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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pseudocode Jurnal Penelitian Ekonomi dan Bisnis International Journal of Advances in Intelligent Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Advances: Jurnal Ekonomi & Bisnis
Edi Noersasongko
Faculty Of Economics And Business, Dian Nuswantoro University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering

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Enhancing Low-Resolution Images of Mustard Leaves Affected by Pests with Thermal Sensor using Super-Resolution Convolutional Neural Network Optimization Susanto, Fredy; Nurtantio, Pulung; Soeleman, Arief; Pujiono, Pujiono; Noersasongko, Edi; Dedi, Dedi
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2841

Abstract

With urban areas facing limited agricultural land, hydroponic systems offer a solution to increase food storage and variety. Hydroponics, a farming technique that relies on water as a growing medium rather than soil, provides essential nutrients and oxygen for plants. This paper explores the use of thermal sensors to capture images of mustard leaves in a hydroponic system. In addition, it also explores thermal sensor images. These images are analyzed to detect pest attacks, with red leaves indicating the presence of pests and green/blue leaves unaffected by pests. These pests emit hot air; consequently, they turn red. The method of increasing resolution is to compare the Long Short-Term Memory (LSTM) algorithm with the Super-Resolution Convolutional Neural Network (SR-CNN) to improve the quality of images obtained from low-resolution sensors (AMG8833/Grid-EYE). The results show that the SR-CNN method is better than the LSTM (Long Short-Term Memory) method, although limitations remain due to the sensor resolution. After conducting the research, it could be observed that using LSTM resulted in a Mean Square Error (MSE) value of 0.001551685, while SR-CNN indicated an MSE value of 8.873. Furthermore, LSTM produces a Peak Signal-to-Noise Ratio (PSNR) value of 37.10797726, whereas SR-CNN achieves a PSNR of 39.199. The accuracy rates (SSIM) for LSTM and SR-CNN are 0.991538522961364 and 0.997747, respectively. These findings show that using the SR-CNN algorithm can effectively improve the quality of images produced by thermal sensors, even though the sensor pixel capacity is limited.
Classification of Breast Cancer Histopathology Images with Attention-Based Multiple Instance Learning Method Setiyani, Safira Hasna; Noersasongko, Edi; Affandy
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 4, November 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i4.2310

Abstract

Breast cancer is one of the deadliest types of cancer among women worldwide. Early detection plays a crucial role in increasing the chances of successful treatment and reducing the risk of death. Various efforts have been made by both the general public and medical professionals to raise awareness, promote early screening, and ensure timely medical intervention. With advances in technology, the use of computer-based systems, particularly in the field of medical image analysis, has become increasingly important. One such application is histopathological image analysis to support the diagnostic process in breast cancer cases. Histopathological image classification has gained significant attention from researchers in recent years, and various machine learning and deep learning techniques have been applied to improve its accuracy. Convolutional Neural Networks (CNNs), as part of the deep learning framework, have shown promising results in identifying tissue patterns in histopathological images. However, despite their high accuracy, CNNs are often less interpretable, making it difficult to understand the reasoning behind their predictions—especially when dealing with subtle features such as small spots, dots, or fine lines that may be overlooked. This study addresses these limitations by proposing a method that not only classifies histopathological images with high accuracy but also enhances readability through localization techniques. The goal is to make the classification process more transparent and clinically useful. Using widely recognized datasets like BreakHIS, the proposed method achieves a classification accuracy of up to 97.50%, demonstrating its potential as a reliable tool in medical diagnostics and breast cancer research.
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets Muhammad Misdram; Edi Noersasongko; Purwanto Purwanto; Muljono Muljono; Fandi Yulian Pamuji
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.26881

Abstract

The problem of dataset imbalance needs special handling, because it often creates obstacles to the classification process. A very important problem in classification is to overcome a decrease in classification performance. There have been many published researches on the topic of overcoming dataset imbalances, but the results are still unsatisfactory. This is proven by the results of the average accuracy increase which is still not significant. There are several common methods that can be used to deal with dataset imbalances. For example, oversampling, undersampling, Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adasyn, Cluster-SMOTE methods. These methods in testing the results of the classification accuracy average are still relatively low. In this research the selected dataset is a medical dataset which is classified as a small dataset of less than 200 records. The proposed method is Gaussian Based-SMOTE which is expected to work in a normal distribution and can determine excess samples for minority classes. The Gaussian Based-SMOTE method is a contribution of this research and can produce better accuracy than the previous research. The way the Gaussian Based-SMOTE method works is to start by determining the random location of synthesis candidates, determining the Gaussian distribution. The results of these two methods are substituted to produce perfect synthetic values. Generated synthetic values are combined with SMOTE sampling of the majority data from the training data, produce balanced data. The result of the balanced data classification trial from the influence of the Gaussian Based SMOTE result in a significant increase in accuracy values of 3% on average.
Analysis Kernel and Feature: Impact on Classification Performance on Speech Emotion Using Machine Learning Jutono Gondohanindijo; Edi Noersasongko; Pujiono Pujiono; Muljono Muljono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29022

Abstract

The main objective of this study is to test the machine learning kernel's selection against the characteristics of the data set used, resulting in good classification performance. The goal of speech emotion recognition is to improve computers' ability to detect and process human emotions in order to improve their ability to respond to interactions between people and computers. It can be applied to feedback on talks, including sentimental or emotional content, as well as the detection of human mental health. One field of data mining work is Speech Emotion Recognition. One of the important things in data mining research is to determine the selection of the kernel Classifier, know the characteristics of datasets, perform Engineering Features and combine features and Corpus Datasets to obtain high accuracy. The research uses analysis and comparison methods using private and public datasets to detect speech emotions. Experimental analysis was done on the characteristics of datasets, selection of kernel classifiers, pre-processing, feature and corpus datasets fusion. Understanding the selection of a classifier kernel that matches the characteristics of the dataset, engineering features and the merger of features and datasets are the contributions of this investigation to improving the accuracy of the classification of speech emotion data. For models with the selection of kernels that match the characteristics of their datasets, this study gave an increase in accuracy of 12.30% for the private dataset and 14.80% for the public dataset, with accuracies of 100.00% and 74.80% respectively. Combining features and public datasets provides an increase in accuracy of 33.62% with an accuracy of 73.95%.
Representasi Strategis Promosi dan Loyalitas Pelanggan pada Ritel Minimarket: Analisis Isi Kualitatif atas Dokumen Resmi Indomaret dan Alfamart Harke Revo Leonard Polii; Sandra J. R Kainde; Happy Tatemba; Edi Noersasongko
Advances: Jurnal Ekonomi & Bisnis Vol. 4 No. 4 (2026): July - August
Publisher : Yayasan Pendidikan Bukhari Dwi Muslim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60079/ajeb.v4i4.914

Abstract

Tujuan: Penelitian ini menganalisis bagaimana promosi dan loyalitas pelanggan direpresentasikan secara strategis dalam dokumen resmi Indomaret dan Alfamart. Fokus penelitian bukan untuk mengukur efektivitas strategi aktual, melainkan untuk membaca bagaimana kedua peritel membingkai promosi, keanggotaan, insentif berulang, dan kanal digital sebagai teks korporat yang ditujukan kepada publik. Metode: Penelitian ini menggunakan desain kualitatif deskriptif-komparatif melalui studi dokumen dan analisis isi kualitatif terhadap 14 dokumen resmi yang divalidasi pada 1 Mei 2026. Korpus mencakup halaman promosi, halaman keanggotaan, dokumen aplikasi digital, blog resmi, profil korporat, serta laporan tahunan. Data dianalisis dengan pendekatan hibrida melalui pembacaan berulang, pengodean unit makna, pengelompokan kategori, audit trail, dan code-recode. Hasil dan Pembahasan: Hasil penelitian menunjukkan bahwa kedua peritel merepresentasikan promosi sebagai proposisi nilai yang menekankan hemat, urgensi, kemudahan, eksklusivitas anggota, dan manfaat berulang. Indomaret lebih menonjolkan variasi tema serta bahasa promosi yang dekat dengan rutinitas belanja harian, sementara Alfamart lebih menonjolkan keteraturan program, mekanisme poin, dan keterhubungan online-offline melalui Alfagift. Perbedaan ini tidak dipahami sebagai oposisi biner, karena Indomaret juga memiliki integrasi digital dan Alfamart juga menggunakan bahasa promosi persuasif. Implikasi: Hasil penelitian menunjukkan bahwa kedua peritel merepresentasikan promosi sebagai proposisi nilai yang menekankan penghematan, urgensi, kemudahan, eksklusivitas anggota, dan manfaat berulang. Indomaret lebih menonjolkan variasi tema serta bahasa promosi yang dekat dengan rutinitas belanja harian, sementara Alfamart lebih menonjolkan keteraturan program, mekanisme poin, dan keterhubungan online-offline melalui Alfagift. Perbedaan ini tidak dipahami sebagai oposisi biner, karena Indomaret juga memiliki integrasi digital dan Alfamart juga menggunakan bahasa promosi persuasif. Orisinalitas: Penelitian ini menggeser unit analisis dari persepsi konsumen ke dokumen korporat sebagai teks strategis serta mengembangkan arsitektur manfaat sebagai lensa analitis untuk membaca hubungan antara promosi, keanggotaan, dan loyalitas digital dalam ritel minimarket.
Augmentation Strategy and Hyperparameter Optimization Using Optuna for Potato Leaf Disease Classification in Uncontrolled Environment Rofiqi, Harri Kurniawan; Noersasongko, Edi; Winarno, Sri; Soeleman, M. Arief
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.4898

Abstract

Image-based classification of potato leaf diseases presents a significant challenge, particularly when data are collected in uncontrolled field environments. While Convolutional Neural Networks (CNNs) and Computer Vision have been widely used for plant disease identification, most previous studies relied on laboratory datasets with uniform lighting and backgrounds, limiting their real-world applicability. This study proposes an integrated framework that combines data augmentation, class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and automated hyperparameter optimization through Optuna to enhance the robustness and accuracy of CNN-based models. A total of 3,076 high-resolution potato leaf images representing seven disease classes were evaluated across five CNN architectures and three training scenarios. The MobileNetV3-Large model achieved the best baseline performance with an accuracy of 0.863 and F1-score of 0.868, while Optuna-based optimization further improved performance to 0.895 accuracy, 0.913 precision, 0.906 recall, and 0.904 F1-score, demonstrating the effectiveness of adaptive optimization in improving model generalization. The integration of augmentation, SMOTE, and Optuna resulted in an intelligent and efficient system resilient to environmental variability, showing strong potential for automatic early detection of potato leaf diseases in real agricultural settings. This research contributes to the advancement of Informatics and Artificial Intelligence by promoting adaptive computer vision approaches for smart agriculture and real-world image-based diagnostic systems.
Co-Authors Affandy Alif Wahyu Oktaputra Dedi Dedi Dwi Jayanti Amanda Sari Dwiza Riana Evanita Evanita, Evanita Fandi Yulian Pamuji Fredy Susanto Happy Tatemba Harke Revo Leonard Polii Hendro Subagyo Jutono Gondohanindijo M Arief Soeleman Moch Arief Soeleman Mochammad Arief Soeleman Mohamad Jamhari Muhammad Misdram Muhammad Munsarif Mula Agung Barata Muljono, - Nita Merlina Nurtantio, Pulung Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Ricardus Anggi Pramunendar Rofiqi, Harri Kurniawan Sandra J. R Kainde Setiyani, Safira Hasna Soeleman, Arief Soeleman, M. Arief Sri Winarno Sudiarti, Ni Made Watimena Syamsiar, Syamsiar Wijaya, Danang Bagus Yuventius Tyas Catur Pramudi