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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 Paradigma 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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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.
Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN Jayadianti, Herlina; Kaswidjanti, Wilis; Utomo, Agung Tri; Saifullah, Shoffan; Dwiyanto, Felix Andika; Drezewski, Rafal
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1505.348-354

Abstract

Reviews are a form of user experience information on a product or service that can be used as a reference for potential consumers’ preferences to buy, use, or consume a product. They can be also used by business entities to find out public opinion about their product or the performance of their business products. It will be very difficult to process the review data manually and it will take a long time. Therefore, sentiment analysis automation can be used to get polarity information from existing reviews. In this study, IndoBERT with Recurrent Convolutional Neural Network (RCNN) was used to automate sentiment analysis of Indonesian reviews. The data used was a sentiment analysis dataset obtained from IndoNLU with sentiment consisting of negative sentiment, neutral sentiment, and positive sentiment. The results of the test showed that IndoBERT with the Recurrent Convolutional Neural Network (RCNN) had better results than the IndoBERT base. IndoBERT with Recurrent Convolutional Neural Network (RCNN) obtained 95.16% accuracy, 94.05% precision, 92.74% recall and 93.27% f1 score.
PERBANDINGAN SEGMENTASI PADA CITRA ASLI DAN CITRA KOMPRESI WAVELET UNTUK IDENTIFIKASI TELUR Saifullah, Shoffan; Sunardi, Sunardi; Yudhana, Anton
ILKOM Jurnal Ilmiah Vol 8, No 3 (2016)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v8i3.75.190-196

Abstract

Citra digital merupakan gambaran yang jelas dari objek yang dapat diolah dengan komputer. Semakin besar ukuran (pixel) citra akan membutuhkan tempat penyimpanan yang besar pula. Dasar pengolahan citra yang dilakukan dalam penelitian ini terletak pada proses segmentasi pengolahan citra. Hal yang perlu dipertimbangkan adalah objek dari citra telur ayam yang akan diidentifikasi. Proses pengolahan citra melibatkan beberapa proses mulai dari akuisisi citra, preprocessing dan proses pengolahan citra sampai hasilnya. Preprocessing dilakukan untuk proses segmentasi yaitu dengan mengubah citra menjadi citra grayscale, dan kemudian diubah menjadi citra hitam putih. Dalam setiap proses dilakukan padding haar untuk mengurangi ukuran (size on disk) dengan matrik haar 8x8. Dan juga dilakukan proses dilasi dan opening untuk membuat objek terlihat jelas serta menghaluskan permukaan untuk menghilangkan noise. Pada proses pengolahannya dilakukan dengan menggunakan segmentasi dan pelabelan dengan didahului dengan perhitungan centroid dan penentuan bounding box untuk mengidentifikasi telur ayam. Perbandingan hasil pengolahan citra asli dengan hasil kompresi dari citra asli menunjukkan bahwa proses segmentasi citra telur ayam memberikan hasil 100% sama (baik citra asli maupun citra kompresi wavelet). Dengan kompresi akan menghemat penyimpanan (disk) dan hasil yang sama diperoleh dalam proses perhitungan objek, luas area, dan penentuan titik centroid.
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction Saifullah, Shoffan; Suryotomo, Andiko Putro
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.937.285-293

Abstract

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the processs identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.
Comparative Analysis of Email Spam Detection Using SVM with TF-IDF and Word2Vec on Multilingual Datasets Katamsyi, Kaifa Ahlal; Akbar, Ahmad Taufiq; Nurkholis, Andi; Prapcoyo, Hari; Akbar, Bagus Muhammad; Saifullah, Shoffan
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.12339

Abstract

The rapid growth of email communication has increased the prevalence of spam emails, which can disrupt productivity and compromise information security. This study presents a comparative analysis of two text representation methods—TF-IDF and Word2Vec—for spam email classification using a Support Vector Machine (SVM) with a Radial Basis Function kernel. The experiments utilized Indonesian and English email datasets totaling 5,421 emails, split into 75% training and 25% testing sets. Two scenarios were evaluated: baseline with default parameters and after hyperparameter optimization using Grid Search combined with K-Fold Cross Validation. The results indicate that TF-IDF consistently outperformed Word2Vec across both languages, achieving the highest accuracy of 0.9562 on the English dataset after tuning. Word2Vec showed substantial improvement following parameter adjustment, reducing the performance gap with TF-IDF. The findings highlight the importance of hyperparameter optimization for enhancing the quality of feature representations and improving classification performance. This study also demonstrates that TF-IDF provides more stable results across different linguistic contexts, while Word2Vec benefits significantly from careful tuning. The results provide practical insights for implementing efficient spam email detection systems in multilingual environments. Future research could explore additional classifiers, deep learning approaches, and contextual embeddings to further improve classification accuracy and robustness.
Co-Authors Abdul Fadlil Adityo Nugroho, Adityo Afiqa, Nurul Agus Sasmito Aribowo Ahmad Taufiq Akbar Ahmad Tri Hidayat Aji Prasetya Wibawa Akbar, Ahmad Taufiq Akbar, Bagus Muhammad Alek Setiyo Nugroho Alfiani, Oktavia Dewi Alin Khaliduzzaman Alin Khaliduzzaman Alisya Amalia Putri Hasanah Andi Muhammad Dirham Dewantara Andi Nurkholis 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 Betty Yel, Mesra Budi Santosa Devia, Elmi Dharmawan, Tio Dreżewski, RafaÅ‚ Drezewski, Rafal Drezewski, Rafał Dwi Wahyuningrum Dwiyanto, Felix Andika Faqihuddin Al-anshori Ghazali, Ahmad Badaruddin Haekal, Haekal 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 Katamsyi, Kaifa Ahlal 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 Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saidah, Andi Santosa, Budi Satya Ghifari Adipratama Seno Aji Putra 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 Yuhefizar Yuhefizar Yuli Fauziah Yuli Fauziyah