Article Per Year (5 Year)
p-Index From 2021 - 2026
8.401
P-Index
This Author published in this journals
All Journal Sainteks JUITA : Jurnal Informatika Proceeding Seminar LPPM UMP Tahun 2014 Sain Akuatik PROSIDING SENATEK FAKULTAS TEKNIK UMP Al Ishlah Jurnal Pendidikan Jurnal SOLMA JRST (Jurnal Riset Sains dan Teknologi) Jurnal Perikanan Universitas Gadjah Mada KACANEGARA Jurnal Pengabdian pada Masyarakat JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Geodika: Jurnal Kajian Ilmu dan Pendidikan Geografi Community Development Journal: Jurnal Pengabdian Masyarakat Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Pengabdian UNDIKMA Agrokompleks Linguistics and Literature Journal Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Proceedings Series on Social Sciences & Humanities Proceedings Series on Physical & Formal Sciences Dulang: Jurnal Pengabdian Kepada Masyarakat Prosiding University Research Colloquium Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Innovative: Journal Of Social Science Research Jurnal Pengabdian Masyarakat dan Riset Pendidikan Engagement: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Kepada Masyarakat Kalam Baktimu : Jurnal Pengabdian Kepada Masyarakat English Journal Antartika Baktimu : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Ultras
. Suwarsito
Universitas Muhammadiyah Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

Published : 100 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 15 Documents
Search
Journal : juita jurnal informatika

 1   2 
Kinerja Algoritma Pelatihan Levenberg-Marquardt dalam Variasi Banyaknya Neuron pada Lapisan Tersembunyi Hindayati Mustafidah; Muhamad Zaeni Budiastanto; Suwarsito Suwarsito
JUITA : Jurnal Informatika JUITA VoL. 7 Nomor 2, November 2019
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1063.391 KB) | DOI: 10.30595/juita.v7i2.5863

Abstract

Algoritma pelatihan Levenberg-Marquardt (LM) merupakan algoritma pelatihan yang paling optimal daripada algoritma pelatihan lainnya ditinjau dari eror yang dihasilkan. Kondisi tersebut menggunakan 10 neuron pada lapisan tersembunyi. Banyaknya neuron dalam lapisan tersembunyi yang digunakan dalam proses pembelajaran berpengaruh pada kinerja jaringan. Sebagai kelanjutan dari penelitian sebelumnya, maka dalam penelitian ini dilakukan analisis terhadap kinerja algoritma pelatihan LM ditinjau dari epoh yang diperlukan oleh jaringan menggunakan beberapa variasi banyaknya neuron dalam lapisan tersembunyi. Epoh dipandang sebagai salah satu parameter jaringan syaraf tiruan yang digunakan sebagai tolok ukur kinerja. Tahapan penelitian yang dilakukan adalah membangun program komputer menggunakan MATLAB untuk menjalankan algoritma pelatihan LM, selanjutnya rata-rata epoh jaringan dalam 20 kali perulangan sebagai data penelitian dianalisis menggunakan ANOVA. Algoritma pelatihan LM dijalankan dengan 5, 10, dan 15 neuron pada lapisan input dengan 1 neuron pada lapisan output, dan variasi banyaknya neuron pada lapisan tersembunyi untuk masing-masing banyaknya neuron pada lapisan input. Variasi banyaknya neuron pada lapisan tersembunyi digunakan untuk menemukan kondisi optimal algoritma pelatihan yang berupa rata-rata epoh paling kecil. Hasil analisis menunjukkan bahwa kondisi optimal algoritma peltihan LM dengan 5 neuron pada lapisan input dicapai pada penggunaan 9 neuron pada lapisan tersembunyi dengan rata-rata epoh sebesar 10.80; untuk 10 neuron pada lapisan input dicapai pada penggunaan 19 neuron pada lapisan tersembunyi dengan rata-rata epoh sebesar 21.52; dan untuk 15 neuron pada lapisan input dicapai pada penggunaan 29 neuron pada lapisan tersembunyi dengan rata-rata epoh sebesar 7.38.
Freshwater Fish Classification Based on Image Representation Using K-Nearest Neighbor Method Suwarsito Suwarsito; Hindayati Mustafidah; Tito Pinandita; Purnomo Purnomo
JUITA : Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1247.934 KB) | DOI: 10.30595/juita.v10i2.15471

Abstract

Indonesia is a maritime and agricultural country with enormous world fishery potential. The large variety of fish is often confusing for ordinary people in recognizing types of fish, especially freshwater fish. It was stated that the types of freshwater fish often consumed by the Indonesian people are bawal (pomfret), betutu, gabus (cork), gurame (carp), mas (goldfish), lele (catfish), mujaer (tilapia), patin (asian catfish), tawes, and nila (tilapia nilotica). Some fish types have similar shapes, so it is tricky to tell them apart. Meanwhile, in the digitalization era today, Artificial Intelligence (AI)-based technology has become a demand in all areas of life. It is overgrowing, not apart from the fisheries sector. Therefore, in this study, the K-Nearest Neighbor (KNN) method was applied as one of the methods in AI to identify and classify freshwater fish species based on their images. The KNN method classifies new data into specific classes based on the distance between the new data and the closest k data through the learning process. This KNN model is built by preparing the dataset stages, separating the dataset into data-train and data-test with a ratio of 70%:30%, then building and testing the model. The dataset is freshwater fish images, totaling 100 images from 10 freshwater fish types. Model testing is done by measuring performance using a confusion matrix. Based on the test results, the model has an accuracy performance of 70%. Thus, KNN can be used as a model to identify freshwater fish species based on their image.
Freshwater Fish Classification Based on Image Representation Using K-Nearest Neighbor Method Suwarsito Suwarsito; Hindayati Mustafidah; Tito Pinandita; Purnomo Purnomo
JUITA: Jurnal Informatika JUITA Vol. 10 No. 2, November 2022
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v10i2.15471

Abstract

Indonesia is a maritime and agricultural country with enormous world fishery potential. The large variety of fish is often confusing for ordinary people in recognizing types of fish, especially freshwater fish. It was stated that the types of freshwater fish often consumed by the Indonesian people are bawal (pomfret), betutu, gabus (cork), gurame (carp), mas (goldfish), lele (catfish), mujaer (tilapia), patin (asian catfish), tawes, and nila (tilapia nilotica). Some fish types have similar shapes, so it is tricky to tell them apart. Meanwhile, in the digitalization era today, Artificial Intelligence (AI)-based technology has become a demand in all areas of life. It is overgrowing, not apart from the fisheries sector. Therefore, in this study, the K-Nearest Neighbor (KNN) method was applied as one of the methods in AI to identify and classify freshwater fish species based on their images. The KNN method classifies new data into specific classes based on the distance between the new data and the closest k data through the learning process. This KNN model is built by preparing the dataset stages, separating the dataset into data-train and data-test with a ratio of 70%:30%, then building and testing the model. The dataset is freshwater fish images, totaling 100 images from 10 freshwater fish types. Model testing is done by measuring performance using a confusion matrix. Based on the test results, the model has an accuracy performance of 70%. Thus, KNN can be used as a model to identify freshwater fish species based on their image.
Expert System for Diagnosing Gourami Fish Diseases Using the Certainty Factor Approach Hindayati Mustafidah; Ilham Gunadi; Cahyono Purbomartono; Suwarsito Suwarsito; Eri Zuliarso
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.26031

Abstract

Gourami is an economically significant fish in the aquaculture sector due to its high market demand and relatively stable price. However, it is also challenging to cultivate, with disease outbreaks being one of the primary difficulties. Early diagnosis of gourami fish diseases requires expertise from fish health specialists, who are often difficult to find due to their limited availability. With advancements in artificial intelligence-based technology, this study developed an expert system to diagnose gourami fish diseases based on observed symptoms. The system employs the Certainty Factor (CF) approach to estimate the likelihood of a particular disease affecting the fish. The Certainty Factor approach utilizes a knowledge base derived from expert knowledge to address uncertainty in diagnosis. The certainty factor weights are determined based on confidence levels from both experts and users to generate an accurate diagnosis. This expert system was developed using data from 20 types of gourami fish diseases and 38 associated symptoms. The system successfully identified diseases with a certain level of confidence and provided appropriate treatment recommendations based on the confidence level obtained. By implementing this expert system, the risk of disease outbreaks can be minimized, thereby improving efficiency and productivity in gourami fish farming while helping maintain fish health and reducing economic losses caused by disease.
Image-Based Classification of Freshwater Fish Species to Support Feed Recommendation Using Random Forest Hindayati Mustafidah; Suwarsito Suwarsito; Rahmat Setiawan; Abdul Karim
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.27358

Abstract

Accurate identification of freshwater fish species plays a vital role in aquaculture, particularly in determining appropriate feed strategies to optimize fish growth. Visual similarities among species—such as color, shape, and surface texture—often hinder novice farmers from correctly recognizing fish types. This study proposes an image-based classification system using the Random Forest algorithm to identify six freshwater fish species: pomfret (bawal), gourami (gurame), catfish (lele), barb (melem), tilapia (nila), and Java barb (tawes) and provide automated feed recommendations. A total of 120 fish images were used as the dataset, collected from various sources, including online repositories and field documentation. Feature extraction was applied to capture color characteristics (HSV), texture patterns (GLCM), and morphological features (regionprops). The model was trained on 70% of the dataset and tested on the remaining 30%. Evaluation results show that the system achieved a classification accuracy of 83.33%, with a precision of 83.53%, recall of 83.33%, and an F1-score of 82.86%. Notably, catfish, barb, and tilapia classes achieved perfect classification, while pomfret and gourami showed room for improvement due to overlapping visual features. The findings indicate that the integration of Random Forest with multi-domain image features offers an effective, affordable, and practical solution to support the digital transformation of small and medium scale aquaculture systems through intelligent species recognition and feed guidance
Co-Authors Abdul Karim Abdurrohman Muzakki Ade Rusman Adi Imantoyo Afiani, Indri Ahmad Qurtubi Akhmad Darmawan Al Hikmatul Zahro Kamila Almira Ulimaz Aman Suyadi Aman Suyadi Anang Widhi Nirwansyah Anna Ulie Nafisha Archristhea Amahoru, Archristhea Arif Ihsanul Fikri Arifudin, Opan Aris Hidayat Aris Hidayat Astika Nurul Hidayah Aulia, Safira Aviza, Aviza Alyama Ayundasari, Cantika Diffa Beny Wijarnako Budi, Larasati Ika Bustanol Arifin Cahyono Purbomartono Cantika Diffa Ayundasari Chodidjah Chodidjah Danang Dwi Harmoko Darmono Dedi Mulyawan, Dedi Diana Indra Dewi Didik Trianto Nugroho, Didik Trianto Dini Siswani Mulia Dinny Fauziah Dwi Ma’rifatun Khasanah Edi Ahyani Eri Zuliarso Esti Sarjanti Esti Sarjanti Euis Meinawati Faiza, Childa Fajriati, Hevi Hanifah Falasifa Azizah Fathurnnadi Shalihati Fitria Aulia Hamzah Syarifuddin, Hamzah Harjono Harjono Harna Adianto Heri Maryanto, Cahyono Purbomartono, Heri Maryanto, Herlin Widasiwi Setianingrum Hilmi, Mukhamad Rizal Hindayati Mustafidah Ibnu Afan Ikhsan Mujahid Ilham Gunadi Istiqomah, Adilah Al Jagad, Nimas Ayu Sekar Kinasih Jaka Purwa Nugraha, Jaka Purwa Jejentri, Jejentri Juanita Juanita kurniawan, rahmat adi Lestari, Viviana Lisma M. Agung Miftahuddin Mahmud, Annisa Kayla Azzira Meilani, Alen Fadila Mira Rosita Moh Aya Sofia Mohammad Ardin Pahlevi Mr. Suwarno, Mr. Muhamad Zaeni Budiastanto Mustolikh Mustolikh Nadifah, Itqon Ngaynun Nadiya Nur Apreli Nasril, Nasril Niken Herawati Ni`maturrohmah Ni`maturrohmah Noerhaliza Rachman Asriana Dwi Nur Mahmudah, Fitri Pambudi, Asidqo Kurnia Luhur Pradita, Mufidah Putri Prayogo, Adhitya Pribadi, Teguh Purnomo Purnomo Putri, Nadia Khasanah Radin Alhif Dirgantara Rahayu, Wiwit Sayekti Rahmat Setiawan Ramli, Akhmad Ratna Kartika Wati Ratna Kartikawati Rifari Baron Rijal, Muhammad Azharul Ririn Windari, Ririn Rizki Herdani, Rizki Rusnendi Rusnendi Sabila Tri Amelia Sakinah Fathrunnadi Shalihati Salim, Agus Nur Saputra, Eqwar Saputri, Seffiana Aprilka Sembiring, Darmawanta Shahiffa Nur Pranannisa Sigit Sriwanto Sri Wahyuni Sufi Alawiyah Sugeng Priyadi Susanto Susanto Susylowati, Dewi Sutomo Sutomo Sutomo Sutomo Tamam, Muhammad Taufik Tito Pinandita Triyadi Haryanto Utbah Aminatul Hofisah Veronica Sovita Sari, Veronica Sovita Vivian Lisma Lestari Viviana Lisma Lestari Viviana Lisma Lestari Wahyu Agung Ciptadi Wakhudin Wildan Ahid Mujamal Wiwin Bregasnia Wiwit Sayekti Rahayu Yuda, Sabik Husni Maula Yunia Dwi Pambudi, Yunia Dwi Yuniarista, Lulu Dwi Zahrah Hana Afifah