGE), Energy Efficient Integrated Planning Framework (EEIPF), and Open Data Impact for Smart Cities Framework (ODISC). Each framework serves to illustrate how examples of smart city investments can influence the economy of a region or city.

TREND ANALYSIS AND CORRELATION OF TOURIST, RESTAURANT AND HOTEL VISITS IN KUNINGAN REGENCY

Rizki Hesananda, Agus Trihandoyo, Ninuk Wiliani, Nidya Sari Rahmawati — Mon, 23 Sep 2024 09:06:12 +0000

This study conducts an in-depth analysis of the tourism sector in Kuningan Regency, focusing specifically on hotel stays, tourist arrivals, and restaurant visits. Utilizing forecasting models and correlation analyses, the research aims to uncover trends and interdependencies within the sector. The primary objective is to identify actionable insights that can inform data-driven decision-making. The study employs the FBProphet algorithm for forecasting future trends and conducts Kendall correlation analysis to examine relationships among key variables. Data collected spans a time series of 84 months, from January 2016 to December 2022. FBProphet accurately predicts trends in hotel stays, while variations exist in predictions for tourist arrivals and restaurant visits. Mean values for hotel stays, tourist arrivals, and restaurant visits are 21,098.67, 135,647.33, and 130,660.83, respectively. Kendall correlation analysis reveals a moderate positive correlation (0.214, p-value = 0.004) between tourist arrivals and restaurant visits, a strong positive correlation (0.324, p-value = 1.291e-05) between tourist arrivals and hotel stays, and a weaker positive correlation (0.176, p-value = 0.019) between restaurant visits and hotel stays. These findings underscore the intricate dynamics of Kuningan Regency's tourism sector, providing stakeholders with critical insights for strategic planning. The research contributes significantly to sustainable growth initiatives by guiding stakeholders in leveraging the interconnected elements of tourism and making well-informed decisions.

XGBOOST HYPERPARAMETER OPTIMIZATION USING RANDOMIZEDSEARCHCV FOR ACCURATE FOREST FIRE DROUGHT CONDITION PREDICTION

Nur Alamsyah, Budiman Budiman, Titan Parama Yoga, R Yadi Rakhman Alamsyah — Mon, 23 Sep 2024 09:19:04 +0000

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.

GAME DEVELOPMENT PROJECT MANAGEMENT USING SCRUM FRAMEWORK: HYPERCASUAL GAME CASE STUDY 'RUSH RUNNER'

Moh. Nurjaman, Habi Baturohmah, Cecep Warman — Mon, 23 Sep 2024 09:25:14 +0000

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.

INTEGRATION OF TELEGRAM BOT AND UPTIME KUMA FOR WI-FI NETWORK MONITORING USING MIKROTIK

Siti Nurjanah, Falentino Sembiring, Rieska Rahayu Ayuningsih — Mon, 23 Sep 2024 09:29:03 +0000

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.

DEVELOPMENT OF FIELD WORK PRACTICE MANAGEMENT IN INFORMATICS INTEGRATED SERVICE SYSTEM

Indah Fitri Astuti, Hendi Hendi, Dedy Cahyadi, Awang Harsa Kridalaksana — Mon, 23 Sep 2024 09:32:23 +0000

Field work practice is crucial for aligning college education with workplace skills, but manual management poses challenges such as time-consuming processes and limited information access, particularly within the Informatics Program at Mulawarman University. This research explores integrating field work practice with mobile technology through Rapid Application Development, resulting in a comprehensive field work practice management mobile app. Data collected from direct observation and student questionnaires inform the app's development. The app covers various field work practice aspects, facilitating efficient student engagement, and Black Box testing confirms its functionality without errors. This research significantly contributes to field work practice management, offering stakeholders an efficient, transparent, and well-integrated solution for field work practice implementation in academic settings, ultimately aiming to enhance convenience and accelerate the field work practice process for all involved parties.

PREDICTING GEN-Z PERSONALITY ON TWITTER BASED ON BIG FIVE MODEL WITH KNN AND SVM

Aang Kisnu Darmawan, Salman Alfarisi, Hozairi Hozairi — Mon, 23 Sep 2024 09:39:52 +0000

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.

COMPARISON OF MACHINE LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS OF DIGITAL IDENTITY APPLICATION USERS

Muhammad Naufal Maulana Abrari, Ferian Fauzi Abdulloh — Mon, 23 Sep 2024 09:41:51 +0000

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.

DESIGNING AN ANDROID-BASED FUTSAL BOOKING APP USING FCFS AND MULTILEVEL FEEDBACK QUEUE ALGORITHMS

Rega Listya Ardani, Asrul Sani — Tue, 24 Sep 2024 07:52:19 +0000

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.

VTUBER PERSONAS IN DIGITAL WAYANG: A REVIEW OF INNOVATIVE CULTURAL PROMOTION FOR INDONESIAN HERITAGE

Tue, 24 Sep 2024 08:06:46 +0000

This study investigates the integration of VTuber into Wayang Digital as a strategic initiative to promote Indonesian cultural heritage. By leveraging advanced technologies such as real-time motion capture and artificial intelligence (AI), the project aims to enhance the animation quality and interaction of VTuber, creating a more immersive and engaging experience for audiences. The research focuses on three key aspects: VTuber integration's effectiveness in attracting international audiences, optimizing real-time motion capture for high-quality animation, and applying AI algorithms to create adaptive, responsive interactions between VTubers and their viewers. Through these innovations, the study aims to enrich the narrative and visual appeal of Wayang Digital, making it more accessible and appealing to a diverse global audience. The findings show that integrating advanced technologies enhances Wayang Digital's storytelling, aesthetics, and effectiveness as a powerful tool for cultural promotion. AI-enabled adaptive interactions create a personalized viewer experience, deepening audience connections with the traditional art form. High-quality animation preserves and effectively communicates Wayang's cultural nuances to audiences, enhancing its impact and cultural promotion. This study underscores the importance of continuous technological innovation and strategic implementation in the preservation and globalization of Indonesian heritage through digital media, suggesting that the future of cultural preservation lies in the seamless integration of tradition with cutting-edge technology.

EVALUATION OF IT GOVERNANCE USING COBIT 2019 ON REGIONAL ASSET MANAGEMENT AGENCY OF DKI JAKARTA

Meidina Herliani, Wella Wella — Mon, 30 Sep 2024 03:19:25 +0000

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.

CLASSIFICATION OF HEART DISEASE USING THE K-NEAREST NEIGHBOR ALGORITHM AND LOGISTIC REGRESSION

I Kadek Agga Sugitha, Agung Triayudi, Endah Tri Esti Handayani — Mon, 30 Sep 2024 00:00:00 +0000

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.

BERKAH JAYA ELECTRIC SHOP APPLICATION IS BASED ON ANDROID

Andiani Andiani, Siti Anzila Nur — Mon, 30 Sep 2024 03:32:06 +0000

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.

DEVELOPMENT OF RESPIRATORY SYSTEM RPG GAME USING UNITY WITH A* (A STAR) ALGORITHM

Yulyanto Yulyanto, Rika Nugraha, Sigit Setya Kusuma — Mon, 30 Sep 2024 00:00:00 +0000

This research addresses the need for more engaging and interactive methods to improve elementary students' understanding of complex scientific concepts, particularly the respiratory system. To overcome the limitations of traditional teaching methods, an educational Role-Playing Game (RPG) incorporating the A* (A-Star) algorithm was developed for optimal game navigation. The study followed the ADDIE development model, which involves Analysis, Design, Development, Implementation, and Evaluation. During the analysis phase, learning needs were determined through interviews and classroom observations. The design phase involved creating game scenarios and integrating educational content with interactive elements. The A* algorithm was applied during development to ensure efficient navigation. The game was implemented in a 5th-grade classroom in Kuningan, and its effectiveness was evaluated using pre-tests, post-tests, and student questionnaires. Results demonstrated a significant increase in students' understanding, with average post-test scores rising from 58 to 85. Feedback from both students and teachers was very positive, with the game receiving a 94.2% acceptance rate. The study suggests that RPG-based educational games with intelligent algorithms like A* can greatly enhance science education by offering a more engaging and effective learning experience, contributing to advancements in technology-based learning and setting a standard for future educational game development.