Garuda - Garba Rujukan Digital

Sentiment Analysis of Social Media Platform Reviews Using the Naïve Bayes Classifier Algorithm Saepudin, Sudin; Widiastuti, Selviani; Irawan, Carti
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1650

The Covid-19 pandemic has caused significant changes in people's lifestyles which are further strengthened by the rapid development of technology. This has resulted in increased use of the internet and accelerated dissemination of information through social media platforms. Not only for self-expression, social media can also be a means of communication, information, education, and even used as a marketing tool. Several social media platforms have recently been popular and widely used, the number of users is increasing from year to year, and each user can provide a rating review of the application. To find out public opinion on social media platforms, sentiment analysis will be carried out on several social media platform applications on the Google Play Store, namely Twitter, Instagram and Tiktok which will later be used as material for evaluating these applications. In this study, the dataset was taken based on ratings from user reviews on the Google Play Store using the NBC (Naïve Bayes Classifier) method with the Python programming language. Based on testing of 1000 comment review data from each application, it was found that the majority gave positive sentiment (Twitter 57.2%, Instagram 74.1%, Tiktok 83.9%), and negative sentiment (Twitter 42.8%, Instagram 25.9%, Tiktok 16.1%) with an accuracy rate of 85.6% for the Twitter application, 83.6% for the Instagram application, and 84.8% for the Tiktok application.

PERANCANGAN ARSITEKTUR PENGELOLAAN TAMAN KOTA BERBASIS WEB MENGGUNAKAN FRAMEWORK ZACHMAN Maulana, Sadam Husen; Saepudin, Sudin; Irawan, Carti
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.6265

Penelitian ini bertujuan untuk merancang arsitektur pengelolaan taman kota di Kota Sukabumi dengan menggunakan Framework Zachman . Pengelolaan taman kota yang efektif dan sangat efisien penting untuk mendukung keberlangsungan lingkungan perkotaan. Taman kota memiliki peran penting sebagai paru-paru kota, kawasan rekreasi, tempat interaksi sosial, dan konservasi keanekaragaman hayati. Namun pengelolaan taman kota menghadapi berbagai tantangan, seperti kurangnya perencanaan terpadu, keterbatasan sumber daya manusia, pendanaan yang tidak stabil, serta sistem monitoring dan evaluasi yang tidak memadai, sehingga dikembangkan arsitektur pengelolaan taman kota dengan menggunakan Framework Zachman . Pada penelitian ini, dibuat model sistem informasi yang sesuai dengan kebutuhan pengelola dan pengguna taman. Hasil penelitian menunjukkan bahwa penggunaan Framework Zachman dalam perancangan arsitektur pengelolaan taman kota dapat meningkatkan konsistensi, konsistensi, dan kejelasan dalam pengelolaan taman kota. Penelitian ini memberikan kontribusi signifikan bagi pengembangan ilmu pengetahuan di bidang arsitektur informasi dan pengelolaan taman kota, serta memberikan manfaat praktis sehingga dapat meningkatkan kualitas layanan taman kota.

Sentiment Analysis of Social Media Platform Reviews Using the Naïve Bayes Classifier Algorithm Saepudin, Sudin; Widiastuti, Selviani; Irawan, Carti
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1650

The Covid-19 pandemic has caused significant changes in people's lifestyles which are further strengthened by the rapid development of technology. This has resulted in increased use of the internet and accelerated dissemination of information through social media platforms. Not only for self-expression, social media can also be a means of communication, information, education, and even used as a marketing tool. Several social media platforms have recently been popular and widely used, the number of users is increasing from year to year, and each user can provide a rating review of the application. To find out public opinion on social media platforms, sentiment analysis will be carried out on several social media platform applications on the Google Play Store, namely Twitter, Instagram and Tiktok which will later be used as material for evaluating these applications. In this study, the dataset was taken based on ratings from user reviews on the Google Play Store using the NBC (Naïve Bayes Classifier) method with the Python programming language. Based on testing of 1000 comment review data from each application, it was found that the majority gave positive sentiment (Twitter 57.2%, Instagram 74.1%, Tiktok 83.9%), and negative sentiment (Twitter 42.8%, Instagram 25.9%, Tiktok 16.1%) with an accuracy rate of 85.6% for the Twitter application, 83.6% for the Instagram application, and 84.8% for the Tiktok application.

IMPLEMENTATION OF THE K-MEANS CLUSTERING ALGORITHM IN ANALYZING PUBLIC SATISFACTION REGARDING PUBLIC SERVICES (STUDI CASE: BALAI PENGUJIAN STANDAR INSTRUMEN TANAMAN INDUSTRI DAN PENYEGAR) Alifah, Atika Juhaedah; Saepudin, Sudin; Irawan, Carti
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2125

With the development of today's modern era, publik service is an important and very necessary thing because it is one of the benchmarks for seeing publik trust and satisfaction with the services provided by an agency. One of the agencies that carries out publi services is the Balai Pengujian Standar Instrumen Tanaman Industri dan Penyegar (BPSI TRI), a government agency under the Ministry of Agriculture. There are a lot of people who will receive services in 2023. Therefore, publik service officers find it difficult to determine publik satisfaction in order to optimize the services provided. To determine community satisfaction, data mining calculations were carried out using the K-Means clustering algorithm method with Community Satisfaction Index (IKM) data in 2023 using 3 (three) categories including unsatisfactory (C1), satisfactory (C2) and very satisfactory) and 2 attributes, namely the behavior of service officers (U7) as well as handling complaints, suggestions and input (U8) then carried out calculations using Microsoft Excel and got the results that C1 (unsatisfactory) 14 respondents, C2 (satisfactory) 39 respondents and C3 (very satisfactory) 98 respondents. Meanwhile, from the results of calculations using python testing, the results showed that C1 (unsatisfactory) was 9 respondents, C2 (satisfactory) was 39 respondents and C3 (very satisfactory) was 103 respondents.

Analisis Motivasi Kinerja Pegawai Kecamatan Cibitung Menggunakan Metode Analytical Hierarchy Process (AHP) Andrean, Okta Teza; Saepudin, Sudin; Irawan, Carti; Mupaat
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2429

This study aims to analyze the factors that influence the performance motivation of Cibitung Subdistrict employees using the Analytical Hierarchy Process (AHP) method with a total of 22 subdistrict employee respondents. The three main criteria analyzed include work environment, rewards, and leadership. Data were obtained through a paired comparison questionnaire, which was then processed using the AHP method to determine the priority weight of each criterion. The results show that leadership is the dominant factor (0.666), followed by rewards (0.601) and work environment (0.534). The Consistency Ratio (CR) value of 0.00086 indicates that the respondents' assessments are consistent. These findings are expected to serve as a basis for policy-making to improve employee performance in the environment.

Classification of Employee Attendance Categories Using the Gradient Boosted Trees Algorithm Safitri, Mutia; Saepudin, Sudin; Irawan, Carti; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.301

Employee attendance is a crucial factor in human resource management as it affects productivity and operational efficiency. However, the recording and analysis of employee attendance often encounter challenges, particularly in terms of the accuracy and effectiveness of the systems used. This study aims to develop an employee attendance classification model using the Gradient Boosted Trees algorithm to improve the accuracy of grouping attendance categories such as Present, Permission, Sick, Leave, and Absent into attendance level categories: High, Medium, and Low. The research method includes collecting employee attendance data throughout the year 2024. The model evaluation is carried out using metrics such as accuracy, precision, recall, and the confusion matrix. The results indicate that the developed model achieves an accuracy of 100.00%, with a mean precision of 100.00% and a mean recall of 100.00%.

Application Of K-Means Clustering Algorithm to Identify the Best-Selling Digital Printing Services Fatahali Ramadhan, Ana; Saepudin, Sudin; Irawan, Carti; Mupaat
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.316

The digital printing industry in Indonesia is experiencing rapid growth thanks to the increasing demand from companies for printing services such as banners, stickers, brochures, and business cards. CV. Copy Paste is one of the companies operating in the digital printing industry that fulfills various printing orders every month. However, the company has difficulty identifying the most popular printing services, which makes it difficult to develop a targeted promotional strategy. In view of this problem, the aim of this study is to group digital printing services according to their popularity using the K-Means Clustering method. This study uses a quantitative approach, collecting sales data from the last 12 months, covering 160 types of services. The steps taken include preliminary data processing, namely attribute selection, data cleaning, and data transformation so that it can be effectively processed using the K-Means algorithm, implemented in the Python programming language. The test results show that digital printing services can be divided into three clusters: 115 less popular services (C1), 31 fairly popular services (C2), and 14 very popular services (C3). The results of this study provide information that can be used as a basis for strategic decisions regarding promotion and service management. In this way, the K-Means Clustering algorithm has proven effective in helping companies group products in a more objective and measurable way based on historical data.

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