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All Journal Teknika Jurnal Sains dan Teknologi Jurnal Simetris JSI: Jurnal Sistem Informasi (E-Journal) International Journal of Advances in Intelligent Informatics IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi WIDYA LAKSANA Jurnal Informatika Kaputama (JIK) EVOLUSI : Jurnal Sains dan Manajemen JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Ilmu Teknik dan Komputer Jurnal Tekinkom (Teknik Informasi dan Komputer) Infotek : Jurnal Informatika dan Teknologi Jurnal Media Informatika JUSTIAN - Jurnal Sistem Informasi Akuntansi J-Intech (Journal of Information and Technology) Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Ilmu Komputer Dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Abdimas Le Mujtamak Mestaka: Jurnal Pengabdian Kepada Masyarakat TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Dedikasi Saintek Jurnal Pengabdian Masyarakat SOROT: Jurnal Pengabdian Kepada Masyarakat Madani: Jurnal Pengabdian Masyarakat dan Kewirausahaan Jurnal Komputer dan Teknologi (JUKOMTEK) DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Indonesian Community Service Journal of Computer Science (IndoComs) Jurnal Nasional Komputasi dan Teknologi Informasi
Riski Annisa
Universitas Bina Sarana Informatika
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Pemanfaatan Interactive Flat Panel dengan Mewujudkan Pembelajaran Digital Interaktif di Sekolah Luar Biasa Panny Agustia Rahayuningsih; Wahyu Nugraha; Riski Annisa; Anna Anna
Indonesian Community Service Journal of Computer Science Vol. 3 No. 2 (2026): Periode Juli 2026
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v3i2.13175

Abstract

Perkembangan teknologi digital menuntut transformasi media pembelajaran di lingkungan Sekolah Luar Biasa (SLB). Keterbatasan pemahaman guru terhadap integrasi teknologi interaktif sering kali membuat perangkat digital di sekolah tidak termanfaatkan secara optimal. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan kompetensi pedagogis digital guru di SLB Negeri Rasau Jaya, Kabupaten Kubu Raya, melalui pelatihan pemanfaatan Interactive Flat Panel (IFP) Hisense 75WM61FE. Metode pelaksanaan kegiatan meliputi tahap analisis situasi mitra, tahap perancangan materi pelatihan, tahap pelaksanaan workshop, serta tahap evaluasi kualitatif melalui praktik mandiri. Hasil kegiatan menunjukkan peningkatan pemahaman guru secara signifikan mengenai perbedaan fundamental antara IFP dengan media konvensional (TV/proyektor) beserta keunggulan spesifikasi teknisnya. Selain itu, guru juga berhasil menguasai dan mengoperasikan enam fitur utama perangkat, yaitu Smart Whiteboard, Screen Mirroring, Video Conference Built-in, Split Screen 4-Way, Multi-Touch 55 Titik, dan pemanfaatan Google Play Store. Melalui pelatihan ini, guru SLB Negeri Rasau Jaya kini mampu mengintegrasikan fitur-fitur inti IFP untuk menciptakan ekosistem pembelajaran digital yang interaktif dan kolaboratif bagi anak berkebutuhan khusus
Perbandingan Kinerja Naïve Bayes, Support Vector Machine, dan K-Nearest Neighbor dalam Analisis Sentimen Mobile Legends Hikmawan Alvin Zikirlah; Muhammad Fazilla; Iltavera Paula; Riski Annisa; Lady Agustin Fitriana
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp228-235

Abstract

The rapid advancement of information and communication technology has significantly increased the popularity of online games in Indonesia, one of which is Mobile Legends: Bang Bang (MLBB) with millions of active users. The abundance of user reviews on digital platforms provides valuable data for analysis using text mining and natural language processing (NLP) approaches. Sentiment analysis is applied to classify user opinions into positive, negative, and neutral categories, offering insights into player satisfaction and perceptions of game quality. This study compares the performance of three classification algorithms Naïve Bayes (NB), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) in analyzing sentiment from Mobile Legends user reviews on the Google Play Store. A total of 5,000 reviews were collected using the web scraping technique and processed through the Knowledge Discovery in Databases (KDD) framework, which includes cleaning, case folding, tokenization, normalization, and stopword removal. Sentiment labeling was performed using a lexicon-based approach with the InSet sentiment lexicon. The dataset was divided into training and testing sets with an 80:20 ratio and evaluated using accuracy, precision, recall, and f1-score metrics. The results show that the SVM algorithm achieved the highest accuracy of 88.1%, followed by KNN at 65.1% and NB at 62.6%. Thus, SVM is recommended as the most effective model for sentiment analysis of Mobile Legends user reviews.
PEMODELAN ANALISIS SENTIMEN ROBLOX MENGGUNAKAN ALGORITMA MACHINE LEARNING Veronika Agnes; Elsa Mutia Sari; Riski Annisa; Lady Agustin Fitriana
Jurnal Komputer dan Teknologi Vol 5 No 1 (2026): JUKOMTEK JANUARI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i1.505

Abstract

The rapid advancement of digital technology has fostered the rise of various interactive online gaming platforms, with Roblox standing out as one of the most prominent. This platform allows users not only to play but also to design and share their own games. As the number of active users increases, the volume of reviews submitted on the Google Play Store also grows. These reviews contain valuable information but require sentiment analysis to automatically understand users’ opinions, satisfaction levels, and complaints. This research aims to conduct sentiment analysis on Roblox user reviews by comparing the performance of three machine learning algorithms—Naïve Bayes, Random Forest, and Decision Tree—to determine which yields the most optimal results. The study follows the Knowledge Discovery in Databases (KDD) framework, which includes several stages: selecting 5,000 reviews, performing text preprocessing (such as cleaning, case folding, tokenizing, normalization, stopword removal, stemming, and labeling), transforming data using word embedding, and evaluating model performance with metrics including Confusion Matrix, Accuracy, Precision, Recall, and F1-Score. The experimental findings indicate that the Decision Tree algorithm achieved the best performance, with an accuracy of 85%, precision of 0.847, recall of 0.850, and a weighted F1-score of 0.848. In contrast, Random Forest obtained an accuracy of 83.6% and a macro F1-score of 0.773, while Naïve Bayes recorded the lowest performance with 64.2% accuracy and a macro F1-score of 0.527. Overall, the Decision Tree algorithm demonstrated superior capability and balance in classifying positive, negative, and neutral sentiments in Roblox user reviews, showing more effective text pattern recognition compared to probabilistic-based methods.
Aplikasi Prediksi Tingkat Kelulusan Mahasiswa Berdasarkan Data Akademik dan Demografi Menggunakan Algoritma Klasifikasi Random Forest Muhammad Arief Rachman; Dinar Ridho Maulana; Billianto Timothy; Riski Annisa
Jurnal Media Informatika Vol. 7 No. 1 (2026): Edisi Januari - Februari
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jumin.v7i1.7605

Abstract

Ketepatan waktu kelulusan mahasiswa merupakan indikator mutu krusial bagi perguruan tinggi, namun pemanfaatan data akademik untuk langkah preventif masih sangat terbatas. Selain itu, terdapat kesenjangan penelitian berupa kurangnya implementasi praktis model prediksi dalam bentuk antarmuka yang siap digunakan oleh manajemen institusi untuk intervensi harian. Penelitian ini bertujuan untuk mengembangkan Sistem Peringatan Dini Akademik proaktif melalui prediksi tingkat kelulusan mahasiswa menggunakan algoritma Random Forest (RF). Data sekunder yang digunakan mencakup 27 variabel prediktor yang mengintegrasikan fitur akademik dan demografi ke dalam klasifikasi status biner. Hasil pengujian menunjukkan performa model yang sangat efektif dengan pencapaian akurasi sebesar 94,44%, serta nilai presisi (0,97) dan recall (0,97) yang sangat andal. Sebagai kontribusi utama, model prediksi ini diimplementasikan ke dalam aplikasi web interaktif berbasis Streamlit untuk menjamin kegunaan praktis dan intuitif bagi pengambil kebijakan. Penelitian ini menyimpulkan bahwa penyediaan sistem peringatan dini yang terintegrasi secara praktis memungkinkan institusi melakukan intervensi personal secara real-time guna membantu mahasiswa menyelesaikan studi tepat waktu secara humanis.
Co-Authors Agung Sasongko Anna Anna Anna Anna Anna Anna Anna Anna, Anna Anna, Anna - Arvin Hidayat Waluya Baihaqi, Muhammad Rivaldi Billianto Timothy Chania Lista Zepani Chika Suhendra Cici Debi Handika Detylia Detylia Dewi Ayu Nur Wulandari Didi Rosiyadi Dinar Ridho Maulana Dwiza Riana Eliska, Puspa Elsa Mutia Sari Emi Wulandari Eva Meilinda Fadilah, Aldi Famela Jessica Fariska Adela Nurhidayah Firly Annisa Luthfi Fransiska Regina Frizi Olivian Grant Christo Budiyanto Hazael Susanto Hidayana, Reymond Syahputra Hikmawan Alvin Zikirlah Iltavera Paula Iltavera Paula Lady Agustin Fitriana Lestari Yusuf Marelita Marelita Maulana, Reza - Meltiana Mohammad Kamal Reza Monikka Nur Winnarto Muhamad Rizki Pratama Muhammad Arief Rachman Muhammad Eka Fadillah Muhammad Fahmi Julianto Muhammad Fazilla Muhammad Fazilla Murdiansyah Murdiansyah Nur W, Monikka Nurfia Oktaviani Syamsiah Panny Agustia Rahayuningsih Parwandar, Robi Rahayuningsih, Agustia Rai Markus Panamuan Raja Sabaruddin Raja Sabaruddin Raja Sabarudin Ramadhani, Zulfikar Ismaya Rendi Rendi Reymond Syahputra Hidayana Robi Parwandar Sahrul Bariyah Sendi Yustisio Siti Nurdiani Sri Murni Suhendra, Chika Talcha Ilham Putri Veronika Agnes Wahyu Nugraha Weiskhy Steven Dharmawan Welly, Welly Winnaarto, Monikka Nur Winny Christiani Thomas Yulia Yulia Yustisio, Sendi Zikirlah, Hikmawan Alvin