Journal of Applied Research In Computer Science and Information Systems
Journal of Applied Research In Computer Science and Information Systems (JARCIS) is dedicated to publishing and disseminating research results and theoretical discussions, applied analysis, and literature studies in the fields of information technology, computer science, and information systems. JARCIS is committed to becoming a quality journal by publishing quality articles in English and being the main reference for researchers. This journal is officially accredited as SINTA 4 based on the Decree of the Director General of Science and Technology, Ministry of Higher Education, Science, and Technology of the Republic of Indonesia Number 355/DST/D.D1/HM.01.01/2026 concerning the New Accreditation Rankings of Scientific Journals for Period 3 of 2025. Journal of Applied Research In Computer Science and Information Systems (JARCIS) published 2 (Two) numbers every years is Publish in Juni, and Desember. Index by : Google Schoolar , Copernicus , SINTA 4 contact us via email: Proletargroup@gmail.com
Implementation of Forward Chaining Method in Laptop Damage Detection Expert System
Expert systems are a branch of artificial intelligence designed to replicate the reasoning ability of specialists. This study applies the forward chaining method to build a web-based expert system for diagnosing laptop malfunctions. The system’s knowledge base was constructed from 20 common laptop malfunction symptoms, identified through literature review, user questionnaires, and interviews with repair technicians, and translated into inference rules. To evaluate performance, the system was tested on 50 malfunctioning laptops. Results show that the expert system achieved an accuracy rate of 85%, indicating its effectiveness in detecting various hardware and software problems. This research demonstrates that forward chaining can support non-expert users in performing early fault detection, thereby reducing repair costs and dependence on professional technicians
Copyright (c) 2026 Julian Chaniago, Hetty Rohayani, Abd Halim https://creativecommons.org/licenses/by-sa/4.0
White Box Testing with Path Testing on the Web-Based Population and Civil Registration Service (Dukcapil) Submission Status Notification Module in Kuningan Barat Subdistrict
Software testing is a stage in ensuring the quality and reliability of the system before it is implemented operationally. This study aims to evaluate the accuracy of the program logic flow in the web-based DUKCAPIL service submission status notification module in Kuningan Barat Village using the white box testing method with a path testing approach. This method is to analyze the internal structure of the program code to ensure that all logic paths have been thoroughly tested. The testing process begins with the identification of the path basis using the control flow (Control Flow Graph), calculation of cyclomatic complexity, and determination of the test path set. The test results show that all logic paths in the notification module have been passed and no logical errors were found in the program flow. Thus, this module is declared to have met the eligibility criteria in terms of internal logic. This study is expected to be a reference in improving the quality of testing web-based public service information systems, especially in terms of the reliability of population administration service notifications.
Copyright (c) 2026 Dimas Abimanyu Panji, safrizal safrizal https://creativecommons.org/licenses/by-sa/4.0
Identifying Damage Types in Solar Panels Through Surface Image Analysis with Naive Bayes
The growing utilization of solar panels as a renewable energy source requires efficient maintenance solutions to guarantee their best functioning. Identifying and categorizing faults on solar panel surfaces is essential for maintenance, as these defects considerably affect energy output and system efficiency. This study investigates the utilization of statistical feature extraction methods alongside Bernoulli Naive Bayes (BNB) and Gaussian Naive Bayes (GNB) algorithms to categorize different defect types, such as cracks, scratches, spots, and non-defective surfaces, through digital image analysis. Statistical criteria, including recall, specificity, and area under the curve (AUC), are employed to assess model performance. The findings indicate that the GNB algorithm surpasses BNB, with a mean average precision (mAP) of 39.83% with an 85:15 training-test ratio, whereas BNB reaches a maximum mAP of 29.25% at a 90:10 ratio. Nonetheless, both models demonstrate constraints in precision, as indicated by a total AUC of 0.644. This work illustrates the potential of statistical feature extraction approaches for defect classification, while emphasizing the necessity for future improvements to boost the efficacy of feature extraction and classification techniques in practical applications
Copyright (c) 2025 Ninuk Wiliani, Titik Khawa Abdul Rahman, Suzaimah Ramli https://creativecommons.org/licenses/by-sa/4.0
Fruit and Vegetable Classification using Convolutional Neural Network with MobileNetV2
Fruits are parts of plants that originate from the plant's pistils and usually contain seeds. Meanwhile, vegetables are leaves, legumes, or seeds that can be cooked. Fruits and vegetables have many variations that can be distinguished based on color, shape, and texture. However, the development of Artificial Intelligence (AI) technology has become pervasive in everyday life, one aspect of which is demonstrated through deep learning, a method of AI learning. Therefore, developing deep learning for tasks such as automatically detecting surrounding objects is necessary. This study aims to classify types of fruits and vegetables by applying a Convolutional Neural Network (CNN) with the MobileNetV2 architecture. In this study, fruits and vegetables encompassing 36 categories, including significant types in daily life, were considered. The results show that the classification system achieved an excellent accuracy rate of 97.31%, demonstrating the effectiveness of using deep learning techniques for this application
Copyright (c) 2025 Muhammad Khoiruddin, Silvester Tena https://creativecommons.org/licenses/by-sa/4.0
Customer Segmentation of Cash Management System Using K-Means Clustering
The effective financial management is essential for running successful business operations. In the banking context, the Cash Management System (CMS) facilitates real-time, automated transaction management. PT Bank Rakyat Indonesia (Persero) Tbk., as one of Indonesia’s largest banks, has implemented CMS since 2009. Despite its benefits, challenges persist, such as customer transactions outside regular working hours and difficulties in segmenting customers based on transaction volume and frequency. This study aims to address these issues by clustering BRI CMS users using the K-Means Clustering method, following the CRISP-DM framework. The research utilized transaction data of 2,727 users from January 2021 to April 2022. Data preparation involved cleaning anomalies and converting non-numeric values to numeric formats. Using the Elbow method, the optimal number of clusters was determined, resulting in three distinct user segments. The clustering revealed actionable insights, such as identifying high-value customers for targeted marketing and improving service strategies. This research offers a novel application of K-Means Clustering and CRISP-DM to CMS data management, contributing to better customer segmentation and strategic decision-making. These findings can help banks optimize resources, improve customer satisfaction, and enhance overall transaction efficiency.
Copyright (c) 2025 Rizki Hesananda, Patri Apriliga https://creativecommons.org/licenses/by-sa/4.0
Enhancing Text Classification Performance: A Comparative Study of RNN and GRU Architectures with Attention Mechanisms
Text classification plays a crucial role in natural language processing, and enhancing its performance is an ongoing area of research. This study investigates the impact of integrating attention mechanisms into a recurrent neural network (RNN) based architectures, including RNN, LSTM, GRU, and their bidirectional variants (BiLSTM and BiGRU), for text sentiment analysis. Three attention mechanisms Multihead Attention, Self Attention, and Adaptive Attention are applied to evaluate their effectiveness in improving model accuracy. The results reveal that attention mechanisms significantly enhance performance by enabling models to focus on the most relevant parts of the input text. Among the tested configurations, the LSTM model with Multihead Attention achieved the highest accuracy of 68.34%. The findings underscore the critical role of attention mechanisms in overcoming traditional RNN limitations, such as difficulty in capturing long-term dependencies, and highlight the potential for their application in broader text classification tasks.
Copyright (c) 2025 Yulita Ayu Wardani, Mery Oktaviyanti Puspitaningtyas, Happid Ridwan Ilmi, Onesinus Saut Parulian https://creativecommons.org/licenses/by-sa/4.0
Comparison of Apriori and Fp-Growth Algorithms in Determining Package Menus at Sate Perawan Restaurant Sawangan Raya
The culinary creative industry holds promising prospects as it is a necessity for society. However, the variety of menu items and high customer demand lead to slow ordering processes, which hinder service at Rumah Makan Sate Perawan. Additionally, some menu items are less popular among customers. To address these issues, a system is needed to assist in determining food and beverage package menus based on association rules. This system aims to facilitate business owners in organizing packages and improving sales. This study employs the Apriori and FP-Growth algorithms, using sales transaction data collected over a four-month period. The research applies a minimum support of 0.1 for food, 0.01 for beverages, and a minimum confidence of 0.6 for both categories. The results indicate that there is no significant difference between the two algorithms in terms of the generated packages, lift ratio evaluation, and runtime. In the food category, 5 association rules were generated with an average lift ratio of 1.1929, while in the beverage category, 2 rules were generated with an average lift ratio of 1.8990.
Copyright (c) 2025 Shabrina Putri, Ninuk Wiliani, Febri Maspiyanti https://creativecommons.org/licenses/by-sa/4.0
K-Means Clustering for Identifying Traffic Accident Hotspots in Depok City
This study applies the K-Means clustering algorithm to support decision-making processes related to identifying traffic accident-prone areas in Depok City over a three-year period (2020-2022). Secondary data was obtained from the Traffic Accident Unit of the Depok Metro Police, encompassing monthly traffic accident recapitulations for each district. The data underwent preprocessing steps, including integration and selection of relevant attributes. Using RapidMiner, the data was clustered into three distinct groups, with the optimal number of clusters determined by the Davies-Bouldin Index (DBI), which yielded a score of 0.896, indicating a satisfactory clustering result. The findings reveal that four districts—Beji, Cimanggis, Pancoran Mas, and Sukmajaya—are identified as high-risk areas for traffic accidents. These results are expected to assist local authorities in implementing targeted safety measures. The study demonstrates that the K-Means clustering method is a viable tool for analyzing traffic accident data and can significantly contribute to improving road safety in urban areas
Copyright (c) 2024 Herry Wahyono, Hari Setiaji, Tri Hartati, Ninuk Wiliani https://creativecommons.org/licenses/by-sa/4.0
Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE)
Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and LSTM models in predicting the closing price of Solana using RMSE as a testing metric, the aim is to determine the efficiency level of both ARIMA and LSTM models. The research results show that the ARIMA model with an order of (2,1,3) achieves an RMSE of 0.019 (1.9%) with an accuracy of 98.1%, while the LSTM model with a data training ratio of 70:30%, a batch size of 64, and 500 epochs has an RMSE of 0.075 (7.5%) with an accuracy of 92.5%. The conclusion drawn from the conducted experiments is that, in the case of using time series data samples from Solana, the ARIMA method demonstrates higher accuracy compared to the LSTM method.
Copyright (c) 2024 Andiani Andiani, Yoel Simanjuntak, Ninuk Wiliani https://creativecommons.org/licenses/by-sa/4.0
Laptop Price Prediction Using Extreme Gradient Boosting Algorithm
The laptop is a support for many people in doing all activities. The number of laptop outputs with various models can affect the price of laptops. The presence of various online and offline stores causes different laptop prices and it becomes difficult to compare prices that are close to the low price range. Based on these problems, a system is needed that can predict laptop prices based on laptop specifications that are useful for people in finding a cheap price range. Data collection in this study came from bhinneka.com with 560 data and pemmz.com with 319 data collected by scrapping method. This research uses the Extreme Gradient Boosting method with evaluation techniques in the form of cross-validation resulting in an R2 score at the Bhinneka store of 0.98 and RMSE of 1250363.29 with the best cross-validation of 8. At Pemmz store produces an R2 score of 0.98 and RMSE of 1073090.92 with the best cross-validation of 6. Both results use data with outliers.
Copyright (c) 2024 Syahrani Adrianty, Febri Maspiyanti https://creativecommons.org/licenses/by-sa/4.0
Telemedicine Application Adoption During the COVID-19 Pandemic: The Lens of the UTAUT Framework Model
The objective of this study is to analyze the impact of various factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, technology characteristics, task characteristics, and self-efficacy within the framework of the UTAUT model on the intention to adopt telemedicine applications, particularly in light of the ongoing Covid-19 pandemic. A quantitative approach is employed for data analysis, with data being gathered through the distribution of online questionnaires. The research sample consists of 350 respondents, and the data is examined using the Structural Equation Modeling (SEM) technique facilitated by the SmartPLS software. The findings reveal a positive and significant relationship between task technology fit and technology fit, as well as between technology characteristics and technology fit. Furthermore, task technology fit is shown to positively and significantly influence the intention to adopt telemedicine applications, where the services are anticipated to offer health assistance to users of the application. This study offers valuable insights for application developers to enhance features and services to attract public interest in utilizing telemedicine applications..
Copyright (c) 2024 Beno Rahman, Agata Pratiwi Prawitasari, Yulius Anggi Setiawan, Lianna wijaya https://creativecommons.org/licenses/by-sa/4.0
Identification of Potential Landslide Areas in Nusaniwe Sub-district using Slope Morphology Method
This study aims to identify potential landslide areas in Nusaniwe Sub-district using the Slope Morphology Method (SMORPH) based on slope and slope shape analysis. The results show that the complex geological and topographical conditions in the area increase the risk of landslides, with areas of high slope and steep slope shapes tending to be landslide hotspots. Interdisciplinary collaboration, community education, and the development of effective mitigation strategies are key in reducing the risk of landslides in Nusaniwe Sub-district and similar areas.
Copyright (c) 2024 Heinrich Rakuasa https://creativecommons.org/licenses/by-sa/4.0
The Current Trend of Culinary Learning from Basically and Self-taught with social media
The large amount of information that can be explored and obtained through social media makes social media one of the media currently most in demand by most internet users because of the ease of access and completeness of the information. The aim of this research is to explore how much social media is used to learn cooking and how effective social media is for learning culinary arts. This research involved 96 respondents and resulted in the most widely used social media being YouTube, namely 62 (65%) apart from Instagram, Facebook, Twitter, TikTok and Facebook. The research results stated that as many as 55 (57%) social media were used to search for information rather than for entertainment, learning, product promotion and status updates. Social media is considered more effective by 67 (70%) for finding information about culinary compared to other electronic media, especially through YouTube social media. No less important, social media is also able to increase user motivation to continue accessing social media compared to other media. In the future, questionnaires will be distributed to more respondents so that results can be maximized. It is possible that trends will change according to the needs of society at large
Copyright (c) 2024 Winanti winanti, Erick Fernando; Nurasiah Nurasiah, Sucipto Basuki, Shofwatun Hasna; Riyanto https://creativecommons.org/licenses/by-sa/4.0
Spatial Temporal Analysis of Land Surface Temperature Changes in Ambon Island from Landsat 8 Image Data Using Geogle Earth Engine
This study aims to analyze land surface temperature changes on Ambon Island using Landsat 8 imagery data and Google Earth Engine. The research method involves the use of Ambon Island boundary data from the Geospatial Information Agency (BIG) as well as Landsat 8 Collection 2 Tier 1 and Real-Time satellite image data that have been calibrated to reflect the reflectance of the Earth's surface to the atmosphere. The analysis steps include temperature conversion from Kelvin to Celsius, land surface temperature classification, and data export to Arc GIS software. The results showed an increase in land surface temperature associated with urban development in Ambon City, highlighting the importance of sustainable urban planning and good resource management to mitigate the negative impacts of land surface temperature increase and support adaptation to climate change.
Integrated Renewable Energy Microgrid Model Based on Blockchain and DeFi: A Simulation Study of P2P Energy Trading and Renewable Energy Certificate Tokenization
This study proposes and evaluates a blockchain-based renewable energy microgrid model and Decentralized Finance (DeFi) that integrates physical energy systems, IoT metering, smart contracts, peer-to-peer (P2P) energy markets, Renewable Energy Certificate (REC) tokenization, and staking mechanisms to create a transparent and incentivized energy-finance ecosystem. The evaluation is conducted through hourly computational simulations for one year (8,760 hours) on the same community microgrid configuration, namely 500 kWp PV and 200 kWh batteries, with a total consumption of 1.25 GWh. Three scenarios are compared to isolate the impact of the digital layer on system performance: S1 (baseline without blockchain and without P2P), S2 (blockchain-P2P with energy trading and RECs without DeFi), and S3 (full integration of blockchain + DeFi). The results show that S1 results in 36% renewable energy penetration with 810 MWh/year of grid energy imports and a system cost of IDR 455 million/year, indicating that the utilization of PV surplus and the role of batteries is still limited. In S2, the implementation of a P2P marketplace and REC tokenization increased renewable energy penetration to 52%, decreased grid imports to 600 MWh/year, and reduced system costs to IDR 382 million/year, due to increased battery utilization and reduced curtailment. In S3, the DeFi staking mechanism (10%/year yield) strengthened green energy utilization incentives, increasing renewable energy penetration to 67%, decreasing grid imports to 430 MWh/year, and decreasing net system costs to IDR 305 million/year after incorporating DeFi revenue of approximately IDR 48 million/year, with stable tokenomics indicated by approximately 55% of tokens being staked. These findings confirm that the gradual integration of blockchain and DeFi can improve the technical and economic efficiency of microgrids, while transforming renewable energy into a productive digital asset.
Copyright (c) 2024 Erick Fernando, Marojahan Tampubolon, Fahmy Rinanda Saputri https://creativecommons.org/licenses/by-sa/4.0
Development of An Application Transforming Handwriting into Digital Form using CNN
This study aims to develop an application to recognize and predict handwriting using a Convolutional Neural Network (CNN) with ResNet50 architecture. The software development life cycle (SDLC) is an incremental model with two increments. The first increment is used to build the model, and the second increment is used to build the user interface. The data used in this study is handwritten images of Latin uppercase, Latin lowercase, and Arabian numerals with 62 classes. The training data used English Handwritten Characters by Dhruvil Dave from Kaggle Dataset. Data was trained and validated using k-fold cross-validation with tenfold and ten epochs for each fold. The model has an accuracy, precision, recall, and f1-score of 66.33%, 73.4%, 66.2%, and 66%, respectively. The functional application can work as expected based on the black box testing. The developed application can predict handwriting with up to 50% accuracy.
Copyright (c) 2023 Claudio Josulin, Yulia Ery Kurniawati https://creativecommons.org/licenses/by-sa/4.0
Influence of Augmanted Reality Text Learning Media Description Of The Learning Independence of Class Students VII SMP in Central Lampung
This research aims to develop Augmented Reality learning media descriptive text material for class VII SMP and testing the effect of using Augmented Reality learning media for descriptive text on the learning independence of class VII SMP students in Central Lampung. The method used to develop Augmented Reality learning media is method development of Borg and Gall. The population in this study were class VII students of SMP Negeri 3 Terbanggi Besar for the 2023/2024 academic year with a sample size of 75 students. This research uses four data collection techniques, namely interviews, observation, questionnaires and tests. The results of development research show two things. First, the Augmented Reality learning media developed with the Borg and Gall concept was declared feasible by validators with an average score of 82,11%. Second, based on the feasibility test of the developed Augmented Reality learning media , an average score of 90.8 was obtained which is included in the very feasible category; The effectiveness test using N-Gain obtained a value of 0.74 which is included in the high category. This means that Augmented Reality learning media is suitable for use in learning descriptive text material and has been proven to be effective in improving students' reading comprehension abilities of descriptive text.
Copyright (c) 2023 SM. Wardatul Fauziah, Iing Sunarti, Mulyanto Widodo, Ranni Rahmayanthi, Siti Samhati https://creativecommons.org/licenses/by-sa/4.0
Recommendations for Selecting Houses in the Bintaro Area Using the AHP Method for Property Sales Companies
This research aims to overcome customer difficulties in choosing a house in the Bintaro area, especially in the context of Property Sales Companies. Customers often face challenges in navigating the process of selecting a home that fits their preferences and needs. The complexity of very complicated and unstructured situations is one of the main obstacles, so an approach is needed that can simplify the process. In order to overcome these problems, this research proposes the application of two main approaches, namely Process Hierarchy Analysis (AHP) and system prototype development. Using AHP, variables such as price, land area, building area, number of bedrooms, and distance from the city center were identified as key elements that were the focus of the research. A criteria priority matrix will be created based on these variables to become the basis for determining optimal housing choices. In addition, this research also tries to overcome barriers in housing selection by adopting a prototype approach to system development. Thus, it is hoped that the system developed can provide a more effective and efficient solution in simplifying the home selection process for customers. By considering the findings of the AHP calculation method. It is hoped that this research can make a significant contribution in improving the house selection process in the Bintaro area. With better solutions, it is hoped that Property Sales Companies can more effectively overcome challenges in meeting customer preferences, thereby increasing their satisfaction.
Copyright (c) 2023 Fadillah Zulfitriansyah, Rudi Sutomo https://creativecommons.org/licenses/by-sa/4.0
Design of an IoT-Based Automatic Switching System Using Blynk Software
Optimizing electrical energy consumption is imperative to preserve ecological balance and conserve finite natural resources. One major contributor to inefficient electrical energy consumption is the indiscriminate use of electronic devices. Lighting systems, for example, fundamental in modern living, are often left on unnecessarily, resulting in substantial energy waste. To address this issue, we've introduced an automatic lighting control system that automates light activation and deactivation based on user-defined schedules, promoting energy efficiency and responsible energy use. The primary objective of our research is to design and deploy a Blynk-controlled IoT-based wireless switch control system that enhances energy efficiency by efficiently managing lighting for example conditions according to user-defined parameters. This approach minimizes energy waste, regulates current supply to lighting fixtures, and encourages responsible electronic device usage. Moreover, the system aligns with government initiatives for sustainable energy use and environmental conservation. This paper delves into the detailed design, development, and practical applications of the IoT-based Blynk-controlled wireless switch control system, emphasizing its technical aspects and underscoring the substantial benefits it offers to users and the environment. Our study represents a significant step towards a more sustainable and energy-efficient future, driven by innovative technology and responsible energy management.
Copyright (c) 2023 Linus Gregorius Radithya, Fahmy Rinanda Saputri, Indra Hadi Prasetya https://creativecommons.org/licenses/by-sa/4.0
Application of the Association Method Using FP-Growth Algorithm to Find Pattern Of Medicine Purchasing Transactions at Pharmacy
This research was conducted with the aim of finding patterns of medicine sales transactions at the Family Pharmacy so that it can be used as a reference for providing medicine stock so that service to consumers can be improved. This study uses the association method by implementing the FP-Growth algorithm. Through the association method, patterns of medicine purchases will be found in the form of medicine that are purchased together. Then, this study wants to test the FP-Growth algorithm in finding patterns of medicine purchases. The data used is data for the second half of 2021. This dataset consists of 20,266 transactions and 1,166 attributes in the form of medicine. The results of the study by setting different minimum support and minimum confidence values, 7 association rules are obtained with a minimum support value of 10% and a minimum confidence of 90% and the accuracy value of each rule formed is 2,683. The level of association rules formed is strong because it shows more than 1. After knowing the association rules that are formed, these rules will be used as a reference in providing medicine stocks and medicine recommendations.
Copyright (c) 2023 Joanna Nugraha, Chanadya Yustia Purnamawati https://creativecommons.org/licenses/by-sa/4.0
Website-Based Digital Wedding Invitation Generator Application
This research aims to answer the problems in making conventional digital invitations at Our invitation. Users can create digital invitations according to their own wishes and designs and can preview the appearance of the invitations instantly. And Admin can add new themes that Users can use. This application is made in the form of a website. The research starts from problem identification and literature study to application development using the waterfall method. The application has passed testing through the user acceptance test, and evaluation is carried out by distributing questionnaires to users with the conclusion that the application meets user needs.
Copyright (c) 2023 Journal of Applied Research In Computer Science and Information Systems https://creativecommons.org/licenses/by-nc-sa/4.0
Information Technology Governance Audit on implementing the XYZ Mobile Application Using the COBIT 5 Framework with the BAI Domain
Information technology is needed in every area of human life, including the company's business needs. In the company's business activities, technology has a very important role. Therefore, information technology governance is needed so that information technology can be utilized optimally. PT XYZ has a sales service application for products with the XYZ label. This study conducted an audit on the XYZ application with the COBIT 5 domain BAI framework to determine the application's maturity level and provide suggestions for future development. The maturity level results in a value of 3,356 with a predetermined process status, and there is still a large enough gap between each subdomain with the results of the average maturity level in the BAI domain. The results of this study are that it can be seen the value of the application maturity level and recommendations for system development that can be carried out.
Mobile Application for Locating Nearby Garbage Collection Sites
Human actions produce waste. The placement of garbage cans is an issue in the Special Region of Yogyakarta. The public is often unaware of the location of garbage cans. The Nearest Garbage Sites Search System (SIPTeST) solves an existing problem. The objective of developing SIPTeST is to enable the public to determine the location of the nearest garbage can based on their current location. SIPTeST is based on both mobile and web platforms. The SIPTeST website is constructed with PHP and MySQL. Android Studio is used to build SIPTeST mobile. In addition to using the user's location, the system uses the user's location to add garbage bin locations. Location-based services (LBS) are implemented by SIPTeST. This system's output is that it assists users in locating the closest garbage cans and viewing photos of their locations. The user can also observe the location history of previously viewed trash cans. Then, users who encounter problems can also submit reports.
Copyright (c) 2023 Journal of Applied and Research Computer Science and Information Systems https://creativecommons.org/licenses/by-nc-sa/4.0
Design of Web-Based Home Service Application Information System on The Sale and Repair of Motor Vehicle Spare Parts Workshop
The sale and repair of motor vehicle spare parts workshopis small and medium business engaged in the sale and repair of motor vehicle spare parts which was established in 2000. Sales are made offline by opening a shop. CV Lancar Motor’s business makes sales by serving buyers who come by explaining the problems experienced by their vehicles then CV Lancar Motor’s experts will fix them.During the internship as Web Development at The sale and repair of motor vehicle spare parts workshopfor 60 woking days, the task given was to create a home service website to serve buyers form home, by bringing mechanics to the customer’s house. Making is done by making briefings and project integration and then followed by testing the website that has been made.The internship process that has been completed at The sale and repair of motor vehicle spare parts workshopprovides many benefits. Like getting experience how to be a Web Development that makes a website., it is hoped that it will provide useful knowledge and become a new experience in order to be able to do a better job in the real world of work.
Web-Based Application Development for Applying Research or Internship Cover Letters at Kalbis Institute
This study aims to develop a web-based application for applying for research or internship cover letters at Kalbis Institute. The software development life cycle (SDLC) used the Rational Unified Process (RUP), and the software modelling used the Unified Modeling Language (UML). RUP has several phases. They are Business Modelling, Requirements, Analysis and Design, Implementation, Testing, and Deployment. The UML used a use case, activity, and class diagram. The application development used Visual Studio Code software with the PHP programming language. The results of this study are a web-based application for submitting research or internship cover letters for web admins and users. Web admin will be operated by academic operations while the users for students. All functional applications can run properly based on black box testing. The users, both academic operation administrators and students, agree that this application can help the administrators and students to process cover letters for research and internship at Kalbis Institute.
Literature Study E-Commerce: Background, Methods Development System, Trend Topic, and Features
This research focuses on conducting a literature review on e-commerce. With such extraordinary developments, users demand more functions and features. This has the impact of continuous development to attract more users to transact on e-commerce platforms. The research uses a systematic literature review approach with 30 appropriate and relevant articles in the 2017 - 2022 timeframe. Findings The findings of e-commerce development occurring at this time have different backgrounds. Transaction service systems and the absence of online marketing media are the most dominant backgrounds for e-commerce development. The topic of e-commerce development that has been widely developed is the sale of clothing, followed by development for SMEs. Many system development uses the waterfall method approach. In addition to discovering 27 features designed just for online shopping. These findings may serve as an example of how future developments in e-commerce might be improved to be more user-friendly and in tune with the requirements of the customer base.
Copyright (c) 2023 Erick Fernando, Yulius Denny , Jullend Gatc https://creativecommons.org/licenses/by-nc-sa/4.0