Article Per Year (5 Year)
p-Index From 2021 - 2026
2.627
P-Index
This Author published in this journals
All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal ULTIMA InfoSys Jurnal Tekinkom (Teknik Informasi dan Komputer) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Generic Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Jurnal Algoritma Jurnal Testing dan Implementasi Sistem Informasi INOVTEK Polbeng - Seri Informatika
M. Rudi Sanjaya
Universitas Sriwijaya
Religion Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
PENERAPAN METODE AHP DAN SMART DALAM PEMILIHAN PROVIDER WIFI DI KOTA PALEMBANG Michael Darwin; M. Rudi Sanjaya; Dedy Kurniawan; Rizka Dhini Kurnia
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.v11i1.6910

Abstract

The demand for fast and stable internet services continues to grow in line with the advancement of digital technology in Palembang City. The wide range of Wi-Fi service providers (Internet Service Providers) with varying characteristics makes it difficult for users to objectively determine the best option. This study aims to apply the Analytic Hierarchy Process (AHP) and Simple Multi Attribute Rating Technique (SMART) methods to support decision-making in selecting the best Wi-Fi provider in Palembang City. The AHP method is used to determine the priority weights of each criterion, including price, internet speed, connection stability, and after-sales service. Meanwhile, the SMART method is used to calculate the preference value of each provider alternative based on the obtained weights. The results indicate that internet speed is the most influential criterion in users’ decision-making, followed by stability, price, and after-sales service. Based on the calculation results, Biznet Home ranked highest with a score of 0.966 (on a maximum scale of 1.0) and is therefore recommended as the best Wi-Fi provider in Palembang City. The integration of AHP and SMART methods proves effective in producing systematic, objective, and easily interpretable decision outcomes for users.
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.
ANALISIS EVALUASI TINGKAT LITERASI KEAMANAN CYBER PENGGUNAAN MEDIA SOSIAL (STUDI KASUS SISWA SMK BUKIT ASAM) Ikhwan Amalsyah; M. Rudi Sanjaya; Endang Lestari Ruskan; Dwi Rosa Indah
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.v11i1.6963

Abstract

This study aims to analyze the level of cyber security literacy among students of SMK Bukit Asam and classify it using the Random Forest algorithm. A quantitative approach was employed with a questionnaire covering four key indicators: knowledge, attitude, behavior, and overall cyber security literacy. A total of 192 students participated as respondents. The results show that 53.13% of students fall into the high literacy category, 35.94% into the medium category, and 10.94% into the low category. The Random Forest model achieved an accuracy of 97.44%, with SI2 and SI4 identified as the most influential features. Beyond describing the students’ generally good level of cyber security literacy, the use of Random Forest also provides an important methodological contribution by revealing attitude-related indicators as the main determining factors in the classification. These findings offer a clearer foundation for designing more targeted and effective digital security education programs in schools.
ANALISIS SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN BANTUAN SOSIAL MENGGUNAKAN METODE WEIGHTED PRODUCT Lulu Monica Sari; M. Rudi Sanjaya; Dedy Kurniawan; Dwi Rosa Indah
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.v11i1.6966

Abstract

Social assistance is one of the government’s efforts to reduce social inequality and achieve social justice for all Indonesian citizens. However, limited budget allocations necessitate the development of an objective selection system to determine eligible recipients. This study aims to apply the Weighted Product (WP) method in analyzing a Decision Support System for determining social assistance recipients in Tanjung Raja Village. Five criteria were used in this study, namely income amount, number of dependents, housing condition, employment status, and asset ownership. The data were obtained through questionnaires that were validated using the Content Validity Index (CVI), yielding a CVI value of 1, which indicates full expert agreement regarding the suitability of the criteria. The results show that the income criterion (C1) has the highest weight of 0.2461, followed by the number of dependents (C2) at 0.1936, employment status (C4) at 0.1907, housing condition (C3) at 0.1897, and asset ownership (C5) with the lowest weight of 0.1797. In the ranking results, alternative A5 obtained the highest vector V value of 0.2076, followed by A3 (0.2061), A4 (0.2024), A1 (0.1931), and A2 (0.1905), indicating that candidate A5 is the most eligible to receive social assistance. The strong validity of the criteria (CVI = 1) and the measurable ranking results demonstrate that the application of the Weighted Product method effectively supports an objective, fast, and accurate decision-making process.
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.
Comparison of SVM and Naive Bayes Algorithms in Sentiment Analysis of User Reviews on Bukalapak M Yasir Alghifari; M. Rudi Sanjaya; Dwi Rosa Indah; Endang Lestari Ruskan
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/dqhpkb12

Abstract

Indonesia’s rapid e-commerce growth has produced a vast volume of user reviews, yet their use for insight extraction remains limited—particularly for the Bukalapak platform. This study compares the performance of Naïve Bayes and Support Vector Machine for sentiment classification on 10,000 Bukalapak reviews. The workflow includes text preprocessing (cleaning, case folding, tokenization, stopword removal, and stemming) and feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF; max_features = 10,000). Evaluation employs 10-fold cross-validation with accuracy, precision, recall, and F1-score, complemented by a paired t-test for significance. Results show SVM outperforming NB (accuracy 84.48% vs. 83.96%; F1 0.8253 vs. 0.8205) with better consistency (standard deviation ±1.08% vs. ±1.24%). The t-test confirms a significant difference (p = 0.019), with SVM’s advantage most evident for the negative class (precision 0.80 vs. 0.78). Both models underperform on the neutral class due to severe class imbalance. These findings provide empirical evidence for algorithm selection in Indonesian e-commerce sentiment analysis and open avenues for future research using deep learning and class-imbalance handling techniques.
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 Ahmad Rifai Ali Ibrahim Anadya Nisrina Salsabila Ananda Khoirunnisa Annisa Khoiriah Annisa Khoiriah Apriansyah Putra Ardina Ariani Ari Wedhasmara Athallah Yasyfi Imran Bayu Wijaya Putra Cherliana Dedy Kurniawan Dwi Rosa Indah Endang Lestari Endang Lestari Ruskan Endang Lestari Ruskan Gabriel Ekoputra Hartono Cahyadi Hayqal Nur Akbari Iin Seprina Ikhwan Amalsyah Jaidan Jauhari Lulu Monica Sari M Yasir Alghifari M. Aqeel Gibran M. Husni Syahbani M. Ronaldo Mgs Afriyan Firdaus Michael Darwin Muhammad Ravi Wijayanto Sanjaya Mukhlis Febriady Nabilah Putri Maharani Naretha Kawadha Pasemah Gumay Netty Herawati Niki Ramadhan Rahma Ardhia Cahyani Rahmat Izwan Heroza Rizka Dhini Kurnia Rusdi Efendi Sutarno Sutarno Wahyu Prawira