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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

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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

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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

 Modern cyber threats require adaptive intrusion detection systems (IDS) capable of identifying complex anomalies within large networks. . Traditional signature-based methods frequently struggle to detect novel zero-day attacks, making unsupervised machine learning, specifically Isolation Forest (IF), a vital research direction. IF detects anomalies by isolating unusual observations through random partitioning mechanisms.. This study evaluates IF for intrusion detection using the highly imbalanced UNSW-NB15 dataset, comprising 2,540,044 records (87% normal traffic, 13% attack traffic). A quantitative approach was applied, evaluating performance across Accuracy, Precision, Recall, F1-score, ROC-AUC, and PR-AUC. Results show an accuracy of 79.3% and a ROC-AUC of 0.6302. However, a low PR-AUC (0.1710) and F1-score (0.18) reveal the model's critical failure in detecting the minority attack class due to severe feature overlap. These findings prove that relying strictly on Accuracy or ROC-AUC is misleading in imbalanced scenarios. Therefore, IF should serve only as a lightweight baseline screening layer; hybrid architectures are necessary to improve detection sensitivity in real-world IDS.
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

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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