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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Script Journal: Journal of Linguistic and English Teaching Syntax Literate: Jurnal Ilmiah Indonesia JURIKOM (Jurnal Riset Komputer) Infotekmesin Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat DEDIKASI PKM Walisongo Journal of Information Technology Indonesian Journal of EFL and Linguistics SWAGATI: Journal of Community Service Advance Sustainable Science, Engineering and Technology (ASSET) Green Engineering: Journal of Engineering and Applied Science Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Muljono, -
Department Of Computer Science, Universitas Dian Nuswantoro, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health Social Sciences Other

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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%.
Adaptive threshold for moving objects detection using gaussian mixture model Moch Arief Soeleman; Aris Nurhindarto; Muslih Muslih; Karis W.; Muljono Muljono; Farikh Al Zami; R. Anggi Pramunendar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14878

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
Application of Green Hydrogen Technology for Industrial Decarbonization: Techno-Economic and Environmental Assessment Amiq Fahmi; Raden Arief Nugroho; Muljono Muljono; Noorsidi Aizuddin Bin Mat Noor
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 2 (2024): April: Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i2.261

Abstract

This study explores the application of green hydrogen technology for industrial decarbonization, focusing on its techno-economic and environmental feasibility. A quantitative approach was used, incorporating system modeling of a solar-based hydrogen production system combined with electrolyzers. The techno-economic assessment involved calculating the Levelized Cost of Hydrogen (LCOH), estimating capital and operational expenditures (CAPEX and OPEX), and evaluating the system's energy efficiency and hydrogen output. The environmental impact was analyzed using Life Cycle Assessment (LCA), comparing the carbon footprint of green hydrogen with fossil-based hydrogen. The results reveal that green hydrogen can reduce carbon emissions by up to 60% compared to fossil hydrogen, primarily due to the use of renewable energy for production. Additionally, the study found significant improvements in energy efficiency as electrolyzer performance and solar capacity increased. The LCOH is expected to decrease steadily as solar panel and electrolyzer prices continue to fall, enhancing the competitiveness of green hydrogen in the energy market. The findings also highlight the potential for heavy industries, such as cement and steel production, to transition from fossil fuels to green hydrogen, contributing to a cleaner industrial energy mix. This transition presents both environmental and economic benefits, with long-term savings from reduced fossil fuel dependency and lower production costs.
Arsitektur Hibrida IndoBERTweet - Convolutional Neural Network (CNN) untuk Klasifikasi Ujaran Kebencian Berbahasa Gaul di Media Sosial Margareta Valencia Suci Handayani; Muljono
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3026

Abstract

Detecting hate speech on Indonesian social media is challenging due to slang, abbreviations, and informal expressions that hinder automated text understanding. Traditional machine learning approaches often fail to capture contextual meaning effectively. This study aims to develop a hate speech detection system for Indonesian slang by evaluating contextual embedding IndoBERTweet combined with a Convolutional Neural Network (CNN) architecture. The research compares the performance of CNN and BiLSTM models using IndoBERTweet and FastText embeddings. A dataset of 1,477 labeled tweets categorized as Hate Speech, Abusive, or Non-Hate Speech was used. Evaluation metrics employed in this study consist of accuracy, precision, recall, F1 score, and AUC ROC. The results show that the IndoBERTweet + CNN model achieves the best performance, with 91.2% accuracy and a 91.1% F1-score, significantly outperforming FastText-based models. IndoBERTweet’s contextual embedding proves effective in handling the linguistic complexity and implicit meanings commonly found in Indonesian slang. These findings highlight the model’s strong capability for robust hate speech detection and open opportunities for its adoption as an automated content-moderation module that identifies and filters toxic narratives on social media platforms.
Optimalisasi Akurasi dan Stabilitas Analisis Sentimen Ulasan E-Commerce Indonesia melalui Fine-Tuning Transformer IndoBERT Alfina Latifa Maysara; Muljono
Infotekmesin Vol 17 No 1 (2026): Infotekmesin: Januari 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v17i1.3037

Abstract

The rapid growth of e-commerce in Indonesia increases the need for sentiment analysis to accurately understand customer perceptions. This study evaluates the effectiveness of the Transformer-based IndoBERT model for sentiment classification on Indonesian e-commerce reviews and compares its performance with four RNN architectures (LSTM, GRU, BiLSTM, and BiGRU). The PRDECT-ID dataset containing 5,400 reviews was processed through preprocessing, an 80:20 data split, RNN training using 5-Fold Cross Validation, and IndoBERT fine-tuning under a hold-out scheme. Unlike previous studies that focused solely on RNN models with a maximum accuracy of 90.7%, this work expands the evaluation by integrating a Transformer-based approach. Results show that IndoBERT achieves 98.52% accuracy and F1-weighted score, outperforming the best RNN models by approximately 0.94–0.95. Paired T-Test and Wilcoxon tests yield p < 0,05, confirming that the performance improvements are statistically significant. IndoBERT demonstrates greater stability and effectiveness for Indonesian sentiment analysis.
A Hybrid VADER–IndoBERT Framework for Robust Sentiment Analysis of Long and Ambiguous Indonesian Texts Margareta Valencia Suci Handayani; Ruri Suko Basuki; Muljono; Raden Arief Nugroho; Dhendra Maruhto; Yo Ceng Giap; Deshinta Arrova Dewi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7377

Abstract

The rapid expansion of digital learning platforms has increased the reliance on user-generated reviews for service evaluation and quality monitoring. However, sentiment analysis of Indonesian reviews remains challenging due to the prevalence of long sentences, mixed sentiments, and ambiguous linguistic expressions. This study introduces a Hybrid VADER–IndoBERT framework designed to improve sentiment classification robustness on complex Indonesian texts. A dataset of 4,904 Ruangguru application reviews was collected through web scraping and processed using a hybrid pipeline consisting of preprocessing, translation-based silver-standard sentiment labeling with VADER, and class balancing via Random Oversampling (ROS). The IndoBERT classifier was evaluated against a Bidirectional Long Short-Term Memory (BiLSTM) baseline. Experimental results show that IndoBERT achieved 90.9% accuracy, outperforming BiLSTM at 86.4%, demonstrating the superiority of Transformer-based architectures in capturing long-range dependencies and handling ambiguous sentiment cues. These findings highlight the effectiveness of integrating lexicon-based and Transformer-based approaches to achieve more robust sentiment analysis on linguistically complex Indonesian texts.
Penerapan Arsitektur Deep Learning EfficientNetB0 Berbasis Citra Digital untuk Meningkatkan Kinerja Sistem Klasifikasi Sampah Organik, Anorganik, dan B3 Anggita Alya Salsabila; Muljono Muljono
JURIKOM (Jurnal Riset Komputer) Vol. 12 No. 6 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i6.9360

Abstract

Waste management in Indonesia remains a major challenge due to increasing waste volumes and the low efficiency of manual sorting processes at Landfills (TPA). This study aims to improve the performance of an automated waste classification system for three categories: organic, inorganic, and hazardous and toxic waste (B3) using deep learning-based computer vision technology. The proposed method is the EfficientNetB0 architecture with a transfer learning approach, whose performance is compared with four other pre-trained architectures (VGG-16, InceptionV3, MobileNetV2, and ResNet50). The dataset used consists of 7,003 valid images collected from public sources and manual acquisition after a data cleaning process. The dataset is divided into 70% as training data, 20% as validation data, and 10% as test data. Data augmentation and class balancing strategies are used to increase variation and overcome data imbalance between classes. Training is conducted in two stages: Feature Extraction and Fine-Tuning, with consistent hyperparameters for a fair comparison. Performance evaluation is performed using accuracy, precision, recall, and f1-score metrics. The test results show that EfficientNetB0 managed to achieve the best performance with an accuracy rate of 96.87%. Modern architectures like EfficientNetB0 have proven capable of extracting complex features with good computational efficiency, thereby holding the potential for use in AI-based automatic waste sorting systems to support more effective and sustainable waste management.
Co-Authors Abimanyu Cahya Pramudhita Aditya Wahyu Ramadhan Agus Sri Giyanti Ahmad Fanani Akbar Dwi Syahputra Al Ghiffari Bintang Saputra Alfina Latifa Maysara Alvino Dwi Rachman Prabowo Angga Adetiya Anggita Alya Salsabila Anisa Larassati Aripin Azhara Devi Sandi Budi Harjo Cinantya Paramita Deshinta Arrova Dewi Dewa Ayu Putri Tesalonika Dhendra Maruhto Edi Noersasongko Eka Saraswati Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Faezal Hartono Fahmi Amiq Fandi Yulian Pamuji Farikh Al Zami Filmada Ocky Saputra Gustina Alfa Trisnapradika Hanny Haryanto Harun Al Azies Heni Indrayani Jutono Gondohanindijo Karis W. Magus Sarasnomo Mangatur Rudolf Nababan Margareta Valencia Suci Handayani Margareta Valencia Suci Handayani Moch Arief Soeleman Muhamad Hasan Basori Muhamad Ni&#039;am Syukri Roni Asmi Muhammad Misdram Muhammad Naufal Muslih Muslih Nina Setyaningsih Noor Ageng Setiyanto, Noor Ageng Noorsidi Aizuddin Bin Mat Noor Novianto Nur Hidayat Nurhindarto, Aris Pujiono Pujiono Pujiono Pujiono Purwanto Purwanto R Arief Nugroho Raden Arief Nugroho Restu Poetra Alqurni Ricardus Anggi Pramunendar Robiatul Adawiyah Ruri Suko Basuki Saraswati, Galuh Wilujeng Satriya, Candra Yudha Septian Enggar Sukmana Sunardi Sunardi Sunardi SUPRIADI RUSTAD Syaiful A. Septemuryantoro Tri Wuryantoro Utari Fatma Dewi Valentina Widya Suryaningtyas, Valentina Widya Wildani Eko Nugroho, Wildani Eko Wise Herowati Wulanda Yuniar Permatasari Yo Ceng Giap