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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 Jurnal Abdimas Mahakam 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) 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 Jurnal Publikasi Teknik Informatika (JUPTI) Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Jurnal Informatika Polinema (JIP) GEMBIRA (Pengabdian Kepada Masyarakat) Buffer Informatika JSE Journal of Science and Engineering
Naufal Azmi Verdikha
Teknik Informatika, Universitas Muhammadiyah Kalimantan Timur
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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.
ANALISIS PENGGUNAAN METODE UNDERSAMPLING CLUSTER CENTROIDS UNTUK MODEL KLASIFIKASI SVM DAN EKSTRAKSI FITUR BOW PADA ULASAN APLIKASI SIREKAP 2024 Hidayati Ramadhani, Novia; Azmi Verdikha, Naufal; Azhima Yoga Siswa, Taghfirul
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 3 (2025): JATI Vol. 9 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i3.13536

Abstract

Sistem Informasi Rekapitulasi (SIREKAP) digunakan sebagai aplikasi perhitungan suara pemilu. Namun, pada awal bulan februari 2024 aplikasi ini mendapat banyak ulasan negatif di platform google play store, dengan rata-rata peringkat 2,7 dari 5. Hal ini menunjukkan adanya ketidakseimbangan pada peringkat ulasan pengguna. Ketidakseimbangan ini dapat mempengaruhi hasil analisis dan evaluasi aplikasi secara keseluruhan. Untuk mengatasi ketidakseimbangan data pada ulasan penelitian in menggunakan algoritma machine learning. Penelitian ini bertujuan untuk mengklasifikasikan teks ulasan aplikasi SIREKAP 2024 menggunakan algoritma SVM dengan ekstraksi fitur BoW serta metode undersampling Cluster Centroids (CC) untuk menangani ketidakseimbangan data. Dataset yang digunakan berjumlah 8235 ulasan. Proses validasi dilakukan menggunakan teknik k-fold cross-validation dengan k=10 untuk memastikan performa secara konsisten pada berbagai subset data. Hasil penelitian menunjukkan bahwa undersampling CC efektif dalam menyeimbangkan distribusi kelas, dengan Imbalanced Ratio (IR) awal sebesar 21,02 berhasil menjadi 1. Selain itu nilai entropi meningkat dari 1,079 menjadi 1,609 yang menunjukkan peningkatan keragaman ketidakpastian data. Namun, meskipun distribusi kelas menjadi seimbang, rata rata nilai IBA mengalami penurunan dari 0,274 pada Skenario I menjadi 0,271 pada Skenario II. Penurunan ini mengindikasikan bahwa teknik CC dapat menyebabkan hilangnya informasi penting pada kelas mayoritas selama proses undersampling.
Analisis Model Klasifikasi Sentimen Publik Terhadap Kebijakan Keberlanjutan IKN Menggunakan BERT Sebagai Feature Extractor dan K-Nearest Neighbor (KNN) Fiqri, Mohammad Hiqmal; Rudiman, Rudiman; Verdikha, Naufal Azmi
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8168

Abstract

This study aims to evaluate the performance of sentiment classification models for public opinions regarding the relocation of Indonesia’s new capital (IKN) using a combination of IndoBERT as a feature extractor and K-Nearest Neighbor (KNN) as a classifier. The dataset consisted of 1,274 YouTube comments related to IKN, which were annotated by an expert in sociology and text analysis. The preprocessing stage involved cleaning numbers, URLs, emojis, and punctuation, as well as removing stopwords using the Sastrawi library. IndoBERT produced 768-dimensional vector representations, which were then classified using KNN with k=5 and Euclidean distance. Evaluation with 5-fold cross validation achieved an accuracy of 73.31%. However, the recall for the positive class was relatively low (0.49), indicating challenges in detecting positive comments due to class imbalance (831 negative, 294 positive, 149 neutral). These findings suggest that the IndoBERT+KNN model performs well on majority classes but struggles with minority classes. The contribution of this research is to provide a critical analysis of the limitations of IndoBERT-based models in Indonesian sentiment classification and to recommend future directions, including data balancing and fine-tuning approaches.
Analisis Sentimen Twitter Atas Isu Hak Angket Menggunakan Pembobotan TF-IDF dan Algoritma SVM Fahrezi, Irqi Anbi; Rudiman; Nauval Azmi Verdikha
Sci-tech Journal Vol. 3 No. 2 (2024): Sci-Tech Journal (STJ) In Press
Publisher : MES Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/stj.v3i2.526

Abstract

Social media has become an important platform for voicing public opinion. One of the most popular and frequently used social media is Twitter. Twitter is a popular social media in Indonesia for discussions on political issues. The topic that is being discussed is the "inquiry right" because of the alleged fraud that occurred in the 2024 elections. The alleged fraud in the 2024 elections raised issues related to the rolling of the right of inquiry aimed at finding out the oddity or fraud. Therefore, a method is needed to classify the opinion whether it is classified as a positive or negative sentiment. This research uses 1113 data obtained from Twitter social media by applying crawling techniques. The data goes through several preprocessing stages then feature extraction using Term Frequency-Inverse Document Frequency, split data, and Support Vector Machine algorithms. The test results using these stages obtained an accuracy of 75%, indicating that the applied method is effective in classifying public sentiment related to the inquiry right issue..  
Klasifikasi Kecelakaan Lalu Lintas Menggunakan Kombinasi Forward Selection, ADASYN, dan Random Forest Aspianur; Taghfirul Azhima Yoga Siswa; Naufal Azmi Verdikha
Buffer Informatika Vol. 11 No. 2 (2025): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

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

Abstract

Kecelakaan lalu lintas menjadi penyebab utama kematian bagi kelompok usia 15-29 tahun, dengan lebih dari 1,3 juta kematian setiap tahunnya. di Indonesia, data dari korps lalu lintas polri menunjukkan bahwa pada tahun 2023 terjadi lebih dari 100 ribu kasus kecelakaan, dengan korban jiwa mencapai 25 ribu orang. Penelitian ini bertujuan untuk mengembangkan model klasifikasi tingkat keparahan kecelakaan lalu lintas dengan mengintegrasikan metode Adaptive Synthetic Sampling (ADASYN) dan Forward Selection ke dalam algoritma Random Forest. Data yang digunakan merupakan data kecelakaan dari Polresta Samarinda periode 2020–2024 yang terdiri dari 35 fitur. Proses penelitian mencakup tahapan data preprocessing, feature selection dengan Forward Selection, dan pembagian data testing dan training dengan 10k-fold validation. Permodelan menggunakan algoritma Random Forest, dan evaluasi model menggunakan confusion matrix untuk mencari akurasi, precision, recall, dan f1-score. Hasil penelitian menunjukkan fitur yang paling signifikan terhadap klasifikasi kecelakaan lalu lintas adalah cuaca, jumlah luka ringan, jumlah luka berat, dan jumlah meninggal dunia. Penerapan Random Forest tanpa penanganan ketidakseimbangan dan tanpa seleksi fitur hanya menghasilkan akurasi 79,26%, precision 29,03%, recall 34,62%, dan f1-score 31,58%. Setelah diterapkan ADASYN, metrik evaluasi meningkat signifikan menjadi akurasi 84,26%, precision 81,82%, recall 84,62%, dan f1-score 83,20%. Peningkatan lebih besar tercapai setelah seleksi fitur Forward Selection, menghasilkan akurasi akhir 95,28%, precision 94,23%, recall 96,15%, dan f1-score 95,18%.
METODE PEMBOBOTAN TF-IDF UNTUK KLASIFIKASI TEKS QUICK COUNT PEMILIHAN WAKIL PRESIDEN INDONESIA 2024 PADA X TWITTER DENGAN METODE SVM Pranata, Ricky Albin; Rudiman, Rudiman; Azmi Verdikha, Naufal
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14934

Abstract

The 2024 Indonesian Vice Presidential Election Quick Count sparked diverse public reactions on X Twitter. The sheer volume and variety of expressed opinions complicate accurate sentiment identification and classification. This study aims to develop a text classification model using Support Vector Machine (SVM) to identify sentiment in election Quick Count-related tweets. Data was acquired through tweet collection, followed by pre-processing, word weighting using TF-IDF, and data splitting for model training and testing. Results indicated that the developed SVM model achieved 77.30% accuracy in tweet sentiment classification. The model's implementation is expected to aid in more effective information filtering and assist stakeholders in understanding public opinion more accurately.
KLASIFIKASI SENTIMEN X-TWITTER PERIHAL PEMINDAHAN IBU KOTA INDONESIA MENGGUNAKAN EKSTRAKSI FITUR TF-IDF DAN METODE SUPPORT VECTOR MACHINE (SVM) Wahyudi, Tri; Rudiman, Rudiman; Verdikha, Naufal Azmi
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15015

Abstract

The classification model has reached the realm of sentiment classification to analyze user sentiment in providing comments. this research aims to classify sentiment regarding the topic of moving the capital city of Indonesia using the Support Vector Machine (SVM) method with TF-IDF weighting. SVM has its own advantages, namely to overcome complex problems in SVM classification using the kernel function. the kernel functions to transform input data into a high dimensional feature space, allowing linear separation of data more easily. there are 3 sentiment categories in this study, namely Negative, Neutral and Positive sentiment. to determine these 3 categories, researchers used expert labelling services. the purpose of this study using the SVM method and TF-IDF feature extraction is to find out and analyze the accuracy results obtained in processing sentiment data regarding the transfer of the capital city of Indonesia. The accuracy results obtained are 64%, this shows that the SVM method with TF-IDF weighting is able to classify sentiment data with fairly good results.
Co-Authors Abdul Rahim Achmad, Arda Fahmi Adhistya Erna Permanasari Ahmad Ilham Ahmad Ridhani Mubaraq Arbansyah Aspianur Asslia Johar Latipah Assliah Johar Latipah Bulan Suci Cahayawati Dwiagam, Junianda Haris Evitasari, Yuliana Dilla Fahrezi, Irqi Anbi Faldi Faldi Faldi Faldi Fendy Yulianto Fiqri, Mohammad Hiqmal Fitri Damayanti Habid, Reza Hasudungan, Rofilde Hendra Saputra Hery Tri Waloyo Hidayati Ramadhani, Novia Husni Thamrin Ilham, Muhammad Fauzan Nur Johar Latipah, Asslia Latipah, Asslia Johar Muhamad Wahyu Tirta Muhammad Najeri Al Syahrin Muhammad Taufiq Sumadi Muhammad Taufiq Sumadi Muthmainnah Muthmainnah Nuraini, Nabila Nurdiansyah, Rendy Octaviany, Dinda Nur Pranata, Aditya Pranata, Ricky Albin Reza Andriyanti Ridhoi, Reno Ririn Wahyuni Rudiman Rudiman, Rudiman Sari, Septa Intan Permata Sayekti Harits Suryawan Susanto, Muhammad Riza Radyaka Syahrandi, Akmal Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Takhta Perlawanan Putra Sinawang Teguh Bharata Adji Tri Wahyudi Trisha NurHalisha Tyas, Intan Kinanthi Damarin Wawan Joko Pranoto Wawan Joko Pranoto Zayni Zayni