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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Transformatika ELINVO (Electronics, Informatics, and Vocational Education) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Journal of Economic, Bussines and Accounting (COSTING) Jurnal Sains dan Informatika Jurnal Informatika Universitas Pamulang Jurnal Sisfokom (Sistem Informasi dan Komputer) Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat IJID (International Journal on Informatics for Development) JISKa (Jurnal Informatika Sunan Kalijaga) JIKA (Jurnal Informatika) Journal of Innovation in Community Empowerment (JICE) International Journal of Health Science and Technology Jurnal INFOTEL Teknomatika: Jurnal Informatika dan Komputer Jurnal Komtika (Komputasi dan Informatika)
Puji Winar Cahyo
Universitas Jenderal Achmad Yani Yogyakarta
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences

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A Comparison of K-Means and Agglomerative Clustering for Users Segmentation based on Question Answerer Reputation in Brainly Platform Cahyo, Puji Winar; Sudarmana, Landung
Elinvo (Electronics, Informatics, and Vocational Education) Vol. 6 No. 2 (2021): November 2021
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.454 KB) | DOI: 10.21831/elinvo.v6i2.44486

Abstract

Brainly is a question and answer (Q&A) site that students can use as a media for questions and answers. Students can also use Brainly to find and share educational information that helps students solve their homework problems. In Brainly, users can answer questions according to their interests. However, it could be that the interest is not necessarily following the competencies possessed. It causes many answers to the questions given not to have a high rating because the answers given are of low quality to be prioritized as the main answer. This study aims to apply the K-Means and Agglomerative Clustering methods to segment users based on the reputation of the answerers by conducting clustering based on track records in answering questions on mathematics subjects. This study used the number of the brightest scores and the number of answers that did not get a rating as the basic features for clustering. The comparison between the two methods used is based on the Silhouette Score, representing the quality of the clustering results, calculated by applying the Silhouette Coefficient method. This study result indicates that the K-Means method gives better results than the Agglomerative Clustering. The Silhouette Score generated by the K-Means method is higher at 0.9081 than the Agglomerative Clustering method, which is 0.8990, which produces two clusters or two segments.
Sentimen Topik Menggunakan Regresi Logistik dan Alokasi Dirichlet Laten sebagai Model Analisis Kepuasan Pelanggan Cahyo, Puji Winar; Aesyi, Ulfi Saidata; Santosa, Bagas Dwi
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1081

Abstract

Buying and selling goods now is more interesting through e-commerce or marketplaces because of the ease of carrying out online transactions. Each transaction usually generates a response from the customer. The transaction response on the Shopee platform is still in paragraph form and needs to be more specific. Therefore, this research aims to build a model analysis of customer satisfaction using the best algorithm between support vector machine (SVM), random forest, and logistic regression. This research method uses sentiment classification with logistic regression because the logistic regression algorithm has the best accuracy, with an accuracy of 90.5. Meanwhile, the SVM algorithm achieved an accuracy of 90.4, and random forest reached 90.2. The three algorithms were tested three times, splitting data train:test at 80:20, 70:30, and 60:40. The best results were obtained by splitting data at 60:40. The best model is used to predict data without labels. The prediction produces 12,844 positive sentiment comment data, 112 negative sentiment comment data, and 70 neutral sentiment comment data. The results of this research continued to topic modeling using latent dirichlet allocation (LDA) to generate a trending topic of customer satisfaction on sales products. Implications of discussing each trend topic can be used as a reference for improving products and services, especially in communicating with customers.
Analisis dan Penanganan Insiden Siber SQL Injection Menggunakan Kerangka NIST SP 800-61R2 dan Algoritma Klusterisasi K-Means Asnawi, Choerun; Hariyadi, Dedy; Aesyi, Ulfi Saidata; Cahyo, Puji Winar
Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v7i2.10527

Abstract

Based on the OWASP Top Ten document in 2021, attacks or vulnerabilities in an application in the form of injection still rank in the top 3. SQL Injection attacks are still classified as injection vulnerabilities so they need special attention from Information & Communication Technology Managers. Badan Siber dan Sandi Negara (BSSN) has published a document related to preventing SQL Injection attacks. However, the document has not included a cyber attack analysis process that uses the K-Means clustering approach. So in this research, a collaborative method of handling cyber attacks in the form of SQL Injection is proposed using the NIST SP 800-61R2 framework as a fundamental for handling cyber attacks and K-Means clustering. Before analyzing cyber attacks, it is better to use a framework or standardization that applies globally. Based on the research conducted, the K-Means clustering algorithm can help cybersecurity analysts in the process of analyzing cyber attacks that occur. The result of this research is that the optimal value is obtained that cyber attacks in the form of SQL Injection, namely 3 clusters. The hope of the research can facilitate cybersecurity analysts in analyzing cyber attacks that are poured into reports to parties in need
METODE FULL COSTING DAN COST PLUS DALAM HARGA JUAL USAHA MIKRO KECIL DAN MENENGAH Gerlan Haha Nusa; David Sulistiyantoro; Puji Winar Cahyo; Inna Zahara; Arif Himawan
Journal of Economic, Bussines and Accounting (COSTING) Vol. 9 No. 1 (2026): COSTING : Journal of Economic, Bussines and Accounting
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/xcanpp60

Abstract

The problem faced by MSMEs is the lack of recording the costs incurred in determining prices. The aim of the research is for MSMEs to find out the cost of production using the full costing method and determine the desired profit using the cost-plus approach. With this determination, it is hoped that it can be used to make various decisions regarding selling prices. The research method used is descriptive qualitative to analyze the data. The research results show that the selling price of beverage products at full costing and cost-plus is lower than the price determined by Omah Pakis. Meanwhile, for food products, the full costing and cost-plus calculations are higher compared to the prices determined by Omah Pakis. The conclusion of this research is that costs are only charged to the elements of raw material and labor costs so that the information on determining selling prices is incomplete. As a result of incomplete cost information data being available, the decisions taken by the owner of Omah Pakis only focus on the short term. Omah Pakis can group production costs by determining the markup to determine the selling price using the cost-plus method.  
Perbandingan LSTM dengan Support Vector Machine dan Multinomial Na ve Bayes pada Klasifikasi Kategori Hoax Puji Winar Cahyo; Ulfi Saidata Aesyi
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5880

Abstract

Hoax is fake news, now massively spread through social media. The impact of hoaxes is that people's misperceptions in understanding of news are very high. With the existence of hoaxes are spreading through social media, it requires the public to think smart when receiving the news. Currently, many ways to prevent hoaxes, right now we have Fact Checker Directory Platform which is a truth platform sourced from several fact check sites. On the truth check platform, every news detected as hoaxes has been categorized into specific type of hoax, manually by the validator. For this reason, this research attempts to automatically categorize the types of hoaxes using comparation of Deep Learning with Machine Learning classifications. Deep Learning uses Long Short Term Memory Network (LSTM), while Machine Learning uses Support Vector Machine (SVM) and Multinomial Naive Bayes. Through the build model process, SVM produces the best accuracy quality of 0.74, Multinomial Na ve Bayes produces an accuracy quality of 0.62 while LSTM displays 0.49. The results of low accuracy in LSTM need to be evaluated on model architecture and data normalization during preprocessing.
Co-Authors Adri Priadana Ahmad Subhan Yazid Al Badawi, M. Abu Amar Arief Ikhwan Wicaksono Arif Himawan Arif Himawan, Arif Asnawi, Choerun David Sulistiyantoro Dedy Hariyadi Fakhriza, Fathur Gerlan Haha Nusa Inna Zahara Kharisma, Kharisma Landung Sudarmana Landung Sudarmana, Landung Muhammad Habibi Muhammad Habibi Muhammad Taufiq Nuruzzaman Nafisa Alfi Sa'diya Naufal Asyhab Nurul Fatimah Nurul Imam Prayogo Qurnia Dwi Yoga Putra Rizky Fauzi Akbar Santosa, Bagas Dwi Saputra, Andika Bayu Subekti, Dayat Ulfi Saidata Aesyi Wardoyo, Budi