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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Applied Data Sciences
Hence Beedwel Lumentut
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Sistem Pendukung Keputusan untuk Memilih Budidaya Ikan Air Tawar Menggunakan AF-TOPSIS Hence Beedwel Lumentut; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7548

Abstract

AbstrakPotensi perikanan budidaya air tawar semakin meningkat, hal tersebut disebabkan produksi ikan sektor penangkapan mendekati “overfishing”. Budidaya perikanan air tawar memiliki beberapa alternatif ikan yang memiliki nilai ekonomis tinggi yaitu ikan Mas, ikan Mujair, ikan Nila, ikan Gurame, ikan Lele dan ikan Patin. Alternatif ikan ini memiliki karakteristik yang berbeda untuk masing-masing jenis pembudidayaannya. Parameter-parameter yang mempengaruhi proses budidaya ikan air tawar tersebut diantaranya: faktor kesesuaian air meliputi: suhu, kecerahan, DO (derivater oksigen), keasaman (pH). Sedangkan pemilihan budidaya perikanan yang menguntungkan bisa dinilai dari faktor finansial yaitu: NPV (Net Present Value), ROI (Return on Investment), BCR (Benefit Cost Ratio), PBP (Pay Back Period) dan BEP (Break Event Point). Sedangkan metode yang dipergunakan untuk pengambilan keputusan yaitu Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) sebagai salah satu model decision dapat digunakan untuk memberikan preferensi kepada para petani budidaya ikan, karena alternatif yang terpilih tidak hanya memiliki jarak terpendek dari solusi ideal positif tetapi juga jarak terpanjang dari solusi ideal negatif. Hasil dari penelitian ini menunjukkan bahwa sistem penunjang keputusan yang mempertimbangkan parameter kondisi lingkungan air dan faktor finansial dapat membantu petani budidaya ikan untuk menentukan jenis budidaya ikan air tawar yang akan dijalankan. Kata kunci—Ikan air tawar, Analisis Finansial, TOPSIS, SPK. AbstractFreshwater aquaculture potential is increasing, one of the reason is production of fishing over the sea is almost deal with "overfishing". Freshwater aquaculture fish have few alternatives such as Carp, Mossambique, Tilapia, Gouramy, Catfish and Pangacius. Each has different type of cultivation. The requirement parameters that influence the process of freshwater cultive is water suitability factors include: Temperature, Brightness, DO (derivated oxygen), acidity (pH) etc. While the selection of profitable aquaculture can be determind from financial bussines as: NPV (Net Present Value), ROI (Return on Investment), BCR (Benefit Cost Ratio), PBP (Payback Period) and BEP (Break Event Point). The methods that used to help the decision-making process that Method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as one of the decision models can be used to give preference to farmers fish farming, because the alternative is chosen not only have the shortest distance from a solution positive ideal but also the longest distance from the negative ideal solution. The results of this study show that decision support systems that take into account the environmental condition of water parameters and financial bussines can help fisherman to determine the type of freshwater Aquaculture culture to be run. Keywords— Fresh Water Fish, Financial Analysis, TOPSIS, SPK
Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method Luther Alexander Latumakulita; Silviani Esther Rumagit; Hence Beedwel Lumentut; Frangky Jessy Paat; Jaidun Ramadhan Kaplale; Enny Itje Sela
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1103

Abstract

This study aims to develop an intelligent system capable of recognizing traditional woven motifs of Puta Dino, a culturally significant textile from Tidore Island. These motifs are visually complex, poorly documented, and hard for the public to distinguish, highlighting the need for a digital tool to support cultural preservation and accurate identification. This research is the first to build a structured Puta Dino motif database and provide an integrated model designed for real-world use. The approach captured primary images of eight validated motifs and applied systematic preprocessing, including normalization and data augmentation, to enhance variability and strengthen the dataset. A lightweight deep learning model predicated on a convolutional neural network was designed to achieve a compromise between accuracy and computational efficiency. The system was evaluated through cross-validation and independent test data, as well as multiple real-world trials utilizing a web interface. These trials involved different image capture scenarios, including from a distance, moderate distance, close and angled views, and when the fabric surface was folded. The model architecture and system interface with the system are illustrated in the relevant figures, and the tables provide performance data on the system’s training, accuracy in motif classification, and achieved results in real-world conditions. The system demonstrated excellent classification accuracy in controlled test conditions. It showed real-world competency, accurately classifying most motifs in various conditions. The data also point to specific issues with motif recognition in extreme distortion cases, which reflect the typical issues of laboratory-to-field model deployment. The outcomes clearly demonstrate both the possibilities and the limitations of the currently available recognition of culturally significant textiles. The study concludes by exploring the possibilities of expanding the dataset and increasing the depth of learning through more sophisticated techniques, as well as enhancing accessibility to promote sustained community and cultural engagement.
Co-Authors Enny Itje Sela Frangky Jessy Paat Jaidun Ramadhan Kaplale Luther Latumakulita Silviani Esther Rumagit Sri Hartati