Article Per Year (5 Year)
p-Index From 2021 - 2026
0.882
P-Index
This Author published in this journals
All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA International Journal Software Engineering and Computer Science (IJSECS) APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Muhamad Ihsan Ashari
Universitas Pamulang
Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Pelatihan User Experience (UX) Design bagi Siswa SMK Bintang Nusantara untuk Meningkatkan Kompetensi Desain Aplikasi Afif Efendi; Muhamad Ihsan Ashari; Meta Susanti
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 3 No 3 (2025): APPA : Jurnal Pengabdian kepada Masyarakat
Publisher : Shofanah Media Berkah

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

Abstract

Perkembangan teknologi digital menuntut sumber daya manusia yang tidak hanya menguasai aspek teknis, tetapi juga memahami desain pengalaman pengguna (User Experience/UX) sebagai salah satu komponen utama dalam pengembangan aplikasi modern. Hasil observasi di SMK Bintang Nusantara menunjukkan bahwa siswa jurusan Teknik Komputer dan Jaringan (TKJ) memiliki dasar pemrograman dan jaringan yang baik, namun belum memahami prinsip UX secara menyeluruh. Kondisi ini berpengaruh pada kualitas rancangan antarmuka dan alur penggunaan aplikasi yang dihasilkan. Kegiatan Pengabdian kepada Masyarakat (PKM) ini dilaksanakan untuk meningkatkan pemahaman siswa mengenai konsep UX Design melalui penyampaian teori dan demonstrasi pembuatan prototipe menggunakan Figma. Metode pelaksanaan mencakup tahap perencanaan, persiapan, pelaksanaan pelatihan, serta evaluasi sederhana melalui observasi keterlibatan dan respons siswa. Hasil kegiatan menunjukkan bahwa peserta memperoleh pemahaman yang lebih baik mengenai pentingnya desain berbasis pengguna, struktur alur interaksi, serta prinsip usability dalam pengembangan aplikasi. Program ini memberikan kontribusi positif dalam memperkuat kesiapan siswa menghadapi tantangan dunia industri digital.
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.
Anomaly-Based Network Intrusion Detection Using Isolation Forest on the Imbalanced UNSW-NB15 Dataset Syifaurachman Syifaurachman; Samso Supriyatna; Muhamad Ihsan Ashari
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10378

Abstract

The increasing complexity of network traffic and the rapid evolution of cyber threats require adaptive intrusion detection systems capable of identifying anomalous behavior in large-scale network environments. Traditional signature-based detection methods are effective in recognizing known attack patterns but are limited in detecting emerging or zero-day threats. Consequently, anomaly-based approaches using machine learning have become an important research direction in modern intrusion detection systems. One of the widely used unsupervised algorithms for anomaly detection is Isolation Forest, which identifies anomalies by isolating observations through random partitioning mechanisms.This study investigates the capability of the Isolation Forest algorithm for intrusion detection using the UNSW-NB15 dataset under imbalanced data conditions. The dataset consists of 2,540,044 network traffic records with approximately 87% normal traffic and 13% attack traffic. A quantitative experimental approach was applied, including data preprocessing, model development using Isolation Forest, and performance measurement using several evaluation metrics, namely Accuracy, Precision, Recall, F1-score, Receiver Operating Characteristic Area Under the Curve (ROC-AUC), and Precision-Recall Area Under the Curve (PR-AUC).The experimental results show that the model achieved an overall accuracy of 79.3% and a ROC-AUC value of 0.6302, indicating moderate capability in distinguishing between normal and malicious traffic. However, the PR-AUC value of 0.1710 reveals limited sensitivity in detecting minority attack instances under highly imbalanced conditions. These findings highlight that evaluation of intrusion detection systems under imbalanced data should not rely solely on accuracy-based metrics. Isolation Forest can serve as a baseline anomaly detection mechanism, but additional strategies are required to improve detection sensitivity in real-world intrusion detection environments.
Pelatihan Pembuatan Website Portofolio untuk Meningkatkan Kesiapan Karier Siswa SMK Binong Permai Meta Susanti; Afif Efendi; Muhamad Ihsan Ashari
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 4 No 2 (2026): APPA : Jurnal Pengabdian kepada Masyarakat 
Publisher : Shofanah Media Berkah

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

Abstract

Perkembangan transformasi digital menuntut lulusan Sekolah Menengah Kejuruan (SMK) agar memiliki keterampilan yang sesuai dengan kebutuhan industri. Selain penguasaan aspek teknis, siswa juga dituntut untuk membangun citra profesional dan menampilkan kompetensi melalui media digital. Website portofolio menjadi salah satu sarana efektif untuk menyajikan profil, keterampilan, pengalaman, serta karya secara sistematis dan mudah diakses. Berdasarkan identifikasi di SMK Binong Permai, ditemukan bahwa sebagian besar siswa belum memahami cara mengembangkan website portofolio sebagai media personal branding sekaligus penunjang kesiapan karier. Oleh karena itu, Program Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan dengan tujuan meningkatkan kemampuan siswa dalam merancang portofolio berbasis web serta menumbuhkan kesadaran akan pentingnya personal branding di era digital. Kegiatan melibatkan 34 peserta dengan metode berupa sosialisasi, pemberian materi, praktik langsung menggunakan HTML, CSS, dan Bootstrap, serta pendampingan selama pelatihan. Evaluasi dilakukan melalui observasi partisipasi dan hasil praktik peserta. Hasil menunjukkan adanya peningkatan pengetahuan mengenai pengembangan portofolio digital serta keberhasilan siswa dalam membuat website sederhana untuk menampilkan kompetensi dan karya. Program ini diharapkan menjadi langkah awal dalam memperkuat kesiapan karier siswa dan mendorong pemanfaatan teknologi digital secara optimal dalam menghadapi persaingan dunia kerja.
Modeling the Reputation of Digital Banks Based on Public Opinion Using a Text Mining Approach with TF-IDF and the Support Vector Machine (SVM) Algorithm Muhamad Ihsan Ashari; Afif Efendi; Dimas Eko Prasetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 2 (2026): AUGUST 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i2.6956

Abstract

This study addressed the growing importance of reputation management in digital banking, where public opinion expressed on social media significantly influences customer trust and business sustainability. The objective of this research was to model the reputation of a digital bank based on public sentiment using a text mining approach. The study employed the CRISP-DM methodology, including data collection, preprocessing, modeling, and evaluation. A total of 1,897 Twitter comments related to the "Jenius" digital banking application were collected from 2023 to 2025. The data underwent preprocessing stages such as case folding, cleansing, tokenizing, normalization, stopword removal, negation handling, and stemming. Feature extraction was performed using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification was conducted using Support Vector Machine (SVM). The performance of SVM was compared with Naïve Bayes and K-Nearest Neighbors (KNN). The results showed that SVM achieved the best performance with an accuracy of 81.58%, outperforming Naïve Bayes (70.26%) and KNN (55.00%). Furthermore, sentiment distribution indicated that positive sentiment dominated public opinion, reflecting a generally favorable perception of the digital bank. In conclusion, the combination of TF-IDF and SVM proved effective for sentiment classification and can be utilized to model digital bank reputation, providing valuable insights for improving service quality and customer satisfaction.
Co-Authors Afif Efendi Afif Dimas Eko Prasetyo Ikhwan Fauzi Meta Susanti Samso Supriyatna Syifaurachman Yudisti Prayigo Permana