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All Journal International Journal of Electrical and Computer Engineering Jurnal Sistem Komputer Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL NASIONAL TEKNIK ELEKTRO Bulletin of Electrical Engineering and Informatics Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) GERVASI: Jurnal Pengabdian kepada Masyarakat Systemic: Information System and Informatics Journal Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems Journal of Innovation Information Technology and Application (JINITA) Science in Information Technology Letters Jurnal INFOTEL Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Saifullah, Shoffan
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Saifullah, Shoffan; Drezewski, Rafal; Yudhana, Anton; Pranolo, Andri; Kaswijanti, Wilis; Suryotomo, Andiko Putro; Putra, Seno Aji; Khaliduzzaman, Alin; Prabuwono, Anton Satria; Japkowicz, Nathalie
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26722

Abstract

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.
Visualization of Islamic Boarding School Location at Yogyakarta with Web-Based Geodesain Alfiani, Oktavia Dewi; Wahyuningrum, Dwi; Saifullah, Shoffan; Haekal, Haekal
Telematika Vol 20 No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.10885

Abstract

Purpose: This research produces a webGIS design that presents the geospatial location of buildings in the Krapyak Yogyakarta boarding school area to facilitate users outside the area when heading to the location of the boarding school whose buildings are scattered.Design/methodology/approach: By combining aerial photos from UAV mapping with Open Street Map. The combined results of both maps are presented in a webGIS built from HTML, CSS and OpenLayers scripting.Findings/result: Building a webGIS to present information on the location of Krapyak Islamic boarding schools that has been equipped with corrected coordinates and routes from the iconic city of Yogyakarta so that immigrants from outside the area can easily understand the use of the webgis. Originality/value/state of the art: From previous research, webGIS development only uses maps presented through openstreetmap where if users use existing online navigation applications have different coordinate system references (Soraya R, 2018). So by equalizing the map reference by combining the results of UAV mapping and correcting the shape of the building presented on openstreetmap, the spatial information from the webgis will have a position accuracy that is more in line with the truth.
The Evaluation of Effects of Oversampling and Word Embedding on Sentiment Analysis Cahyana, Nur Heri; Fauziah, Yuli; Wisnalmawati, Wisnalmawati; Aribowo, Agus Sasmito; Saifullah, Shoffan
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1077

Abstract

Generally, opinion datasets for sentiment analysis are in an unbalanced condition. Unbalanced data tends to have a bias in favor of classification in the majority class. Data balancing by adding synthetic data to the minority class requires an oversampling strategy. This research aims to overcome this imbalance by combining oversampling and word embedding (Word2Vec or FastText). We convert the opinion dataset into a sentence vector, and then an oversampling method is applied here. We use 5 (five) datasets from comments on YouTube videos with several differences in terms, number of records, and imbalance conditions. We observed increased sentiment analysis accuracy with combining Word2Vec or FastText with 3 (three) oversampling methods: SMOTE, Borderline SMOTE, or ADASYN. Random Forest is used as machine learning in the classification model, and Confusion Matrix is used for validation. Model performance measurement uses accuracy and F-measure. After testing with five datasets, the performance of the Word2Vec method is almost equal to FastText. Meanwhile, the best oversampling method is Borderline SMOTE. Combining Word2Vec or FastText with Borderline SMOTE could be the best choice because of its accuracy score and F-measure reaching 91.0% - 91.3%. It is hoped that the sentiment analysis model using Word2Vec or FastText with Borderline SMOTE can become a high-performance alternative model.
Geographic-Origin Music Classification from Numerical Audio Features: Integrating Unsupervised Clustering with Supervised Models Pranolo, Andri; Sularso, Sularso; Anwar, Nuril; Putra, Agung Bella Utama; Wibawa, Aji Prasetya; Saifullah, Shoffan; Dreżewski, Rafał; Nuryana, Zalik; Andi, Tri
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13400

Abstract

Classifying the geographic origin of music is a relevant task in music information retrieval, yet most studies have focused on genre or style recognition rather than regional origin. This study evaluates Support Vector Machine (SVM) and Convolutional Neural Network (CNN) models on the UCI Geographical Origin of Music dataset (1,059 tracks from 33 non-Western regions) using numerical audio features. To incorporate latent structure, we first applied K-means clustering with the optimal number of clusters (k=2) determined by the Elbow and Silhouette methods. The cluster assignments were used as auxiliary signals for training, while evaluation relied on the true region labels. Classification performance was assessed with Accuracy, Precision, Recall, and F1-score. Results show that SVM achieved 99.53% accuracy (95% CI: 97.38–99.92%), while CNN reached 98.58% accuracy (95% CI: 95.92–99.52%); Precision, Recall, and F1 mirrored these values. The differences confirm SVM’s superior performance on this dataset, though the near-perfect scores also suggest strong separability in the feature space and potential risks of overfitting. Learning-curve analysis indicated stable training, and cluster supervision provided small but consistent benefits. Overall, SVM remains a reliable baseline for tabular music features, while CNNs may require spectro-temporal representations to leverage their full potential. Future work should validate these findings across multiple datasets, apply cross-validation with statistical significance testing, and explore hybrid deep models for broader generalization.
Otsu Method for Chicken Egg Embryo Detection based-on Increase Image Quality Suhirman Suhirman; Shoffan Saifullah; Ahmad Tri Hidayat; Rr Hajar Puji Sejati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1724

Abstract

Detection of chicken egg embryos using image processing has limitations and needs some processes for improvement. By human vision, the previous process used binoculars and candling using light/beams directed at the chicken eggs in the incubator. In this study, we propose the application of image segmentation using the Otsu method in detecting chicken egg embryos. This method uses image segmentation with increased image quality (preprocessing) by several methods such as resizing, grayscaling, image adjustment, and image enhancement. These processes produce a better image and can be used for input in the segmentation process. In addition, this study compares several segmentation methods in detecting chicken egg embryos, such as thresholding, Otsu basic, and k-means clustering. The results show that our proposed method produced segmentation images to detect chicken egg embryos of 200 datasets images. This method has a faster process and can create a uniform segmentation than other methods. However, other methods can also detect chicken egg embryos. The method’s accuracy proposed in this study increased by 1.5% compared to other methods. In addition, the resulting SSIM value has a percentage close to and more than 90%, which means that the segmentation of the results obtained can be used to detect chicken egg embryos.
Essay auto-scoring using N-Gram and Jaro Winkler based Indonesian Typos Herlina Jayadianti; Budi Santosa; Judanti Cahyaning; Shoffan Saifullah; Rafal Drezewski
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2473

Abstract

Writing errors on e-essay exams reduce scores. Thus, detecting and correcting errors automatically in writing answers is necessary. The implementation of Levenshtein Distance and N-Gram can detect writing errors. However, this process needed a long time because of the distance method used. Therefore, this research aims to hybrid Jaro Winker and N-Gram methods to detect and correct writing errors automatically. This process required preprocessing and finding the best word recommendations by the Jaro Winkler method, which refers to Kamus Besar Bahasa Indonesia (KBBI). The N-Gram method refers to the corpus. The final scoring used the Vector Space Model (VSM) method based on the similarity of words between the answer keys and the respondent’s answers. Datasets used 115 answers from 23 respondents with some writing errors. The results of Jaro Winkler and N-Gram methods are good in detecting and correcting Indonesian words with the accuracy of detection averages of 83.64% (minimum of 57.14% and maximum of 100.00%). In contrast, the error correction accuracy averages 78.44% (minimum of 40.00% and maximum of 100.00%). However, Natural Language Processing (NLP) needs to improve these results for word recommendations.
Co-Authors Abdul Fadlil Adityo Nugroho, Adityo Afiqa, Nurul Agung Tri Utomo Agus Sasmito Aribowo Agus Sasmito Aribowo Ahmad Taufiq Akbar Ahmad Tri Hidayat Aji Prasetya Wibawa Akbar, Bagus Muhammad Alek Setiyo Nugroho Alfiani, Oktavia Dewi Alin Khaliduzzaman Alin Khaliduzzaman Alisya Amalia Putri Hasanah Andi Muhammad Dirham Dewantara Andiko Putro Suryotomo Andri Pranolo Anton Satria Prabuwono Anton Satria Prabuwono Anton Yudhana Arianti, Berliana Andra Arief Hermawan Awang Hendrianto Pratomo Azlan, Faris Farhan Azrul Mahfurdz Bambang Yuwono Bambang Yuwono Betty Yel, Mesra Budi Santosa Devia, Elmi Dharmawan, Tio Dreżewski, RafaÅ‚ Drezewski, Rafal Drezewski, Rafał Dwi Wahyuningrum Dwiyanto, Felix Andika Faqihuddin Al-anshori Felix Andika Dwiyanto Ghazali, Ahmad Badaruddin Haekal, Haekal Hari Prapcoyo Herlina Jayadianti Heru Cahya Rustamaji Hidayat, Ahmad Tri Humairoh, Nanda Lailatul Ismail, Amelia Ritahani Isna Nur Aini Ivana Puspita Sari Japkowicz, Nathalie Judanti Cahyaning Junaidi Junaidi Kaswijanti, Wilis Khaliduzzaman, Alin Kusuma, M. Apriandi Lean Karlo Tolentino Luh Putu Ratna Sundari Mubarak, Zulfikar Yusya Muhammad Nur Hendra Alvianto Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nur Heri Cahyana Nuril Anwar, Nuril Nuryana, Zalik Opi Irawansah, Opi Prapcoyo, Hari Putra, Agung Bella Utama Putra, Seno Aji Rabbimov Ilyos Rabbimov, Ilyos Rafal Drezewski Rafal Drezewski Rafal Drezewski Rafal Drezewski Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saidah, Andi Santosa, Budi Satya Ghifari Adipratama Seno Aji Putra Siti Khomsah, Siti Suhirman SUHIRMAN SUHIRMAN Sularso Sularso, Sularso Sunardi - Sunardi - Sunardi Sunardi Sunardi, Sunardi Taufiq Akbar, Ahmad Tri Andi, Tri Tundo, Tundo Tuti Purwaningsih, Tuti Wahyu Adjie Saputra Wilis Kaswidjanti Wilis Kaswidjanti Wilis Kaswijanti Wisnalmawati Wisnalmawati Yuhefizar Yuhefizar Yuli Fauziah Yuli Fauziyah