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All Journal TEKNIK INFORMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal ULTIMA InfoSys Jurnal Teknologi Sistem Informasi dan Aplikasi JUTEKIN (Jurnal Manajemen Informatika) INTEK: Informatika dan Teknologi Informasi Jurnal Sistem Cerdas Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Journal of Information Systems and Informatics bit-Tech Jurnal ABDINUS : Jurnal Pengabdian Nusantara INFORMASI (Jurnal Informatika dan Sistem Informasi) Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Journal of Computer System and Informatics (JoSYC) Budimas : Jurnal Pengabdian Masyarakat International Journal of Advances in Data and Information Systems Infotek : Jurnal Informatika dan Teknologi Jurnal Ilmiah Teknologi Informasi dan Robotika Bulletin of Computer Science Research Jurnal Informatika, Komputer dan Bisnis (JIKOBIS) Jurnal Ilmu Komputer dan Informatika Journal of Computer Science and Informatics Engineering Jurnal Nasional Teknologi Komputer Jurnal ADAM : Jurnal Pengabdian Masyarakat JOMPA ABDI: Jurnal Pengabdian Masyarakat Journal of Information System and Artificial Intelligence Jurnal Pengabdian Masyarakat Bangsa Teknomatika: Jurnal Informatika dan Komputer Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Jurnal Pengabdian Kepada Masyarakat Radisi International Journal of Applied Mathematics and Computing. Dharma: Jurnal Pengabdian Masyarakat SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat INOVTEK Polbeng - Seri Informatika Teknologi : Jurnal Ilmiah Sistem Informasi
Setyaningsih, Putry Wahyu
Universitas Mercu Buana Yogyakarta
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Pengembangan Aplikasi E-Booking Konser K-Pop Berbasis QRIS dengan Pendekatan User-Centered Design untuk Optimalisasi Pengalaman dan Efisiensi Transaksi Ozmar Azhari; Putry Wahyu Setyaningsih; Septian Eka Ady Buananta; Fandevi Maitri; Francka Sakti Lee
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.981

Abstract

The increase in the number of K-Pop concerts in Indonesia drives the need for a ticket e-booking system that is not only efficient but also capable of accommodating transaction characteristics with high demand levels and optimal integration of digital payment systems. The main issue with the current e-ticketing system lies in the platform's general nature (multi-event marketplace) and the suboptimal integration of user experience with digital payment systems like QRIS. This research aims to design and develop a mobile-based K-Pop concert e-booking application integrated with QRIS and empirically test its impact on transaction efficiency and user experience. The method used is Research and Development (R&D) with a System Development Life Cycle (SDLC) Waterfall model approach, complemented by a quasi-experimental design (posttest control group design). Testing was conducted on 60 respondents divided into experimental and control groups, with variables measured including transaction time, transaction success rate, usability using the System Usability Scale (SUS), and user satisfaction. The research results show that the K-Party application is capable of effectively and integratively supporting the e-booking process. Statistical analysis shows that the use of QRIS has a significant impact on transaction efficiency and user experience, as well as contributing to business performance improvements, including a 36.2% increase in revenue and a 5-10% increase in audience numbers. Thus, the developed system is not only technically feasible but also empirically proven to add value in the context of the digital entertainment industry.
Analisis Sentimen Timnas Indonesia pada Data Tidak Seimbang Menggunakan Perbandingan Naïve Bayes dan IndoBERT Maharani Navila Salsa Bela; Putry Wahyu Setyaningsih
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.9601

Abstract

Social media platform X is widely used by the public to express opinions on the performance of the Indonesian National Team, especially in the fourth round of the 2026 World Cup Qualifiers. In this phase, the Indonesian National Team suffered two consecutive defeats, namely 2–3 to Saudi Arabia and 0–1 to Iraq, which triggered an increase in emotional responses and public criticism on social media. This condition makes sentiment analysis important to understand public perception more objectively. This study aims to analyze the sentiment of social media users X and compare the performance of the Naïve Bayes and IndoBERT models in imbalanced data conditions. The research data amounted to 1,268 tweets that were processed through a pre-processing stage, then automatically labeled using a lexicon-based approach as an initial labeling into two classes, namely positive and negative. The dataset was divided into training data and test data with a ratio of 70:30. The data distribution shows the dominance of negative sentiment at 84.1% and positive at 15.9%. Classification was performed using TF-IDF-based Naïve Bayes and IndoBERT-base-p1, with data imbalance management using random oversampling and class weighting. The results show that Naïve Bayes without treatment achieved 84% accuracy but failed to recognize the positive class. After oversampling, the positive class recall increased to 45%. IndoBERT achieved 85% accuracy, with positive recall increasing from 35% to 43% and the positive class F1-score increasing by 47% after applying class weighting. Despite the relatively high accuracy, the evaluation shows the importance of considering performance on minority classes. Overall, IndoBERT with class weighting provided more balanced results. However, the use of lexicon-based automatic labeling is a limitation of this study.
Benchmarking Local Development Environments: Analyzing the Performance of XAMPP, MAMP, and Laragon Albert Yakobus Chandra; Putry Wahyu Setyaningsih
Bulletin of Computer Science Research Vol. 5 No. 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i3.493

Abstract

In the rapidly evolving landscape of web application development, the choice of a local development environment significantly influences both productivity and performance. This study aims to benchmark three widely utilized local server solutions—XAMPP, MAMP, and Laragon—through a rigorous performance analysis grounded in information technology principles. By examining critical performance metrics such as load times, resource utilization, scalability, and compatibility with various programming languages and frameworks, we provide a holistic view of each platform's capabilities.Utilizing empirical testing methodologies, including stress testing and response time measurements, this research evaluates the environments under varying workloads to simulate real-world application development scenarios. Additionally, we explore factors such as ease of installation, configuration flexibility, and community support, which are essential for developers in selecting an appropriate development environment. The findings reveal significant differences in performance and user experience among the three platforms, emphasizing the implications of server performance on developer efficiency, project timelines, and overall software quality. This study contributes to the body of knowledge in the information technology field by providing actionable insights for practitioners, educators, and researchers. Ultimately, it serves as a foundational resource for informed decision-making regarding local development environments in web application projects, fostering a deeper understanding of how these tools impact the software development lifecycle.
Pengembangan Website Galeri Produk UMKM sebagai Upaya Peningkatan Ekonomi Lokal di Kalurahan Bangunharjo Putry Wahyu Setyaningsih; Albert Yakobus Chandra; Irfan Pratama
Abdimas Galuh Vol 8, No 1 (2026): Maret 2026
Publisher : Universitas Galuh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25157/ag.v8i1.22244

Abstract

Program pengabdian ini dilaksanakan untuk menjawab kebutuhan penguatan promosi UMKM di Kalurahan Bangunharjo yang belum memiliki platform digital terintegrasi. Melalui pendekatan partisipatif, tim bersama mitra melakukan analisis kebutuhan, pengumpulan konten, perancangan antarmuka, serta pengembangan website galeri produk berbasis domain bangunharjo.id. Proses pengembangan melibatkan dokumentasi produk, penyusunan profil usaha, integrasi Google Maps, serta uji coba berulang guna memastikan kesesuaian dengan kebutuhan UMKM. Hasil implementasi menunjukkan bahwa website berfungsi efektif sebagai etalase digital yang menampilkan informasi UMKM secara terstruktur, sekaligus memperkuat identitas digital melalui elemen branding BUMKal dan penerapan prinsip UI/UX modern. Dampak program meliputi peningkatan kapasitas pemasaran, literasi digital pelaku UMKM, serta kredibilitas usaha lokal. Secara keseluruhan, platform ini menjadi instrumen strategis dalam mendukung penguatan ekonomi desa. Untuk ke depan, disarankan adanya pembaruan konten berkala dan integrasi lebih lanjut dengan media sosial serta fitur pemasaran digital.
Perbandingan Naïve Bayes, SVM, dan XGBoost dengan SMOTE untuk Analisis Sentimen Ulasan DANA dan OVO Eko Wardianto; Putry Wahyu Setyaningsih
Journal of Computer System and Informatics (JoSYC) Vol 7 No 3 (2026): May 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i3.10002

Abstract

User reviews of the DANA and OVO digital wallet applications provide valuable insights into user satisfaction and service-related issues. However, the large volume of review data and the imbalanced distribution of sentiment classes pose significant challenges for automated sentiment analysis. This study aims to compare the performance of Naïve Bayes, Support Vector Machine (SVM), and XGBoost for sentiment classification of user reviews, while evaluating the effectiveness of the Synthetic Minority Over-sampling Technique (SMOTE) in handling class imbalance for each algorithm. The dataset consists of 4,987 user reviews classified into three sentiment categories: positive, negative, and neutral. The research process includes text preprocessing, TF-IDF feature extraction, model training, and performance evaluation using a confusion matrix and standard classification metrics. The experimental results indicate that SMOTE significantly improves the performance of the Naïve Bayes classifier, whereas it provides little to no performance improvement for SVM and XGBoost; therefore, the final SVM and XGBoost models are trained using the original dataset. Among the evaluated algorithms, SVM achieves the best overall performance with an accuracy of 85.14% and a macro F1-score of 77.60%, followed by XGBoost with an accuracy of 82.32%. Furthermore, application-specific analysis reveals that the model achieves higher accuracy on DANA reviews (87.55%) than on OVO reviews (82.13%), suggesting differences in linguistic characteristics and sentiment distributions across the two platforms. This study provides a systematic comparison of machine learning algorithms for Indonesian digital wallet sentiment analysis and demonstrates that selective application of data balancing techniques can improve classification performance without necessarily benefiting all algorithms.
Sentiment Analysis of BPD DIY Mobile Banking Application Using SVM and KNN Methods Nabil Fauzan; Putry Wahyu Setyaningsih
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/qyebc428

Abstract

This study aims to conduct sentiment analysis on user reviews of the BPD DIY Mobile Banking application available on the Google Play Store. The analysis is crucial due to the increasing number of user complaints regarding technical performance and user experience that have not been systematically addressed. Two machine learning algorithms, the Support Vector Machine (SVM) and the K-Nearest Neighbour (KNN), were used to classify reviews into positive and negative sentiments.  The dataset comprises 1,211 user reviews collected through web scraping and processed with comprehensive preprocessing stages, including cleaning, tokenizing, case folding, stopword removal, normalization, and stemming. The novelty of this research lies in the integration of Indonesian-specific preprocessing techniques and a comparative evaluation of two classification models, which are rarely applied in similar studies focused on regional banking applications.  The results indicate that SVM outperforms KNN, achieving 81.48% accuracy, 82.30% precision, and 88.50% recall, while KNN only reaches 55.56% accuracy, 63.00% precision, and 65.50% recall. With this level of accuracy, the SVM-based model can be effectively utilized for real-time sentiment monitoring and to identify critical issues in user experience. These findings offer strategic insights for BPD DIY to enhance application quality, particularly in addressing technical problems frequently highlighted by users.
Perancangan Kerangka Knowledge Management System Terintegrasi untuk Meningkatkan Berbagi Pengetahuan di PT. XYZ Septian Eka Ady Buananta; Ozmar Azhari; Putry Wahyu Setyaningsih
INFORMASI (Jurnal Informatika dan Sistem Informasi) Vol 18 No 1 (2026): INFORMASI (Jurnal Informatika dan Sistem Informasi)
Publisher : LPPM STMIK Indonesia Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37424/informasi.v18i1.522

Abstract

Dalam ekonomi digital yang berkembang pesat, pengelolaan pengetahuan yang efektif menjadi faktor penting dalam menciptakan keunggulan kompetitif, khususnya pada industri berbasis teknologi. PT. XYZ, yang bergerak di sektor smart home, menghadapi beberapa tantangan utama, yaitu silo pengetahuan, keterbatasan akses informasi, dan hilangnya tacit knowledge akibat pergantian karyawan. Penelitian ini menggunakan desain penelitian kualitatif-deskriptif yang mengintegrasikan Systematic Literature Review (SLR) berbasis PRISMA dengan wawancara semi-terstruktur yang melibatkan 7 informan kunci organisasi. Data sekunder diperoleh dari 19 sumber peer-reviewed dan dokumen studi kasus, sedangkan data primer diperoleh melalui wawancara dengan informan untuk memvalidasi temuan literatur dalam konteks PT. XYZ. Penelitian ini berfokus pada tiga pertanyaan utama, yaitu KPI apa yang dapat digunakan untuk menilai efektivitas KMS dalam meningkatkan efisiensi operasional dan inovasi, mekanisme apa yang paling tepat untuk mendokumentasikan tacit knowledge, serta bagaimana desain KMS memengaruhi penggunaan sistem dan hasil kinerja yang dapat diukur. Hasil penelitian mengusulkan kerangka KMS terintegrasi, rekomendasi teknologi seperti MS Teams, Confluence, Guru, dan Loom, serta KPI terukur sebagai target validasi, mencakup penurunan waktu pencarian dokumen dari 15 menjadi 12 menit atau sebesar 20%, peningkatan first-contact support resolution dari 70% menjadi 85%, dan peningkatan tingkat penggunaan kembali pengetahuan dari 40% menjadi 65%. .
Comparison of SVM and Random Forest with RFE for Diabetes Prediction Anggi Vandryan; Putry Wahyu Setyaningsih
Journal of Computer Science and Informatics Engineering Vol 5 No 3 (2026): July
Publisher : Ali Institute of Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosie.v5i3.1838

Abstract

Individuals with a high Body Mass Index (BMI) are among the most vulnerable groups to developing Type 2 Diabetes Mellitus due to insulin resistance caused by visceral fat accumulation. However, most existing machine learning models have been developed using general population data without considering the specific characteristics of high-risk individuals. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms in predicting diabetes risk among individuals with a BMI ≥ 25, while also evaluating the impact of Recursive Feature Elimination (RFE) on improving model performance. The Pima Indians Diabetes Dataset from the UCI Machine Learning Repository was used as the data source. After filtering records based on BMI, a total of 662 instances were included in the analysis. The preprocessing stage consisted of median imputation for invalid values, feature normalization using StandardScaler, and feature selection using RFE to select four features for each model. The dataset was divided into training and testing sets using a 70:30 ratio. Model performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results indicate that the RF-RFE model achieved the best performance, with an accuracy of 78%, a recall of 68% for the diabetes class, and an F1-score of 72%, representing a significant improvement over the RF model without RFE (74% accuracy and 60% recall). The combination of Random Forest and Recursive Feature Elimination proved to be the most effective approach for reducing false negatives, which is particularly important in the context of early clinical detection of diabetes
Pemetaan Potensi Desa untuk Penyusunan Masterplan Desa berbasis Desa Mandiri Budaya di Kalurahan Bangunharjo Azfa Mutiara Ahmad Pabulo; Tutut Dewi Astuti; Putry Wahyu Setyaningsih
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 10 No 2 (2026): Volume 10 Nomor 2 Tahun 2026
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v10i2.27436

Abstract

Desa Bangunharjo adalah desa pertanian dan 50% penduduknya bekerja sebagai petani. Karena sulit untuk memaksimalkan potensi pariwisata, budaya, dan UMKM guna mendapatkan status Desa Budaya Mandiri. Tujuan proyek layanan masyarakat ini adalah menyusun daftar sumber daya yang dimiliki oleh desa-desa pedesaan, seperti lahan pertanian dan infrastruktur (aset fisik) serta pengetahuan lokal dan lembaga (aset non-fisik). Daftar ini akan digunakan untuk menyusun rencana induk desa yang terintegrasi. Metode partisipatif yang digunakan melibatkan BUMKal, pelatihan bagi petugas survei, pengumpulan data berdasarkan zonasi, dan penggunaan teknologi. Total luas sawah yang ditemukan adalah satu hektar, potensi Sungai Code, dan 17 usaha kecil dan menengah (UKM) yang belum dimanfaatkan. Rencana induk komunitas akan mencakup empat bagian utama: Komunitas Budaya, Desa Pariwisata, Desa Wirausaha, dan Desa Utama. Pelatihan digital BUMKal, pertumbuhan e-commerce, dan kerja sama dengan pemerintah daerah adalah ide-ide untuk membuat hal-hal lebih berkelanjutan. Proyek ini menunjukkan bahwa strategi berbasis aset dan partisipatif dapat membantu desa berkembang secara inklusif dan berkelanjutan. Hasil akhir dari kegiatan ini adalah tersusunnya master plan desa sebagai pedoman pengembangan Desa Bangunharjo menuju Desa Mandiri Budaya.
ANALISIS PERBANDINGAN KINERJA KNN DAN LOGISTIC REGRESSION PADA KLASIFIKASI RISIKO STROKE Asri Rahma Ayunanda; Putry Wahyu Setyaningsih
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 4 (2026): Agustus 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i4.3812

Abstract

Stroke is a primary cause of mortality globally, making accurate risk classification vital for early intervention. This study aims to evaluate the effectiveness of K-Nearest Neighbor (KNN) compared to Logistic Regression in detecting stroke risk. Utilizing 4,981 medical records from Kaggle, the SMOTE technique was implemented to address data imbalance across 80:20 and 70:30 split scenarios. Results indicate that Logistic Regression outperforms KNN, providing more consistent performance in detecting high-risk classes. The most optimal performance was achieved by Logistic Regression at an 80:20 ratio, reaching 80.00% recall and 74.12% accuracy. This study demonstrates that Logistic Regression is a more effective and sensitive method for identifying clinical stroke risk factors in medical decision support systems. Keywords: Classification; KNN; Logistic Regression; SMOTE; Stroke   Abstrak Stroke merupakan penyebab kematian utama secara global, sehingga klasifikasi risiko yang akurat sangat penting untuk intervensi dini. Penelitian ini bertujuan mengevaluasi efektivitas algoritma K-Nearest Neighbor (KNN) dibandingkan Logistic Regression dalam mendeteksi risiko stroke. Dengan menggunakan dataset 4.981 rekam medis dari Kaggle, teknik SMOTE diterapkan untuk menangani ketidakseimbangan data pada skenario pembagian 80:20 dan 70:30. Hasil menunjukkan Logistic Regression mengungguli KNN dengan performa lebih konsisten dalam mendeteksi kelas risiko. Kinerja paling optimal dicapai Logistic Regression pada rasio 80:20 dengan nilai recall 80,00% dan akurasi 74,12%. Penelitian ini membuktikan Logistic Regression adalah metode yang lebih efektif dan sensitif dalam mengidentifikasi faktor risiko klinis stroke untuk mendukung sistem keputusan medis. Kata kunci: Klasifikasi; KNN; Logistic Regression; SMOTE; Stroke
Co-Authors Ahsan, Moh Albert Yakobus Chandra Albert Yakobus Chandra Anggi Vandryan Arisandi, Bustomi Arita Witanti Asri Rahma Ayunanda Azfa Mutiara Ahmad Pabulo Azfa Mutiara Ahmad Pabulo, Azfa Mutiara Ahmad Azhari, Ozmar Bambang Sugiantoro Buananta, Septian Eka Ady Budianto, Alexius Endy Bustomi Arisandi Eko Wardianto Fandevi Maitri Francka Sakti Lee Gani, Muhammad Irfan Abdul Ibnu Rivansyah Subagyo Ibrahim, Norshahila Imara Gumilar, Nanang Indah Susilawati Indah Susilawati Irfan Juliardi Saputra Irfan Pratama Korsyawati Nasela, Fera Krisna Widatama M Bastian Maharani Navila Salsa Bela Muhammad Ausathul Fikri Mutaqin Akbar Nabil Fauzan Nabil Fauzan Ozzi Suria Panji Kurniawan Prasetyaningrum, Putri Taqwa Putri Prasetyaningrum Reny Yuniasanti Rizky Kurnia Pratama Septian Eka Ady Buananta Setianingsih, Wahyu Siti Rokhmah Siti Rokhmah Sitompul, Jessica Sandra Subagyo, Ibnu Rivansyah Suharjo, Imam Syeda Azwa Asif Tutut Dewi Astuti Widatama, Krisna Wiryanto, Yusuf Aji Yudi prayudi