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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Of Computer, Network Security and Information System (IJCONSIST) AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Jurnal Ilmiah Teknik Informatika (TEKINFO) Sisfo: Jurnal Ilmiah Sistem Informasi Digital Transformation Technology (Digitech) MASALIQ: Jurnal Pendidikan dan Sains JEECS (Journal of Electrical Engineering and Computer Sciences) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Innovative and Creativity Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) International Journal of Information Engineering and Science Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara
Ani Dijah Rahajoe
Department Of Computer Science, Faculty Of Informatics, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
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Journal : bit-tech

 1 
Comparison of Fine-Tuning InceptionV3 and Xception for Eye Disease Classification Based on Fundus Images Irsyad Rafi Naufaldi; Ani Dijah Rahajoe; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3195

Abstract

Eye diseases represent a major global health concern that can lead to visual impairment and even blindness if not detected early. The shortage of ophthalmologists and unequal distribution of medical services highlight the need for automatic eye disease detection system increasingly essential. Therefore, the role of Artificial Intelligence (AI), particularly Deep Learning, is highly needed. This study aims to compare the performance of two CNN architectures InceptionV3 and Xception. Unlike previous studies, this paper provides a comparative Fine-Tuning analysis of two CNN models on multiclass eye disease. The approach applied is transfer learning with a fine-tuning technique on several final layers to achieve higher accuracy by optimizing pretrained models using large-scale datasets such as ImageNet. The dataset consists of 4,184 fundus images covering multiple eye disease with balanced class distribution, ensuring diversity that supports model generalization. Divided into train, valid, and test sets with a ratio of 70:15:15. The training employed Adam optimizer, a batch size of 16, a learning rate of 0.0001, and implements early stopping to prevent overfitting. The performance of the model was assessed using evaluation metrics including accuracy, precision, recall, and F1-score. Experimental results indicate that the Xception model achieved superior performance with an accuracy of 87.78%, precision of 0.89, recall of 0.88, and an F1-score of 0.88, outperforming InceptionV3 with an accuracy of 85.56%, indicates the model is reliable for preliminary diagnosis. These findings suggest that the architecture in Xception is more efficient in extracting features from limited yet complex medical datasets.
Waste Classification Using YOLOv8 and One Factor At a Time Muhammad Aldi Maulana; Eva Yulia Puspaningrum; Ani Dijah Rahajoe
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3209

Abstract

Solid waste management has become a significant global environmental challenge that affects both ecosystem sustainability and human well-being. The increasing volume of waste generated from daily human activities highlights the urgent need for technology-based solutions that support efficient waste sorting, recycling, and resource recovery. This study proposes an automatic waste classification system using the YOLOv8 algorithm, a state-of-the-art deep learning model capable of performing real-time object detection with high accuracy. A dataset consisting of 1,800 labeled waste images representing five main categories plastic, glass, metal, paper, and organic was used for model training and evaluation. To enhance performance, the One Factor at a Time (OFAT) approach was applied for hyperparameter optimization, focusing on learning rate, batch size, and number of epochs. Two models were compared: the default YOLOv8 configuration and the optimized YOLOv8 OFAT model. Experimental results show that the optimized YOLOv8 OFAT achieved a mAP@0.5:0.95 of 86.1%, slightly higher than the default YOLOv8 model with 85.8%. Although the improvement of 0.3% appears modest, it indicates better model consistency and reliability across various data conditions. The integration of the OFAT technique into YOLOv8 represents a novel contribution, demonstrating that systematic hyperparameter tuning can significantly enhance the efficiency and robustness of automated waste detection systems, thereby supporting environmental sustainability and the realization of a green economy.
Implementation of PSO Optimization on the LightGBM Algorithm for Air Pollution Classification Muchamad Dicky Alifiansyah; Ani Dijah Rahajoe; Eva Yulia Puspaningrum
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3243

Abstract

The survival of living things is highly dependent on the important role of air. Clean air that is free from pollution is a standard for a quality environment that supports life. The Machine Learning approach can be an alternative in conducting data-based air pollution monitoring to assist in making the right decisions to deal with air pollution early on. This research aims to optimize the performance of the Light Gradient Boosting Machine (LightGBM) algorithm in air pollution classification combined with PSO optimization. The LightGBM or Light Gradient Boosting Machine algorithm is a Gradient Boosting algorithm that has decision tree-based learning, but in its application, LightGBM is prone to overfitting because it is sensitive to hyperparameters. Therefore, optimization techniques are needed to maximize performance. Particle Swarm Optimization (PSO) is an optimization method inspired by the movement of flocks of birds searching for optimal solutions. The data used is the Air Pollution Standard Index data. The research method includes data collection, data preprocessing, splitting the data, PSO optimization, model training, and model evaluation. The results show that PSO optimization can improve the performance of the LightGBM model. The LightGBM model with PSO optimization produced an evaluation matrix with an accuracy of 0.9510, precision of 0.9256, recall of 0.9261, and F1-score of 0.9247, demonstrating the model's ability to accurately classify air pollution. Meanwhile, the LightGBM model without optimization produced an evaluation matrix with an accuracy of 0.9455, precision of 0.9201, recall of 0.9170, and F1-score of 0.9182.
Sensitivity Test Of Simple Additive Weighting And Weighted Product Methods For Toddler Nutritional Status Nadia Dita Salsabila; Ani Dijah Rahajoe; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3713

Abstract

Toddler nutritional status is an important indicator of child health and development and requires accurate assessment. In Posyandu, nutritional evaluation is often performed manually, which may lead to inefficiencies and inconsistencies when processing large amounts of data. Decision Support Systems (DSS) can assist health workers in conducting more systematic and objective assessments. Previous studies have applied multicriteria decision-making methods such as Simple Additive Weighting (SAW) and Weighted Product (WP) in various decision-making contexts. However, most studies mainly focus on producing ranking results and rarely examine how sensitive these methods are when criteria weights change. In addition, only limited research evaluates these methods using real anthropometric data collected from community health services such as Posyandu. Therefore, this study aims to analyze and compare the sensitivity of the SAW and WP methods in determining toddler nutritional status using empirical anthropometric data. The dataset consists of 412 toddlers collected from Posyandu activities, including gender, age, weight, height, and body mass index, which were converted into nutritional indicators. Sensitivity was assessed by modifying each criterion weight under two scenarios (0.5 and 1) and measuring the percentage change in the resulting preference values. The results show that the SAW method produced a change of 4%, whereas the WP method showed a change of 0.0028%. These findings indicate that SAW is more responsive to weight variations, while WP produces more stable preference values. The results provide empirical insight into the behavior of different multicriteria decision-making methods when applied to real nutritional monitoring data.
Co-Authors Achmad Ario Dwi Maulana Agung Subekti, Mohamad Rafli Agussalim Agussalim Agussalim, Agussalim Ainur Rahim Akash, Fransisco Rivaldi Andre Leto Andreas Nugroho Sihananto Andriano Lukas Angelo A Beltran Angelo A Beltran Jr Angga Dwi Cahyono Aninditya Daniar Anna Fauziah Arif Arizal arif arizal Arif Dwi Putra Arif Setyo Wibowo Aryo Bagus Satrio Wicaksono Azaidane, Dandi Azmi Maulana Mahardika Bawazir Fadhil Mohammad Bawazir Fadhil Muhammad Bimantoro, Bisma Satrio Brahmantio Widyo Trenggono Budi Mukhamad Mulyo Chaurina, Agfanadita Rezkia Denisa Septalian Alhamda Dhian Satria Yudha Kartika Dian Agus Prawinata Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fairus Irhab Adinata Efendi Fajar Indra Nur Alam Feriza, Reyana Dinda Maulan Fransiska, Amelia Hamdan Yuwafi Mastu Wijaya Henni Endah Wahanani I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Ida Retno Moeljani Indartono, Taqiyya Irsyad Rafi Naufaldi Jr, Angelo A. Beltran Kartini Kartini Khansa Alyssa Fauziyah M Mahaputra M. Mahaputra Made Hanindia Prami Swari Mas Nurul Hamidah Maulana, Hendra Megantara, Sofia Ramadhani Muchamad Dicky Alifiansyah Muchlisiniyati Safeyah Muhammad Aldi Maulana Muhammad Fahreal Bernov Muhammad Farhan Maulana Muhammad Iqbal Al Afgany Muhammad Rizky Firdaus Muhammad Suriansyah Muttaqin, Faisal Nadia Dita Salsabila Nafiendra Praba Hendyka Nurlaili, Afina Lina Nurlaili, Afina Lina P. Eko Prasetyo Pahlevy, Mohammad Reza Pangesti N Perkasa, Laurensius Gading Surya Piter Rudi Irson Rivaldo Lapon Pradana, Ilham Akbar Pramnesti, Adisty Regina Putra Bramantyo, Adam Putra, Brian Akhdan Rangga Laksana Aryananda Retno Mumpuni Retno Mumpuni Reza Aminullah Rifki Fahrial Zainal Rino Zakaria Satrio Budi Wahyuono Shagi Hisyam Al Fathony Soffiana Agustin, Soffiana Subekti, Mohamad Rafli Agung Suriansyah, Muhammad Suryantari, Putu Anggi Syariful Alim Waskito, Muhammad Rizal Winarko, Edi Yushinta Aristina Sanjaya