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All Journal Information System for Educators and Professionals : Journal of Information System Informatics for Educators and Professional : Journal of Informatics JURNAL MANAJEMEN INFORMATIKA (JUMIKA) Angkasa: Jurnal Ilmiah Bidang Teknologi JURIKOM (Jurnal Riset Komputer) JOURNAL INFORMATICS, SCIENCE & TECHNOLOGY Jurnal ICT : Information Communication & Technology Jurnal Accounting Information System (AIMS) Narada : Jurnal Desain dan Seni Jurnal Dinamika Informatika (JDI) Hello World Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistim Informasi dan Teknologi AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) Journal of Literature Review Bianglala Informatika Linier: Literatur Informatika dan Komputer
Arif Rinaldi Dikananda
STMIK IKMI Cirebon
Arts Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Social Sciences Other

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Peningkatan Keterampilan Digital Pemuda Dan Pelaku UMKM Kota Cirebon Melalui Pelatihan Video Editing Arif Rinaldi Dikananda; Bani Nur Hakim; Aulia Nur Rochmah; Mochamad Arief Saputra
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

Video editing training is a strategic solution to the challenge of low digital skills of youth and Micro, Small and Medium Enterprises (MSMEs) in Cirebon City. This program aims to empower the community through capacity building in creating effective and attractive promotional video content. Activities were carried out through a participatory and practice-based approach, including an introduction to editing software, basic visual storytelling techniques, and content optimization for social media. The results of the training showed a significant improvement in participants' skills, with 85% able to produce promotional videos independently. In addition, this training encourages collaboration between creative youth and MSME players, expands the reach of digital marketing, and opens new job opportunities in the creative industry. With the improvement of digital skills, MSME players become more competitive, while youth have alternative income in the digital era. This program contributes to the growth of the creative economy and the reduction of unemployment in Cirebon City.
The Optimization of Learning Media Through Augmented Reality to Improve Student Learning Comprehension Saeful Anwar; Tati Suprapti; Yoga Nugraha; Arif Rinaldi Dikananda
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 18, No 2 (2026): Mei
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v18i2.3869

Abstract

This study presents the development and evaluation of an Augmented Reality (AR) based learning media optimized to enhance students’ comprehension of camera architecture concepts. The AR system was developed using Unity 3D integrated with Vuforia SDK, implementing a marker-based AR approach to ensure stability and compatibility with limited mobile device specifications. The system architecture consists of a mobile AR client, image-marker recognition module, 3D visualization engine, and learning interaction layer designed based on multimedia learning principles and cognitive load theory. A five-stage development framework was employed: planning, material collection, assembly, implementation, and evaluation. The AR media was applied in an undergraduate informatics course involving 30 students, using a one-group pretest–posttest design. Learning outcomes were analyzed using paired t-tests, Wilcoxon tests, normalized gain, and effect size measurements. Results show significant improvements across all cognitive dimensions (p < 0.001), with very large effect sizes (dz = 3.13) and a moderate normalized gain (g = 0.42). The findings indicate that AR provides strong practical impact on higher-order cognitive skills, particularly application and analysis, while highlighting limitations related to measurement instrument validity, absence of a control group, and limited sample generalizability, which will be addressed in future research through experimental comparison and extended system performance testing.
Analisis Kualitas Jaringan Hotspot Menggunakan Metode Quality of Service (QoS) dalam Mendukung Kegiatan Belajar Mengajar Di Sekolah Menengah Kejuruan Negeri 1 Gebang Mochammad Fatha Mudzhaffar; Martanto; Arif Rinaldi Dikananda; Ahmad Rifai
Jurnal Dinamika Informatika Vol. 14 No. 1 (2025): Jurnal Dinamika Informatika Volume 14 Nomor 1
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v14i1.513

Abstract

Hotspot adalah jaringan nirkabel yang menyediakan akses internet kepada pengguna melalui perangkat Wi-Fi. Kualitas jaringan hotspot sangat penting dalam mendukung berbagai aktivitas, termasuk kegiatan belajar mengajar. Untuk menilai performa jaringan, metode Quality of Service (QoS) digunakan sebagai pendekatan standar dalam mengukur parameter-parameter utama jaringan, seperti throughput, packet loss, delay, dan jitter. Penelitian ini bertujuan untuk menganalisis kualitas jaringan hotspot di SMK Negeri 1 Gebang menggunakan metode QoS. Hasil penelitian menunjukkan bahwa nilai throughput berada dalam kategori "Buruk" hingga "Sangat Buruk" pada jam-jam trafik tinggi (12:00-15:00), dengan nilai berkisar antara 150-318 kbps, sehingga memerlukan optimasi jaringan. Di sisi lain, parameter packet loss tercatat 0%, yang menempatkannya dalam kategori "Sangat Baik." Nilai delay berkisar antara 10,12 ms hingga 30,01 ms, menunjukkan responsivitas jaringan yang baik dalam kategori "Sangat Baik." Sementara itu, nilai jitter berada dalam kategori "Baik" meskipun mengalami sedikit fluktuasi pada jam sibuk. Secara keseluruhan, meskipun performa jaringan dinilai baik dalam aspek packet loss, delay, dan jitter, peningkatan kualitas throughput sangat diperlukan untuk memastikan koneksi yang stabil dan berkualitas, khususnya pada jam trafik tinggi. Temuan ini memberikan dasar untuk pengembangan strategi optimasi jaringan guna mendukung kegiatan pendidikan secara lebih efektif.
Pengembangan Model Pengelompokan Jenis Bencana Alam di Jawa Baratmenggunakan Algoritma K-Means Panji Adi Putra; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Bianglala Informatika Vol. 13 No. 1 (2025): Maret 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/bianglala.v13i1.11998

Abstract

Salah satu masalah terbesar yang dihadapi masyarakat Jawa Barat adalah bencana alam.Analisis berbasis data diperlukan untuk memahami pola kejadian bencana dan mendukung kebijakanmitigasi yang efektif karena berbagai jenis bencana. Untuk menganalisis data kejadian bencana diJawa Barat selama periode 2020–2023, penelitian ini menggunakan pendekatan KnowledgeDiscovery in Databases (KDD).Tahapan KDD meliputi pembuatan dataset, preprocessing untuknormalisasi dan penanganan data hilang, serta transformasi guna menentukan atribut utama.Algoritma K-Means digunakan dalam proses data mining untuk mengelompokkan wilayahberdasarkan jenis bencana dan intensitasnya. Tahap terakhir adalah interpretasi hasil, yang bertujuanuntuk memahami pola distribusi bencana. Hasil klasterisasi menghasilkan lima kluster utama. Cluster0 menunjukkan dominasi kejadian banjir dan kebakaran lahan, sering ditemukan di dataran rendahdengan karakteristik lingkungan yang rawan pembakaran liar. Cluster 1 didominasi oleh kejadiantanah longsor di wilayah perbukitan yang curah hujannya tinggi. Cluster 2 mencerminkan kombinasikejadian hujan angin dan kekeringan di daerah pedesaan dengan sumber daya air terbatas. Cluster 3menunjukkan kejadian bencana dengan frekuensi rendah dan distribusi yang merata, seringkali terkaitdengan daerah urban. Sementara itu, Cluster 4 memiliki tingkat heterogenitas tertinggi, mencakupberbagai jenis bencana dengan intensitas bervariasi di wilayah pegunungan dan lembah. Kualitasklasterisasi diukur menggunakan Davies-Bouldin Index (DBI) sebesar 0.085, mengindikasikanpemisahan kluster yang baik. Selain itu, analisis Performance Vector menunjukkan jarak total antarkluster sebesar 2.311, dengan jarak terbesar pada Cluster 4 (4.672). penelitian ini diharapkan dapatmembantu dalam perencanaan dan alokasi sumber daya yang lebih tepat sasaran untuk mitigasibencana.
Application of the K-Means Algorithm in the Segmentation of 3kg Lpg Customers Ginaselvia Ananda; Nana Suarna; Agus Bahtiar; Arif Rinaldi Dikananda; Faturrohman
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.v5i2.1853

Abstract

This research was motivated by PT Sumber Perkasa Mandiri's need to understand the purchasing patterns of 3 kg LPG gas customers more accurately in order to improve the effectiveness of its marketing strategy. The purpose of this study was to apply the K-Means Clustering algorithm to form customer segmentation based on transaction behavior. The method used is a quantitative approach with sales data analysis of 850 records through the stages of data selection, preprocessing, attribute transformation, and modeling using RapidMiner Studio. Model evaluation was carried out using the Davies-Bouldin Index to determine the optimal number of clusters. The results of the study show the formation of two main clusters, namely the premium customer cluster with high purchase frequency and high loyalty, and the low-activity customer cluster that only makes purchases when necessary. The best DBI value at K=2 of 0.057 indicates excellent cluster separation quality. These findings conclude that K-Means Clustering is effective in identifying differences in consumption behavior, and its implications provide a strategic basis for companies to design loyalty programs for high-value customers and more intensive promotions for low-activity customers.
Sentiment Analysis of “Cek Bansos” Application Reviews on Google Play Store Using the Naïve Bayes Algorithm NoviFirda Aini; Odi Nurdiawan; Tati Suprapti; Arif Rinaldi Dikananda; Fathurrohman
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.v5i2.1883

Abstract

The rapid development of digital public services requires a deeper understanding of user perceptions and experiences regarding government applications, including Cek Bansos. This study aims to identify the polarity of user reviews by applying the Multinomial Naïve Bayes algorithm to review data collected from the Google Play Store. The methodology includes text preprocessing, sentiment labeling, feature extraction using TF–IDF, and model training and evaluation based on accuracy, precision, recall, and F1-score. The results show that the model achieves an accuracy of 79.5%, with very high performance in the negative class (recall 0.97) but poor performance in the neutral class due to data imbalance. The dominance of negative sentiment in the dataset indicates that users face significant technical difficulties, particularly in registration, verification, and service access. These findings demonstrate that Multinomial Naïve Bayes is effective as a baseline model for sentiment analysis; however, improving data balance and quality is necessary to produce a more stable, accurate, and representative model for evaluating digital public services.
Analysis of the Effectiveness of Manual Deployment and CI/CD Github Actions in the Braisee Application Nenda Alfadil Seputra; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade Kurnia
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.v5i2.1916

Abstract

In the modern cloud-based software development ecosystem, the speed and reliability of the deployment process are critical elements. This study aims to evaluate the effectiveness of implementing Continuous Integration/Continuous Deployment (CI/CD) using GitHub Actions compared to manual methods for the machine learning API of the Braisee application hosted on Google Cloud Run. Using a quantitative approach with a comparative experimental design across ten testing iterations, this research measures deployment time efficiency, error rates, and system stability. The experimental results show a significant performance disparity, where the automated method based on GitHub Actions is considerably more efficient, with an average total duration of 111–167 seconds, reducing operational time by 40–60% compared to the manual method, which requires 297–364 seconds. In terms of reliability, the automated method achieves a 100% success rate with high consistency, whereas the manual method demonstrates substantial vulnerability to human errors such as mistyped project IDs and inconsistent image tagging. It is concluded that implementing CI/CD through GitHub Actions is a superior solution that improves time efficiency and ensures the stability of cloud-based applications compared to manual procedures.
Comparative Analysis of Serverless Container Service Performance Between Google Cloud Run and AWS App Runner in Cross-Cloud Architecture Muhammad Adithya Pratama; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade Kurnia
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.v5i2.1919

Abstract

Research on the performance of serverless container services is becoming increasingly important as the need for modern distributed and cross-cloud architectures grows. This study analyzes the performance of two leading serverless services, Google Cloud Run and AWS App Runner, in a cross-cloud architecture scenario. Testing was conducted using identical parameters, including container configuration, region, memory, vCPU, and concurrency. Performance testing included p95 latency, throughput, and error rate metrics using loads of up to 1000 virtual users. The results showed that Google Cloud Run provided more stable performance with p95 latency of 47–71 ms, throughput of 436–438 RPS, and 0% error rate. In contrast, AWS App Runner showed p95 latency of 490–651 ms with throughput variation of 388–410 RPS and an error rate of 2–4.41%. The difference in performance was due to autoscaling mechanisms, cross-cloud communication overhead, and resource contention. This study provides empirical evidence for selecting the optimal serverless service for distributed architectures.
Optimalisasi Evaluasi Kepuasan Mahasiswa Pada Mata Kuliah Data Manajemen Berbasis Machine Learning Bani Nurhakim; Arif Rinaldi Dikananda; Ryan Hamonangan; Ryan Ryan Agung Prasetyo
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 9 No 2 (2024): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2024)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v9i2.3197

Abstract

Evaluasi kepuasan mahasiswa merupakan aspek penting dalam memastikan kualitas pendidikan yang optimal, terutama pada mata kuliah Data Manajemen yang berperan vital dalam pengembangan keterampilan pengelolaan data di era digital. Mata kuliah ini tidak hanya mengajarkan teori dasar, tetapi juga mencakup penerapan praktis melalui proyek dan simulasi. Namun, permasalahan muncul ketika institusi sulit mengidentifikasi secara akurat kelompok mahasiswa dengan tingkat kepuasan rendah dan faktor-faktor spesifik yang memengaruhi kepuasan mereka. Hal ini dapat menghambat upaya peningkatan kualitas pembelajaran karena strategi yang diterapkan tidak selalu tepat sasaran. Maka dari itu, diperlukan metode yang efektif dan berbasis data untuk mengidentifikasi pola kepuasan mahasiswa secara mendalam, sehingga dapat digunakan sebagai acuan dalam perbaikan layanan dan pengajaran. Penelitian ini menggunakan metode machine learning dengan algoritma K-Means untuk mengelompokkan mahasiswa berdasarkan tingkat kepuasan mereka terhadap mata kuliah Data Manajemen. Data dikumpulkan melalui survei yang melibatkan 354 mahasiswa, dengan menggunakan lima dimensi kualitas layanan: tangible, reliability, responsiveness, assurance, dan empathy. Setiap dimensi diukur melalui 30 pertanyaan yang dirancang untuk menangkap persepsi mahasiswa terhadap berbagai aspek perkuliahan. Setelah data terkumpul, proses clustering dilakukan menggunakan algoritma K-Means untuk mengidentifikasi pola dalam jawaban responden. Metode Elbow digunakan untuk menentukan jumlah cluster optimal, dan Davies-Bouldin Index (DBI) dipakai untuk mengukur kualitas pengelompokan. Hasil penelitian menunjukkan bahwa dua cluster merupakan jumlah optimal, dengan distribusi yang merata antara mahasiswa dengan kepuasan tinggi dan rendah, masing-masing 50%. Cluster pertama menggambarkan mahasiswa dengan penilaian baik, sementara cluster kedua berisi mahasiswa dengan penilaian kurang baik. Temuan ini memberikan wawasan mendalam tentang kelompok mahasiswa yang memerlukan perhatian lebih dalam proses pembelajaran. Dengan menerapkan analisis berbasis K-Means, institusi pendidikan dapat mengidentifikasi dan merancang strategi peningkatan layanan secara lebih efektif, berdasarkan kebutuhan dan pola spesifik dari masing-masing kelompok mahasiswa. Penelitian ini membuktikan bahwa machine learning dapat memberikan pemahaman lebih mendalam terkait faktor-faktor yang memengaruhi kepuasan mahasiswa dan mendukung perencanaan kebijakan pendidikan yang tepat sasaran dan berkelanjutan.
Rancang Bangun Sistem Informasi Stok dan Restok Toko Sembako Berbasis Mobile untuk Mendukung Pengelolaan Penjualan, Persediaan, dan Pelaporan: Systematic Literature Review (SLR) Fania Lusiano; Arif Rinaldi Dikananda
Journal of Literature Review Vol. 2 No. 2 (2026): JULI-DESEMBER
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/79swh592

Abstract

Perkembangan teknologi informasi telah mendorong transformasi digital pada berbagai sektor usaha, termasuk Usaha Mikro, Kecil, dan Menengah (UMKM). Salah satu permasalahan yang masih sering ditemui pada toko sembako adalah pengelolaan stok barang, proses restok, pencatatan penjualan, serta penyusunan laporan yang masih dilakukan secara manual. Kondisi tersebut menyebabkan ketidaksesuaian data persediaan, keterlambatan pengadaan barang, serta rendahnya efektivitas pengambilan keputusan. Penelitian ini bertujuan mengidentifikasi, menganalisis, dan mengevaluasi berbagai penelitian terdahulu mengenai sistem informasi stok dan restok berbasis mobile untuk mendukung pengelolaan penjualan, persediaan, dan pelaporan pada toko sembako maupun usaha ritel sejenis. Metode penelitian yang digunakan adalah Systematic Literature Review (SLR) dengan mengacu pada pedoman Kitchenham. Tahapan penelitian meliputi perumusan pertanyaan penelitian, pencarian artikel pada basis data ilmiah, seleksi berdasarkan kriteria inklusi dan eksklusi, penilaian kualitas artikel, ekstraksi data, serta sintesis hasil penelitian. Hasil kajian menunjukkan bahwa penerapan sistem informasi berbasis mobile mampu meningkatkan akurasi data persediaan, mempercepat proses pencatatan transaksi, mempermudah proses restok, serta menghasilkan laporan yang lebih cepat dan akurat dibandingkan sistem manual. Selain itu, penelitian terdahulu menunjukkan bahwa pengembangan sistem berbasis mobile memiliki potensi besar dalam meningkatkan efisiensi operasional UMKM. Temuan penelitian ini diharapkan menjadi dasar dalam pengembangan sistem informasi stok dan restok berbasis mobile pada penelitian selanjutnya.
Co-Authors Abrar Hiswara Ade Irma Purnamasari Ade Irma Purnamasari Ade Rizki Rinaldi Agie Maulana Agus Surip Agus Surip Ahmad Faqih Ahmad Faqih Ahmad Faqih Ahmad Rifai Ahmad Rifa’i Ariska Sari Aruka Wijaya Aulia Nur Rochmah Bambang Irawan Bani Nur Hakim Bani Nurhakim Cistia Ningrum Dadang Sudrajat Dede Rohman Denni Pratama Denni Pratama Dian Ade Kurnia Dias Bayu Saputra Edi Wahyudin Fania Lusiano Farid Ali Ma&#039;ruf Fathurrohman Fathurrohman Fathurrohman Fathurrohman Faturrohman Fidya Arie Pratama Ginaselvia Ananda Harry Budi Santoso Hilpad Hidayat Iin Irfan Ali Irfan Ali Irfan Ali Julia Eka Yanti Kanda Muhamad Ishak Lukman Dwi Kusuma Martanto . Mochamad Arief Saputra Mochammad Fatha Mudzhaffar Muhamad Abdurohman Muhamad Aji Pratama Muhammad Adhari Febriyan Muhammad Adithya Pratama Nana Suarna Nenda Alfadil Seputra Nining Rahaningsih Nova Nur Sakbani NoviFirda Aini Nurilah Odi Nurdiawan Panji Adi Putra Pratama, Fidya Arie Putri Apriyani Raditya Danar Dana Raja Al-Fath Hidayat Rinovian Rais Ryan Hamonangan Ryan Ryan Agung Prasetyo Saeful Anwar Saeful Anwar Siskawati suroso, amat Tati Suprapti Tiar Imam Muarif Tio Prasetya Umi Hayati Yoga Nugraha Yudhistira Arie Wijaya