Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Ikhwan Fauzi
Universitas Pamulang
Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Analisis Sentimen Publik Pada Media Sosial Tiktok Terhadap Program Makan Bergizi Gratis (MBG) Dengan Algoritma Support Vector Machine (SVM) Yudisti Prayigo Permana; Ikhwan Fauzi; Muhamad Ihsan Ashari
Algoritma: Jurnal Ilmu Komputer dan Informatika Vol 10, No 1 (2026): April 2026
Publisher : Universitas Islam Negeri Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v10i1.28894

Abstract

The rapid growth of social media has made digital platforms a primary space for expressing public opinion on government policies, including the Free Nutritious Meal Program (MBG). TikTok, as a widely used platform, allows users to share opinions openly through comments. This study aims to analyze public sentiment toward the MBG program based on TikTok comments using the Support Vector Machine (SVM) algorithm. Relevant comments were collected and classified into positive, neutral, and negative categories. The data then underwent preprocessing stages, including cleaning, case folding, normalization, tokenization, stopword removal, and stemming. Text data were transformed into numerical form using the TF-IDF method. The dataset was split into training and testing data with an 80:20 ratio. Results show that most comments are positive (60.64%), followed by neutral (32.94%) and negative (6.41%). The highest accuracy (79.71%) was achieved using linear and sigmoid kernels, indicating SVM’s effectiveness for sentiment analysis.
Optimalisasi Pemanfaatan Media Sosial sebagai Sarana Digital Marketing melalui Tiktok Affiliate dan Tiktok Seller untuk Mendorong Kewirausahaan Digital bagi Organisasi Masyarakat Pemuda Muhammadiyah Parung Panjang Donna Oktar Endras Wanto; Ikhwan Fauzi; Muhamad Iqbal
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 4 No 1 (2026): APPA : Jurnal Pengabdian kepada Masyarakat 
Publisher : Shofanah Media Berkah

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

Abstract

Pemanfaatan media sosial sebagai sarana pemasaran digital masih belum optimal di kalangan masyarakat, khususnya pada anggota Organisasi Pemuda Muhammadiyah Parung Panjang. Sebagian besar peserta masih menggunakan media sosial sebatas hiburan dan belum memahami potensi monetisasi yang dapat dihasilkan melalui platform seperti TikTok. Selain itu, keterbatasan dalam pembuatan konten promosi dan kurangnya pemahaman mengenai fitur TikTok Affiliate dan TikTok Seller menjadi kendala utama dalam pengembangan usaha digital. Sebagai upaya untuk mengatasi permasalahan tersebut, kegiatan Pengabdian kepada Masyarakat (PKM) ini dilaksanakan dengan tujuan meningkatkan literasi digital dan keterampilan kewirausahaan berbasis media sosial. Kegiatan ini difokuskan pada pelatihan pemanfaatan TikTok Affiliate sebagai model bisnis tanpa stok serta TikTok Seller sebagai platform penjualan digital. Metode pelaksanaan meliputi sosialisasi, penyampaian materi, demonstrasi, praktik langsung, serta pendampingan intensif kepada peserta. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta terhadap konsep digital marketing, kemampuan dalam membuat akun TikTok Seller dan Affiliate, serta keterampilan dalam menghasilkan konten promosi sederhana. Selain itu, hasil evaluasi menunjukkan peningkatan nilai dari pre-test sebesar 37% menjadi 65% pada post-test, yang mengindikasikan peningkatan pemahaman peserta secara signifikan. Kegiatan ini diharapkan dapat mendorong pertumbuhan kewirausahaan digital berbasis komunitas serta meningkatkan daya saing masyarakat di era ekonomi digital.
Evaluasi Mutual Information dalam Klasifikasi Dokumen Ilmiah Menggunakan Random Forest dan CBOW Mufidah Karimah; Dendi Sunardi; Ikhwan Fauzi
BETRIK Vol. 17 No. 02 (2026): Jurnal Ilmiah BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : PPPM Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/aqfp3679

Abstract

Scientific document classification in large-scale digital repositories requires an efficient process because manual categorization is time-consuming and may introduce inconsistencies. This study evaluates the performance of a Random Forest Classifier (RF) using Continuous Bag-of-Words (CBOW) for feature representation and Mutual Information (MI) for feature selection in scientific document classification. The dataset contains 5,560 titles and abstracts of nuclear-related scientific documents distributed across 10 categories. After data cleaning, 5,374 documents were used for experiments with an 80:20 training-test split. Experiments were conducted using CBOW vector dimensions of 100, 200, 300, 400, and 500 under two scenarios: RF+CBOW and RF+CBOW+MI. Performance was evaluated using precision, recall, F1-score, and accuracy. The RF+CBOW scenario achieved the highest accuracy of 73% at a vector dimension of 100, while the scenario with MI reached a maximum accuracy of 71% across several dimensions. Adding MI increased recall to 73% at dimension 100 but did not improve overall performance and reduced all reported macro metrics to 69% at dimension 300. These findings indicate that feature selection does not necessarily improve classification performance; dataset characteristics and representation dimensionality also influence model effectiveness.
Co-Authors Dendi Sunardi Donna Oktar Endras Wanto Mufidah Karimah Muhamad Ihsan Ashari Muhamad Iqbal Yudisti Prayigo Permana