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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 IPSSJ
Arif Rinaldi Dikananda
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Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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RANCANG BANGUN APLIKASI SISTEM INFORMASI PENDATAAN PELAUT BERBASIS WEB Arif Rinaldi Dikananda; Saefullah Fasa; Irfan Ali; Gifthera Dwilestari
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.473

Abstract

PT. Abdi Marine is one of the companies that has not used a web-based information system in the marine data collection section, where the data processing system is still manual. It often happens that seafarers' registration and flight date research takes up a lot of paper and seafarer data storage space, the calculation of the date is less accurate and making reports of incoming and outgoing seafarers' data takes a lot of time. To emphasize and learn in understanding the problems as described, the problem formulation that researchers can explain is to design a computerized marine crew data collection information system, create a database of data services for managers to carry out their work. The purpose of this research is to find out, develop and create an ongoing data collection application system into the PHP and HTML programming language using the MySQL database. So that researchers can draw conclusions in processing sailor crew data collection by implementing applications that have been designed and built in a systematic and structured manner, so that the level of damage in the process of implementing sailor crew data collection can be resolved.
RANCANG BANGUN SISTEM INFORMASI PERSEDIAAN BARANG BERBASIS WEB PADA PT PARAGON FURNITAMA INDUSTRY Arif Rinaldi Dikananda; Shofian Yunus; Saeful Anwar; Odi Nurdiawan
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 3: Jursima Vol.10 No.3
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i3.474

Abstract

PT. Paragon Furnitama Industry is one of the companies engaged in the production of fabric and leather sofas, back seats, and chair cushions, at this time the inventory process is still done manually because it still uses records in books and Microsoft Excel, the process sometimes finds several problems including data redundancy, discrepancies in stock of goods with records, and providing long reports because data validation is needed first. So that the information received by the parties concerned is very difficult to obtain quickly. To emphasize and study the problems as described, the problem formulation that researchers can explain is to design an inventory information system so that the company's performance is getting better. The Design and Build of this Goods Inventory Information System is built based on a website. The design of the information system uses the Software Development Life Cycle (SDLC) with the waterfall method so that this design system is expected to improve performance and performance, especially those related to processing inventory data to making inventory reports at PT. Paragon Furnitama Industry.
Bibliometrik Analysis: Konten Video Untuk Meningkatkan Daya Tarik Pariwisata Arif Rinaldi Dikananda; Dadang Sudrajat; Fatihanursari Dikananda; Rudi Kurniawan; Martanto
Prosiding SISFOTEK Vol 8 No 1 (2024): SISFOTEK VIII 2024
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

The use of video content as a marketing tool in the tourism industry has seen a significant increase in recent years. This research aims to explore and develop effective video content strategies in increasing tourism appeal and influencing tourists' decisions to visit certain destinations. Research methods include bibliometric analysis of video content used in tourism marketing, as well as experiments to test the effectiveness of various video content strategies. The results of the study show that the characteristics of travel vlogs that include personal narratives, attractive visuals, and relevant information can increase user travel intentions. Additionally, audience engagement through short videos has proven to be a key factor in increasing travel interest. This research makes a new contribution in understanding the role of video content in tourism marketing and developing a video marketing strategy model that can be applied by the tourism industry to increase the attractiveness of tourist destinations. By utilizing the results of this study, the tourism industry can optimize the use of video content to reach a wider audience and increase positive perceptions of tourist destinations.
ANALISIS SEGMENTASI PELANGGAN VOUCHER WIFI DENGAN METODE K-MEANS Fahmi Naufal; Martanto; Arif Rinaldi Dikananda; Rohman, Dede
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5169

Abstract

AirNet Teknologi, yang bergerak di bidang konsultasi komputer dan penyedia layanan internet WiFi, melakukan penelitian untuk menerapkan teknik clustering menggunakan algoritma K-Means dalam menganalisis data penjualan voucher WiFi di cabang Talun dari 31 Oktober 2023 hingga 31 April 2024. Penelitian ini bertujuan untuk mengidentifikasi pola pembelian pelanggan berdasarkan jenis paket, durasi penggunaan, dan harga, guna meningkatkan strategi pemasaran perusahaan. Dataset yang dianalisis mencakup detail transaksi seperti tanggal, jenis produk, durasi penggunaan, dan total pembayaran. Proses analisis dilakukan menggunakan RapidMiner Studio dengan tahapan Knowledge Discovery in Database (KDD), termasuk seleksi data, praproses, dan evaluasi hasil clustering menggunakan Davies-Bouldin Index (DBI). Hasil menunjukkan jumlah klaster optimal adalah K = 6 dengan nilai DBI 0.182, menandakan kualitas clustering yang baik. Pelanggan dikelompokkan ke dalam enam klaster dengan karakteristik berbeda, yang dapat digunakan untuk menargetkan promosi dan program loyalitas. Penelitian ini menekankan pentingnya analisis data dalam pengambilan keputusan strategis, memungkinkan AirNet Teknologi untuk menyusun strategi pemasaran yang lebih efektif, meningkatkan kepuasan pelanggan, dan memperkuat posisinya di pasar layanan WiFi.
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.
DETEKSI POLA CANDLESTICK MENGGUNAKAN YOLOV8 UNTUK ANALISIS TEKNIKAL BERBASIS CITRA Maulana Manshur; Odi Nurdiawan; Arif Rinaldi Dikananda; Fathurrohman
Integrative Perspectives of Social and Science Journal Vol. 3 No. 04 April (2026): Integrative Perspectives of Social and Science Journal
Publisher : PT Wahana Global Education

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

Abstract

Perkembangan teknologi computer vision dan deep learning telah meningkatkan analisis data keuangan, khususnya dalam pengenalan pola candlestick untuk pengambilan keputusan investasi. Namun, identifikasi pola secara manual cenderung subjektif, tidak konsisten, dan rentan terhadap kesalahan, terutama pada pola dengan kemiripan visual tinggi. Penelitian ini bertujuan mengevaluasi kinerja model YOLOv8 dalam mendeteksi pola candlestick secara otomatis serta menganalisis kemampuannya dalam membedakan pola yang memiliki kemiripan morfologis tinggi. Metode yang digunakan adalah pendekatan kuantitatif eksperimental dengan dataset sebanyak 4.435 citra yang diperoleh dari Roboflow. Model dilatih menggunakan konfigurasi standar YOLOv8 selama 100 epochs. Evaluasi dilakukan menggunakan metrik precision, recall, F1-score, dan mean Average Precision (mAP) pada rentang IoU 0.5–0.95. Hasil penelitian menunjukkan bahwa model mencapai precision 0.877, recall 0.898, F1-score 0.88, dan mAP sebesar 0.903. Model mampu mendeteksi pola dengan baik, namun performa menurun pada pola dengan kemiripan visual tinggi. Dengan demikian, YOLOv8 dinilai efektif untuk pengembangan sistem analisis teknikal berbasis citra yang lebih objektif dan efisien.
Optimization of Classification of Tea Leaf Disease Images Using LBP–HOG and MobileNetV2 Ezar Qotrunnada; Odi Nurdiawan; Arif Rinaldi Dikananda; Aris Pratama Putra; Bani Nurhakim
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.v5i2.1861

Abstract

This study was motivated by the need for an accurate and efficient system for detecting tea leaf diseases, given that the current method Manual identification has limitations in terms of consistency, speed, and It also depends on expert labor. To address these challenges, the study It developed a classification model for detecting diseases in tea leaves using a combination of features Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) integrated with the MobileNetV2 architecture. The research method includes the following stages: importing the dataset, data partitioning, exploratory data analysis (EDA), preprocessing, features, and training four model scenarios: baseline MobileNetV2, LBP-based model, HOG-based model, and hybrid LBP–HOG model. Evaluation is done with the metrics of accuracy, precision, recall, and F1-score. The results show that the baseline model achieved 91.67% accuracy, the LBP model achieved 60.67%, the HOG model achieved 68.67% accuracy, and the hybrid model achieved 66.67% accuracy. These findings indicate that MobileNetV2 is still the most optimal model, but the integration of texture features and gradients provides a deeper understanding of the characteristics of disease patterns. This study emphasizes the importance of exploring classic features to enriching visual representation in lightweight CNN models, as well as providing a contribution to the development of plant disease diagnosis systems that are efficient.
Co-Authors Ade Irma Purnamasari Ahmad Faqih Aria Pratama Aris Pratama Putra Bani Nurhakim Dadang Sudrajat Dyana Fitri Ega Salsa Nugraha Eka Wisnu Ramadhona Ezar Qotrunnada Fahmi Naufal Fathurrohman, Fathurrohman Fatihanursari Dikananda Fauziah Noor Musid Gifthera Dwilestari Irfan Ali Martanto Maulana Manshur Moch Feri Izulhaq Mulyawan Mulyawan Musofi Musofi Nana Suarna nur syarief abdullah Nurjana Adi Wijaya Odi Nurdiawan Rohman, Dede Rudi Kurniawan Saeful Anwar Saefullah Fasa Shofian Yunus Tio Prasetya