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All Journal ComEngApp : Computer Engineering and Applications Journal Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri Sistemasi: Jurnal Sistem Informasi Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Sebatik Jurnal Teknoinfo ICETIA Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika IJISTECH Information System Journal (INFOS) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT) Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sains dan Informatika : Research of Science and Informatic Jurnal Informatika Ekonomi Bisnis Journal Of Artificial Intelligence And Software Engineering Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK)
Suwanto Sanjaya
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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Klasifikasi Citra Daging Babi dan Daging Sapi Menggunakan Deep Learning Arsitektur ResNet-50 dengan Augmentasi Citra Sarah Lasniari; Jasril Jasril; Suwanto Sanjaya; Febi Yanto; Muhammad Affandes
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 3 No. 4 (2022): Juni 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v3i4.4167

Abstract

Beef is an example of an animal protein-rich food. The consumption of meat in Indonesia is increasing year after year, in tandem with the country's growing population. Many traders purposefully combine beef and pork in order to maximize profits. With the naked eye, it's difficult to tell the difference between pork and beef. In Muslim-majority countries, the assurance of halal meat is crucial. This study uses Deep Learning with the Convolutional Neural Network (CNN) method and ResNet-50 with data augmentation to classify images of beef and pork. The original meat picture databases contain 457 images, however following the data augmentation process, there are 2742 images in total, divided into three classes. The distribution of training and test data is 90 percent:10 percent in the comparison test scenario between the two original data schemes and supplemented data. With an average of 87.64 % accuracy, 87.59 % recall, and 90.90 % precision, the Confusion Matrix is the best classification performance model. There was no evidence of overfitting based on observations from the visualization of the training and testing process.
Klasifikasi Citra Daging Sapi dan Daging Babi Menggunakan CNN Arsitektur EfficientNet-B6 dan Augmentasi Data M. Fadil Martias; Jasril Jasril; Suwanto Sanjaya; Lestari Handayani; Febi Yanto
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 4 (2023): Juni 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i4.6195

Abstract

In daily life, beef often serves as a staple food for humans. However, the high and expensive price of beef has prompted traders to adulterate it with pork for the sake of profit. Such adulteration has serious implications in the Islamic religion, where not all types of meat are considered halal (permissible for consumption), such as pork. As a result, consumers often remain unaware that the beef they purchase has been adulterated with pork. At a glance, both types of meat exhibit similar appearance and texture, making them difficult to differentiate. This research aims to classify beef and pork using a deep learning model with the Convolutional Neural Network (CNN) method, combined with data augmentation. The model used is EfficientNet-B6 with variations in the testing scenario. The variations include the ratio of training and testing data, learning rates, and optimizer for EfficientNet-B6. Data augmentation is performed using techniques such as random rotation, shifting, image scaling, vertical and horizontal flipping, and nearest pixel filling. Evaluation results using the confusion matrix show that the model with data augmentation achieves the highest accuracy for the classes of beef, pork, and adulterated samples at 92.00%, while the model without augmentation achieves an accuracy of 91.67%. However, from this experiment, the best scenario to avoid misclassifying pork and adulterated samples as beef can be obtained. This scenario involves a model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01, which achieves the highest precision for the beef class at 96.05%. The research findings demonstrate that the use of data augmentation on images can improve the model's performance, and the model with data augmentation, a 90:10 data split, SGD optimizer, and a learning rate of 0.01 exhibits the best performance in classifying beef images.
Penerapan Semi-Supervised Deep Learning dengan AdaMatch untuk Klasifikasi Penyakit Paru-paru pada Citra X-ray Dada: Application of Semi-Supervised Deep Learning with AdaMatch for Classification of Lung Disease on Chest X-ray Image Zalwana, Hilya; Negara, Benny Sukma; Irsyad, Muhammad; Sanjaya, Suwanto; Fikry, Muhammad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2811

Abstract

Penyakit paru-paru, seperti Coronavirus Disease 2019 (COVID-19) dan pneumonia, masih menjadi tantangan kesehatan yang memerlukan diagnosis cepat dan akurat. Citra chest X-ray (CXR) banyak digunakan untuk mendukung diagnosis, namun interpretasinya masih bergantung pada radiolog dan ketersediaan data berlabel. Keterbatasan data berlabel menjadi kendala dalam pengembangan model deep learning berbasis supervised learning. Penelitian ini menerapkan pendekatan semi-supervised deep learning menggunakan AdaMatch dengan DenseNet-169 untuk klasifikasi multikelas citra CXR menjadi COVID-19, Pneumonia, dan Normal. Dataset publik Mendeley Data yang digunakan terdiri atas 5.228 citra CXR, dengan pembagian 70% data pelatihan, 10% validasi, dan 20% pengujian. Tiga skenario proporsi data berlabel, yaitu 5%, 10%, dan 20%, digunakan untuk mengevaluasi performa model. AdaMatch memanfaatkan data berlabel dan tidak berlabel melalui mekanisme adaptive thresholding, distribution alignment, dan consistency regularization. Hasil terbaik diperoleh pada skenario 20% data berlabel dengan akurasi 98,19%, sensitivitas 98,23%, dan F1-score 98,24%. Performa tersebut mendekati model supervised learning pembanding yang memperoleh akurasi 98,95%, sensitivitas 98,98%, dan F1-score 98,98%. Temuan ini menunjukkan bahwa AdaMatch merupakan pendekatan semi-supervised yang efektif untuk meningkatkan klasifikasi citra CXR pada kondisi keterbatasan data berlabel.
Penerapan Algoritma K-Means Clustering pada Kinerja Mesin Screw press Fikri Kurnia Rahman; Jasril; Suwanto Sanjaya; Lestari Handayani; Fitri Insani
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2002

Abstract

The screw press is one of the machines used in the process of separating oil from tanks containing Fresh Fruit Bunches (FFB). The machine consists of a twin-screw system that functions to extract oil from the pressing unit, with back pressure applied by a hydraulic double cone. The mixed fruit residue is compreWCSSd, causing the oil contained within the residue to be released due to the pressure exerted by the press machine. Maintenance and repair of machinery are eWCSSntial activities to support productive operations in any sector. Therefore, it is necessary to conduct analysis to identify patterns in machine conditions within the factory. One effective approach to discovering machine condition patterns is through clustering techniques. Clustering is a method of grouping data based on certain parameters to form clusters of objects that share similar characteristics. In this study, data were collected from PT. XYZ for the period of April 2024 to May 2024, with a total of 23,002 records. The analysis was conducted using the K-Means Clustering algorithm, with testing carried out on 3 to 15 clusters. Based on the evaluation using the Davies-Bouldin Index (DBI), the most optimal clustering result was obtained with 3 clusters, achieving the lowest DBI value of 0.386. Meanwhile, using the Elbow Method, the optimal number of clusters was determined to be 4, as indicated by the Elbow point on the WCSS graph, with a Sum of Square Error (WCSS) value of 270. Therefore, it can be concluded that the clustering results using the K-Means Clustering algorithm are relevant for identifying machine condition patterns and are expected to assist in monitoring and managing the condition of the screw press machine.
Application of Information Gain Feature Selection and SMOTE in XGBoost Algorithm for Asthma Disease Classification Fioni Nikmatul Fajar; Fitri Insani; Suwanto Sanjaya; Iis Afrianty
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9384

Abstract

Asma merupakan salah satu penyakit kronis pada sistem pernapasan yang prevalensinya terus meningkat dan memerlukan deteksi dini untuk mencegah komplikasi serius. Salah satu tantangan dalam klasifikasi asma menggunakan machine learning adalah ketidakseimbangan kelas yang menyebabkan model cenderung memprediksi kelas mayoritas sehingga kemampuan mendeteksi kasus asma menjadi rendah. Penelitian ini mengusulkan penerapan SMOTE dan seleksi fitur Information Gain dalam algoritma XGBoost untuk mengatasi permasalahan tersebut. Dataset yang digunakan terdiri dari 2.392 data dengan 28 atribut, di mana tahapan penelitian meliputi preprocessing, seleksi fitur menggunakan Information Gain yang mengurangi fitur menjadi 22 fitur, penyeimbangan data menggunakan SMOTE, pembagian data dengan rasio 90:10, 80:20, dan 70:30, serta klasifikasi menggunakan XGBoost. Pengujian dilakukan terhadap empat skenario pendekatan untuk membandingkan kontribusi setiap metode yang diterapkan. Evaluasi dilakukan menggunakan data uji seimbang dan data uji asli dengan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa skenario terbaik diperoleh pada kombinasi Information Gain + SMOTE + XGBoost dengan rasio 90:10 pada data uji seimbang, menghasilkan akurasi 75%, presisi 87,5%, recall 58,33%, dan F1-score 70%. Hasil tersebut menunjukkan bahwa kombinasi seleksi fitur dan penyeimbangan data mampu meningkatkan kemampuan model dalam mendeteksi penyakit asma.
Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi M Alfandri Aqilah; Jasril Jasril; Suwanto Sanjaya; Fitri Insani
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
Analisis Efektivitas IndoBERT untuk Klasifikasi Multilabel Terjemahan Hadis Bukhari Menggunakan Logistic Regression Achmad Yamin Harahap; Nazruddin Safaat H; Surya Agustian; Suwanto Sanjaya; Teddie D
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1219

Abstract

Hadith serves as the second source of guidance after the Quran, directing Muslims in various aspects of life; the *Sahih al-Bukhari* collection is among the most renowned. The complex nature of their meanings often encompassing multiple categories of messages poses a significant challenge for manual text classification, particularly as data volume grows. In this study, the content of the hadith often includes multiple message types, such as recommendations, prohibitions, and general information. This research aims to evaluate an automated classification system for Indonesian translations of *Sahih al-Bukhari* hadith, categorizing them into three classes: Information, Recommendation, and Prohibition. The study is motivated by the vast number of hadith, which requires significant time and deep understanding for people to grasp the core message of each one. This classification system is intended to facilitate the identification of primary messages, thereby making the processes of searching, studying, and understanding hadith more effective and efficient. IndoBERT is employed to generate contextual vector representations capable of capturing deeper semantic meaning, while Logistic Regression is selected for its efficiency and stability with high-dimensional data. Evaluation is conducted using a train-validation-test split approach, alongside accuracy and macro F1-score metrics. The study achieved an average F1-score of 67.43%, demonstrating that the combination of IndoBERT and Logistic Regression yields strong, consistent classification performance for this multi-label task.
Enhancing Hate Speech and Offensive Language Detection using CatBoost with RoBERTa-based Contextual Embeddings Muhammad Elfarizi; Surya Agustian; Fitra Kurnia; Suwanto Sanjaya; Fitri Insani
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6637

Abstract

The widespread dissemination of hate speech and offensive content on social media platforms has become a critical societal issue, highlighting the need for reliable automated detection systems. This study proposes a hybrid approach that leverages frozen embeddings from the pre-trained language model cardiffnlp/twitter-roberta-base-offensive as a high-level semantic feature extractor, combined with the CatBoost gradient boosting algorithm as the final classifier. The proposed method was evaluated on the HASOC 2021 English dataset through six experimental scenarios and compared with a TF-IDF baseline using CatBoost's default hyperparameters. Experimental results demonstrate that the proposed approach achieved a Macro F1-score of 0.7924 for the binary classification task (Task 1A) and 0.6113 for the multiclass classification task (Task 1B), outperforming the TF-IDF baseline, which achieved scores of 0.7724 and 0.5798, respectively. The proposed system demonstrated a clear performance improvement and achieved results comparable to those of the top-ranked teams on the official HASOC 2021 leaderboard, while avoiding the computational cost associated with fine-tuning large pre-trained language models.
A Support Vector Regression Approach for Predicting the Remaining Useful Life of Turbofan Engines Muhammad Vio Hardiansyah; Fitri Insani (Scopus ID: 57190404820); Lestari Handayani; Jasril Jasril; Suwanto Sanjaya
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 11 No. 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Turbofan engines are crucial components in the aviation and manufacturing industries, where estimating the Remaining Useful Life (RUL) has a significant impact on operational efficiency and safety. This study aims to predict the RUL of turbofan engines using the Support Vector Regression (SVR) method, a machine learning approach that has proven effective in modeling nonlinear relationships between variables. Operational data related to turbofan engines include operational parameters, sensors, and maintenance records. The initial stage of this research involves data analysis based on unit number, time, operational control, and sensor parameters. This process begins with preprocessing to initialize the initial data values, normalize, and select sensors that have stagnant values, as these sensors do not affect the machine learning system. Subsequently, regression calculations are performed to compare predicted values and actual values using the Support Vector Regression method optimized with Grid Search Optimization. In this study, testing was conducted with Parameters C [1, 10, 50, 100] and ε [1, 5, 10, 50], resulting in the best model with an RMSE error of 19.56 and MAE of 14.73.
Penggunaan Dual Attention Network pada DenseNet-169 untuk Klasifikasi Multi-kelas Citra X-Ray Dada Azizah Tasykira Paramitha El Razi; Benny Sukma Negara; Muhammad Irsyad; Suwanto Sanjaya; Siti Ramadhani
Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) Vol. 4 No. 1 (2026): JURNAL PENGEMBANGAN TEKNOLOGI INFORMASI DAN KOMUNIAKSI (JUPTIK)
Publisher : Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/juptik.v4i1.4333

Abstract

Klasifikasi multi-kelas citra X-ray dada menjadi salah satu pendekatan yang dapat membantu membedakan kondisi COVID-19, normal, dan pneumonia secara otomatis. Namun, kemiripan karakteristik visual antar kelas dapat menyebabkan model kesulitan dalam mengekstraksi fitur yang relevan. Penelitian ini mengintegrasikan Dual Attention Network (DANet) pada DenseNet-169 untuk meningkatkan representasi fitur melalui kombinasi Grouped Channel Attention Module dan Strip Spatial Attention Module. Dataset yang digunakan terdiri atas 5.228 citra X-ray dada yang dibagi menjadi data latih dan data uji dengan rasio 80:20. Model DenseNet-169 baseline dan DenseNet-169 dengan DANet dievaluasi menggunakan akurasi, presisi, recall, F1-score, sensitivitas, spesifisitas, ROC-AUC, confusion matrix, dan Grad-CAM. Hasil pengujian menunjukkan bahwa DenseNet-169 dengan DANet memperoleh akurasi 98,47%, presisi 98,55%, recall 98,50%, F1-score 98,50%, sensitivitas 98,50%, dan spesifisitas 99,22%. Nilai ROC-AUC yang diperoleh pada kelas COVID-19, normal, dan pneumonia masing-masing sebesar 0,9994, 0,9989, dan 0,9980. Hasil tersebut menunjukkan bahwa DenseNet-169 dengan DANet memiliki kemampuan klasifikasi dan diskriminasi yang baik pada ketiga kelas. Visualisasi Grad-CAM menunjukkan bahwa DANet membantu model menghasilkan perhatian yang lebih terarah.
Co-Authors Abdussalam Al Masykur Achmad Yamin Harahap Adrian Maulana Afiana Nabilla Zulfa Ahmad Fauzan Ahmad Paisal Ahmad, Rizmah Zakiah Nur Al Fiqri, M. Faiz Alwis Nazir Alwis Nazir Alwis Nazir Alwiz Nazir Amalia Hanifah Artya Annisa Putri Arif Mudi Priyatno Ariq At-Thariq Putra Aulia Ramadhani Azizah Tasykira Paramitha El Razi Baehaqi Cut Lira Kabaatun Nisa Darmila Deny Ardianto Dodi Efendi efni humairah Eka Pandu Cynthia Elin Haerani Elvia Budianita Erni Rouza, Erni Ersad Alfarsy Absar, Ersad Alfarsy Fadhilah Syafria Fadhilla Syafria Fakhrezi, Muhammad Dzaki Febi Yanto Felian Nabila Fikri Kurnia Rahman Fioni Nikmatul Fajar Fitra Kurnia Fitri Insani Fitri Insani Fitri Insani Fitri Insani Fitri Insani (Scopus ID: 57190404820) Fitri, Dina Deswara Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Hafez Almirza Harni, Yulia Hartini Hartini Iis Afrianty Iis Afrianty Ikhwanul Akhmad DLY Irman Hermadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Karina Julita Kurniawan, Saifur Yusuf Lestari Handayani Lestari Handayani Lestari Handayani Lia Anggraini Lola Oktavia M Alfandri Aqilah M. Fadil Martias Masaugi, Fathan Fanrita Maulana Junihardi Mazdavilaya, T Kaisyarendika Megawati Megawati Morina Lisa Pura Muhammad Affandes Muhammad Affandes Muhammad Elfarizi Muhammad Fikry Muhammad Irfan Syah Muhammad Irsyad Muhammad Irsyad Muhammad Irsyad Muhammad Vio Hardiansyah Nabyl Alfahrez Ramadhan Amril Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Nazruddin Safaat H Negara, Benny Sukma Novi Yanti Novriyanto Novriyanto Novriyanto Pangestu, Yoga Pizaini Pizaini Puspa Melani Almahmuda Putri Ayuni, Desy Radili, Adi Rahma Shinta Rahmad Abdillah Ramadhan, Muhammad Ilham Ramu Will Sandra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Saputra, Nugroho Wahyu Sarah Lasniari Sarah Lasniari Shahira, Fayza Siti Ramadhani Sugandi, Hatami Karsa SURYA ADITYA GD Surya Agustian Syaputra, Muhammad Dwiky Teddie D Ulfah Adzkia Vitriani, Yelfi Yani, Susmi Syahfrida Yelfi Vitriani Yeni Fariati Yusra Yusra, Yusra Yusril Hidayat Zalwana, Hilya