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All Journal Semesta Teknika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Insect (Informatics and Security) : Jurnal Teknik Informatika ILKOM Jurnal Ilmiah JURIKOM (Jurnal Riset Komputer) Systemic: Information System and Informatics Journal Jurnal Pengabdian Dharma Laksana JURNAL MAHAJANA INFORMASI Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Respati Jurnal Tekinkom (Teknik Informasi dan Komputer) Bubungan Tinggi: Jurnal Pengabdian Masyarakat JITU : Journal Informatic Technology And Communication Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Abdimas: Papua Journal Of Community Service Jurnal Pengabdian Masyarakat Journal of Community Empowerment FRAMEWORK Jurnal Ilmu Komputer dan Informatika Jurnal Keperawatan Sehat Mandiri
Muhammad Rizki Setyawan
Program Studi Teknik Informatika, Universitas Muhammadiyah Sorong
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Other

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Detection of Curcuma and Turmeric Differences Utilizing Fuzzy Tsukamoto Android-Based CCN Model Putra, Fajar Rahardika Bahari; Setyawan, Muhammad Rizki; Ilham, Ahmad; Suseno, Dimas Adi
ILKOM Jurnal Ilmiah Vol 17, No 3 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i3.2857.276-291

Abstract

Turmeric and curcuma are herbs that are often used in medicine and cooking. However, their similar shapes and colours make it difficult for people, especially in Southwest Papua, to distinguish between them directly. According to the Central Statistics Agency (BPS) in 2023, turmeric production reached 18,302 units, far higher than turmeric, which only reached 2,950 units. Based on field interviews in Southwest Papua, more than 60% of respondents had difficulty distinguishing turmeric from turmeric. To address this issue, this research develops an Android-based classification system by integrating the Fuzzy Tsukamoto algorithm with Convolutional Neural Network (CNN) models. Five CNN models VGG16, MobileNetV2, NASNetMobile, EfficientNetB2, and EfficientNetB3 were selected based on their balance between computational efficiency (MobileNetV2, NASNetMobile), depth and proven stability (VGG16), and modern scalable architectures (EfficientNetB2 and B3). Each model was combined with fuzzy logic to enhance classification accuracy. he dataset consisted of 800 images of curcuma and turmeric obtained from Kaggle and field collections. The data were divided into training, validation, and testing sets, and augmented through a series of transformations including rescaling to a range of 0 to 1, rotation up to 40 degrees, horizontal shift of 20%, angular distortion (shear) of 20%, zoom up to 30%, horizontal flipping, and brightness adjustment. Empty areas generated during augmentation were filled using the nearest pixel value with the ‘nearest’ mode to preserve image integrity. Training was performed using the AdamW optimizer and fine-tuning. Model evaluation employed accuracy, precision, recall, F1-score, and confusion matrix metrics. The results showed that the VGG16 model performed best, achieving 97% accuracy, 98% precision, 97% recall, and 98% F1-score, as confirmed by the classification report and confusion matrix. This model was also the most stable when tested on the Android system, while EfficientNetB2 and B3 produced less satisfactory outcomes. These findings demonstrate that combining CNN and Fuzzy Tsukamoto improves the classification accuracy of images with high visual similarity. The proposed system has the potential to be applied as a direct plant identification tool in the field and can be further extended to classify other visually similar plants
Sentiment Analysis towards Jokowi Post-Presidential Term Using CNN-BiLSTM with Multi-head Attention on Platform X Setyawan, Muhammad Rizki; Putra, Fajar Rahardika Bahari; Ramadhani, Ardhina
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2843.150-161

Abstract

The development of social media has changed the way the public expresses political opinions, especially regarding the evaluation of President Joko Widodo’s (Jokowi) leadership after his term. Platform X (formerly Twitter) has become the primary source of public opinion data, but the use of informal language and sarcasm makes accurate sentiment analysis challenging. This study creates a sentiment analysis model that uses deep learning with a CNN-BiLSTM structure and a multi-head attention mechanism. The dataset consists of 52,643 tweets that have been labeled and embedded using IndoBERT. To address class imbalance, the SMOTE method was applied to the training data, enabling the model to better learn from minority classes. The results indicate that the model achieves a high accuracy of 98.78%, with an average precision, recall, and F1-score of 0.98. These findings indicate that the model is not only accurate but also reliable in distinguishing each sentiment class. A comparison with other model variants suggests that the complete combination of CNN-BiLSTM and Multi-Head Attention delivers the best performance, although the improvement is relatively small.
Perancangan Electronic Nose (E-Nose) untuk Analisis dan Klasifikasi Aroma Daging Menggunakan PCA dan LDA Rizki Setyawan, Muhammad; Fadlil, Abdul; Yudhana , Anton
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2254

Abstract

Meat is a vital food commodity prone to adulteration through species mixing or chemical contamination such as formalin and borax. This study aimed to design and test an Electronic Nose (E-Nose) system for aroma pattern analysis and meat classification using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Samples included pure meat (beef, chicken, pork), mixed meat, and chemically contaminated meat. Aroma data were captured using an array of gas sensors sensitive to Volatile Organic Compounds (VOCs) and standardized prior to analysis. PCA reduced eight sensor features into three principal components explaining a total variance of 79.63%. PC1, PC2, and PC3 accounted for 46.10%, 20.58%, and 12.96% of variance, respectively, showing clustering patterns among samples with minor overlap. LDA provided clearer class separation with three discriminant components LD1, LD2, and LD3 explaining 77.13%, 16.63%, and 4.59% of between-class variance, totaling 98.34%. LD1 separated pure, mixed, and contaminated meat, LD2 distinguished variations due to contaminant type and species, and LD3 refined separation of similar classes. Classification evaluation achieved an overall accuracy of 82%. Most classes were well classified, while classes 1 and 10 experienced misclassification due to similar aroma patterns. The findings confirm that E-Nose combined with PCA and LDA is a rapid, non-destructive, and efficient method for detecting meat authenticity and adulteration, showing strong potential for food quality monitoring in the field
Co-Authors Abdul Fadlil Abdul Fadlil Ahistasari, Asih Ahmad Ilham Amri, Irman Anton Yudhana Aronggear, Karim Paul Bahari Putra, Fajar Rahardika Bekti Wiji Lestari Dewi Astria Faroek Elisabeth Serlina Elensia Bupu Ermin Ermin Ermin, E Fajar, Fajar Rahardika Bahari Putra Fakhri , La Jupriadi Fitriyani Tella Haris Haris, Haris Hermansa Hermansa Hilda Histiarini, Aprisa Rian Ilham, Ahmad Kahar, Muhammad Syahrul Kayatun, Siti Nur La Jupriadi Fakhri Marlinda Indah Eka Budiarti, Marlinda Indah Eka Mirga Maulana Rachmadhani Mohammad Arief Nur Wahyudien Mohammad Arief Nur Wahyudien Muh. Fadli Hasa Muhammad Jundullah Muzakkir Pangri Nabila Novita Sari Nurul Widya Har Putra, Fajar Rahardika Bahari Rachmadanur Rachmadanur Rahardika Bahari Putra, Fajar Rahmatullah Bin Arsyad Ramadhani, Ardhina Rawi, Rais Dera Pua Reflita Reflita Rinanda Tri Setiawan Sangadji, Zulkarnain Simori, Pascalina Magrice Slameto, Andika Agus Soekarta, Rendra Soleha Rahayu Muklis Hayu Sundari Sundari Surahmanto Suseno, Dimas Adi Tasman Tasman Teguh Hidayat Iskandar Alam Tella, Fitriyani tivani nurizqi Umar Rusli Marasabessy Verra Widhi Astuti Wattimena, Mikhael Yudhana , Anton