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All Journal Jurnal Informatika Jurnal Ilmiah Matrik JURNAL ILMIAH INFORMATIKA Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat METIK JURNAL Building of Informatics, Technology and Science Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics JATI (Jurnal Mahasiswa Teknik Informatika) IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Intelligent Decision Support System (IDSS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sains Komputer dan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Jurnal Informatika Terpadu Jurnal Informatika Teknologi dan Sains (Jinteks) Sci-Tech Journal Sewagati: Jurnal Pengabdian Masyarakat Indonesia Safari : Jurnal Pengabdian Masyarakat Indonesia Adopsi Teknologi dan Sistem Informasi Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) GEMBIRA (Pengabdian Kepada Masyarakat) Buffer Informatika JSE Journal of Science and Engineering Jurnal Abdimas Mahakam
Naufal Azmi Verdikha
Teknik Informatika, Universitas Muhammadiyah Kalimantan Timur
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Analysis of FastText with Support Vector Machine for Hate Speech Classification on Twitter Social Media Nuraini, Nabila; Latipah, Asslia Johar; Verdikha, Naufal Azmi
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.21107

Abstract

Hate speech refers to sentences or words that aim to demean or insult individuals, groups, or communities based on factors such as ethnicity, religion, race, or social class. In this study, Natural Language Processing (NLP) techniques were employed using FastText feature extraction and SVM algorithm for text classification. The evaluation was conducted using F1 Score as the performance metric. The data was divided using the Cross-Validation method with 10 folds, and the experiment was performed with four SVM kernels: RBF, Linear, Polynomial, and Sigmoid. The results of this research, based on the effectiveness of the FastTextSVM method combination, demonstrate a strong performance in hate speech classification. By adopting FastText parameters from previous studies and involving four SVM kernels, this research achieved a satisfactory average F1 Score. The results obtained for the Polynomial kernel showed the best performance with an F1 Score of 0.813, followed by the Linear kernel with 0.809, the RBF kernel with 0.808, and the Sigmoid kernel with 0.805. This indicates that the F1 Score results do not show significant differences in outcomes.
Perancangan UX (User Experience) Sistem Informasi Lifeskill Menggunakan Metode UCD di Universitas Muhammadiyah Kalimantan Timur (UMKT) Bulan Suci Cahayawati; Naufal Azmi Verdikha; Muhamad Wahyu Tirta
Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Vol. 2 No. 1 (2024): Januari : Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat
Publisher : Asosiasi Riset Ilmu Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/pandawa.v2i1.458

Abstract

The development of information technology is important, especially in the field of education, in supporting the learning process for the better. Muhammadiyah University of East Kalimantan, which carries the IT Based Paperless concept, is one of the campuses that supports the development of information technology. SI Lifeskills was introduced to record and integrate the development of students' academic achievements, where this system helps Life Skills courses to be more organized and effective. The system development demand was carried out in order to increase needs, especially in terms of user experience (UX). This service is carried out as a form of activity to increase comfort in learning services using a User Centered Design (UCD) approach. The evaluation stages carried out in this research used Heuristic Evaluation with 10 parameter aspects. The Heuristic Evaluation test results obtained Severity Ratings 0 with 8 points and Severity Ratings 1 with 2 points. The evaluation results show that the system is comfortable to use with problems that have minimal impact on the user so that repairs are not needed if time is limited.
Klasifikasi Ujaran Kebencian di Twitter Menggunakan Fitur Ekstraksi Glove dengan Support Vector Machine(SVM) Ahmad Ilham; Naufal Azmi Verdikha; Assliah Johar Latipah

Publisher : Program Studi Teknik Informatika, Fakultas Teknik, Universitas Yudharta Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35891/explorit.v15i2.4108

Abstract

Twitter merupakan platform media sosial yang gratis dan bebas dipergunakan. Kebebasan tersebut mengakibatkan tidak terlepasnya banyak pengguna twitter yang membuat tweet dengan kalimat yang mengandung ujaran kebencian. Penelitian ini menggunakan fitur ekstraksi GloVe dan algoritma SVM untuk membuat model machine learning yang dapat mengidentifikasi ujaran kebencian menggunakan dataset twitter. Fokus penelitian ini adalah membandingkan kernel SVM, yaitu Sigmoid dan RBF dengan parameter C = 10 dan C=1. Model dievaluasi menggunakan F1 Score dengan teknik cross validasi untuk mengukur performa model dalam klasifikasi ujaran kebencian. Hasil penelitian menunjukkan bahwa kernel RBF dengan parameter C = 10 memiliki nilai rata-rata F1 Score tertinggi sebesar 0,682, sementara kernel sigmoid dengan parameter C = 10 memiliki nilai rata-rata F1 Score terendah sebesar 0,4520.
Klasifikasi Teks Quick Count Pemilihan Presiden 2024 pada Twitter menggunakan Metode TF-IDF dan Naive Bayes Pranata, Aditya; Rudiman, Rudiman; Verdikha, Naufal Azmi
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1279

Abstract

The 2024 Indonesian Presidential Election generated various responses on X Twitter platform related to the Quick Count. The large number of diverse opinions makes identifying and categorizing sentiments difficult. This study aims to evaluate the accuracy of the Naive Bayes method with TF-IDF weighting in text classification regarding the Quick Count of the 2024 Presidential Election on X Twitter. Data was obtained through crawling, resulting in 2113 tweets, which experts in data labelling then labelled. The preprocessing stage includes case folding, cleansing, stopword removal, and stemming. Words are weighted using TF-IDF, and then the data is divided into 80% for training and 20% for testing. Text classification using the Naive Bayes algorithm achieved an accuracy of 74.46%, indicating a pretty good accuracy in classifying text related to the 2024 Presidential Election Quick Count on X Twitter.
Implementasi Data Mining Algoritma Apriori Pada Data Transaksi Pik Store Achmad, Arda Fahmi; Abdul Rahim; Naufal Azmi Verdikha
Jurnal Informatika Polinema Vol. 11 No. 2 (2025): Vol. 11 No. 2 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v11i2.6860

Abstract

Peningkatan laju pertumbuhan konsumen dirasa dapat dimanfaatkan oleh Pik Store untuk semakin berkembang dengan jajaran produk rekomendasi. Data dari bulan Maret hingga Juni 2023 menunjukkan banyak data transaksi yang diperoleh sebanyak 2016 data. Ruang lingkup penelitian ini adalah penerapan apriori dapat memberikan rekomendasi tata letak produk agar dapat diimplementasikan. Tujuan penelitian ini adalah mengidentifikasi pola pembelian konsumen dalam menemukan kombinasi item produk yang sering dibeli secara bersamaan menggunakan dengan metode asosiasi serta memberikan rekomendasi tata letak paket item produk berdasarkan pola pembelian yang diidentifikasi untuk meningkatkan penjualan di toko Pik Store. Metode penelitian melibatkan pengumpulan data dan penerapan algoritma Apriori untuk training dan validasi. Hasil dari penelitian ini adalah algoritma yang dapat merekomendasikan tata letak produk pada toko Pik Store. Tingkat confidence tertinggi yang didapat adalah 86% dengan rata – rata 61% dimana penulis mengatur nilai minimal support 7%. Penelitian selanjutnya diharapkan dapat mengembangkan model dengan tingkat confidence yang lebih tinggi dengan menerapkan metode yang lebih baik.
ANALISIS KLASIFIKASI ULASAN APLIKASI SIREKAP 2024 MENGGUNAKAN EKSTRAKSI FITUR DISTILBERT DAN METODE SUPPORT VECTOR MACHINE Ridhoi, Reno; Verdikha, Naufal Azmi; Yulianto, Fendy
JURNAL ILMIAH INFORMATIKA Vol 13 No 01 (2025): Jurnal Ilmiah Informatika (JIF)
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/jif.v13i01.9753

Abstract

This study aims to classify reviews of the SIREKAP 2024 application automatically using the DistilBERT feature extraction method and the Support Vector Machine (SVM) algorithm. The data used includes 8,538 user reviews from the Google Play Store with five Rating categories as the target variable. After undergoing 10-Fold cross-validation, the average F1-Score obtained was 36.62%, with the highest performance reaching 37.16%. The analysis indicates that data imbalance is the main obstacle in improving the model's accuracy, particularly in the minority class. The study concludes that the combination of DistilBERT and SVM yields suboptimal results and requires further optimization. Recommendations are provided to improve model accuracy and enhance the quality of the application based on user reviews.
ANALISIS SENTIMEN ULASAN PENGGUNA TERHADAP APLIKASI K24KLIK DI GOOGLE PLAY STORE DENGAN ALGORITMA NAÏVE BAYES Damayanti, Fitri; Rahim, Abdul; Azmi Verdikha, Naufal
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13298

Abstract

Aplikasi K24Klik adalah platform yang menyediakan layanan kesehatan dan farmasi sehingga memudahkan siapa saja mendapatkan informasi obat dan melakukan pemesanan tanpa harus mengunjungi apotek secara langsung. Meskipun bermanfaat, pengguna masih mengeluhkan aplikasi K24Klik, seperti keterlambatan pengiriman, pembatalan tanpa persetujuan pengguna, dan sering terjadi gangguan pada sistem. Oleh karena itu, penelitian bertujuan untuk mengetahui efektifitas algoritma Naive Bayes yang diterapkan dalam menganalisis sentimen ulasan pengguna terhadap aplikasi K24Klik di Google Play Store. Pada kajian ini, metode yang diterapkan dalam menganalisis sentimen ulasan pengguna meliputi, Pre-processing data seperti case folding, tokenizing, stopword removal dan stemming, serta Algoritma Naïve Bayes diimplementasikan dalam mengelompokkan sentimen ulasan kedalam kategori positif, netral dan negatif. Dalam penelitian ini dilakukan dengan 3 perbandingan pembagian data latih dan data uji, seperti 90:10, 80:20, dan 70:30, untuk menentukan rasio yang efektif dalam melakukan analisis sentimen. Dari penelitian ini didapatkan hasil dimana rasio 80:20 menunjukkan hasil terbaik dengan akurasi 80%, presisi 83%, recall 96%, dan F1-score 89%. nilai tersebut membuktikan kemampuan algoritma Naïve Bayes dalam menganalisis sentimen ulasan pengguna aplikasi dengan sangat baik. Hasil pengujian ini diharapkan dapat menambah masukan bagi pengelola aplikasi dalam memperbaiki kualitas layanan di masa mendatang.
Analisis DistilBERT dengan Support Vector Machine (SVM) untuk Klasifikasi Ujaran Kebencian pada Sosial Media Twitter Azmi Verdikha, Naufal; Habid, Reza; Johar Latipah, Asslia
METIK JURNAL (AKREDITASI SINTA 3) Vol. 7 No. 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.583

Abstract

Hate speech is a significant issue in content management on social media platforms. Effective classification of hate speech plays a crucial role in maintaining a safe social media environment, combating discrimination, and protecting users. This study evaluates a hate speech classification model using SVM with linear and polynomial kernels. The dataset used consists of labeled Indonesian-language tweets. The importance of developing an effective classification model to address hate speech has led to the utilization of DistilBERT as a feature extraction method. However, DistilBERT has high-dimensional features, necessitating dimensionality reduction to reduce model complexity. Therefore, in this study, the PCA dimensionality reduction method is implemented with various scenarios of dimensionality, namely 10, 20, 30, 40, and 50. Evaluation is performed using F1-Score, and the entire study is evaluated using 10-fold cross-validation. The evaluation results indicate that in the scenario with a linear kernel, the model achieves the highest F1-Score of 0.75 in the 50-dimensional scenario. Meanwhile, in the scenario with a polynomial kernel, the model achieves the highest F1-Score of 0.7857 in the 50-dimensional scenario. These findings demonstrate that the use of a polynomial kernel with 50 dimensions yields the best performance in classifying hate speech.
Sentiment Analysis of Disney+ Hotstar App User Reviews on Google Playstore Using the Naïve Bayes Method Octaviany, Dinda Nur; Rahim, Abdul; Verdikha, Naufal Azmi
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.729

Abstract

User reviews of the Disney+ Hotstar application on the Google Play Store present a variety of sentiments, particularly concerning the paid subscription feature. This study aims to analyze these sentiments using the Naïve Bayes classification method, categorizing user opinions into positive, negative, and neutral classes. A total of 30,571 Indonesian- language reviews were collected through web scraping, followed by a preprocessing phase that included case folding, stopword removal, and stemming. The Term Frequency-Inverse Document Frequency (TF- IDF) technique was applied to weight the significance of words. The dataset was split into 80% training and 20% testing portions. The classification model achieved an accuracy of 78%, showing reliable performance in identifying sentiment patterns. However, performance on the neutral class was lower, indicating room for improvement through better preprocessing or class balancing. The findings provide insights for Disney+ Hotstar to better understand user perceptions and guide enhancements to the subscription service.
KLASIFIKASI ULASAN APLIKASI SIREKAP 2024 DENGAN EKSTRAKSI FITUR WORD2VEC DAN METODE SUPPORT VECTOR MACHINE (SVM) Muthmainnah, Muthmainnah; Azmi Verdikha, Naufal; Yulianto, Fendy
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13244

Abstract

Pemilu Indonesia memanfaatkan teknologi, termasuk aplikasi SIREKAP 2024, untuk meningkatkan transparansi dan efisiensi. Penelitian ini menganalisis ulasan pengguna aplikasi SIREKAP 2024 dari Google Play Store dengan pendekatan machine learning. Ekstraksi fitur dilakukan menggunakan Word2Vec (Skip-gram), sementara klasifikasi menggunakan Support Vector Machine (SVM). Data ulasan dikumpulkan melalui teknik scraping dan diproses melalui tahapan praproses serta penyeimbangan data menggunakan class_weight='balanced'. Hasil menunjukkan bahwa tanpa penyeimbangan data, model menghasilkan F1-Score sebesar 29,02%. Dengan penerapan class_weight='balanced', skor meningkat menjadi 32,05%. Optimasi parameter dengan nilai C=1 dan max_iter=65 memberikan F1-Score tertinggi sebesar 36,02%, meningkat 7% dari konfigurasi awal. Studi ini menyoroti pentingnya penyeimbangan data dan konfigurasi parameter yang tepat dalam meningkatkan performa model klasifikasi. Namun, model yang digunakan masih belum sepenuhnya sesuai untuk data yang tersedia.
Co-Authors Abdul Rahim Achmad, Arda Fahmi Adhistya Erna Permanasari Ahmad Ilham Ahmad Ridhani Mubaraq Alam, Fajar Arbansyah Arbansyah Aspianur Asslia Johar Latipah Assliah Johar Latipah Bulan Suci Cahayawati Dwiagam, Junianda Haris Evitasari, Yuliana Dilla Fahrezi, Irqi Anbi Fajar Alam Faldi Faldi Faldi Faldi Fattah, Mi'raj Fendy Yulianto Fiqri, Mohammad Hiqmal Fitri Damayanti Habid, Reza Hasudungan, Rofilde Hendra Saputra Hery Tri Waloyo Hidayati Ramadhani, Novia Husni Thamrin Ikram, Muhammad Ilham, Muhammad Fauzan Nur Johar Latipah, Asslia Latipah, Asslia Johar Muhamad Wahyu Tirta Muhammad Fauzan Nur Ilham Muhammad Najeri Al Syahrin Muhammad Taufiq Sumadi Muhammad Taufiq Sumadi Muthmainnah Muthmainnah Nuraini, Nabila Nurdiansyah, Rendy Octaviany, Dinda Nur Pranata, Aditya Pranata, Ricky Albin Rahmad Fahrozi, Mu. Aldi Reza Andriyanti Ridhoi, Reno Ririn Wahyuni Rudiman Rudiman, Rudiman Sari, Septa Intan Permata Sayekti Harits Suryawan Susanto, Muhammad Riza Radyaka Syahrandi, Akmal Taghfirul Azhima Yoga Siswa Takhta Perlawanan Putra Sinawang Teguh Bharata Adji Tri Wahyudi Trisha NurHalisha Tyas, Intan Kinanthi Damarin Wawan Joko Pranoto Wawan Joko Pranoto Wisnu Ismunandar Zayni Zayni