Claim Missing Document
Check
Articles

Found 5 Documents
Search

Rancang Bangun Sistem Informasi Aset Berbasis Web Menggunakan Metode Waterfall Pada Ditjen Hubla Sumatera Utara Hariyanto, Eko; Edo, Edo; Nasution, Darmeli; Muhammad Irfan Sarif
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.705

Abstract

Penelitian ini bertujuan merancang dan membangun sistem informasi aset berbasis web pada Direktorat Jenderal Perhubungan Laut (Ditjen Hubla) Sumatera Utara untuk mengatasi kelemahan pengelolaan aset yang masih dilakukan secara manual menggunakan buku dan spreadsheet terpisah. Kondisi tersebut menimbulkan risiko kesalahan pencatatan, duplikasi data, keterlambatan pelaporan, serta kesulitan pemantauan kondisi dan mutasi aset secara akurat dan real-time. Solusi yang diusulkan adalah pengembangan sistem informasi manajemen aset berbasis web dengan metode Waterfall melalui tahapan identifikasi masalah, studi pustaka, pengumpulan data, analisis kebutuhan, perancangan menggunakan Data Flow Diagram dan Entity Relationship Diagram, implementasi dengan PHP dan MySQL, serta pengujian menggunakan metode blackbox. Sistem yang dihasilkan menyediakan fitur autentikasi pengguna, pengelompokan aset, pendataan aset, pengadaan, mutasi, dan penyajian laporan terpusat bagi admin dan pimpinan instansi. Hasil pengujian menunjukkan seluruh fungsi utama berjalan sesuai kebutuhan pengguna, termasuk validasi input, keterkaitan antar data, dan kemudahan akses laporan. Implementasi sistem ini mampu meningkatkan efisiensi operasional, akurasi data, dan transparansi pengelolaan aset, sekaligus menjadi dasar pengembangan lanjutan, seperti integrasi barcode/QR code dan analitik aset untuk mendukung pengambilan keputusan strategis organisasi.
Model EPAM-2025 Untuk Analisis Keselarasan Opini Publik dan Kebijakan Literasi Digital Wiyono, Tri; Muhammad Irfan Sarif; Andika Dwi Aryo H; Ahmad Syaukani; Muhammad Zikri Ramadhan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9367

Abstract

Transformasi digital yang berlangsung cepat menuntut peningkatan literasi digital yang lebih komprehensif. Namun, berbagai indikator nasional menunjukkan penurunan pada aspek etika digital, keamanan siber, dan kemampuan berpikir kritis. Penelitian ini bertujuan menganalisis kesenjangan antara opini publik dan kebijakan literasi digital nasional melalui pendekatan komputasional berbasis Natural Language Processing (NLP). Untuk tujuan tersebut, dikembangkan model hibrida orisinal EPAM-2025 (Entity–Policy Alignment Model) sebagai kerangka pengukuran keselarasan kebijakan. Dataset penelitian terdiri atas 2.165 tweet berbahasa Indonesia yang diperoleh secara etis melalui API platform X (Twitter). Prosedur analisis mencakup pembersihan data, tokenisasi, ekstraksi entitas menggunakan Named Entity Recognition (NER), analisis sentimen, serta pengukuran kesamaan semantik berbasis TF-IDF cosine similarity. Skor keselarasan dihitung menggunakan formula Sa = αSm + βSs(norm). Hasil penelitian menunjukkan dominasi opini netral (92,24%) serta tingkat kesamaan semantik yang sangat rendah (<0,1), menandakan bahwa terminologi kebijakan digital seperti digital safety dan digital ethics belum terinternalisasi dalam wacana publik. Model EPAM-2025 juga menunjukkan performa evaluatif yang stabil dengan klasifikasi tepat pada dua kategori aktif (“Tidak Selaras” dan “Perlu Analisis Lanjut”). Penelitian ini memberikan kontribusi metodologis melalui pengembangan pendekatan kuantitatif yang objektif untuk mengukur keselarasan opini publik terhadap kebijakan, serta membuka peluang pemanfaatan analitik opini publik dalam mendukung perumusan kebijakan nasional berbasis bukti.
MEDAN SMART TOURISM INFORMATION SYSTEM BASED ON IOT AND ACO FOR ROUTE RECOMMENDATIONS AND VISITOR MANAGEMENT Septia Harliansyah; Muhammad Irfan Sarif; Zulham Sitorus; Eko Wahyudi
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 4 No. 12 (2025): NOVEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v4i12.1553

Abstract

The abstract serves as a concise summary of your research paper, highlighting the essential components that provide readers with an overview of your work. It should effectively capture the key issues addressed, the primary objectives of the study, the methods utilized, and the significant results achieved. This summary must be written in a single cohesive paragraph, limited to a maximum of 200 words. Ensure to follow the formatting specifications: use Times New Roman font, size 11, with single spacing, and present it in italics. The goal is to engage the reader while successfully conveying the importance and impact of your findings.
Sosialisasi Etika Berkomunikasi Digital sebagai Upaya Pencegahan Bullying di Lingkungan SMK Negeri 1 Stabat: Penelitian Dwika Ardya; Muhammad Irfan Sarif; Afif Asri; Dhimas Prayogi
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.4550

Abstract

The rapid development of digital technology among students brings both positive impacts and challenges, one of which is the increasing risk of digital bullying in school environments due to limited understanding of digital communication ethics. This Community Service Program (PKM) aims to improve students’ understanding of digital communication ethics as an effort to prevent bullying at SMKN 1 Stabat. The methods employed include socialization activities, interactive discussions, and the presentation of case studies related to communication behavior in digital spaces. Evaluation was conducted by comparing students’ levels of understanding before and after the program. The results indicate significant improvements across all assessed aspects. Understanding of digital communication ethics increased from 40% to 85%, knowledge of digital bullying rose from 45% to 90%, and students’ awareness of the impacts of bullying improved from 60% to 88%. These percentage increases demonstrate that the socialization of digital communication ethics is effective in enhancing students’ digital literacy and contributes to the creation of a safe and bullying-free school environment. This program is expected to serve as a sustainable preventive education model within educational institutions.
Analisis Sentimen Masyarakat terhadap Isu Korupsi Dana Bencana di Indonesia Menggunakan Metode Bidirectional Long Short-Term Memory (Bi-LSTM) Prabowo, Toni; Muhammad Irfan Sarif; Sebayang, Aradi; Ferdillah, Tengku Didi; Muhammad Azuan
Jurnal Komputer Teknologi Informasi Sistem Informasi (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.756

Abstract

Corruption of disaster relief funds and social assistance is a critical issue that undermines social justice and public trust in government integrity in Indonesia. This phenomenon has triggered a massive wave of opinions on social media, necessitating deep computational analysis to objectively understand public perception dynamics. This study aims to implement and evaluate the performance of a Deep Learning algorithm, specifically Bidirectional Long Short-Term Memory (Bi-LSTM), in classifying public sentiment related to the issue of disaster fund corruption. The dataset comprises 1,358 textual data points categorized into negative, neutral, and positive sentiments, with a significant dominance of the negative class (926 entries). The proposed model architecture integrates a 300-dimensional embedding layer, a Bi-LSTM layer to capture bidirectional context, and a combination of Global Max Pooling and Global Average Pooling for optimal feature extraction. The experimental results demonstrate that the model achieved an accuracy of 0.75, with a Weighted F1-score of 0.76 and a Macro F1-score of 0.65. Confusion Matrix analysis reveals that the model is highly effective in identifying negative sentiments but faces challenges in distinguishing minority classes due to data imbalance and linguistic ambiguities such as sarcasm. These findings provide deep insights for policymakers regarding public sentiment and demonstrate both the potential and limitations of the Bi-LSTM method in processing informal and sarcastic Indonesian text within the context of political and corruption discourse. Keywords: Sentiment Analysis, Bi-LSTM, Disaster Fund Corruption, Deep Learning, Natural Language Processing