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All Journal Jurnal Teknologi Informasi Cyberku Jurnal Elektronika dan Teknik Informatika Terapan International Journal of Technology and Education Research Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Putu Samuel Prihatmajaya
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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OPTIMASI MULTI SCALE RETINEX CITRA BAWAH AIR DENGAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION Pulung Nurtantyo Andono; Putu Samuel Prihatmajaya; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 1 (2019): Jurnal Teknologi Informasi CyberKU Vol. 15, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (661.556 KB)

Abstract

Penelitian tentang perbaikan citra sudah sejak lama dilakukan dan hingga saat ini masih dilakukan penelitian tentang perbaikan citra. Beberapa penelitian yang telah dilakukan menghasilkan beberapa usulan metode untuk perbaikan citra. Penelitian di bidang computer vision untuk lingkungan bawah air menjadi tantangan dari beberapa peneliti untuk melakukan image restoration. Karena untuk citra bawah air sering banyak menghadapi permasalahan intensitas cahaya, pertikel-partikel yang banyak mengganggu pandangan, ditambah jika terjadi gelombang akan membuat kestabilan dalam menggambil citra terganggu sehingga bisa mengakibatkan noise yang besar jika dibandingkan pengambilan citra di darat. Dalam penelitian ini pembahasan yang dilakukan adalah perbaikan citra bawah air dengan menggunakan MSCR (Multi Scale Retinex) dengan mengoptimalkan pembobotan dari MSCR dengan menggunakan teknik PSO(Particle Swarm Optimization) sebagai teknik optimasinya, sehingga mendapatkan tingakatan error yang lebih rendah. Sehingga hasil MSE (Mean Square Error) yang di dapatkan oleh MSCR adalah sebesar 5218,4249 dan hasil yang didapatkan dengan menggunakan PSO adalah sebesar 4955,0757
Pengujian Piranti Tempat Sampah Otomatis Berbasis Sistem Tertanam Menggunakan Mikrokontrol Arduino Uno Fransiska Delsiana; Christy Mahendra; Putu Samuel Prihatmajaya
Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK ) Vol. 1 No. 2 (2023): Juni: Jurnal Elektronika dan Teknik Informatika Terapan (JENTIK)
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v1i2.385

Abstract

Trash cans are very important in maintaining the cleanliness and health of the environment, especially in densely populated urban areas. Garbage bins are a necessity for today's society. Currently, technology is developing very rapidly in the development of industrial products, including automatic trash cans. This study aims to create an automatic trash bin based on Arduino Uno and an Ultrasonic Sensor device for object detection, LCD, LED, Speaker, and Servo Motors. The result of this research is an automation system in the form of an automatic trash can system using Arduino Uno. Testing of embedded system input and output devices worked well with tests of up to 40 times with a result of 90%. These results indicate that the embedded system input and output devices for automatic trash bins can function optimally, effectively and efficiently as industrial products.
Certainty Factor vs. Dempster-Shafer: Evaluating Accuracy in an Android Expert System for Tilapia Disease Diagnosis Christy Mahendra; Putu Samuel Prihatmajaya; Eduardus Gerry Henri; Jonathan Brian Wijaya; Agnes Florentina Santoso; Suyudi
International Journal of Technology and Education Research Vol. 3 No. 03 (2025): July - September, International Journal of Technology and Education Research (
Publisher : International journal of technology and education research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63922/ijeter.v3i03.2006

Abstract

This study aims to develop an Android-based expert system to help fish farmers detect diseases in tilapia (Oreochromis niloticus) quickly and accurately. This system implements two inference methods, namely Certainty Factor (CF) and Dempster-Shafer (DS), which are then compared to assess their effectiveness and accuracy in the diagnosis process. The research was conducted in Purwonegoro Subdistrict, Banjarnegara Regency, which is one of the centers of tilapia farming in Central Java.The knowledge base in this system is compiled based on disease symptom data obtained from interviews with experts and scientific literature references. The developed Android application allows users to enter symptoms that appear on fish to get diagnosis results along with confidence levels and treatment suggestions. System testing is carried out using real case data from the field, and the diagnosis results are compared with evaluations by experts.The results show that both methods are able to provide fairly accurate diagnoses. The Certainty Factor method excels in terms of speed and simplicity in calculation, while the Dempster-Shafer method is better able to handle uncertainty from non-specific symptom combinations. The accuracy of the Dempster-Shafer method is slightly higher than the Certainty Factor, but the difference is not statistically significant.This expert system is expected to be a practical solution for fish farmers in identifying diseases early on, thus supporting the increase in productivity and efficiency of tilapia farming in the research area.
Pengukuran Kualitas Pengalaman Pengguna dan Ketepatan Diagnosa Sistem Pakar untuk Penyakit Ikan Nila Berbasis Android Christy Mahendra; Putu Samuel Prihatmajaya; Suyudi; Eduardus Gerry Henri; Agnes Florentina Santoso; Jonathan Briant Wijaya
Prosiding Vol 7 No 1 (2025): SNISTEK
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/psnistek.v7i1.10739

Abstract

This study aims to evaluate the user experience quality and diagnostic accuracy of an Android-based expert system application developed to assist in diagnosing diseases in tilapia fish. The evaluation employed the User Experience Questionnaire (UEQ) and the Certainty Factor (CF) approach. UEQ results indicate that the application delivers a highly satisfying user experience, with the highest scores on stimulation (1.68) and attractiveness (1.60), both categorized as Excellent. These results suggest that the application provides an engaging and enjoyable interactive experience. Other dimensions such as clarity (1.34), efficiency (1.37), accuracy (1.18), and novelty (1.20) fall into the Good category, indicating the system is intuitive, functional, and reliable, though there is still room for improvement.In terms of expert system performance, the Certainty Factor method successfully delivered relevant diagnostic results. Tricodiniasis emerged as the primary diagnosis with 100% confidence based on the user's selected symptoms. However, the appearance of several other diseases with similarly high confidence scores indicates the existence of overlapping symptoms across different conditions. This highlights the importance of using probabilistic approaches to manage uncertainty in disease diagnosis. Overall, the application demonstrates strong performance in user interface design, user interaction, and expert system accuracy, making it suitable as a supporting tool for diagnosing tilapia fish diseases
Co-Authors Agnes Florentina Santoso Eduardus Gerry Henri Fransiska Delsiana Jonathan Brian Wijaya Jonathan Briant Wijaya mahendra, christy Pulung Nurtantyo Andono Ricardus Anggi Pramunendar Suyudi