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All Journal Techno Nusa Mandiri : Journal of Computing and Information Technology
Widi Astuti
085866669076
Computer Science & IT

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ANALYSIS SENTIMENT ON THE ACCEPTANCE OF CPNS 2021 ON TWITTER SOCIAL MEDIA USING TEXTBLOB Widi Astuti
Jurnal Techno Nusa Mandiri Vol 19 No 1 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v19i1.2980

Abstract

The progress of information technology is growing rapidly along with the development of hardware and software developed by the world's largest companies. These advances have a significant impact on human life. Many jobs in human life use the help of technology. data mining technology, one of which is used in the field of research. In the process, data mining will extract valuable information by analyzing the existence of certain patterns or relationships from large data. Government agencies in Indonesia periodically organize the recruitment and selection of Candidates for Civil Servants (CPNS).
DEVELOPMENT OF A SYSTEM TRANSFORMATION OF NEW STUDENT ADMISSION SERVICES AND VALUE MANAGEMENT Widi Astuti; Fajar Sarasati
Jurnal Techno Nusa Mandiri Vol 21 No 1 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i1.4985

Abstract

Service activities at Tegal Dinamika Vocational School still use a manual system which has a high risk of data being lost and the authenticity of its value is doubtful. Based on these conditions, it is necessary to create a web-based grade processing information system to help convey information on student grades and attendance, as well as online registration data. The aim of this research is to provide a breakthrough in the transformation of services at Vocational School Dinamika Tegal in terms of registering new students, conveying information on exam results and attendance recaps to students and parents, which can be accessed online via the website, so that all users, even people with physical limitations, can access the website. that easily. The research method for web design uses the SDLC waterfall method. Apart from that, to determine website performance, researchers conducted sample usability testing to find out whether the website was user friendly. The results of this research with the creation of this application, the process of conveying value information from teachers to students has become more effective and efficient, this evidence can be seen from the distribution of questionnaires in the school environment which shows that 97% agree with this research, increasing the success of data processing and making it easier for the administrative side to be more organized and no more data errors. The uniqueness of this research focuses on more disability-friendly web design and user experiences that are accessible to all individuals including those with disabilities. The conclusion of this research is that the website is able to manage grade data and student attendance recaps that have been input by the teacher, making it easier for prospective new students to obtain registration information and register online and can be accessed anywhere and at any time through the system
SENTISTRENGTH-BASED SENTIMENT ANALYSIS TO UNDERSTAND THE LOYALTY AND SHOPPING INTERESTS OF DIGITAL BUSINESS MARKETPLACE Widi Astuti; Elly Firasari; F. Lia Dwi Cahyani; Fajar Sarasati; Rendi Septian
Jurnal Techno Nusa Mandiri Vol. 23 No. 1 (2026): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/z9qneg62

Abstract

In Indonesia's dynamic digital economy, customer reviews on marketplace platforms like TikTok Shop, Shopee, and Tokopedia are strategic assets for understanding consumer loyalty and online shopping interest. However, extracting information from thousands of informal reviews presents a significant challenge for rapid business decision-making. This study aims to implement an automated sentiment analysis system by comparing three major machine learning algorithms: Logistic Regression (LR), Naive Bayes (NB), and K-Nearest Neighbors (KNN), utilizing the sentiment strength feature of the Indonesian SentiStrength method. The research dataset consists of 881 reviews collected through crawling techniques and subjected to text preprocessing stages including case folding, cleaning, tokenization, stemming, and stop word removal. Automatic labeling using SentiStrength resulted in a sentiment distribution consisting of Neutral (41.9%), Positive (40.2%), and Negative (17.9%). The data was then divided into training and test data to evaluate the performance of the three algorithms.  Experimental results show that all three models performed very reliably in classifying customer opinions. Based on an evaluation using the Classification Report, K-Nearest Neighbors (KNN) provided the most optimal results with an accuracy rate of 99%, followed by Naive Bayes with 96% accuracy, and Logistic Regression with 94%. The high performance of these three models demonstrates that using SentiStrength sentiment scores as input features is highly effective in minimizing language ambiguity. Managerially, this research contributes to digital business practitioners' ability to monitor public perception in real-time to formulate more responsive marketing strategies and maintain customer retention in the marketplace ecosystem
Co-Authors F. Lia Dwi Cahyani Fajar Sarasati Firasari, Elly Rendi Septian