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All Journal Indonesian Journal on Software Engineering (IJSE) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknoinfo EVOLUSI : Jurnal Sains dan Manajemen Tematik : Jurnal Teknologi Informasi Komunikasi WASANA NYATA
Rousyati, Rousyati
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Humanities Computer Science & IT Engineering Social Sciences Veterinary Other

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Pengelompokan Siswa Penyandang Disabilitas Berdasarkan Tingkat Tunagrahita Menggunakan Algoritma K-Medoids Pratmanto, Dany; Wati, Fanny Fatma; Rousyati, Rousyati; Crisna, Aditia
Indonesian Journal on Software Engineering (IJSE) Vol 5, No 1 (2019): IJSE 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (366.164 KB) | DOI: 10.31294/ijse.v5i1.6550

Abstract

Abstract: Mentally retarded children have obstacles in the activity of the name of the child who still needs proper education in the learning process. SLB Shanti Yoga is one of the best schools that provides educational facilities for children with special needs for people with mental disabilities. The number of criteria determining the level of mentally retarded students makes SLB Shanti Yoga have difficulty in dividing the class according to the results of observations made. So from that research was made to classify data on students with mental retardation to determine the class occupied so that the school can prepare it. The K-Medoids algorithm of clustering techniques can help in grouping students who will occupy classes including light, medium, and heavy classes. The class that has the highest number of students is the heavy mental retardation class while the class that has the lowest number of students is the moderate mental retardation class, with known data grouping results, SLB Shanti Yoga can prepare the class to be used for teaching and learning activities. Keywords: Mentally retarded, data mining, clustering, K-Medoids
Pengembangan Sistem Informasi Anggaran Desa Berbasis Cloud Computing untuk Meningkatkan Transparansi dan Akuntabilitas Pengelolaan Keuangan Desa Bumiharja Aji, Sopian; Pratmanto, Dany; Rousyati, Rousyati; Melly Agustin; Tasya Desti Setiawan; Afida Nurul Yasmin; Andri Miftahul Akhyar
TEMATIK Vol 10 No 2 (2023): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2023
Publisher : LPPM POLITEKNIK LP3I BANDUNG

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

Abstract

Development of a Cloud Computing-Based Village Budget Information System to Increase Transparency and Accountability in Bumiharja Village Financial Management. The Village Budget Information System has become a key component in efforts to increase efficiency and transparency in village financial management. This article discusses cloud computing-based developments, with a focus on Bumiharja Village. The use of cloud computing technology allows easier and safer access to village budget data, integrating all aspects of village financial management into one connected platform. With this system, stakeholders, including village residents, village government, and auditors, can monitor village budgets in real-time. This contributes to a high level of transparency because budget and expenditure data can be accessed publicly. In addition, this system helps increase accountability in village financial management. With proper and structured recording, errors and misuse of funds can be more easily detected. By providing accurate data, village governments can plan budgets more wisely, and village residents can have a better understanding of fund allocation. The development of a cloud-based computing system is a progressive step in creating an efficient, transparent, and accountable village financial management system. This means that this is a breakthrough that has the potential to have a positive impact on development and economic growth in Bumiharja Village.
IMPLEMENTASI METODE PIECES FRAMEWORK PADA TINGKAT KEPUASAN PENGGUNAAN APLIKASI MYPSB MEJASEM Faqih, Husni; Rousyati, Rousyati; Mubarok, Husni; Pangestu, Andika Tulus; Akbar, Muhammad Taufan
Jurnal Teknoinfo Vol 18, No 1 (2024): Januari
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v18i1.3399

Abstract

The need for digitizing schools is currently quite massive due to calls from the central and regional governments. In addition, the development of digital technology has changed the behavior of people who rely more on digital technology so that it is easy and fast to do their activities. One form of school digitization on the school management side is online acceptance of new students. The MyPSB Mejasem application is an online new student admissions information system that is used by SDN Mejasem Barat 01. To improve the service quality of this application, an evaluation is needed regarding user satisfaction with the MyPSB Mejasem application. The PIECES Framework method, which is a framework for classifying a problem, opportunity, and direction in the scope of information system analysis, is the analytical method used because it can provide real results on the effectiveness of the system used. This method has six indicators in its analysis such as Performance, Information, Economics, Control and Security, Efficiency and Service. From measurements with the PIECES Framework, the average value of the six indicators is 4.30, which is a very good value with satisfactory category results according to the characteristics of the Rikert scale assessor.
Sentiment Analysis on Fuel Purchase Policy Through MyPertamina Application Using NB and SVM Methods Optimized by PSO as Weight Optimation Rousyati, Rousyati; Pratmanto, Dany; Ardiansyah, Angga; Aji, Sopian
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.5131

Abstract

Sentiment analysis on the MyPertamina application can serve as a means to extract customer opinions about the application. This method involves collecting reviews from users who have utilized the MyPertamina application and classifying these reviews as positive or negative using sentiment analysis algorithms. After the reviews are classified, themes discussed in positive and negative reviews can be extracted, such as ease of use, payment speed, or technical issues. This provides a general overview of user expectations for the MyPertamina application and areas that may need improvement. Sentiment analysis of MyPertamina application comments using Naïve Bayes (NB) and Support Vector Machine (SVM) methods is a process to evaluate whether user comments on the MyPertamina application are positive or negative. NB and SVM are machine learning methods used to predict the category of an input based on given training data. In this study, user comments on the MyPertamina application are used as input and classified as positive, negative, or neutral based on previous training data. The goal of this sentiment analysis is to understand user perceptions of the MyPertamina application and enhance its quality. The research concludes that the implementation of data mining can assist in categorizing sentiments of MyPertamina reviews. The NB algorithm with the addition of Particle Swarm Optimization (PSO) proves to be the most effective method in this study compared to NB alone, SVM, and SVM + PSO. The NB algorithm with PSO optimization yields an accuracy of 79.49%, the highest precision of 79.57%, recall of 79.38%, and the highest AUC of 95.30%, falling into the category of excellent classification.
Analisa Sentimen Persepsi Masyarakat Terhadap Aplikasi Bea Cukai Mobile Menggunakan Algoritma Naive Bayes Dan K-Nearest Neighbors Rousyati, Rousyati; Pratmanto, Dany; Widodo, Andrian Eko; Fatmawati, Kulum; Saputra, Rangga Diva
Evolusi : Jurnal Sains dan Manajemen Vol 12, No 2 (2024): Jurnal Evolusi 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/evolusi.v12i2.23576

Abstract

AbstractThis study analyzes user sentiment towards the Bea Cukai Mobile application using Naive Bayes and K-Nearest Neighbors (KNN) algorithms. Data was collected from 600 reviews on Google Play Store, equally divided between positive and negative sentiments. After preprocessing, the data was analyzed using both algorithms. Results show that Naive Bayes outperformed with 79.96% accuracy, 87.76% recall, and 96.40% AUC, compared to KNN's 78.34% accuracy, 75.32% recall, and 92.20% AUC. However, KNN excelled in precision with 81.06% versus Naive Bayes' 77.21%. The study concludes that Naive Bayes is more effective in providing accurate classification and distinguishing between positive and negative classes, while KNN is more precise in predicting positive classes. These findings offer valuable insights into user perceptions of the Bea Cukai Mobile application and the effectiveness of algorithms in sentiment analysis.Keywords: Sentiment analysis, Bea Cukai Mobile application, Naive Bayes, K-Nearest Neighbors, Google Play Store, User reviewsAbstrakPenelitian ini menganalisis sentimen pengguna terhadap aplikasi Bea Cukai Mobile menggunakan algoritma Naive Bayes dan K-Nearest Neighbors (KNN). Data diperoleh dari 600 ulasan di Google Play Store, terbagi sama rata antara sentimen positif dan negatif. Setelah melalui tahap preprocessing, data dianalisis menggunakan kedua algoritma tersebut. Hasil menunjukkan bahwa Naive Bayes memiliki performa lebih baik dengan akurasi 79,96%, recall 87,76%, dan nilai AUC 96,40%, dibandingkan KNN dengan akurasi 78,34%, recall 75,32%, dan AUC 92,20%. Namun, KNN unggul dalam hal presisi dengan 81,06% dibanding Naive Bayes 77,21%. Penelitian ini menyimpulkan bahwa Naive Bayes lebih efektif dalam memberikan klasifikasi akurat dan membedakan kelas positif dan negatif, sementara KNN lebih tepat dalam memprediksi kelas positif. Hasil ini memberikan wawasan berharga tentang persepsi pengguna terhadap aplikasi Bea Cukai Mobile dan efektivitas algoritma dalam analisis sentimen.Kata kunci: Analisis sentimen, Aplikasi Bea Cukai Mobile, Naive Bayes, K-Nearest Neighbors, Google Play Store, Ulasan pengguna
Evaluasi Kinerja Naive Bayes dan K-Nearest Neighbors pada Analisis Sentimen Ulasan Aplikasi SnackVideo Pratmanto, Dany; Fandhilah, Fandhilah; Rousyati, Rousyati; Aji, Sopian
Indonesian Journal on Software Engineering Vol 11, No 1 (2025): IJSE 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijse.v11i1.26165

Abstract

Abstrak  Penelitian ini membandingkan performa algoritma Naive Bayes (NB) dan K-Nearest Neighbors (KNN) dalam analisis sentimen review aplikasi SnackVideo. Dataset berisi 2000 ulasan dengan distribusi seimbang antara sentimen positif dan negatif. Metodologi penelitian mengikuti CRISP-DM, meliputi pengumpulan data melalui web scraping, preprocessing data (transform case, tokenization, stopword removal, dan stemming/lemmatization), pembentukan model klasifikasi dengan NB dan KNN, evaluasi model menggunakan metrik akurasi, recall, presisi, dan AUC, serta komparasi hasil. Hasil evaluasi menunjukkan NB memiliki akurasi 80,46%, recall 88,26%, presisi 77,71%, dan AUC 96,90%, sedangkan KNN memiliki akurasi 78,84%, recall 75,82%, presisi 81,56%, dan AUC 92,70%. Secara umum, NB lebih unggul dalam akurasi, recall, dan AUC, sehingga direkomendasikan sebagai algoritma yang lebih efektif dan efisien untuk analisis sentimen pada dataset besar. KNN lebih cocok untuk kasus yang membutuhkan presisi tinggi dalam prediksi positif.Kata kunci: Analisis Sentimen, Aplikasi SnackVideo, Naive Bayes, K-Nearest Neighbors, Preprocessing Data, Evaluasi Model AbstractThis study compares the performance of the Naive Bayes (NB) and K-Nearest Neighbors (KNN) algorithms in sentiment analysis of SnackVideo app reviews. The dataset consists of 2000 reviews with a balanced distribution between positive and negative sentiments. The research methodology follows CRISP-DM, including data collection via web scraping, data preprocessing (transform case, tokenization, stopword removal, and stemming/lemmatization), sentiment classification model building with NB and KNN, model evaluation using metrics such as accuracy, recall, precision, and AUC, and comparison of results. The evaluation results show that NB achieves an accuracy of 80.46%, recall of 88.26%, precision of 77.71%, and AUC of 96.90%, while KNN achieves an accuracy of 78.84%, recall of 75.82%, precision of 81.56%, and AUC of 92.70%. Overall, NB outperforms KNN in terms of accuracy, recall, and AUC, making it a more effective and efficient algorithm for sentiment analysis on large datasets. KNN is more suitable for cases requiring high precision in positive predictions.Keywords: Sentiment Analysis, SnackVideo App, Naive Bayes, K-Nearest Neighbors, Data Preprocessing, Model Evaluation
Enhance Artificial Intelligence Literacy for Islamic Boarding School Students Using the Asset Based Community Development Method Setyadi, Heribertus Ary; Agustina, Candra; Haryanto, Wawan; Rousyati, Rousyati
WASANA NYATA Vol 9, No 1 (2025)
Publisher : STIE AUB Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36587/wasananyata.v9i1.1970

Abstract

Use of artificial intelligence (AI) in teaching and learning has become an increasingly important topic in modern education context. AI offers a wide range of potential to enhance students' learning experiences through better personalization and adaptation to individual needs. From initial observations in several Islamic boarding schools in Banjarsari Surakarta, it was found that understanding of students and teachers about AI and AI supporting applications was still lacking. As part of efforts to improve education quality and community readiness to face a digital era, Bina Sarana Informatika University, Surakarta City Campus, took an initiative to implement a community service program in form of training on using AI in education for students. Community service method used in this activity is the Asset Based Community Development (ABCD) approach which aims to empower communities by utilizing existing potential and resources. Implementation stages include needs analysis (Discovery), expectations formulation (Dream), design of training modules (Design), finalization of plans with FGD (Define), and training implementation (Destiny). AI workshop for Islamic boarding schools has been successfully implemented with good results. From questionnaires that have been filled out by all participants, it shows that workshop materials presented are very useful, materials and tutors delivery method are satisfactory. Workshop participants who are satisfied or rate it good are 63% and those who rate it as very satisfied or very good are 32%.
Co-Authors Afida Nurul Yasmin Aji, Sopian Akbar, Muhammad Taufan Andri Miftahul Akhyar Andrian Eko Widodo Angga Ardiansyah Anggraini, Recha Abriana Candra Agustina Crisna, Aditia Dany Pratmanto Fandhilah Fandhilah Faqih, Husni Fatmawati, Kulum Haryanto, Wawan Heribertus Ary Setyadi Husni Mubarok Melly Agustin Pangestu, Andika Tulus Saputra, Rangga Diva Tasya Desti Setiawan Wati, Fanny Fatma Zahra, Arvano Salma Fatimatus