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All Journal Jupiter JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi ICON-CSE Annual Research Seminar Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Sisfokom (Sistem Informasi dan Komputer) INTECOMS: Journal of Information Technology and Computer Science Jurnal ULTIMA InfoSys Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Riset Informatika JOISIE (Journal Of Information Systems And Informatics Engineering) CCIT (Creative Communication and Innovative Technology) Journal Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Journal of Information Systems and Informatics Jurnal Tekinkom (Teknik Informasi dan Komputer) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Journal of Applied Science, Engineering, Technology, and Education Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Generic Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Algoritma Malcom: Indonesian Journal of Machine Learning and Computer Science Journal Of Artificial Intelligence And Software Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Dwi Rosa Indah
Universitas Sriwijaya
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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.
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.
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.
HYBRID FINE-TUNING INDOBERT DAN ENSEMBLE TF-IDF LOGISTIC REGRESSION UNTUK ANALISIS SENTIMEN ULASAN APLIKASI ZALORA : HYBRID FINE-TUNING INDOBERT DAN ENSEMBLE TF-IDF LOGISTIC REGRESSION UNTUK ANALISIS SENTIMEN ULASAN APLIKASI ZALORA Al Ikhsan Faiq; M. Rudi Sanjaya Sanjaya; Dwi Rosa Indah; Endang Lestari Ruskan
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.v11i1.6924

Abstract

Sentiment analysis of e-commerce app reviews is essential to capture user perception and guide service improvements. However, review datasets are typically imbalanced—especially for the neutral class—making accuracy-only evaluation inadequate. This study proposes a hybrid approach that combines IndoBERT fine-tuning with a TF–IDF + logistic regression ensemble, augmented with probability calibration via temperature scaling, a dedicated neutral threshold rule, and a rating-based prior for low-confidence predictions. To avoid data leakage, the dataset is first split using stratified sampling into 72% training, 8% validation, and 20% testing; oversampling is applied only on the training split. Training uses label smoothing and early stopping (patience=2). The best validation configuration achieves macro-F1 of 0.8158 (T=0.941; α=0.70; t_neu=0.55; γ=0.10; τ=0.60). On the test set, the proposed model reaches 86.77% accuracy, 81.71% macro-F1, and 86.76% weighted-F1. An ablation study shows consistent gains from the TF–IDF+LR baseline to the full hybrid model, with the most notable improvement in the neutral class.
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.
COMPARATIVE STUDY OF MACHINE LEARNING MODELS FOR CLASSIFYING SENTIMENT IN GOOGLE GEMINI APP REVIEWS Muhammad Dzaky Alifayoezra; Ali Ibrahim; Yadi Utama; 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.v11i1.7201

Abstract

The rising number of reviews for the Google Gemini app on the Google Play Store reflects diverse user opinions regarding the performance of this AI-based application. To identify sentiment patterns, this research conducted a comparative study of three classification algorithms—Support Vector Machine (SVM), Naive Bayes, and Random Forest—using 14,479 raw reviews collected through scraping. These reviews then went through several preprocessing steps, including case folding, text cleaning, tokenization, normalization, stopword removal, and stemming. After being labeled based on ratings, the dataset formed a highly imbalanced class distribution, consisting of 11,252 positive reviews and 1,571 negative reviews, and was subsequently split using the Hold-Out method with an 80% training and 20% testing ratio. Evaluation using the Confusion Matrix along with accuracy, precision, recall, and F1-score metrics showed that SVM achieved the best performance, producing 91% accuracy, 93% precision, 97% recall, and a 95% F1-score, outperforming Random Forest and Naïve Bayes, which each reached 90% accuracy. Overall, these results highlight SVM as the most effective algorithm for classifying sentiment in Google Gemini reviews, while the predominance of positive feedback suggests a relatively high level of user satisfaction, although model performance on the minority (negative) class remains a challenge due to data imbalance.
Implementasi Algoritma K-Nearest Neighbors dalam Analisis Sentimen Ulasan Aplikasi Bank Aladin: Implementation of the K-Nearest Neighbors Algorithm for Sentiment Analysis on Aladin Bank Application Reviews Putri, Taniya Raisha Dwiva; Sanjaya, M. Rudi; Firdaus, MGS Afriyan; Indah, Dwi Rosa
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2633

Abstract

Digitalisasi layanan Aladin Bank Syariah melahirkan variasi ulasan di Google Play Store yang mencakup aspek kepraktisan hingga permasalahan teknis aplikasi. Guna memahami polaritas opini tersebut, penelitian ini melakukan evaluasi sentimen pengguna dengan memanfaatkan metode klasifikasi K-Nearest Neighbors (KNN). Proses analisis melibatkan pengelompokan ulasan ke dalam kelas positif, netral, dan negatif menggunakan 80% data hasil scraping, yang diproses melalui pembatasan 6.000 fitur pembobotan TF-IDF. Pengujian model mencatatkan tingkat akurasi sebesar 86,71%, di mana algoritma menunjukkan keunggulan signifikan dalam mengidentifikasi sentimen positif (F1-score 0,93) dan berkinerja cukup baik pada sentimen negatif (F1-score 0,68). Walaupun pengenalan terhadap kelas netral masih belum maksimal (F1-score 0.08), perolehan nilai rata-rata keseluruhan adalah 0.86, yang mengindikasikan efektivitas model secara umum. Temuan riset ini dapat dioptimalkan oleh manajemen bank sebagai landasan evaluasi untuk menyempurnakan kualitas layanan perbankan digital mereka.
The Influence of Experience-Centric IT Governance on Digital Ethics Perception in Social Commerce Naretha Kawadha Pasemah Gumay; Mira Afrina; Dwi Rosa Indah; Winda Kurnia Sari; Widya Sartika
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5750

Abstract

Co-Authors Afif, Hasnan Afriyan Firdaus Ahmad Fali Oklilas Akbari, Rahmat Alif Al Ikhsan Faiq Alghifari, M Yasir Ali Ibrahim Ali Ibrahim Alif, Rahmat Aliyah, Raden Ayu Alfirah Allsela Meiriza, Allsela Alvionita, Vinna Ananda Khoirunnisa Andhika Setiadi Anna Dwi Marjusalinah Annisa Khoiriah Apriansyah Putra Ardina Ariani Ari Wedhasmara Ariani, Ardina Ariansyah Saputra Arrijal Ismail, Ahmad Arwin Permata Putra Astriani, Yulia Athalina, Ghita Athalina, Githa Bayu Wijaya Putra Cherliana Choirunnisa Qonitah Corriny, Nety Damayanti, Indri Dwi Danendra, Muhammad Archi Daffa Danny Matthew Saputra dedi kurniawan Dedy Kurniawan Devi Indra Meytri Devi Karlina Dhilarofii Russandwi, Saras Endang Lestari Endang Lestari Ruskam Endang Lestari Ruskan Endang Lestari Ruskan Ermatita - Errissya Rasywir Faiq, Al Iksan Fatimah Azzahrah Fatinah, Fitriasari Felicia, Yohana Firdaus, Masagus Afriyan Firman Wijaya Fitri Wulandari Gumay, Naretha Kawadha Pasema Gumay, Naretha Kawadha Pasemah Hardini Novianti Haristina Putri, Maulida Harlili Harlili Hasbiallah, Muhammad Jidan Huda Ubaya Huda Ubaya Ikhwan Amalsyah Imran, Athallah Yasyfi Indri Dwi Damayanti Izzatul Maula, Nurly Jaidan Jauhari Jayawarsa, A.A. Ketut Karlina, Devi Ken Dhita Tania Liana Andini, Rahmah Lufiah, Fara Lulu Monica Sari M HUSNI SYAHBANI, M HUSNI M Yasir Alghifari M. Rudi Sanjaya M. Rudi Sanjaya M. Rudi Sanjaya Sanjaya MARIA BINTANG Megah Mulya Merlin, Chalia Meylani Utari Mgs Afriyan Firdaus Mgs Afriyan Firdaus MGS. Afriayan Firdaus Miftahurrohmah Haque Mira Afrina Moses Rinaldy Muhammad Dzaky Alifayoezra Muhammad Fandra Eka Pratama Muhammad Raihan Udda Rahmany Mukhlis Febriady Mutia Farahdilla Naretha Kawadha Pasemah Gumay Naretha Kawadha Pasemah Gumay Novitas Sari Nugroho, Doni Tri Nurfadillah, Nadya Oktadini, Nabila Rizky Pacu Putra Perdianza, Muhammad Egi Pratama, Muhammad Fandra Eka Purwita Sari Purwita Sari, Purwita Putra, Julian Putri Eka Sevtiyuni Putri Ratna Sari, Putri Ratna Putri, Indah Arsita Putri, Taniya Raisha Dwiva Rachmad, M. Ichsan Farel Rachmad, Muhammad Ichsan Farel Rafliandi Ardana Rahma Ardhia Cahyani Rahma Destriani Rahma, Syabilla Mutia Rahman, Fachri Auliya Rahmat Alif Akbari Rahmat Izwan Heroza Rahmayuni, Septa Ramadhini, Reffina Ricy Firnando Riski, M. Rido Rizka Dhini Rizka Dhini Kurnia Royan Dwi Saputra Rusdi Rivaldo Sabrina, Dea Fitri Samsuryadi Samsuryadi Sanjaya , M. Rudi Sanjaya, M Rudi Sanjaya, M. Rudi Saputra, Danny Matthew Saras Dhilarofii Russandwi Seprina, Iin Septamuyassar, Nawfal Siti Nurhanifah Syahbani, M. Husni Syahnel, Ahmad Hidayat Tammam, Bimmo Fathin Thuraya, Zafira Trimaysella, Anne Tsabitah, Laila Vinna Alvionita Widya Sartika Wijaya Putra, Bayu Wijaya, Firman Wijayanto, Muhammad Ravi Winda Kurnia Sari Windy Indrianti Wiwik Handayani Yadi Utama Yadi Utama Yanto, Dimas Hadi Yudha Pratomo Yunita Yunita Zahirah, Nabilah