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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 Algoritma Jurnal Testing dan Implementasi Sistem Informasi
M. Rudi Sanjaya
Universitas Sriwijaya
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Health Professions Public Health

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Analisis Usability Aplikasi Pengolahan Data Berbasis Web Pada Perpustakaan Sekolah Berdasarkan Permodelan Nielsen Dedy Kurniawan; M. Rudi Sanjaya; Ahmad Rifai; Sutarno Sutarno
Generic Vol 13 No 2 (2021): Vol 13, No 2 (2021)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

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

Abstract

Web-based data processing applications have an important role to avoid human error, especially in school libraries. However, in developing a system, consideration of the level of user satisfaction is needed. So the purpose of this study is to analyze the usability of web-based data processing applications in school libraries based on Nielsen's modeling. The method of data collection was performed through filling out questionnaires by 51 students of Public Junior High School 2 Jejawi, OKI Regency. The questionnaire consists of 15 questions using a 4-point Likert scale. The results obtained indicate that the data are valid and reliable. In addition, the web-based data processing application shows that the five usability attributes of Nielsen, namely learning ability, efficiency, memories, few error, and satisfaction have good results, with an average usability score of 80.4. In conclusion, web-based processing applications are acceptable
PERANCANGAN APLIKASI INVENTARIS BARANG BERBASIS WEB DENGAN PENGUJIAN MENGGUNAKAN PEMODELAN NIELSEN M. Rudi Sanjaya; Jaidan Jauhari; Dedy Kurniawan
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 4 No 1 (2021): Jurnal Tekinkom
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v4i1.218

Abstract

The development of technology is increasingly fast and developing, so the need for a technology is one of the webs-based software technology, with this web being used to make difficult work easy, the purpose of this research is to design and build an inventory application for Islamic boarding schools Miftahul Jannad. in the survey village by applying the Nielsen modeling. The research method used is the method of observation, interviews and literature studies, the approach method is the Nielsen modeling method, where Nielsen's modeling consists of learnability, efficiency, memorability, few errors, satisfaction, the results of Nielsen's modeling are obtained to test the validity of learnability with counted r counts. 0.868, 0.845, 0.736, 0.619, Efficiency with r count 0.813, 0.461.0.636, 0.557, for Memorability is 0.773, 0.830, 0.814, 0.578 for memorability with the calculated r value of 0.773, 0.830, 0.814, 0.578, then for few errors obtained a value of 0.621, 0.765, 0.704, 0.653, then for satisfaction with a value of 0.732, 0.559, 0.662, 0.652, which means that with the calculated r value is more r table, where the r table is obtained 0.320, the data in the Nielsen modeling is valid, while for the reliability test it is stated that the results of the Nielsen modeling test are reliable.
PELATIHAN PEMBUATAN DESAIN WEB DAN MEDIA PEMBELAJARAN INOVATIF M. Rudi Sanjaya; Dwi Rosa Indah; Annisa Khoiriah
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025): Volume 6 No 3 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i3.44990

Abstract

Pengetahuan, pemahaman tentang sebuah tren teknologi masih kurang, sehingga perlunya pemahaman tren teknologi, supaya memudahkan proses dalam pembelajaran dengan pembelajaran yang inovatif, Teknologi adalah hasil dari penerapan ilmu pengetahuan untuk menciptakan alat, sistem, dan proses yang bertujuan mempermudah kehidupan manusia serta meningkatkan efisiensi dalam berbagai aspek. Dengan perkembangan yang pesat, teknologi telah menjadi fondasi utama dalam mendorong inovasi terutama dalam bidang Pendidikan, Adapun tujuan dari kegiatan ini menghasilkan atau dapat membuat desain web dan media pembelajaran yang inovatif,  metode pengabdian kepada Masyarakat dengan menggunakan metode pendampingan, sosialisasi. Adapun hasil kegiatan pengabdian pengabdian kepada masyarakat berupa pembuatan desain web dan pembuatan media pembelajaran inovatif Dimana 28 responden jika n-2 maka hasil 26 adalah 0.3739 dibulat menjadi 0.374 dengan signifikan 5%, hasil pengujian validitasnya adalah  0.561, 0.571, 0.519, 0.793, 0.646, 0.722, 0.422, 0,831, 0.570, 0.771 menunjukkan di atas 0.30 maka di nyatakan valid, sedangkan pada uji realibilitas di atas 0.70 maka dinyatakan reliabel.
PELATIHAN CANVA AI UNTUK MENINGKATKAN KUALITAS GURU DI SMA NEGERI 10 PALEMBANG M. Rudi Sanjaya; Annisa Khoiriah; Dwi Rosa Indah; Mgs. Afriyan Firdaus
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 7 No. 1 (2026): Vol. 7 No. 1 Tahun 2026
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v7i1.54247

Abstract

Perkembangan teknologi kecerdasan buatan Artificial Intelligence telah membawa perubahan signifikan dalam dunia pendidikan, khususnya dalam pengembangan media pembelajaran digital. Namun, masih banyak guru yang belum memanfaatkan teknologi AI secara optimal dalam proses pembelajaran. Program Kreativitas Mahasiswa (PKM) ini bertujuan untuk meningkatkan kualitas dan kompetensi guru di SMA Negeri 10 Palembang melalui pelatihan pemanfaatan Canva AI sebagai alat bantu pembuatan media pembelajaran yang inovatif, interaktif, dan efektif. Metode pelaksanaan kegiatan meliputi tahap persiapan, pelatihan, pendampingan, serta evaluasi. Pelatihan dilaksanakan secara langsung dengan memberikan materi pengenalan Canva AI, praktik pembuatan desain pembelajaran berbasis AI, serta pendampingan dalam penerapan hasil desain ke dalam kegiatan belajar mengajar. Evaluasi dilakukan melalui observasi, kuesioner, dan analisis hasil karya guru sebelum dan sesudah pelatihan. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan guru dalam memanfaatkan Canva AI, yang ditandai dengan meningkatnya kualitas media pembelajaran yang lebih kreatif dan menarik. Dengan demikian, pelatihan Canva AI diharapkan mampu mendukung peningkatan profesionalisme guru serta berkontribusi pada terciptanya proses pembelajaran yang lebih adaptif terhadap perkembangan teknologi digital.
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.
Penerapan Random Forest dan XGBoost untuk Analisis Sentimen pada Ulasan Aplikasi M-Pajak Cherliana; M. Rudi Sanjaya; Dwi Rosa Indah; Dedy Kurniawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3370

Abstract

This study aims to evaluate the quality of digital tax services by analyzing the sentiment expressed in user reviews of the M-Pajak app. A dataset of 6,829 reviews was classified into negative, neutral, and positive sentiment, and the study tested the performance of the Random Forest and XGBoost algorithms. Although the test results showed high accuracy rates of 85.21% in the hold-out validation scheme and 90.14% in stratified k-fold cross-validation, an in-depth evaluation using a confusion matrix revealed significant model bias toward the majority class. Key findings indicate that these accuracy figures are misleading because both models completely failed to classify the neutral class, yielding extremely low F1-scores (0.00–0.11). This phenomenon confirms that the primary issue lies not in algorithm selection, but in the extreme data distribution imbalance and the ambiguity of rating-based labeling. The scientific contribution of this research lies in demonstrating that the evaluation of sentiment classification systems must go beyond conventional accuracy metrics. By prioritizing performance stability across each class, the resulting system is expected to provide fairer and more objective evaluation results for public data.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi m.tix – XXI Di Google Play Store Menggunakan Metode Decision Tree Dan Support Vector Machine (SVM) Rahma Ardhia Cahyani; M. Rudi Sanjaya; Dwi Rosa Indah; Dedy Kurniawan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3371

Abstract

This study aims to analyze review sentiment and compare the performance of the Decision Tree and Support Vector Machine (SVM) algorithms on a dataset of 14,000 reviews from the m.tix – XXI app, which were classified as 57.4% positive, 33.8% negative, and 8.8% neutral. Through the pre-processing stage, 200 data points were deemed invalid, leaving 13,800 data points suitable for analysis. The dataset was then split into two categories: 80% training data (11,040 reviews) and 20% testing data (2,760 reviews), to support more accurate model performance measurement. The main contribution of this study lies in identifying the advantages of SVM in handling review data, with evaluation results showing that SVM achieved an accuracy of 86%. This is evidenced by the significant superiority of SVM’s F1-score across all categories, particularly for positive sentiment (0.93), negative sentiment (0.80), and neutral sentiment (0.08) compared to the Decision Tree’s accuracy of 83%, with F1-scores of 0.92 for positive sentiment, 0.73 for negative sentiment, and 0.03 for neutral sentiment. This research can be utilized by m.tix-XXI management as a foundation for evaluating and improving the quality of the m.tix-XXI application’s services.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi M2U ID Menggunakan Metode Random Forest dan Support Vector Machine (SVM) Anadya Nisrina Salsabila; M. Rudi Sanjaya; Ali Ibrahim; Endang Lestari Ruskan
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3375

Abstract

The M2U ID app is the primary digital banking platform of PT Bank Maybank Indonesia Tbk, serving as a crucial tool for supporting customers’ online transactions. The objective of this study is to analyze user review sentiment on the Google Play Store by comparing the performance of the Random Forest and Support Vector Machine (SVM) algorithms. A total of 16,270 review data points were collected via web scraping and processed through preprocessing stages and feature extraction using N-Gram-based TF-IDF with Chi-Square feature selection using the SelectKBest approach. Given the significant imbalance in data distribution, this study applied class weighting techniques as well as hyperparameter optimization using Grid Search and 5-Fold Cross-Validation. Testing on 3,254 test data points indicated that SVM performed more optimally with an accuracy rate of 82% and an F1-Macro score of 0.6344, compared to Random Forest, which yielded an accuracy of 73% and an F1-Macro score of 0.5832. The main contribution of this study is an in-depth analysis of classification errors in the minority (neutral) class, which has a low F1-score (0.16–0.17). The results of the error analysis show that the model’s limitations are caused by the ambiguity of technical features and the overlap of vocabulary in reviews with minimal emotional content.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi Kitabisa: Donasi & Zakat Menggunakan Metode Support Vector Machine (SVM) dan Naive Bayes Nabilah Putri Maharani; M. Rudi Sanjaya; Ali Ibrahim; M. Husni Syahbani
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3376

Abstract

The rapid advancement of digital technology has spurred the emergence of online philanthropy platforms like Kitabisa, which collect a large volume of user reviews. Reviews on the Google Play Store reflect both satisfaction levels and service issues, but their unstructured nature makes manual analysis difficult. This study evaluates user sentiment on the Kitabisa platform by comparing the Support Vector Machine (SVM) and Naive Bayes models. A dataset of 11,887 reviews was processed through preprocessing and word weighting using the TF-IDF approach. The evaluation results show that the Support Vector Machine outperformed Naive Bayes with an accuracy of 84.05% and an F1-score of 0.93, while Naive Bayes achieved an accuracy of 81.73% and an F1-score of 0.92. Theoretically, this study reinforces the literature regarding the superiority of Support Vector Machines for unstructured text data. Additionally, the results of this research produce an automated evaluation framework that can be used by application developers as a basis for improving service quality in accordance with user perceptions accurately.
E-Service Quality Analysis of the MyTelkomsel Application Using CSI, IPA, and PGCV Ananda Khoirunnisa; Dwi Rosa Indah; Ardina Ariani; Ari Wedhasmara; Naretha Kawadha Pasemah Gumay; M. Rudi Sanjaya; Mukhlis Febriady
ULTIMA InfoSys Vol 17 No 1 (2026): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/si.v17i1.4497

Abstract

This study evaluates the e-service quality of the MyTelkomsel application in response to ongoing user concerns, particularly slow service response and the limited effectiveness of the automated support system. A structured questionnaire based on the five SERVQUAL dimensions was used and demonstrated strong validity and reliability. Data were collected from one hundred users in Java and Sumatera. The analysis combined three methods: the Customer Satisfaction Index, Importance Performance Analysis, and Potential Gain in Customer Value. The findings show that the application achieved a Customer Satisfaction Index score of 67.75 percent, indicating that users are generally satisfied but expect further improvement. The Importance Performance Analysis recorded a suitability level of 74.54 percent, with several attributes placed in the high-importance low-performance quadrant, including login security, chatbot speed, complaint handling, and personal data protection. The Potential Gain in Customer Value results indicate that chatbot-related attributes and transaction reliability provide the highest potential for increasing customer value. Overall, the study highlights specific service attributes that require priority enhancement to strengthen user satisfaction and service quality.
Co-Authors Ahmad Rifai Ali Ibrahim Anadya Nisrina Salsabila Ananda Khoirunnisa Annisa Khoiriah Annisa Khoiriah Ardina Ariani Ari Wedhasmara Bayu Wijaya Putra Cherliana Dedy Kurniawan Dwi Rosa Indah Endang Lestari Endang Lestari Ruskan Gabriel Ekoputra Hartono Cahyadi Ikhwan Amalsyah Jaidan Jauhari Lulu Monica Sari M. Husni Syahbani Mgs Afriyan Firdaus Michael Darwin Muhammad Ravi Wijayanto Sanjaya Mukhlis Febriady Nabilah Putri Maharani Naretha Kawadha Pasemah Gumay Rahma Ardhia Cahyani Rahmat Izwan Heroza Rizka Dhini Kurnia Sutarno Sutarno