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All Journal Bulletin of Network Engineer and Informatics (BUFNETS) Jurnal Ilmiah Informatika dan Ilmu Komputer
Rama Ijon Turnip
Universitas Sam Ratulangi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Pengembangan Aplikasi Berbasis Java Untuk Manajemen Pemesanan Paket Wisata Maritim Sulawesi Utara Aditya Lapu Kalua; Megastin Massang Lumembang; Dewi Christa Kobis; Rama Ijon Turnip
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 4 No. 2 (2025): Volume 4 Nomor 2 September 2025
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v4i2.63

Abstract

Pariwisata maritim di Sulawesi Utara menyimpan potensi besar sebagai daya tarik utama dengan kekayaan alam laut dan destinasi eksotisnya. Namun, sistem pemesanan paket wisata yang masih dilakukan secara manual seringkali menyebabkan kesalahan pencatatan, keterlambatan konfirmasi, hingga penurunan kualitas layanan. Penelitian ini bertujuan mengembangkan aplikasi manajemen pemesanan berbasis Java untuk membantu pelaku usaha wisata dalam mengelola proses pemesanan secara efisien dan terstruktur. Metode pengembangan yang digunakan adalah Rapid Application Development (RAD), yang memungkinkan iterasi cepat dan penyesuaian berdasarkan umpan balik pengguna. Aplikasi ini dilengkapi dengan fitur pengelolaan data pelanggan, jadwal perjalanan, sistem pembayaran online, serta laporan statistik. Implementasi sistem menunjukkan peningkatan efisiensi operasional, pengurangan kesalahan manual, dan peningkatan pengalaman pengguna. Solusi ini diharapkan menjadi bagian dari percepatan transformasi digital sektor pariwisata di Sulawesi Utara.
Comparative Analysis of Seven Machine Learning Algorithms for Morphology-Based Classification of Cammeo and Osmancik Rice Varieties Aditya Kalua; Mochamad Agung Wibowo; Luther Alexander Latumakulita; Wisard Widsli Kalengkongan; Rama Ijon Turnip
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/scp7n107

Abstract

Accurate varietal identification of rice grains is crucial for quality assessment and data-driven decision-making in agricultural informatics. This study aims to comparatively eval-uate seven machine learning algorithms for morphology-based classification of Cammeo and Osmancik rice varieties and to identify the most suitable model for structured numerical grain-feature data. Using a dataset of 3,810 instances with seven image-derived morpho-logical features, a systematic comparison was conducted across Logistic Regression, Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, and k-Nearest Neighbors. The models were evaluated based on classification quality and computational efficiency. Results show that MLP achieved the highest overall predictive performance with an accuracy of 93.03% and an F1-score of 94.17%. However, when balancing accuracy against computational overhead, SVM emerged as the optimal” sweet spot” for industrial implementation, offering a competitive 92.50% accuracy with a 93-fold reduction in execution time compared to MLP. Naive Bayes demonstrated the fastest computational runtime (0.0022 seconds total). The study identifies a distinct trade-off between predictive quality and runtime efficiency, recommending MLP for high-fidelity research and SVM for real-time agricultural informatics applications.
Co-Authors Aditya Kalua Dewi Christa Kobis Luther Alexander Latumakulita Megastin M. Lumembang Mochamad Agung Wibowo Wisard Widsli Kalengkongan