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All Journal ComEngApp : Computer Engineering and Applications Journal Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi INTEK: Informatika dan Teknologi Informasi Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics Algoritme Jurnal Mahasiswa Teknik Informatika
Novitasari, Dian C Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Transportation Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Implementation of LSTM Method on Tidal Prediction in Semarang Region Ambadar, Panreshma Rizkha; Novitasari, Dian C Rini; Farida, Yuniar; Hafiyusholeh, Moh; Setiawan, Fajar
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.8932

Abstract

Semarang is the capital of the Central Java province, located in the north and directly adjacent to the Java Sea. Having an almost flat land condition with a slope of about 0-2%, Semarang City has the opportunity to experience tidal flooding. The occurrence of tides does not have a fixed period. So, it is necessary to predict the height of the tide and the ebb of the seawater. Thus, this research aims to predict tides in the Semarang area using the LSTM method. The data used is tidal data in Semarang waters from 2020 to 2024. The advantage of the LSTM method is its ability to effectively remember time series data or data with long-term dependence. LSTM can store past information using special cells contained in its structure. This research on tidal prediction using the LSTM method with 70% training data trial batch size 32 and epoch 200 obtained the smallest error value, namely the MAE value of 0.0388 and MAPE of 0.0313 which is the best LSTM result.
Detection of Diabetic Retinopathy Using Hybrid InceptionResNetV2-KELM Method Musfiroh, Musfiroh; Novitasari, Dian C Rini; Hakim, Lutfi; Damayanti, Adelia; Haq, Dina Zatusiva; Aisah, Siti Nur
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11967

Abstract

Diabetic Retinopathy (DR) is a complication of Diabetes Mellitus (DM), both type 1 and type 2 DM. Based on its severity, DR is divided into mild DR, moderate DR, severe DR, and proliferative DR stages. Manual detection is difficult because there is a fairly small difference between normal and DR. The Computer-Aided Diagnosis (CAD) system is a solution for detecting the severity of DR quickly and accurately so that DR sufferers do not get worse, which can cause blindness. This study uses fundus images from the Mesindor dataset consisting of four classes, namely normal, mild DR, moderate DR, and severe DR, with the InceptionResNetV2-KELM hybrid method. InceptionResNetV2 is used as a feature extraction and Kernel Extreme Learning Machine (KELM) as its classification. Several types of kernels are applied as model trials. The results show the highest sensitivity lies in the polynomial kernel experiment with a sensitivity value of 99.88%, an accuracy of 99.88%, and a specificity of 99.96%. The method used is able to detect very well and is quite time-effective compared to conventional CNN.
Co-Authors Abdulloh Hamid Adzan, M. Sailul Ahmad Hanif Asyhar Ambadar, Panreshma Rizkha Andriyani , Mayrisa Damayanti, Adelia FAJAR SETIAWAN Fatmawati Fatmawati Hani Khaulasari Haq, Dina Zatusiva Hartanto, Salsabila Jannah, Rachma Raudhatul Lutfi Hakim Moh. Hafiyusholeh Musfiroh Musfiroh, Musfiroh Nurwilda , Siti Pramesti, Diah Devi Puspitasari, Wahyu Tri Putri, Risma Madurahma Ramadanti , Alvin Nuralif Rimuljo Hendradi Rinda Nariswari Rizal Amegia Saputra Sholahuddin, Muhammad Zulfikar Siti Nur Aisah Sri Wahyuni Wulandari, A’isyah Yuniar Farida