Article Per Year (5 Year)
p-Index From 2021 - 2026
3.562
P-Index
This Author published in this journals
All Journal Information Management For Educators And Professionals (IMBI) Jurnal Informasi dan Komputer JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Informatika Teknologi dan Sains (Jinteks) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Student Research JURSIMA Journal of Computation Science And Artificial Intelligence
Arif Rinaldi Dikananda
Unknown Affiliation
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

Published : 25 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 25 Documents
Search

 1   2   3 
Sales Data Analysis using Linear Regression Algorithm on Raw Water Sales Rohayati, Eti; Martanto; Arif Rinaldi Dikananda; Dede Rohman
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.809

Abstract

This study aims to assess the effectiveness of linear regression algorithm in predicting raw water demand by considering customer transaction data, raw water volume, and seasonal variables. The method used is Knowledge Discovery in Databases (KDD), including data selection, preprocessing, transformation, data mining, and result evaluation. The dataset is divided 80% for training and 20% for testing. The analysis results show that the linear regression model has a coefficient of determination (R²) of 0.77, which means that the model can explain 77% of the data variability. The prediction error value is low, with Mean Absolute Error (MAE) 0.06, Mean Squared Error (MSE) 0.01, and Root Mean Squared Error (RMSE) 0.08, indicating good accuracy. In the comparison between actual and predicted values, for actual data of 7,000 liters, the model predicts 7,984.70 liters. The variable number of customer transactions has the greatest influence on raw water demand, with a coefficient of 16,940.46, while seasonal factors have less influence. Based on these findings, it can be concluded that the linear regression algorithm is effective in predicting raw water demand, however further development is required to improve accuracy at extreme values, by adding variables or using more complex algorithms.
Implementasi Algoritma K-Means Clustering Dalam Pengelompokkan Data Jumlah Kerusakan Rumah Berdasarkan Kondisi Di Jawa Barat Fauziah Noor Musid; Arif Rinaldi Dikananda; Fathurrohman
Journal of Student Research Vol. 1 No. 3 (2023): Mei: Journal of Student Research
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jsr.v1i3.1155

Abstract

Berdasarkan data yang dipublikasikan oleh Badan Penanggulangan Bencana Daerah (BPBD), Jawa Barat merupakan provinsi dengan jumlah kejadian bencana alam tertingggi di Indonesia sebanyak 3006 kejadian untuk periode 2015-2021. Kondisi ini mengharuskan BPBD (Badan Penanggulangan Bencana Daerah) dan Pemda Provinsi Jawa Barat untuk memperhatikan penanggulangan bencana serta penanganan dampak bencana. Bencana-bencana yang terjadi dapat mengakibatkan dampak yang merusak berbagai bidang. Salah satu dampak yang sangat berpengaruh adalah dampak kerusakan rumah. Kerusakan rumah akibat bencana merupakan dampak yang menyangkut kerusakan pada bidang ekonomi, sosial dan lingkungan. Karena itu, penanganan dampak kerusakan rumah harus dilakukan secara matang, tepat serta cara penanganannya harus berkembang setiap saat. Agar kedepannya bisa melakukan penanganan seperti yang diinginkan, maka perlu diketahui klaster/kelompok bencana berdasarkan dampak jumlah kerusakan rumah berdasarkan kondisi akibat bencana yang telah terjadi di Jawa Barat, dengan cara melakukan pengimplementasian algoritma K-Means clustering untuk mengklasterisasikan data jumlah kerusakan rumah berdasarkan kondisi yang diambil dari portal resmi data terbuka milik Pemda Provinsi Jawa Barat yaitu Open Data Jabar. Dalam kaitannya dengan data dampak bencana, teknik pengelompokan pada data mining sangat berguna dalam mengelompokkan data dampak bencana berupa keruskan rumah berdasarkan kemiripannya. Proyek tugas akhir ini mengimplementasikan algoritma k-means untuk mengklasterisasi data jumlah kerusakan rumah akibat bencana berdasarkan kondisinya yang terjadi di Jawa Barat dan menghasilkan 2 klaster/kelompok dengan nilai dbi teroptimal sebesar 0,118 dimana klaster 0 berisi data yang berasal dari 16 Kabupaten di Jawa Barat dan klaster 1 yang berisi data yang berasal dari 9 Kota di Jawa Barat.
VISUAL SEMIOTIC ANALYSIS OF GAMIFICATION ELEMENTS IN DUOLINGO GERMAN Afifah, Azah; Odi Nurdiawan; Arif Rinaldi Dikananda
Journal of Computation Science and Artificial Intelligence (JCSAI) Vol. 3 No. 1 (2026): Journal of Computation Science and Artificial Intelligence (JCSAI)
Publisher : PT. Berkah Digital Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58468/jcsai.v3i1.24

Abstract

The advancement of digital technology has accelerated the growth of Mobile Assisted Language Learning (MALL), with Duolingo as one of the most popular gamified language-learning applications. This study analyzes the visual semiotics of gamification elements in the Duolingo German interface (versions 2022–2025). A descriptive qualitative approach with a case study design is employed. Data consist of screenshots of gamification elements (XP, streak, badges, leaderboard, mascot, and feedback animations) and related literature, analyzed using Roland Barthes’ semiotics, the Shannon and Weaver communication model, and Self Determination Theory within a sociocultural framework. The findings show that visual gamification elements construct the myth of an ideal learner who is always productive, consistent, and competitive. These elements have a dual motivational effect: they can both strengthen and undermine the needs for competence, autonomy, and relatedness, depending on users’ cultural context and meaning-making. The study enriches visual semiotics and gamified language learning research and offers UI/UX recommendations for developers and educators to design more humanistic and meaningful learning experiences. Abstrak Kemajuan teknologi digital telah mempercepat pertumbuhan Pembelajaran Bahasa Berbantuan Seluler (Mobile Assisted Language Learning/MALL), dengan Duolingo sebagai salah satu aplikasi pembelajaran bahasa berbasis gamifikasi yang paling populer. Studi ini menganalisis semiotika visual elemen gamifikasi dalam antarmuka Duolingo Jerman (versi 2022–2025). Pendekatan kualitatif deskriptif dengan desain studi kasus digunakan. Data terdiri dari tangkapan layar elemen gamifikasi (XP, streak, lencana, papan peringkat, maskot, dan animasi umpan balik) dan literatur terkait, yang dianalisis menggunakan semiotika Roland Barthes, model komunikasi Shannon dan Weaver, dan Teori Penentuan Diri dalam kerangka sosiokultural. Temuan menunjukkan bahwa elemen gamifikasi visual membangun mitos tentang pembelajar ideal yang selalu produktif, konsisten, dan kompetitif. Elemen-elemen ini memiliki efek motivasi ganda: mereka dapat memperkuat dan melemahkan kebutuhan akan kompetensi, otonomi, dan keterkaitan, tergantung pada konteks budaya dan pemahaman makna pengguna. Studi ini memperkaya semiotika visual dan penelitian pembelajaran bahasa yang digamifikasi, serta menawarkan rekomendasi UI/UX bagi pengembang dan pendidik untuk merancang pengalaman belajar yang lebih humanistik dan bermakna.
Application of the K-Means Algorithm in the Segmentation of 3kg Lpg Customers Ananda, Ginaselvia; Suarna, Nana; Bahtiar, Agus; 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.
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.
Co-Authors Ade Irma Purnamasari Afifah, Azah Ahmad Faqih Ananda, Ginaselvia Aria Pratama Candra, Candra Rahmawati Dadang Sudrajat Dede Rohman Denni Pratama Dian Ade Kurnia Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Fachry Abda El Rahman Fahmi Naufal Fathurrohman, Fathurrohman Fatihanursari Dikananda Faturrohman Fauziah Noor Musid Gifthera Dwilestari Hanifah Nur Aulia Ica, Ica Pandia Irfan Ali Laylatunna'imah Martanto Moch Feri Izulhaq Muhammad Adithya Pratama Mulyawan Mulyawan Mulyawan Musofi Musofi Nana Suarna nur syarief abdullah Nurjana Adi Wijaya Odi Nurdiawan Rohayati, Eti Rohman, Dede Rudi Kurniawan Saeful Anwar Saefullah Fasa Santi Nurjulaiha Shofian Yunus Suarna, Nana Tati Suprapti Tio Prasetya Wiratriyana