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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Informatika dan Teknik Elektro Terapan Akrab Juara : Jurnal Ilmu-ilmu Sosial Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Computing and Informatics Research
Fuad Nur Hasan
Unknown Affiliation
Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Education Medicine & Pharmacology

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ANALISIS KLASIFIKASI INDEKS STANDAR PENCEMARAN UDARA JAKARTA TAHUN 2025 MENGGUNAKAN ALGORITMA RANDOM FOREST Nur Fachmi, Andhika; Fuad Nur Hasan; Anisa Permata Sari; Ferdinan Restu Ramadhan; Reihan Dwi Patria; Salma Pudjiati
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8477

Abstract

Pencemaran udara di DKI Jakarta telah menjadi isu lingkungan kritis dengan fluktuasi konsentrasi polutan yang kompleks, menuntut metode pemantauan yang lebih presisi dibandingkan pendekatan konvensional. Penelitian ini bertujuan untuk mengklasifikasikan tiga kategori utama Indeks Standar Pencemaran Udara (ISPU) menggunakan algoritma Machine Learning Random Forest. Studi ini memanfaatkan dataset harian terbaru periode Januari hingga Agustus 2025 yang mencakup parameter PM10, PM2.5, SO2, CO, O3, dan NO2. Guna mengatasi ketidakseimbangan distribusi kelas pada data kategori ISPU, diterapkan teknik Synthetic Minority Over-sampling Technique (SMOTE) pada tahap pra-pemrosesan. Hasil evaluasi model menunjukkan kinerja yang sangat impresif dengan tingkat akurasi mencapai 99,50% pada data pengujian. Analisis feature importance mengidentifikasi bahwa PM2.5 merupakan parameter paling dominan dengan kontribusi pengaruh sebesar 30,68% terhadap penentuan kualitas udara. Temuan ini memvalidasi efektivitas Random Forest sebagai instrumen sistem peringatan dini yang andal serta menekankan urgensi kebijakan pengendalian emisi partikulat di Jakarta.
Public Opinion Sentiment Analysis of Government Fuel Purchasing Policy by the Private Sector Using Support Vector Machine (SVM) Methods Muhammad Rossi Satria Fitrah; Afif Al Qifary; Ahmad Maulana Wahyudi; Dea Deswina Sumarna; Muhammad Nabiel Alfarizi; Fuad Nur Hasan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1970

Abstract

Government policies that provide opportunities for the private sector to participate in the purchasing and distribution of fuel oil (BBM) have triggered various reactions within society. The diversity of opinions expressed on social media reflects public perceptions of the effectiveness and potential impacts of these policies. This study aims to examine public sentiment toward the government policy by applying the Support Vector Machine (SVM) method. Data were collected from various social media platforms containing public responses to the issue of private sector involvement in fuel purchasing. The analysis process consisted of several stages, including data collection, data preprocessing (comprising cleansing, tokenizing, stopword removal, and stemming), feature extraction using the Term Frequency Inverse Document Frequency (TF-IDF) approach, and sentiment classification using the SVM algorithm. The results show that the SVM algorithm performs well in classifying public opinions into two sentiment categories, positive and negative, with a relatively high level of accuracy. The analysis indicates that the majority of public opinions tend to be negative, driven by concerns over potential price disparities, weakened government oversight, and possible socio-economic impacts. The findings of this study are expected to provide constructive input for the government in evaluating and developing energy policies that are more transparent and oriented toward public interest.
Sentiment Analysis of Public Opinion on Rupiah Redenomination Policy Using Support Vector Machine and SMOTE Haidar Aslam; Haikal Nurul Barki; Adhi Prasetyo Wibowo; Faqih Al Araf; Abdul Hamid Musawir; Fuad Nur Hasan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1982

Abstract

The government’s planned rupiah redenomination has generated a substantial wave of public opinion across social media platforms. This study aims to analyze public sentiment by examining comments on YouTube and classifying them into two categories: positive and negative. The data are collected through web scraping conducted on December 21, 2025, using the keyword “rupiah redenomination.”Given the pronounced imbalance between negative and positive opinions, this study applies the Synthetic Minority Over-sampling Technique (SMOTE) to balance the class distribution within the training data. The research pipeline consists of text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and classification using a linear-kernel Support Vector Machine (SVM). Experimental results indicate that the SVM model achieves an accuracy of 88.28%. The application of SMOTE is shown to effectively enhance the model’s ability to identify the minority class, with the recall for positive sentiment reaching 0.71. Furthermore, the analysis reveals that public opinion is predominantly negative (83.93%), reflecting widespread concern regarding the potential economic implications of the policy.
Co-Authors Abdul Hamid Musawir Adhi Prasetyo Wibowo Afif Al Qifary Agil Aqshol Mahenda Ahmad Maulana Wahyudi Alfarezi Hidayat Pratama Anisa Permata Sari Ari Abdilah Audy Abdillah Hidayat Dea Deswina Sumarna Eka Kusuma Pratama Elah Nurlelah Faqih Al Araf Ferdinan Restu Ramadhan Gata, Windu Haidar Aslam Haikal Nurul Barki Kurani Mega Asteroid Mochamad Wahyudi Muhammad Nabiel Alfarizi Muhammad Rafly Muhammad Rossi Satria Fitrah Nur Fachmi, Andhika Nurlaelatul Maulidah Reihan Dwi Patria Salma Pudjiati Taufik Ramlan Alfiansyah Yusuf Bachtiar