Article Per Year (5 Year)
p-Index From 2021 - 2026
10.235
P-Index
This Author published in this journals
All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnal Informatika KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Jurnal Informasi dan Komputer Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) ICIT (Innovative Creative and Information Technology) Journal E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) MEANS (Media Informasi Analisa dan Sistem) Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Instal : Jurnal Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) JURSIMA BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Jurnal Inovasi dan Teknologi Pendidikan SISFOTENIKA Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Tati Suprapti
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Social Sciences Other

Published : 83 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 83 Documents
Search

 5   6   7   8   9 
Peningkatan Efisiensi Model Asosiasi Pada Data Transaksi Penjualan Sembako Dengan Algoritma FP-Growth Yusuf Sidiq, Yusuf Sidiq; Kurniawan, Rudi; Suprapti, Tati
Informasi Interaktif : Jurnal Informatika dan Teknologi Informasi Vol 10 No 1 (2025): JII Volume 10, Number 1, Januari 2025
Publisher : Program Studi Informatika Fakultas Teknik Universitas Janabadra

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Dengan meningkatnya volume data transaksi di sektor ritel, termasuk toko sembako, analisis pola transaksi menjadi kebutuhan penting untuk mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan untuk mengidentifikasi aturan asosiasi yang memenuhi kriteria minimum support sebesar 0,95 dan minimum confidence sebesar 0,94, serta mengungkap pola transaksi signifikan dalam data penjualan. Analisis ini diharapkan dapat memberikan wawasan penting untuk mengoptimalkan pengelolaan stok dan merancang strategi promosi yang lebih efektif. Penelitian ini menerapkan metode Knowledge Discovery in Database Process (KDD), yang meliputi beberapa tahapan: pengumpulan data transaksi penjualan, preprocessing untuk membersihkan dan menyusun data, transformasi data ke format yang sesuai untuk analisis, penerapan algoritma FP-Growth untuk menghasilkan aturan asosiasi, serta evaluasi hasil. Data yang digunakan berasal dari transaksi toko sembako dalam periode tertentu yang mencakup berbagai jenis produk.Hasil eksperimen menunjukkan bahwa tidak ada aturan asosiasi yang memenuhi kriteria minimum support 0,95 dan minimum confidence 0,94. Namun, analisis lebih lanjut menemukan bahwa item dengan nilai support tertinggi adalah beras, dengan nilai sebesar 0,912. Selain itu, pola asosiasi dengan confidence tertinggi adalah kombinasi daging sebagai premis dan beras sebagai konklusi, dengan nilai confidence sebesar 0,941. Hasil ini menunjukkan bahwa meskipun kriteria awal tidak terpenuhi, pola pembelian tertentu tetap dapat dimanfaatkan untuk analisis mendalam. Diskusi penelitian ini menyoroti potensi algoritma FP-Growth dalam mengidentifikasi pola transaksi yang relevan, meskipun diperlukan penyesuaian parameter awal. Penelitian ini memberikan kontribusi praktis bagi pengelolaan toko sembako, khususnya dalam pengaturan stok dan strategi promosi berbasis data. Penelitian lanjutan disarankan untuk menggunakan dataset yang lebih besar serta parameter yang lebih fleksibel guna mendapatkan hasil yang lebih komprehensif.
FP-Growth Algorithm for Association Model Optimization in Household Sales Data Zulfa Hana Aqliyah; Rudi Kurniawan; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.760

Abstract

This research aims to find the value of support and confidence parameters needed so that associations between products can be identified and get the value of support, confidence, lift for the association rules found, and identify products that have the highest support value in frequent itemsets. The method used is Knowledge Discovery in Databases (KDD) with the stages of data collection, data pre-processing, data transformation, data mining, dan interpretation and evaluation. Sales transaction data was collected from January 1 to September 30, 2024, focusing on support and confidence values. The results showed that the association was successfully found with a parameter value of support 0.02 and confidence 0.5. In the association found, the products SWEAT BRONZE PANTS MINI M5 and SWEAT BRONZE PANTS MINI L5 have a support value of 0.004, confidence of 0.073, and lift of 1.421. These values indicate that although the frequency of this association is low, its strength exceeds that of a random association, which can be used in marketing strategies like product bundling.The product “SENSI PEREKAT S20” has the highest support of 0.149 (14.9%. The findings provide insight into the use of data mining algorithms to design data-driven marketing strategies and more efficient inventory management.
Analysis of Beverage Sales Data Using the FP-Growth Algorithm at Sini Aja Cafe Widisa Adi Kumara; Rini Astuti; Willy Prihartono; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.772

Abstract

The growth of information technology and data mining techniques has greatly helped analyze consumer purchasing behavior, particularly in marketing and inventory management. This study aims to uncover association patterns between products frequently bought by customers at Sini Aja Cafe and to measure these patterns' support and confidence values. The research uses Knowledge Discovery in Databases (KDD), including stages like data selection, preprocessing, transformation, applying the FP-Growth algorithm, and interpreting results. Data from 1,083 beverage sales transactions at Sini Aja Cafe from August 1 to October 31, 2024. The findings reveal five significant association rules when applying a minimum support of 0.1 (10%) and confidence of 0.3 (30%). Notably, if customers buy Red Velvet Oreo, there is a 56% chance they will also buy Thai Tea. Thai Tea sales dominate with a support value 0.557 (55.7%). The support values of the association rules range from 0.141, categorized as medium, and the confidence values range from 0.235, categorized as low. These findings offer valuable insights for the cafe owner to optimize operations, enhance customer satisfaction, and increase profits.
Optimizing Naïve Bayes Algorithm Through Principal Component Analysis To Improve Dengue Fever Patient Classification Model Santi Nurjulaiha; Rudi Kurniawan; Arif Rinaldi Dikananda; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.798

Abstract

Dengue fever is an infectious disease that has a significant impact on public health in tropical regions, including Indonesia. Early detection and proper classification of DHF patients is essential to reduce severity and mortality. For this reason, a method that can improve the accuracy in diagnosing this disease is needed. Principal Component Analysis (PCA) and Naïve Bayes (NB) are two commonly used techniques in medical data analysis. PCA is used to reduce the dimensionality of data to reduce complexity, while Naïve Bayes is used for classification of data based on probability. This study aims to optimize the use of PCA and Naïve Bayes in improving the accuracy of the dengue patient classification model. The method used in this study involves processing a medical dataset of dengue patients containing various clinically relevant attributes. The dataset was then processed using PCA to reduce dimensionality and identify key features that affect classification. Next, Naïve Bayes was applied to classify the data based on the selected features. This study compares the performance of classification models that use a combination of PCA and Naïve Bayes with models that only use Naïve Bayes without dimensionality reduction. The results show that the use of PCA in data processing significantly improves the accuracy of the classification model compared to the model that only uses Naïve Bayes. The combination of PCA and Naïve Bayes produces a more efficient model and has a higher accuracy rate in identifying patients with DHF risk. Thus, the application of PCA and Naïve Bayes in the classification of DHF patients can be an effective tool in assisting the medical diagnosis process, which in turn can reduce misdiagnosis and improve patient recovery rates. This research contributes to the development of artificial intelligence technology in the medical field, especially to improve the accuracy of dengue disease diagnosis, and serves as a basis for further research in the use of machine learning techniques in healthcare. This study analyzes the performance of the Naïve Bayes algorithm in classifying dengue fever patient data, by comparing models that use Principal Component Analysis (PCA) as a dimension reduction method and models that do not use it. The results show that the Naïve Bayes model without PCA has an accuracy of 49.96%, which is close to the random guess rate. This finding indicates that the model is less effective in recognizing patterns in the data. In contrast, the application of PCA successfully increased the model's accuracy to 50.03%
Optimizing Email Spam Classification Using Naïve Bayes and Principal Component Analysis Shinta Virgiana; Rudi Kurniawan; Tati Suprapti
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.803

Abstract

In the ever-evolving digital era, email spam filtering is an important challenge to maintain the security and comfort of email services. The Naïve Bayes algorithm is widely used for spam email classification because of its ability to manage large data, although there are still limitations in terms of accuracy, precision and recall. This research aims to improve spam email classification performance by combining Naïve Bayes and Principal Component Analysis (PCA) to optimize model accuracy and explore optimal parameters in the reduction dimension. The research methodology goes through the Knowledge Discovery in Database (KDD) stages which include selection, preprocessing, transformation using PCA, development of a classification model using Naïve Bayes, and evaluation of model performance. The dataset used consists of emails categorized as spam and non-spam. The experimental results show that the combination of Naïve Bayes and PCA achieves the highest accuracy of 99.24% with 7 principal components. The fixed number of components approach shows better performance compared to preserving variance, emphasizing the importance of selecting appropriate PCA parameters in improving the effectiveness of model classification. This research shows that PCA not only reduces the complexity of the dataset but also increases the efficiency of the classification algorithm.
Optimizing Sentiment Analysis on the Linux Desktop Using N-Gram Features Hidayat, Muhamad Taufiq; Kurniawan, Rudi; Suprapti, Tati
Jurnal Informatika Vol 12, No 1 (2025): April
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i1.24773

Abstract

Linux, or GNU/Linux, is a widely used open-source operating system built on the Linux kernel that is available for anyone to use, known for its security and privacy advantages. With advancements in information technology, protecting privacy has become increasingly challenging due to data extraction practices done by major tech companies. This has encouraged some Mastodon users to switch to Linux, with many expressing their opinions on using Linux as their main operating system. This research seeks to analyze the sentiments of Mastodon users toward Linux through sentiment analysis to understand whether the trend is predominantly positive, negative, or neutral. The methodology used includes collecting data with the help of the Mastodon.py library witch then gets manually labelled with the assistance of a linguistic expert as well as a linguistic rule proposed by previous research. The text mining process includes preprocessing steps which includes feature extraction with n-Gram to gain the most optimize result as well as employing feature selection using TF-IDF. The Naïve Bayes algorithm is employed for text classification. The entire process of data analysis is conducted with the help of AI Studio (RapidMiner) software. The results show that the highest-performing model for sentiment analysis is achieved with an n-gram value of 3, revealing user sentiment polarity towards Linux on Mastodon as follows: 42% positive, 28% negative, and 30% neutral. The sentiment analysis model has an accuracy of 63%, with a precision of 70%, recall of 80%, and an f1-score of 74% which shows that this method is able to optimize the sentiment analysis process.
Transformasi Digital Umkm Sebagai Strategi Inovasi Dan Peningkatan Daya Saing Di Era Industri 4.0 Kurniawan, Rudi; Suprapti, Tati; Fikri, Achmad; Aditia
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 4 : Mei (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Micro, Small and Medium Enterprises (MSMEs) play a crucial role in the local economy, but often face challenges in marketing and financial management. Lack of knowledge and skills in marketing strategies and accounting leads to limited market reach and lack of efficiency in business management. This research aims to empower MSMEs through digital transformation to increase innovation and competitiveness in the industry 4.0 era. The methods used in this research include training on digital marketing and technology-based accounting systems for MSME players in Tarikolot District. The results show that the application of digital technology, such as online marketing and the use of accounting applications, can improve business efficiency and expand the MSME market. With digital transformation, MSMEs are more adaptive to changing market trends and able to compete with larger businesses. This study confirms that support in the form of education and technology implementation is essential to sustainably improve the competitiveness of MSMEs.
Peningkatan Kompetensi Digital Pengurus Koperasi Melalui Pelatihan Operator Komputer Madya Suprapti, Tati; Lukman Rohmat, Cep; Muhaimin, Ahmad; Sri Nurmala, Ai
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 4 : Mei (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Intermediate Computer Operator Training is a program designed to improve the digital competence of cooperative administrators in facing the challenges of the digitalization era. The program aims to provide an in-depth understanding of the use of office software, data management, and digital applications relevant to cooperative operations. The training method includes interactive theoretical and practical sessions to improve participants' skills in operating computers and managing information effectively. The results of the training showed a significant increase in participants' understanding of digital technology. In addition, the training also had a positive impact on the efficiency of the cooperative board's work in administrative and financial management. Evaluations conducted through tests and questionnaires showed that most participants felt more confident in using computers and supporting software after attending the training. In the era of digital transformation, increasing digital literacy for cooperative boards is an urgent need to improve the competitiveness and sustainability of cooperative businesses. With this training, it is expected that cooperatives can be more adaptive to technological developments and be able to take advantage of digitalization to increase productivity and transparency in business management.
Clustering Data on Participants’ Reactions to Online Shop Posts on Facebook Using K-Means Algorithm With Elbow Method Technique Arifin, Imam; Rahaningsih, Nining; Suprapti, Tati; Narasati, Riri
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i02.132

Abstract

One of the social media platforms that not only serves as a place to share stories and statuses but also as a place to sell is Facebook. The data used is a dataset from Kaggle totaling 6666 data with 10 attributes and then sampled with the Slovin technique and obtained 377 sample data which will be processed using RapidMiner software with K-Means Algorithm and then optimized with Elbow Method technique, evaluation using (Cluster Distance Performance) to find the average within centroid distance value and the Davies-Bouldin Index (DBI) value. The results obtained are, the average within centroid distance value of the 3rd clustering is proven in the cluster distance performance operator obtained ???? = 3: 200237.353, ????=5: 118343.557, ????=7: 75339.476, then the ideal of clusters in this study proven by the Elbow Method is when ???? = 5, and Davies-Bouldin Index (DBI) value which is close to zero is when ???? = 3 with a value of k = 3: 0.394. In addition, clustering based on the number of likes and comments can help sellers identify the most active group of participants and potentially become loyal customers.
Implementasi Game Edukasi Berbasis Android Dalam Pembelajaran Alat Musik Tradisional Jawa Barat Suprapti, Tati; Apriliani, Yuni
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 2 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Indonesia is a country that is rich in culture, one of the famous ones from Indonesia is its traditional musikal instruments, especially the West Java area which has many musikal instruments called waditra. Waditra is a term for sound instruments commonly used as traditional musikal instruments. Limitations occur because of the impossibility of teaching staff to directly show some musikal instruments from West Java in the learning process of Cultural Arts in recognizing musikal instruments. By utilizing the provision of applications that contain elements of education, it gives birth to new ways in one's learning process. From the problems that occur, to overcome them by making an educational game in introducing musikal instruments from the regions in Indonesia. By introducing the identity of each of these musikal instruments, it is able to provide insight to the children of SD Negeri Cidenok, Majalengka Regency. Based on the ADDIE development method (Analysis, Design, Development, Implementation, Evaluation), able to make educational games that are useful for learning media systematically for children at SD Negeri Cidenok, Majalengka Regency. The results obtained from blackbox testing, which is to test the functions in the game so that it runs well. In this trial, it was found that every game scene functions and runs properly so that this research is said to be successful because the game is worthy of being used as an innovative and creative learning medium and helps children in understanding local musikal instruments.
Co-Authors Abdul Hakim Abdul Mukhyidin Abrar Bayan, Athaullah Achmad Suharno Adam Firmansyah Ade Irma Purnamasari Ade Irma Purnamasari Aditia agus bahtiar Ahmad Faqih Ahmad Faqih Aldi Setiawan Ali Ali Alpian Novansyah, Indi Amaliah, Novi Andi Ardiansyah Andri Yanto Apriliani, Yuni Aribah, Firyal Arif Rinaldi Dikananda ASEP SAEFUDDIN Auliya Azhar, Alwan Cep Lukman Rohmat Christian Anderson Wint's II, Hans Darussalam, Luthvi Nurfauzi Dayanti, Resda Dian Ade Kurnia Dodi Solihin Doni Anggara Dwi Prasetyo Faujatun Hasanah Fazrian, Vivi Feri Irawan Irawan Fikri, Achmad Fitri Adha Hariyati Airi Fitriani Agustina Fitriani Fitriani Gifthera Dwilestari Gifthera Dwilestari Gilang Perwati, Intan Gilang Ramadhan Gustiani Regina Pratama Putri Gustino, Gustino Habiballoh, Hafshoh Hadianti, Isan Hafshoh Habiballoh Hajaroh, Hajaroh Hartati Hartati Hayati, Umi Hendriyansyah, Hendriyansyah Hidayat, Manarul Hidayat, Muhamad Taufiq Hidayat, Peri Husni Mubarok Ilham Kurniawan Imam Arifin imam maulana, imam Indrawan, Heru Irfan Ali Irma Purnamasari, Ade Kaslani Khoirunisa, Irma Lestari, Hasanah Lukman Rohmat, Cep Mahda, Muhammad Manarul Hidayat Martanto . Maryam, Beby Muhaimin, Ahmad Muhamad Basysyar, Fadhil Mulyawan Nana Siti Nurjanah Narasati, Riri Narasati Naufan, Muhammad Hilmy Nining Rahaningsih Nur Amalia Nurhakim, Bani Nurmala, Sri Pratiwi, Intan Purnamasari, Ade Irma Raditya Danar Dana Rananda Deva Rian Raudotul Janah, Fina Rini Astuti Rini Astuti Riri Narasati Rizki Ani, Fitri Rosdiana Rosdiana Rudi Kurniawan Rudi Kurniawan Rudi Kurniawan Ruli Herdiana Ryan Hmonangan Saeful Anwar Saeful Anwar, Saeful Sajidan, Dzikri Santi Nurjulaiha Shalihah, Ghina Shinta Virgiana Silalahi, Ryan H Siti Aisah, Iis siti azhar Sri Nurmala, Ai Suarna, Nana Suharno, Achmad Sukma Maula, Intan Syahputra Simbolon, Vrendi Amro Syajida, Hanna Syaripah, Imas Tegar Lazuardi, Muhammad Tengku Riza Zarzani N Tohidi, Edi Tri Aditama Tri Gustiane, Indri Umi Hayati Umi Hayati Utami Aryanti Vinna Agustina Wahyudin, Edi Warni Ayu Hermina, Bintang Widiawati, Fitri Widisa Adi Kumara Wijaya, Yudhitira Arie Willy Prihartono Yudhistira Arie Wijaya Yusuf Sidiq, Yusuf Sidiq Zaki Nur Rahmat Hidayat Zulfa Hana Aqliyah