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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Ilmiah Informatika Komputer Jurnal Ilmiah Teknologi dan Rekayasa Prosiding KOMMIT IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah KOMPUTASI InComTech: Jurnal Telekomunikasi dan Komputer Communications in Science and Technology MIND (Multimedia Artificial Intelligent Networking Database) Journal Journal of Applied Data Sciences Proceeding International Conference on Mathematics and Learning Research Prosiding SeNTIK STI&K
Sarifuddin Madenda
Universitas Gunadarma
Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics

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Metode Pengolahan Citra Untuk Mendeteksi Karies Gigi Linda Wahyu Widianti; Sunny Arief Sudiro; Sarifuddin Madenda; Johan Harlan
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Metode Pengolahan Citra Untuk Mendeteksi Karies Gigi
Metode Penentuan Keyframe Berdasarkan Kesamaan Event Pada Pengelompokanframe Video Menggunakan Histogram Bin Warna HCL Ire Puspa Wardhani; Lussiana, ETP; Sunny Arief Sudiro; Sarifuddin Madenda; Prihandoko
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Penelitian ini merupakan bagian dari penelitian tentang pencarian video berbasis konten dimana salah satu prosesnya adalah pengelompokan frame video dengan menggunakan metode penentuan keyframe berdasarkan kesamaan event dengan menggunakan histogram bin warna, salah satu tahapan proses penentuan keyframe diawali dengan proses ekstraksi file video yaitu memisahkan frame-frame dalam video tersebut, selanjutnya, frame-frame tersebut diekstraksi berdasarkan fitur warna local dan global dengan menggunakan histogram bin warna 3D. Prosespengelompokan frame-frame ini berada dalam satu event yang sama, sehinggahasilnya berupa cuplikan-cuplikan atau klip-klip video event. Metode penentuan keyframe ini sebagai ID yang akan merepresentasikan setiap klip video event dan menghasilkan tiga jenis data, pertama adalah keyframe-keyframe dengan fitur bin warnanya masing-masing sebagai ID dari setiap klip video event. Kedua adalah klip-klip video event yang masing-masing berisikan kelompok frame sesuai dengan event atau event saat pembuatan video, dan Ketiga adalah data file video itu sendiri. Tiga data ini kemudian disimpan dalam sebuah basis data,, dan metode penentuan keyframe ini sangat berhubungan dengan klip video event yang diwakilkannya dan setiap klip video event memiliki keterhubungan dengan file videonya sendiri sehingga nantinya akan berpengaruh terhadap hasil pencarian dan temu kembali video berbasis konten
An optimized transfer learning-based approach for Crocidolomia pavonana larvae classification Risnawati, Risnawati; Rodiah, Rodiah; Madenda, Sarifuddin; Tri Susetianingtias, Diana
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2270-2281

Abstract

The increasing demand for mustard greens has driven farmers to continuously improve mustard greens cultivation. One of the challenges in mustard greens cultivation is the presence of insect pests. A significant pest in mustard greens is Crocidolomia pavonana (C. pavonana). C. pavonana damages plants by feeding on various parts, especially the leaves. The initial step in controlling them is insect pest monitoring. Monitoring aims to establish the control threshold. C. pavonana larvae have four instar stages: instar 1, 2, 3, and 4. Identification of the instar larval stages utilizes deep convolutional neural network (CNN) to classify C. Pavonana larvae on mustard greens using ResNet50V2 and DenseNet169 architectures optimized to enhance classification accuracy. The classification evaluation results show that both DenseNet169 and ResNet50V2 models achieve high accuracy, with DenseNet169 reaching the highest accuracy at 97.1%, while ResNet50V2 achieves an accuracy of 94.2%. The lower loss values on the test data compared to the validation data indicate that the deep learning models have successfully captured the patterns in C. pavonana images for classification. This classification process is expected to be one of the activities in monitoring the instar larvae to improve the accuracy of insecticide spraying and enhance mustard greens production.
Multi-Label Classification of Indonesian Voice Phishing Conversations: A Comparative Study of XLM-RoBERTa and ELECTRA Hidayat, Ahmad; Madenda, Sarifuddin; Hustinawaty, Hustinawaty
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.858

Abstract

Mobile phones have become a primary means of communication, yet their advancement has also been exploited by cybercriminals, particularly through voice phishing schemes. Voice phishing is a form of social engineering fraud carried out via telephone conversations to illegally obtain personal or financial information. The complexity of voice phishing continues to increase, as a single conversation may involve multiple fraudulent schemes simultaneously, necessitating the application of multi-label classification to comprehensively identify all motives of fraud. Previous studies have predominantly utilized single-label approaches and foreign-language data, making them less relevant to the Indonesian language context and unable to produce speaker segmentation outputs for conversational analysis. This study contributes by developing a multi-label voice phishing classification system specifically for Indonesian telephone conversations to address this gap. Audio data were collected from open sources and simulated recordings, resulting in a total of 300 samples labeled into six categories: five phishing modes and one non-phishing category. The proposed system consists of a preprocessing pipeline that includes noise reduction, speaker segmentation, automatic transcription, and text cleaning to preserve the context of two-way conversations. Two machine learning models based on transformer architectures, XLM-RoBERTa and ELECTRA, are employed to identify various fraud schemes that may occur simultaneously within a single conversation. The dataset was split into training, validation, and testing sets with two division ratios for performance evaluation. Several combinations of hyperparameters were tested to obtain the most optimal model configuration. Evaluation was conducted using a supervised learning approach and various performance metrics. The experimental results show that XLM-RoBERTa achieved the highest average accuracy of 97.04 ± 1.15% and the highest average F1-score of 92.66 ± 2.59%. These results highlight the novelty of applying multi-label classification in the Indonesian language context for voice phishing detection, contributing to more effective fraud identification in real-world telephony systems.
Solar module defects classification using deep convolutional neural network Cahyaningtyas, Rizqia; Madenda, Sarifuddin; Bertalya, Bertalya; Indarti, Dina
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1818

Abstract

Solar modules are essential components of a solar power plant, that are designed to withstand scorching heat, storms, strong winds, and other natural influences. However, continuous usage can cause defects in solar modules, preventing them from producing electrical energy optimally. This paper proposes the development of a deep learning-based system for identifying and classifying solar module surface defects in solar power plants. Module surface condition are classified into five categories: clean, dirt, burn, crack, and snail track. The dataset used consists of 8,370 images, including primary image data acquired directly from the mini solar power plant at the Renewable Energy Laboratory of PLN Institute of Technology, and secondary image data obtained from public repositories. The limitation in the number of images in each category was overcome using data augmentation techniques. The proposed classification model combines Deep Convolutional Neural Networks (DCNN) with transfer learning models (DenseNet201, MobileNetV2, and EfficientNetB0) to perform supervised image classification. Training and testing results on the three models demonstrated that the combination of DCNN + DenseNet201 provided the best performance, with a classification accuracy of 97.85%, compared to 97.25% accuracy for DCNN + EfficientNetB0 and 94.98% for DCNN + MobileNetV2. This research shows that DCNN-based image classification reliably diagnoses solar module defects and supports using RGB images for surface defect classification. Applying the developed system to solar power plant maintenance management can help in accelerating the process of identifying panel defects, determining defect types, and performing panel maintenance or repairs, while ensuring optimal power production.
Fine-tuning GloVe Embedding with Contextual Information for Synthetic Batik Pattern Generation Khalida, Rakhmi; Madenda, Sarifuddin; Harmanto, Suryadi; Wiryana , I Made
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i3.30738

Abstract

Batik is an Indonesian cultural heritage that is rich in philosophical and symbolic meanings. To support preservation and innovation, this research examines the use of Generative Adversarial Networks (GAN) in generating synthetic batik patterns based on natural language description. The main challenge lies in the interpretation of semantically complex cultural texts. This research proposes a fine-tuning approach of the GloVe word insertion model with a batik domain-specific corpus. The dataset consists of 3,100 batik images of Parang and Kawung motifs, each accompanied by 10 textual descriptions. Two approaches were evaluated: GloVe generalized pre-training and GloVe enhanced. The GAN architecture combines multimodal input and up sampling techniques to generate images from text. Intrinsic evaluation results showed that the customized GloVe model improved the average cosine similarity value to 0.99. A paired t-test between the general model and the refined results yielded p < 0.01, indicating a statistically significant improvement. Extrinsic evaluation using Fréchet Inception Distance (FID) and Inception Score (IS) showed an improvement in visual quality: FID decreased from 64.5 to 48.1, and IS increased from 2.37 to 3.23. These findings demonstrate the effectiveness of semantic enhancement for improving the synthesis of culturally meaningful visuals. In addition to the technical contribution, this study demonstrates the potential of AI in the preservation of Indonesia's cultural heritage through.
Co-Authors Achmad Fahrurozi Ahmad Hidayat Antonius Angga Kurniawan Aqwam Rosadi Kardian Atit Pertiwi Aviarini Indrati Bertalya Bertalya Cahyaningtyas, Rizqia Catur Suharinto Dewi Agushinta R. Dewi Putrie Lestari Diana Tri Susetianigtias Dina Indarti Djati Kerami Eri Prasetya Wibowo Eri Prasetyo Wibowo, Eri Prasetyo Ernastuti Ernastuti Ety Sutanty Farina Pramanik Fitrianingsih Fitrianingsih Hariyanto Hariyanto Harmanto, Suryadi Hustinawaty Hustinawaty, Hustinawaty Ichsani Mursidah Ire Puspa Wardhani Johan Harlan Johan Harlan Johan Harlan Jufriadif Na`am, Jufriadif Julius Santony Khalida, Rakhmi LATIFAH Linda Wahyu Widianti Lingga Hermanto Lussiana, ETP Metty Mustikasari Muhammad Subali Prayitno, Ragiel Hadi Prihandoko . Rahayu Noveandini Rama Dian Syah Rifki Kosasih Rini Arianty Risnawati Risnawati Robby Candra Rodiah Rodiah Ruddy J Suhatril S Suryadi Setia Wirawan SHABIRA, DIYANK SIAGIAN, AL HAFIZ AKBAR MAULANA Slamet Riyanto Sumijan Sumijan Sunny Arief Sudiro Suryadi Harmanto Suryarini Widodo Susetianingtias, Diana Tri Tavipia Rumambi Tri Susetianingtias, Diana Tubagus Maulana Kusuma Wiryana , I Made