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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Ecotrophic, Journal of Environmental Science Elkom: Jurnal Elektronika dan Komputer Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Elementer (Elektro dan Mesin Terapan) JUTIK : Jurnal Teknologi Informasi dan Komputer Matrix : Jurnal Manajemen Teknologi dan Informatika Jurnal Teknologi Terapan MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA The International Journal of Remote Sensing and Earth Sciences (IJReSES)
I Ketut Swardika
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences

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VERTICAL DISTRIBUTION OF CHLOROPHYLL-A BASED ON NEURAL NETWORK TAKAHIRO OSAWA; CHAO FANG ZHAO; NUARSA I WAYAN; I KETUT SWARDIKA; YASUHIRO SUGIMORI
International Journal of Remote Sensing and Earth Sciences Vol. 2 (2005)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2005.v2.a1353

Abstract

An algorithm of estimating Vertical distribution of Chlorophyll-a (Chl-a) was evaluated based on Artificial Neural Networks (ANN) method in Hokkaido field in the northwest of Pacific Ocean. The algorithm applied to the data of SeaWiFS on OrbView-2 and AVHRR on NOAA off Hokkaido, has been applied on September 24, 1998 and September 28, 2001. Ocean color sensor provides the information of the photosynthetic pigment concentration for the upper 22% of the euphotic zone. In order to model a primary production in the water column derived from satellite, it is important to obtain the vertical profile of Chl-a distribution, because the maximum value of Chl-a concentration used to lie in the subsurface region. A shifted Gaussian model has been proposed to describe the variation of the chlorophyll-a (Chl-a) profile which consists of four parameters, i.e. background biomass (B0), maximum depth of Chl-a (zm), total biomass in the peak (h), and a measurement of the thickness or vertical scale of the peak (cr). However, these parameters are not easy to be determined directly from satellite data. Therefore, in the present study, an ANN methodology is used. Using in-situ data from 1974 to 1994 around Japan Islands, the above four parameters are calculated to derive the Chl-a concentration, sea surface temperature, mixed layer depth, latitude, longitude, and Julian days. The total of 6983 profiles of Chl-a and temperature are used for ANN. The correlation coefficients of these parameters are 0.79 (B0), 0.73 (h), 0.76 (cr) and 0.79 (zm) respectively. A site called A-linc off Hokkaido is used to evaluate Chl-a concentration in each depth. After comparing with in-situ data and ANN model, the results show good agreement relatively. Therefore, the ANN method is applicable and available tool to estimate primary production and fish resources from the space.
ESTIMATION OF TUNA FISHING GROUND IN LOW LATITUDE REGION USING SEA SURFACE HEIGHT GRADIENT DERIVED FROM SATELLITE ALTIMETRY: APPLICATION TO NORTHEASTERN INDIAN OCEAN Susumu Kanno; Yasuo Furushima; I Wayan Nuarsa; I Ketut Swardika; Atsushi Ono
International Journal of Remote Sensing and Earth Sciences Vol. 3 (2006)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2006.v3.a1209

Abstract

In order to improve the method for prediction of tuna fishing ground, the modification of the analysis about satellite altimeter data was made as trial. In this study, we focused on the satellite altimeter, TOPEX/POSEIDON series, to improve the method of fishing ground prediction. Fishery data were supplied as hook rate by local fishing information around Indonesia and hearing infromation. The gradient of sea surface height is calculated between the neighbor grid which has the maximum gradient. Result showed that the fishery data with hook rate over 0.8 are grouped in a zone from 1.0E-06 of sea prediction of fishing ground quantitatively, but also reasonable accuracy as shown in the change in the standard deviation. This method can be utilized for the effective fishing plan with the resource protection and the economy in the fishing operation in near future.
BIO-OPTICAL CHARACTERISTIC OF CASE-2 COASTAL WATER SUBSTANCES IN INDONESIA COAST I Ketut Swardika
International Journal of Remote Sensing and Earth Sciences Vol. 4 (2007)
Publisher : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30536/j.ijreses.2007.v4.a1218

Abstract

The result of our study in the bio-optical characteristic of mixed water substances or referred as water leaving radiance of chlorophyll-a in case-2 water. Apparent optical properties of chlorophyll-a(chl-a) influence by others water constituents eq.particle backscattering, and yellow substances absorption coefficients. We studied varies Chl-a concentration from 0.001 ug/l,-65.0 ug/l, mixed by suspended particle (SS) concentration from 0.01 mg/l-50.0mg/l, and yellow substances absorption coeficients (ay) from 0.001m - 5.0m. We used the simple radiative transfer equation in seawater method to simulate the Normalized water leaving radiance (NLw)of Chl-a with concentration less than 1 ug/l and less influence from other substances similiar to NLw of pure sea water characteristic. This high reflected at blue band. Otherwise, chl-a concentrations more than 1 ug/l, are similiar to the absorption characteristic of Chl-a with flourescene peak at 680 nm. The Cross characteristic (Hinge point) occurs at 530 nm. Higher SS concentration causes NLw characteristic of Chl-a change, where hinge point moves toward the longer wavelength. Higher yellow substance absorption coeficients cause NLw characteristic of Chl-a has strange behavior. To keep the NLw Chl-a characteristic SS concentration should be no more than 1 mg/l, and ay coeficient no more than 0.01m.
Analisis Temporal Gerakan Kata BISINDO Menggunakan Landmark Tangan dan LSTM dengan Keluaran Suara Berbasis ESP32 Secara Real-time I Gusti Agung Made Yoga Mahaputra; Putri Alit Widyastuti Santiary; I Ketut Swardika
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3258

Abstract

Indonesian Sign Language (BISINDO) serves as a primary communication medium for the deaf community; however, limited public understanding often creates barriers during daily interactions. This study aims to develop a real-time BISINDO word-level translation system using hand landmark extraction and temporal modeling with Long Short-Term Memory (LSTM). The system employs MediaPipe Hands to detect 21 hand landmarks per frame, which are then processed as sequential motion patterns to classify five BISINDO words: saya, terima kasih, maaf, nama, and kamu. A total of 250 gesture samples were recorded under controlled lighting conditions as the primary dataset. The processed sequences were used to train the LSTM model, which was subsequently integrated with an ESP32 microcontroller and a DFPlayer Mini module to produce direct audio output. Experimental results show that the model achieved an average accuracy of 86%, with precision and recall values ranging from 0.81 to 0.94. The confusion matrix analysis indicates that most gestures were correctly classified, although some errors occurred in gestures with similar initial motion trajectories. Integration testing demonstrated an average system latency of 3.8 seconds and an audio output success rate of 85%. These findings indicate that the proposed system is capable of translating BISINDO word-level gestures accurately, responsively, and consistently in real-time conditions. This study provides a strong foundation for the broader development of sign language translation systems, with potential enhancements in vocabulary expansion, multi-user datasets, and hardware optimization for deployment in real-world environments.
Sistem Inspeksi Visual Berbasis Web-camera Menggunakan YOLO untuk Deteksi Real-time Spaghetti defect   pada 3D Printing FDM I Gusti Agung Made Yoga Mahaputra; Putri Alit Widyastuti Santiary; I Ketut Swardika
Elkom: Jurnal Elektronika dan Komputer Vol. 19 No. 1 (2026): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v19i1.3976

Abstract

Print failure remains a major challenge in fused deposition modeling (FDM) 3D printing because it may lead to material waste, production delays, and potential damage to printer components. One frequent failure mode is the spaghetti defect  , in which extruded filament forms irregular strands due to poor bed adhesion, object displacement, or extrusion outside the intended printing path. This study develops a low-cost visual inspection system based on a web-camera and Raspberry Pi for real-time detection of spaghetti defect   using a YOLO object detection model. The proposed system captures visual data from the printing area, processes image frames locally on the edge device, and displays the detected defect  location using a bounding box, class label, and confidence score. The dataset consisted of 100 images, including 50 normal printing images and 50 spaghetti defect   images, divided into training, validation, and testing sets using a 70:20:10 ratio. Defect  images were annotated using bounding boxes to enable location-based defect  detection. The evaluation results show that the model achieved a precision of 91.30%, recall of 88.24%, F1-score of 89.74%, mAP@0.5 of 92.10%, and mAP@0.5:0.95 of 87.30%. Real-time testing also demonstrated that the system could detect spaghetti defect   with confidence scores ranging from 0.87 to 0.90. These results indicate that the integration of a web-camera, Raspberry Pi, and YOLO has potential as an economical and portable early monitoring system for FDM 3D printing failures. 
Co-Authors Atsushi Ono Budiada, I Made CHAO FANG ZHAO Dewa Ayu Indah Cahya Dewi Harry Saptarini, Ni Gusti Ayu Putu I Gusti Agung Made Yoga Mahaputra I Gusti Agung Made Yoga Mahaputra I Nyoman Kusuma Wardana I Nyoman Mudiana I Wayan Arthana I Wayan Nuarsa Ida Bagus Irawan Purnama NUARSA I WAYAN Putri Alit Widyastuti Santiary Putu Indah Ciptayani Santiary, Putri Alit Widyastuti Sugirianta, Ida Bagus Ketut Susumu Kanno Takahiro Osawa Widyastuti Santiary, Putri Alit YASUHIRO SUGIMORI Yasuo Furushima