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All Journal Journal of Applied Engineering and Technological Science (JAETS) Jurnal Dinamika Informatika (JDI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Jurnal Informatika Dan Tekonologi Komputer
Rahmaddeni Rahmaddeni
Universitas Sains dan Teknologi Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Sara Detection on Social Media Using Deep Learning Algorithm Development M. Khairul Anam; Lucky Lhaura Van FC; Hamdani Hamdani; Rahmaddeni Rahmaddeni; Junadhi Junadhi; Muhammad Bambang Firdaus; Irwanda Syahputra; Yuda Irawan
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.5390

Abstract

Social media has become a key platform for disseminating information and opinions, particularly in Indonesia, where SARA (Ethnicity, Religion, Race, and Intergroup) issues can fuel social tensions. To address this, developing an automated system to detect and classify harmful content is essential. This study develops a deep learning model using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to detect SARA-related comments on Twitter. The method involves data collection through web scraping, followed by cleaning, manual labeling, and text preprocessing. To address data imbalance, SMOTE (Synthetic Minority Over-sampling Technique) is applied, while early stopping prevents overfitting. Model performance is evaluated using precision, recall, and F1-score. The results demonstrate that SMOTE significantly improves model performance, particularly in detecting minority-class SARA comments. CNN+SMOTE achieves a accuracy of 93%, and BiLSTM+SMOTE records a recall of 88%, effectively capturing patterns in SARA and non-SARA data. With SMOTE and early stopping, the model successfully manages class imbalance and reduces overfitting. This research supports efforts to curtail hate speech on social media, especially in the Indonesian context, where SARA-related issues often dominate public discourse.
Perbandingan Algoritma Naive Bayes dan Decission Tree untuk Prediksi Penyakit Kanker Paru-Paru Mulia Gusti Firmansyah; M. Khairuddin; M fadillah; Lusiana Efrizoni; Rahmaddeni Rahmaddeni
Jurnal Dinamika Informatika Vol. 13 No. 1 (2024): Jurnal Dinamika Informatika
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v13i1.309

Abstract

In this study, we compared the performance of two machine learning algorithms, Naïve Bayes and Decission Tree, for diagnosing lung diseases using patient health datasets. The main objective of this study is to evaluate the accuracy, precision, recall, and F1 score of the two algorithms to determine which method is more effective in predicting lung diseases. The results showed that the tree classification algorithm outperformed Naïve Bayes in terms of accuracy, reaching 95% in an 80:20 split, compared to the 78% accuracy achieved by Naïve Bayes on the same data. Further analysis showed that most patients in this dataset were high risk with 365 patients, followed by risk with 332 patients, and low risk with 303 patients. The decision tree structure proved to be more effective in handling the complexity of the data and produced more accurate predictions, improving efficiency by creating a new "Risk_Score". These results show that decision trees are a better method than Naïve Bayes for diagnosing lung diseases and can provide a solid foundation for developing accurate machine learning models for future health research.
Penerapan Algoritma K-Means Clustering dalam Menganalisis Tren Konsumen E-Commerce Andika Mahesa Putra; Rahmaddeni Rahmaddeni; Candra Saputra; Rahmat Hidayatullah; Safril Irsandi
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol. 6 No. 2: DESEMBER 2025
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v6i2.7419

Abstract

Penelitian ini membahas penerapan algoritma K-Means untuk menganalisis perilaku konsumen pada platform e-commerce. Dengan menggunakan dataset yang terdiri dari 3.900 entri pelanggan, data diproses melalui tahapan preprocessing, normalisasi Min-Max, dan encoding. Jumlah klaster optimal ditentukan menggunakan metode Elbow dan Silhouette Score, menghasilkan dua segmen utama pelanggan: aktif dan pasif. Visualisasi dengan PCA menunjukkan pemisahan klaster yang jelas. Hasil ini memberikan wawasan strategis bagi perusahaan untuk menyusun pendekatan pemasaran berbasis data yang lebih efektif, serta meningkatkan loyalitas pelanggan dan pendapatan bisnis. 
Klasterisasi Lagu Populer dan Eksplorasi Subgenre Spotify 2024 dengan K-Medoids Alfia Nurlaili Tahiyat; Bima Maulana; Ade Eka Saputra; Lusiana Efrizoni; Rahmaddeni Rahmaddeni
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 1 (2025): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i1.5699

Abstract

Spotify's genre classification system remains too broad, often grouping songs with distinct characteristics into the same category. For example, Pop Ballads and Dance Pop are frequently classified under "Pop" despite significant differences in tempo, emotion, and production style. This leads to inaccurate song recommendations. This study applies the K-Medoids algorithm to enhance song classification based on Spotify Playlist Count, Spotify Playlist Reach, and Spotify Popularity. The CRISP- DM methodology guides business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Clustering results without popularity ranking reveal three main groups: songs with low playlist count but high reach (dominated by light hip-hop), songs with high playlist count and reach (dominated by contemporary R&B), and songs with low popularity (dominated by dance). After ranking by popularity, clusters became more defined, with alternative pop dominating the high-reach cluster, contemporary R&B in the popular cluster, and dance pop in the less popular cluster. Evaluation using a Silhouette Score of 0.5014 indicates good cluster quality. Additionally, this study successfully identified the 15 most popular songs on Spotify in 2024. These findings can help Spotify refine its recommendation system by incorporating subgenre-based classification, ensuring more accurate search results aligned with user preferences and evolving music trends.
Penerapan Algoritma Support Vector Machine dan XGBoost Dalam Mengklasifikasikan Sentimen Opini Publik Terhadap Aplikasi Uber Rizky Rizaldi; M Ridho; Arraihan Tahta Ainullah; Lusiana Efrizoni; Rahmaddeni Rahmaddeni; M Fahrel Dea Putra
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 1 (2025): Maret : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i1.5735

Abstract

The development of application-based transportation services such as Uber has driven an increase in the number of public opinions distributed through various digital platforms. Sentiment analysis of this public opinion is important to understand user perceptions of Uber services. This study applies the Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) algorithms to classify public opinion sentiment, by optimizing data imbalance using the Synthetic Minority Oversampling Technique (SMOTE). The data used comes from Uber reviews on public platforms, which are grouped into positive, negative, and neutral sentiments. The experimental results show that the SVM algorithm has superior performance with an accuracy of 94%, while XGBoost experienced an increase in accuracy of up to 93% after applying SMOTE. This study provides insight into the effectiveness of machine learning algorithms in sentiment analysis and its implementation in the development strategy of application-based transportation services. Abstrak: Perkembangan layanan transportasi berbasis aplikasi seperti Uber telah mendorong peningkatan jumlah opini publik yang disalurkan melalui berbagai platform digital. Analisis sentimen terhadap opini publik ini menjadi penting untuk memahami persepsi pengguna terhadap layanan Uber. Penelitian ini menerapkan algoritma Mesin Vektor Pendukung (SVM) dan Peningkatan Gradien Ekstrem (XGBoost) untuk mengklasifikasikan sentimen opini publik, dengan mengoptimalkan ketidakseimbangan data menggunakan Synthetic Minority Oversampling Technique (SMOTE). Data yang digunakan berasal dari ulasan Uber di platform publik, yang dikategorikan ke dalam sentimen positif, negatif, dan netral. Hasil eksperimen menunjukkan bahwa algoritma SVM memiliki performa lebih unggul dengan akurasi mencapai 94%, sementara XGBoost mengalami peningkatan akurasi hingga 93% setelah penerapan SMOTE. Penelitian ini memberikan wawasan mengenai efektivitas algoritma pembelajaran mesin dalam analisis sentimen serta implikasinya terhadap strategi pengembangan layanan transportasi berbasis aplikasi.  
Co-Authors Ade Eka Saputra Alfia Nurlaili Tahiyat Anam, M Khairul Andika Mahesa Putra Arraihan Tahta Ainullah Bima Maulana Candra Saputra Hamdani Hamdani Irwanda Syahputra Junadhi Junadhi Lucky Lhaura Van FC, Lucky Lhaura Lusiana Efrizoni m fadillah M Fahrel Dea Putra M Ridho M. Khairuddin Muhammad Bambang Firdaus Mulia Gusti Firmansyah Rahmat Hidayatullah Rizky Rizaldi Safril Irsandi Yuda Irawan