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All Journal ComEngApp : Computer Engineering and Applications Journal Jupiter JSI: Jurnal Sistem Informasi (E-Journal) Jurnas Nasional Teknologi dan Sistem Informasi Annual Research Seminar RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Digital Teknologi Informasi Jurnal Abdi Masyarakat Indonesia Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Puan Indonesia The Indonesian Journal of Computer Science Jurnal Testing dan Implementasi Sistem Informasi INOVTEK Polbeng - Seri Informatika
Bayu Wijaya Putra
Universitas Sriwijaya
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Penerapan artificial intelligence media desain website pembelajaran inovatif Sanjaya, M. Rudi; Ruskan, Endang Lestari; Indah, Dwi Rosa; Putra, Bayu Wijaya; Afif, Hasnan; Seprina, Iin; Faiq, Al Iksan; Wijayanto, Muhammad Ravi; Imran, Athallah Yasyfi; Danendra, Muhammad Archi Daffa; Rachmad, M. Ichsan Farel
Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Vol. 7 No. 1 (2026)
Publisher : Universitas Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jp2m.v7i1.24377

Abstract

Program Kreativitas Mahasiswa (PKM) ini bertujuan untuk meningkatkan kompetensi digital guru melalui penerapan teknologi Artificial Intelligence (AI) dalam desain website sekolah dan pengembangan media pembelajaran inovatif di SMA Negeri 10 Palembang.  Kegiatan ini dilatarbelakangi oleh kebutuhan guru untuk beradaptasi dengan era pembelajaran digital yang menuntut keahlian, kreativitas, efisiensi, dan interaktivitas tinggi. Metode pengabdian kepada masyarakat menggunakan pendampingan, pelatihan, praktik, diskusi. Melalui pelatihan berbasis praktik, guru dibimbing menggunakan AI dalam pembuatan desain website sekolah yang dinamis serta pengembangan media pembelajaran interaktif seperti pembuatan media pembelajaran aplikasi Gamma, ChatGPT, Wix Studio, Web Flow.  Hasil kegiatan di ukur dan di evauasi menggunakan test pre test dan post test dimana hasil tersebut menunjukkan peningkatan kemampuan guru dalam mengintegrasikan teknologi AI (ChatGPT, Gamma, Wix Studio, Web Flow) pada proses pembelajaran inovatif, kreatif, kolaboratif, dan berorientasi teknologi di  SMA Negeri 10 Palembang. sekolah SMA N 10 Palembang . Program ini berkontribusi nyata dalam mendorong transformasi digital pendidikan serta memperkuat peran guru di SMA Negeri 10 Palembang sebagai inovator dalam lingkungan belajar yang modern dan adaptif yang berbasis teknologi digital.
Pengembangan Sistem Informasi Penomoran Surat Berbasis Web untuk Digitalisasi Administrasi Kelurahan Plaju Darat Bayu Wijaya Putra; Lulu Usni Dwi Putri; Nabila Nabila; Aprillia Syafitri; Ezanovia Ezanovia; Yesinta Florensia; Muhammad Ali Buchari; Hasnan Afif; Rusdi Efendi; Dewi Sartika; Anna Dwi Marjusalinah; Sri Turatmiyah; Abdiansah Abdiansah; Karen Nazzua Putri Pratami
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 2 (2026): Maret 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i2.1028

Abstract

Pengelolaan surat di Kelurahan Plaju Darat sebelumnya masih manual sehingga sering terjadi ketidakteraturan penomoran, keterlambatan pencarian arsip, dan rendahnya akurasi administrasi. Kegiatan pengabdian ini bertujuan menerapkan Sistem Informasi Penomoran Surat berbasis web untuk meningkatkan efisiensi dan akuntabilitas pelayanan. Pendekatan Participatory Action Research (PAR) digunakan melalui tahapan identifikasi masalah, analisis kebutuhan, pengembangan sistem, pengujian, pelatihan, dan evaluasi. Sistem dikembangkan menggunakan CodeIgniter dan MySQL, kemudian diuji dengan Black Box Testing serta User Acceptance Testing. Hasil pre-test menunjukkan rata-rata nilai 51 dan meningkat menjadi 86,13 pada post-test, atau peningkatan 68,88% setelah pelatihan. Evaluasi kepuasan pengguna menunjukkan skor sangat baik, berada pada rentang 4,35–4,65, dengan nilai tertinggi pada efisiensi pencarian arsip dan akurasi penomoran otomatis. Program ini berhasil meningkatkan kompetensi aparatur dan efektivitas administrasi, serta mendukung transformasi digital kelurahan.
Analysis of User Reviews for The Mytelkomsel App Using Naïve Bayes and Random Forest Methods M. Rudi Sanjaya; Annisa Khoiriah; Rahmat Izwan Heroza; Bayu Wijaya Putra
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2220

Abstract

While sentiment analysis of local application reviews predominantly utilizes native Indonesian data, these datasets frequently suffer from colloquial ambiguities and informal structures that degrade classifier performance. This study addresses this gap by implementing a language-filtering mechanism to separate and analyze English and Indonesian user opinions from the MyTelkomsel application, specifically justifying the inclusion of English reviews due to their superior grammatical structure and syntactic consistency, which inherently enhances feature extraction. A systematic methodology was employed, encompassing data collection from the Google Play Store, comprehensive pre-processing (case folding, tokenization, stopword removal, and stemming), and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Evaluated using Naïve Bayes and Random Forest algorithms on 25,000 customer feedbacks, the models were compared across accuracy, precision, recall, and F1-score. The empirical results demonstrated that Random Forest outperformed Naïve Bayes, achieving a higher accuracy of 86.85% compared to 86.36%. This superiority stems from Random Forest’s robust capability to mitigate class imbalance and minimize error distribution across sentiment categories. Ultimately, this approach provides precise, actionable insights into service quality, enabling Telkomsel to effectively distinguish user satisfaction, target operational improvements, and mitigate customer churn.
EVALUASI USER ACCEPTANCE PLATFOR EVALUASI USER ACCEPTANCE PLATFORM TOKOPEDIA MELALUI FRAMEWORK UTAUT3 DAN ANALISIS KEPUTUSAN TOPSIS DENGAN IMPLEMENTASI RSTUDIO Muhammad Ravi Wijayanto Sanjaya; M. Rudi Sanjaya; bayu wijaya putra; Gabriel Ekoputra Hartono Cahyadi; Endang Lestari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.6938

Abstract

This study aims to evaluate user acceptance of the Tokopedia e-commerce platform in Indonesia by applying the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) framework combined with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis implemented in RStudio. Data were collected through an online questionnaire distributed through social networks, generating responses from 200 Indonesian users. Each UTAUT3 construct (Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value, Habit, and Personal Innovativeness) was measured using a five-point Likert scale. The TOPSIS method was then applied to determine the ranking and relative importance of each construct in shaping user acceptance. The results indicate that Effort Expectancy (EE) and Personal Innovativeness (PI) are the most influential factors, reflecting users' appreciation of Tokopedia's ease of use and their openness to adopting the digital platform. Conversely, Habit (HB) showed the lowest score, indicating that routine use is still limited among some users. These findings provide valuable insights for Tokopedia and other digital commerce platforms to improve user engagement and service optimization in Indonesia's rapidly growing online market. The findings of this study suggest that platform development should focus more on promotional programs to improve user habits in using Tokopedia as a primary e-commerce platform.
Implementasi Sistem Informasi Pengaduan Warga Dan Inventaris Barang Pada Kelurahan Plaju Darat Palembang Bayu Wijaya Putra; Niki Ramadhan; M. Ronaldo; Wahyu Prawira; M. Aqeel Gibran; Netty Herawati; Iin Seprina; Endang Lestari Ruskan; Rusdi Efendi; M. Rudi Sanjaya; Apriansyah Putra; Hayqal Nur Akbari
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.989

Abstract

Pengelolaan pengaduan warga dan inventaris barang di Kelurahan Plaju Darat Palembang selama ini masih menghadapi kendala, seperti data yang tersebar di berbagai media, sulit dikategorikan, serta minimnya arsip digital yang terintegrasi. Kondisi ini menyebabkan proses tindak lanjut pengaduan dan pengelolaan inventaris kurang efektif. Program pengabdian kepada masyarakat ini bertujuan untuk mengembangkan sistem informasi berbasis web yang terintegrasi dengan website kelurahan, sehingga dapat meningkatkan efisiensi pelayanan publik. Metode pelaksanaan meliputi wawancara, analisis kebutuhan, perancangan prototype, implementasi dengan framework CodeIgniter, pengujian blackbox dan keamanan sistem, serta sosialisasi kepada perangkat kelurahan dan warga. Hasil kegiatan menunjukkan bahwa sistem informasi pengaduan warga dan inventaris barang berhasil diimplementasikan dan diakses melalui domain kelurahanplajudarat.id. Evaluasi melalui kuesioner kepada 62 peserta menunjukkan tingkat penerimaan dan kepuasan yang sangat baik (85,01%). Program ini tidak hanya meningkatkan efektivitas pengelolaan data, tetapi juga mendorong partisipasi aktif masyarakat dalam menyampaikan pengaduan secara mandiri. Ke depan, sistem ini diharapkan menjadi model berkelanjutan yang dapat direplikasi di kelurahan lain untuk mendukung pelayanan publik berbasis teknologi informasi.
Analysis of User Reviews for The Mytelkomsel App Using Naïve Bayes and Random Forest Methods M. Rudi Sanjaya; Annisa Khoiriah; Rahmat Izwan Heroza; Bayu Wijaya Putra
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2220

Abstract

While sentiment analysis of local application reviews predominantly utilizes native Indonesian data, these datasets frequently suffer from colloquial ambiguities and informal structures that degrade classifier performance. This study addresses this gap by implementing a language-filtering mechanism to separate and analyze English and Indonesian user opinions from the MyTelkomsel application, specifically justifying the inclusion of English reviews due to their superior grammatical structure and syntactic consistency, which inherently enhances feature extraction. A systematic methodology was employed, encompassing data collection from the Google Play Store, comprehensive pre-processing (case folding, tokenization, stopword removal, and stemming), and Term Frequency-Inverse Document Frequency (TF-IDF) vectorization. Evaluated using Naïve Bayes and Random Forest algorithms on 25,000 customer feedbacks, the models were compared across accuracy, precision, recall, and F1-score. The empirical results demonstrated that Random Forest outperformed Naïve Bayes, achieving a higher accuracy of 86.85% compared to 86.36%. This superiority stems from Random Forest’s robust capability to mitigate class imbalance and minimize error distribution across sentiment categories. Ultimately, this approach provides precise, actionable insights into service quality, enabling Telkomsel to effectively distinguish user satisfaction, target operational improvements, and mitigate customer churn.
Implementation of the TOPSIS Method and Usability Method for Marketplace Application Based on Data Visualization M. Rudi Sanjaya; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/2w11eh43

Abstract

The rapid development of technology in online marketplaces has significantly influenced consumer shopping behavior, with applications such as Shopee, Tokopedia, Zalora, and Bukalapak leveraging advances in information and communication technology to provide faster and more efficient shopping experiences. However, frequent system disruptions often affect user satisfaction, emphasizing the need for improved information systems. This study, conducted in South Sumatra with 334 respondents, utilized questionnaire data that were processed and visualized using R, where decision-support metrics were analyzed through the TOPSIS method with equal weights and a normalized respondent data matrix calculate_topsis  function(data, weights = c(0.2, 0.2, 0.2, 0.2, 0.2)), normalized_matrix as.matrix(data responden), and the methodology integrated both the usability approach and the TOPSIS method within an R Shiny environment. The findings show that data visualization effectively applied the usability and TOPSIS methods, with usability evaluation results indicating average scores of Memorability (4.263), Satisfaction (4.186), Learnability (4.146), Efficiency (4.101), and Low Error Rate (3.749), where Memorability achieved the highest score, while the TOPSIS results highlighted Learnability as the most significant factor.
Optimization of Sentiment Analysis on Tokopedia User Reviews Using Gridsearchcv and Smote with Machine Learning Algorithms Athallah Yasyfi Imran; M. Rudi Sanjaya; Bayu Wijaya Putra; Gabriel Ekoputra Hartono Cahyadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 3 (2025): November
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5ax8km80

Abstract

Understanding user sentiment from e-commerce reviews is essential for platform improvement and business strategy. This study compares three machine learning algorithms—Logistic Regression, Random Forest, and XGBoost—for sentiment classification of Indonesian-language Tokopedia reviews. A dataset of 6,822 user reviews was preprocessed through tokenization, stopword removal, and TF-IDF vectorization. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training set. Models were evaluated using accuracy, precision, recall, and F1-score. Results demonstrate that Random Forest achieved the highest accuracy at 86.86%, followed by Logistic Regression at 84.86%, and XGBoost at 82.60%. The application of SMOTE significantly improved classification performance across all models, particularly for minority sentiment classes. These findings indicate that tree-based ensemble methods, especially Random Forest, are effective for sentiment analysis in imbalanced e-commerce datasets. This research provides practical insights for e-commerce platforms to implement automated sentiment monitoring systems, enabling faster response to customer feedback and targeted service improvements. However, the study is limited to Tokopedia reviews and may not generalize to other platforms or languages. Future work should explore deep learning approaches and cross-platform validation to enhance model robustness.
Co-Authors . Apriansyah A. Noviar Satria Mukti AA Sudharmawan, AA Abdiansah, Abdiansah Ade Iriani Sapitri Afif, Hasnan Ali Ibrahim Allsela Meiriza, Allsela Anggun Islami Anna Dwi Marjusalinah Annisa Darmawahyuni Apriansyah Putra Apriansyah Putra Aprillia Syafitri Ariansyah Saputra Ariansyah Saputra Athallah Yasyfi Imran Badia Perizade Bambang Tutuko Bambang Tutuko Buchari, Muhammad Ali Danendra, Muhammad Archi Daffa Darmawahyuni, Annisa Dedy Kurniawan Dedy Syamsuar Dewi Sartika DEWI SARTIKA Dwi Rosa Indah Endang Lestari Endang Lestari Ruskan Ezanovia Ezanovia Fahreza, Irvan Faiq, Al Iksan Faizah, Ovie Nur Firdaus Firdaus Firdaus Firdaus Firdaus Florensia, Yesinta Gabriel Ekoputra Hartono Cahyadi Gabriel Ekoputra Hartono Cahyadi Hardini Novianti Hasnan Afif Hayqal Nur Akbari Iin Seprina Imran, Athallah Yasyfi Irvan Fahreza Islami, Anggun Ismail, Ahmad Arrijal Jambak, Muhammad Ihsan Junia Kurniati, Junia Karen Nazzua Putri Pratami Kesuma, Lucky Indra Khoiriah, Annisa Kurniawati, Junia Lulu Usni Dwi Putri M Rudi Sanjaya M. Ali Buchari M. Aqeel Gibran M. Ronaldo M. Rudi Sanjaya M. Rudi Sanjaya M.Rudi Sanjaya M.Yusuf Al-Hakim Syah Maharani, Masayu Nadila Marjusalinah, Anna D. Masayu Nadila Maharani Maula, Nurly Izzatul Meylani Utari Mira Afrina Muhamad Akbar Muhammad Fachrurrozi Muhammad Fachrurrozi Muhammad Ichsan Hadjri Muhammad Naufal Rachmatullah Muhammad Rafie Chautie Muhammad Ravi Wijayanto Sanjaya Nabila Nabila Netty Herawati Niki Ramadhan Oktadini, Nabila Rizky Pacu Putra Purwita Sari Purwita Sari Purwita Sari, Purwita Rachmad, M. Ichsan Farel Rahmat Fadli Isnanto Rahmat Izwan Heroza Rudi Sanjaya Rusdi Efendi Rusdi Efendi Sanjaya, M Rudi Sanjaya Sanjaya, M. Rudi Sapitri, Ade Iriani Saputra, Ariansyah Saputri, Nyimas Dewi Murnila Seprina, Iin Sevtiyuni, Putri Eka Siti Nurmaini Sri Turatmiyah Suci Dwi Lestari Suci Dwi Lestari Wahyu Prawira Wijayanto, Muhammad Ravi Yesinta Florensia