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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Intelligent Computing and Health Informatics (JICHI) Djtechno: Jurnal Teknologi Informasi Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) Journal of Future Artificial Intelligence and Technologies
Dhendra Marutho
Teknik Informatika, STMIK PROVISI Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health Social Sciences

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Enhanced multi-ethnic speech recognition using pitch shifting generative adversarial networks Nugroho, Kristiawan; Hadiono, Kristophorus; Sutanto, Felix; Marutho, Dhendra; Farooq, Omar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2904-2911

Abstract

Research in the field of speech recognition is a challenging research area. Various approaches have been applied to build robust models. A problem faced in speech recognition research is overfitting, especially if there is insufficient data to train the model. A large enough amount of data can train the model well, resulting in high accuracy. Data augmentation is an approach often used to increase the quantity of dataset. This research uses a data augmentation approach, namely pitch shifting, to increase the quantity of speech dataset, which is then processed into spectrogram data and then classified using a generative adversarial network (GAN). Using the pitch shifting-generative adversarial network (PS-GAN) model, this research produces high accuracy performance in multi-ethnic speech recognition, namely 98.43%, better than several similar studies.
Comprehensive Exploration of Machine and Deep Learning Classification Methods for Aspect-Based Sentiment Analysis with Latent Dirichlet Allocation Topic Modeling Setiadi, De Rosal Ignatius Moses; Marutho, Dhendra; Setiyanto, Noor Ageng
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-3

Abstract

This research explores the effectiveness of machine learning (ML) and deep learning (DL) classification methods in Aspect-Based Sentiment Analysis (ABSA) on product reviews, incorporating Latent Dirichlet Allocation (LDA) for topic modeling. Using the Amazon reviews dataset, this research tests models such as Naive Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Units(GRU). Important aspects such as the product's quality, practicality, and reliability are discussed. The results show that the RF and DL models provide competitive performance, with the RF achieving an accuracy of up to 94.50% and an F1 score of 95.45% for the reliability aspect. The study's conclusions emphasize the importance of selecting an appropriate model based on specifications and data requirements for ABSA, as well as recognizing the need to strike a balance between accuracy and computational efficiency.
HYBRID DEEP LEARNING RANDOM FOREST OPTIMASI PEMILIHAN FITUR UNTUK PREDIKSI CHURN INDUSTRI TELEKOMUNIKASI Mutiarachim, Atika; Marutho, Dhendra; Yuniarti, Nur Atika; Pramudya, Ryan Arya; Tyoso, Jaluanto Sunu Punjul
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.7052

Abstract

Customer churn merupakan tantangan kritis dalam industri telekomunikasi yang berdampak signifikan terhadap profitabilitas perusahaan. Penelitian ini mengusulkan pendekatan hybrid machine learning untuk memprediksi customer churn dengan mengintegrasikan deep learning dan random forest serta mengoptimalkan performa melalui seleksi fitur chi-square dan information gain. Dataset IBM Telco Customer Churn yang terdiri dari 7.043 sampel dengan 31 atribut digunakan dalam penelitian ini. Metodologi penelitian meliputi preprocessing data, implementasi 10-fold cross validation, aplikasi metode seleksi fitur, dan evaluasi performa menggunakan confusion matrix serta metrik klasifikasi biner. Hasil penelitian menunjukkan bahwa implementasi seleksi fitur secara signifikan meningkatkan akurasi prediksi, di mana akurasi tanpa seleksi fitur mencapai 97.00% (Deep Learning) dan 98.68% (Random Forest), sedangkan dengan seleksi fitur chi-square meningkat menjadi 97.97% (Deep Learning) dan 98.72% (Random Forest). Performa terbaik dicapai oleh kombinasi Random Forest dengan seleksi fitur information gain yang menghasilkan akurasi 98.75%, precision 98.37%, recall 99.96%, dan F-measure 99.16%. Temuan ini membuktikan efektivitas kombinasi algoritma ensemble dengan teknik seleksi fitur dalam mengoptimalkan prediksi customer churn untuk mendukung strategi retensi pelanggan yang lebih tepat sasaran
Evaluation of a Semantic Representation-Based Retrieval Model on a Text Dataset Generated from Image Transformation Firmansyah, Muhammad; Marutho, Dhendra; Ilham, Ahmad; Saputra, Irwansyah
Journal of Intelligent Computing & Health Informatics Vol 6, No 2 (2025): September
Publisher : Universitas Muhammadiyah Semarang Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jichi.v6i2.19240

Abstract

The increasing demand for efficient multimodal information retrieval has driven significant research into bridging visual and textual data. While sophisticated models like CLIP offer state-of-the-art semantic alignment, their substantial computational requirements present challenges for deployment in resource-constrained environments. This study introduces a lightweight retrieval framework that leverages the BLIP image captioning model to transform image data into rich textual descriptions, effectively reframing cross-modal retrieval as a text-to-text task. We systematically evaluated three retrieval models BM25, SBERT, and T5 on caption-transformed MSCOCO and Flickr30K datasets, utilizing both classical metrics (Recall@5, mAP) and semantic-aware metrics (SAR@5, Semantic mAP). Experimental results demonstrate that T5 achieves superior semantic performance (SAR@5 = 0.561, Semantic mAP = 0.524), surpassing SBERT (SAR@5 = 0.524) and outperforming the lexical BM25 baseline (SAR@5 = 0.312). Notably, the proposed BLIP+T5 pipeline attains 88% of CLIP’s semantic accuracy while reducing inference latency by approximately 60% and decreasing GPU memory consumption by over 60%. These findings underscore the potential of caption-based retrieval frameworks as scalable, cost-effective alternatives to computationally intensive multimodal systems, especially in latency-sensitive and resource-limited scenarios. Future work will explore fine-tuning strategies, domain-adapted semantic metrics, and robustness under real-world conditions to further advance retrieval effectiveness.
Media Pembelajaran Interaktif untuk Mengoptimalkan Pembelajaran Sanggar Belajar di Malaysia Solichan, Achmad; Sari, Nova Christina; Marutho, Dhendra; Ramdani, Aditya Putra; Al Amin, Muhammad Zainudin; Ansor, Basirudin; Puterizahra, Arviza
Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) Vol. 6 No. 2 (2025): Vol. 6 No. 2 Oktober 2025
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/sitechmas.v6i2.6131

Abstract

Dalam era digital yang terus berkembang, penggunaan teknologi dalam pendidikan telah menjadi semakin penting. Salah satunya di negara Malaysia, banyak sanggar belajar berusaha untuk mengintegrasikan teknologi dalam metode pengajaran mereka untuk meningkatkan efektivitas pembelajaran. Salah satu inovasi yang menjanjikan adalah penggunaan media pembelajaran interaktif melalui aplikasi permainan. Media pembelajaran interaktif menawarkan berbagai keunggulan dalam proses belajar-mengajar. Metode ini mampu menarik minat siswa melalui elemen interaktif dan gamifikasi, yang dapat membuat proses belajar menjadi lebih menyenangkan dan memotivasi dapat lebih cenderung untuk terlibat aktif dalam pembelajaran dan dapat meningkatkan retensi informasi. Aplikasi permainan sebagai media pembelajaran juga memberikan kesempatan untuk pembelajaran yang lebih personalisasi. Siswa dapat belajar sesuai dengan kecepatan mereka sendiri dan mendapatkan umpan balik langsung, yang membantu mereka memahami konsep dengan lebih baik. Pengabdian masyarakat di sanggar belajar Malaysia berlangsung dengan sangat baik. Motivasi guru dan siswa dalam proses pembelajaran interaktif, meningkat. Peningkatan hasil dari pembelajaran interaktif dapat dilihat pada survei hasil kepuasan peserta terhadap metode pembelajaran ini. Survei kepuasaan menghasilkan nilai 19% puas, dan 81% sangat puas, terhadapa kegiatan ini.
Co-Authors Aditya Putra Ramdani Al Amin, Muhammad Zainudin Basirudin Ansor De Rosal Ignatius Moses Setiadi Dian Kristiawan Nugroho Farooq, Omar Ilham, Ahmad Jaluanto Sunu Punjul Tyoso Kristophorus Hadiono Muhammad Firmansyah, Muhammad Mutiarachim, Atika Noor Ageng Setiyanto, Noor Ageng Nova Christina Sari Puterizahra, Arviza Ryan Arya Pramudya, Ryan Arya Saputra, Irwansyah SUTANTO, FELIX Yuniarti, Nur Atika