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ARSITEKTUR SISTEM 3D KERATON KESULTANAN TERNATE MENGGUNAKAN PHOTOGRAMMETRY STRUCTURE from MOTION BERBASIS VIRTUAL REALITY Arief, Assaf; Fuad, Achmad; Turuy, Seh; Kapita, Syarifuddin N
IJIS - Indonesian Journal On Information System Vol 11, No 1 (2026): APRIL
Publisher : POLITEKNIK SAINS DAN TEKNOLOGI WIRATAMA MALUKU UTARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36549/ijis.v11i1.474

Abstract

Keraton Kesultanan Ternate merupakan warisan budaya Maluku Utara yang rentan terhadap kerusakan fisik, sementara dokumentasi konvensional dinilai belum mampu menyajikan representasi bangunan secara menyeluruh dan interaktif. Penelitian ini merancang dokumentasi digital Keraton dalam bentuk model tiga dimensi (3D) menggunakan metode photogrammetry berbasis Structure from Motion (SfM) yang diintegrasikan ke dalam lingkungan Virtual Reality (VR) melalui arsitektur sistem tiga lapisan (Business–Application–Technology). Metodologi mencakup akuisisi citra multi-sudut, pemrosesan SfM (Align Photos, Dense Cloud, Mesh, Texture) menggunakan Agisoft Metashape, optimasi model via Blender, hingga integrasi navigasi imersif berbasis Unity. Hasil rancangan degan validasi lapangan, menunjukkan bahwa pendekatan ini layak untuk menghasilkan representasi digital Keraton yang akurat, aksesibel, dan edukatif tanpa keterbatasan akses fisik, sekaligus menjadi arsip permanen untuk mendukung pelestarian warisan budaya lokal di era digital.Kata Kunci: Photogrammetry, Structure from Motion, Model 3D, Virtual Reality, Pelestarian Budaya.
The idealPosition System: Sebagai Solusi Pendukung Keputusan untuk Menentukan Pemain Bola yang Ideal Berdasarkan Posisi Pemain Faisal F Taran; Abdul Mubarak; Firman Tempola; Achmad Fuad; Salkin Lutfi
JUSIFO : Jurnal Sistem Informasi Vol 6 No 2 (2020): December
Publisher : Program Studi Sistem Informasi, Fakultas Sains dan Teknologi, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/jusifo.v6i2.6468

Abstract

In North Maluku, especially in Ternate, many football schools have been opened to train and find potential Indonesian National players. One of them has contributed to producing Indonesian National player candidates, namely Sekolah Sepak Bola (SSB) Tunas Gamalama. The problems that occur at this time at SSB Tunas Gamalama, managers and coaches are difficult to determine the ideal player to fill each position. Oftentimes, SSB Tunas Gamalama students choose a position according to their idol football players, and also because of the popularity of these positions. The tendency of SSB Tunas Gamalama students to be like this results in an imbalance of potential players in certain position. During this time the coach takes a long time and is often subjective in selecting players at every position available. In this research, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used as a method of decision support. This article aims to build a Decision Support System (DSS) in determining the ideal football player based on the player's position (The idealPosition System) using the TOPSIS method. This research produces DSS which can be used to determine the ideal soccer player based on the player's position.
A RECALL-ORIENTED STACKING ENSEMBLE FOR EXTREME RAINFALL EARLY WARNING IN TERNATE Achmad Fuad; Muhammad Sabri Ahmad; Muhammad Ridha Albaar; Yasir Muin
JIKO (Jurnal Informatika dan Komputer) Vol 9 No 2 (2026)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v9i2.11857

Abstract

Extreme-rainfall detection in small tropical islands is difficult because extreme events are rare and false negatives have substantial disaster-mitigation consequences. This study aims to (1) compare imbalance-aware machine-learning and deep temporal models, (2) determine whether an out-of-fold stacking ensemble can maximize sensitivity, and (3) explain model behavior using SHAP. ERA5 hourly data for a representative Ternate grid point were collected from 1 January 2005 to 6 February 2026 and aggregated into 7,707 daily records; only 12 days met the extreme threshold of 75 mm/day. A 30-day sequence was used to predict the next-day class, and three expanding temporal test folds were evaluated. Weighted loss, focal loss, balanced tree learning, validation-based threshold selection, and leakage-controlled stacking were applied. Across 2,229 pooled test days containing four extreme events, the stacking model detected all events (recall 1.000) but produced low precision (0.0023), F2-score 0.0115, specificity 0.2256, and 1,723 false positives. Balanced Random Forest provided the best overall trade-off, with recall 0.750, F2-score 0.0316, specificity 0.7951, and MCC 0.0570. Therefore, stacking is useful as a high-sensitivity screening layer, but it is not yet suitable as a standalone operational warning model. Local observations and additional event samples are required for calibration.