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All Journal Jurnal Jurusan Ilmu Keolahragaan ELKHA : Jurnal Teknik Elektro Journal of Software Engineering Journal of Intelligent Systems JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Musamus Journal of Primary Education Abdimasku : Jurnal Pengabdian Masyarakat Asean Journal of Sport for Development and Peace Indonesian Research Journal on Education Jurnal Informatika: Jurnal Pengembangan IT Scientific Journal of Informatics ACPES Journal of Physical Education, Sport, and Health
Catur Supriyanto
Universitas Dian Nuswantoro
Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Optimizing Deep Learning Models with Custom ReLU for Breast Cancer Histopathology Image Classification Nugroho, Wahyu Adi; Supriyanto, Catur; Pujiono, Pujiono; Shidik, Guruh Fajar
Scientific Journal of Informatics Vol. 11 No. 3: August 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i3.12722

Abstract

Purpose: The prompt identification of breast cancer is crucial in preventing the considerable damage inflicted by this dangerous form of cancer, which is widely happened across the globe. This study seeks to refine the efficacy of a deep learning-driven approach for the precise diagnosis of breast cancer by employing diverse bespoke Rectified Linear Units (ReLU) to improve the model's performance and reduce inaccuracies within the system. Method: This study focuses on analyzing a deep learning approach utilizing the BreakHis dataset with 7,909 images, incorporating changes to the ReLU activation function across different pre-trained CNN models. It then evaluates performance through measurement such as accuracy, precision, recall, and F1-Score. Result: Based on our experiment results, it can be shown that the DenseNet201 models with a custom LeakyReLU excel beyond the typical ReLU, achieving the highest accuracy, recall, and F1-Score at 99.21%, 99.21%, and 99.11%, respectively. Simultaneously, ResNet152, utilizing LessNegativeReLU (α=0.05), achieved the highest precision at 99.11%. The VGG11 model exhibited the most notable performance enhancement, with improvements ranging from 1.39% to 1.59%. Novelty: The research is original in optimizing a model for accurate breast cancer diagnosis. The proposed model is superior to the model utilizing the default activation function. This finding indicates that the study significantly enhances performance while effectively minimizing errors, thereby necessitating further exploration into the effectiveness of the customized activation function when applied to other medical imaging modalities.
Pendampingan Penggunaan Media Pembelajaran Game Edukasi "Code.org" bagi Siswa SMP Ibu Kartini Semarang Astuti, Yani Parti; Subhiyakto, Egia Rosi; Umaroh, Liya; Sutojo, Totok; Supriyanto, Catur
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1799

Abstract

Transformasi pendidikan lewat kebijakan merdeka belajar adalah perwujudan demi mewujudkan SDM Unggul Indonesia yang memiliki Profil Pelajar Pancasila. Merdeka belajar ditujukan untuk jenjang pendidikan dasar dan pendidikan menengah seperti SMP/SMA/SMK/Sederajat. Jenjang SMP yang pada kurikulum 2013 tidak ada mata pelajaran TIK, tapi pada saat akan masuk SMA dituntut untuk bisa materi TIK secara dasar. Untuk itu tim pengabdian Udinus menawarkan adanya suatu kerja sama yang intinya memberi pelatihan dan pendampingan kepada siswa – siswa SMP Ibu Kartini belajar materi tentang TIK. Agar materi yang akan diberikan tidak membosankan, maka materi tersebut akan diambil tema game edukasi. Banyak geme edukasi yang tersebar di dunia teknologi saat ini, maka tim pengabdi memilih metode yang sesuai dengan siswa SMP dan bisa menjadi bekal siswa – siswa tersebut dalam menghadapi program merdeka belajar saat SMA nanti. Metode yang akan diambil adalah metode pemahaman logika dalam pemrograman computer yang ada pada game edukasi code.org. Dalam game tersebut siswa bisa mengerjakan 20 game yang tingkat kesulitannya berdasarkan logika pemrograman computer yang nantinya akan dipelajari di SMA. Dengan pelatihan game edukasi ini, siswa bisa benar – benar mengerti tentang logika pemrograman. Pada akhir pelatihan ini, siswa akan mendapatkan sertifikat dari code.org bila bisa menyelesaikan 20 game dengan benar.
Menavigasi Dunia Digital dengan Meningkatkan Literasi Office, TI, dan Internet di Kalangan Siswa-Siswi Pondok Pesantren Raudhatul Qur'an Paramita, Cinantya; Andono, Pulung Nurtantio; Sudibyo, Usman; Rafrastara, Fauzi Adi; Supriyanto, Catur
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 2 (2023): Mei 2023
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/ja.v6i2.1338

Abstract

Peningkatan popularitas penggunaan perangkat komputer semakin berkembang di berbagai lapisan masyarakat. Pondok pesantren, yang sebelumnya dianggap sebagai tempat yang kurang produktif dan hanya diperuntukkan bagi mereka yang beragama, kini melakukan inovasi untuk meningkatkan peran dan potensi dalam mendukung kemaslahatan lingkungan sekitarnya. Pondok Pesantren Raudhatul Qur’an di Kauman Semarang telah banyak menciptakan siswa yang berhasil menghafal Al-Quran. Setelah menyelesaikan studi di pondok, banyak dari mereka yang melanjutkan pendidikan ke sekolah formal atau menjadi pemuka agama yang memberikan pengajaran dan bimbingan kepada masyarakat dalam memahami agama Islam di lingkungan mereka. Oleh karena itu, pelatihan teknologi komputer diperlukan untuk memberikan pengetahuan dan keterampilan bagi para santri agar dapat dimanfaatkan untuk membantu mengurus keperluan administrasi di pondok pesantren dan berguna bagi masa depan mereka. Sebanyak 53 santri diikutsertakan untuk mengikuti pelatihan yang mencakup pengenalan dasar teknologi informasi [1] seperti hardware, software, penggunaan aplikasi office seperti Word, Excel, dan PowerPoint, serta internet untuk komunikasi dan pengiriman data digital. Berdasarkan hasil pelatihan yang dilaksanakan, para santri memberikan respon positif seperti yang terlihat pada diagram 3 dan 4. Pada diagram 3 menunjukkan bahwa 81,4% dari para santri sangat tertarik dengan pelatihan tersebut, sementara hanya 13,9% yang merasa biasa-biasa saja dan 10,7% yang terpaksa mengikuti. Selain itu, hasil perbandingan pretest dan postest pada diagram 4 menunjukkan peningkatan yang signifikan setelah para santri mengikuti pelatihan tersebut.
Pemanfaatan Algoritma K-Means untuk Membuktikan Implementasi Undang-Undang Pelanggaran Hukum Korupsi di Pengadilan Negeri Banjarmasin Paramita, Cinantya; Rafrastara, Fauzi Adi; Supriyanto, catur
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5216

Abstract

This research aims to demonstrate the implementation of the Anti-Corruption Law in the Banjarmasin District Court by utilizing the K-Means algorithm. Corruption, which persists in Indonesia over a prolonged period, has reached a critical level, making it crucial to enforce the law fairly and firmly. In this study, the panel of judges in the Banjarmasin District Court was analyzed using the K-Means Clustering method and silhouette coefficient to decide corruption cases that result in state losses. The research findings indicate that the optimal number of clusters is 3, with a value of 0.686. However, there is also a lowest value among the 4 clusters, which is 0.454. These clusters are then divided into three categories of enforcement, namely cases that have been executed (108 cases), cases that will be executed (26 cases), and cases that have not been executed (2 cases). All clusters have a silhouette score of 0.742, indicating successful enforcement. This research provides concrete evidence that the panel of judges in the Banjarmasin District Court has implemented the Anti-Corruption Law while considering state losses. By utilizing the K-Means algorithm, this study also contributes to a better understanding of enforcement practices in the court. It is expected that the results of this research will support efforts to enhance the implementation of the Anti-Corruption Law in Indonesia, particularly in the Banjarmasin District Court
Deteksi Malware menggunakan Metode Stacking berbasis Ensemble Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4606

Abstract

Serangan malware kian hari kian memprihatinkan. Evolusi malware yang cepat dan semakin destruktif menimbulkan kekhawatiran bagi banyak pihak. Oleh karena itu, deteksi malware yang efektif sangat dibutuhkan. Data mining memainkan peran yang krusial dalam bidang ini, mengingat algoritma-algoritma yang ada pada data mining bisa dilatih hingga menghasilkan akurasi yang paling tinggi. Untuk mengklasifikasi suatu file, apakah tergolong malware atau tidak, dalam penelitian ini metode stacking digunakan karena dapat meningkatkan akurasi jika dibandingkan dengan algoritma-algoritma klasifikasi konvensional. Empat Algoritma dilibatkan dalam eksperimen yang dilakukan, yaitu: Neural Network, Random Forest, kNN, dan Logistic Regression. Tiga algoritma pertama digunakan sebagai classifier pada level 0, sementara itu Logistic Regression digunakan classifier pada level 1 (meta classifier).  Dengan kombinasi 4 algoritma tersebut, akurasi yang diperoleh adalah sebesar 98.7%, dan akurasi tersebut merupakan yang paling tinggi jika dibandingkan dengan masing-masing algoritma jika dieksekusi secara individual.
Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti; Ahmed, Foez
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5207

Abstract

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.
Student-Athlete Feedback on The 2023 Student Athletic Championships Khuddus, Lutfhi Abdil; Yuhantini, Eva Ferdita; Supriyanto, Catur; Prabowo, Suryanto Agung; Bahauddin, Muhammad Arja; Sulistyana, Caturia Sasti
ACPES Journal of Physical Education, Sport, and Health (AJPESH) Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Negeri Semarang (UNNES) in cooperation with ACPES (ASEAN Council of Physical Education and Sport)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/ajpesh.v4i2.21458

Abstract

Sports are one of the means of shaping individual and collective character and national spirit. One of the Student Athletics Championship events is a school-level athletic competition to find potential seeds in Indonesia ranging from elementary to high school students. This competition is held starting from regional qualifications to the National Championship. Participants who participated were 32,000 from more than 200 schools and the most participants came from East Java, namely 5104 participants. This study aims to determine the athletes’ thoughts on the Student Athletics Championship event in assessing the negative and positive impacts of the event. The research design used was descriptive cross-sectional with a survey method of 71 athletic athletes selected by random sampling. The study was conducted at the Thor Field, Surabaya in March-October 2024. The instrument used was a post-event survey question with 4 question perspectives, namely the level of satisfaction with the initial stage of organizing the event, belief in the benefits of participating in the event, the level of satisfaction with the final stage of assessment and reward-giving, and plans and recommendations to friends or relatives to participate in the next event. The data analysis used in this study was descriptive. The study results showed that most athletes had a positive perception of participating in the 2023 SAC event and believed they would participate and recommend the next SAC event with an average of 97% (69 athletes). Athletes who participated stated that the 2023 SAC.
Vision Transformer for Pneumonia Classification with Grad-CAM Explainability Darmawan, Immanuel Julius; Supriyanto, Catur
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11532

Abstract

Pneumonia is still one of the main causes of death around the world, especially in kids and older people. To lower the death rate, early and accurate diagnosis is very important. Chest X-ray (CXR) imaging is widely used for this purpose, but manual reading of CXR images can be time-consuming and may lead to differences in interpretation between observers. To address this problem, this study presents a pneumonia classification model based on the Vision Transformer (ViT) architecture combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to make the model’s decisions more interpretable. The model was trained on a publicly available CXR dataset with 5,863 images that were split into Normal and Pneumonia classes, using a 70:15:15 split for training, validation, and testing. The ViT model achieves an accuracy of 96.41% on the test set and a high recall for pneumonia cases, while class weighted loss helps to maintain more balanced predictions between the two classes. The Area Under the Curve (AUC) of 0.975 indicates strong discrimination between pneumonia-positive and normal samples. Grad-CAM visualizations, supported by a randomization test and occlusion analysis, provide an initial qualitative view of the lung regions that influence the model’s predictions and often overlap with radiologically plausible areas. However, the heatmaps have not been formally evaluated by radiologists, and the correspondence between highlighted regions and pneumonia consolidation patterns has not yet been quantitatively validated. Therefore, the proposed ViT Grad-CAM framework should be regarded as an exploratory step toward explainable pneumonia classification on chest X-rays rather than a system that is ready for clinical deployment.
Co-Authors . Safuan, . Abdollah, Mohd. Faizal Abdul Rachman Syam Tuasikal Abu Salam Ahmed, Foez Al Fahreza, Muhammad Daffa Alamsyah, Sayyidul Aulia Amalia Amalia Amalia, Syafira Rosa Amiral, Afinzaki Andreas Wilson Setiawan Antony Eka Aditya, Antony Eka Astuti, Yani Parti Bahauddin, Muhammad Arja Bayu Satria, Zaky Indra Darmawan, Immanuel Julius Dyan Yuliana Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erna Sri Rahayu, Erna Sri Fauzi Adi Rafrastara Gede Doddy Tisna MS Guruh Fajar Shidik Hapsari Peni Heru Agus Santoso HIMAWAN WISMANADI Hussein, Jasim Nadheer Junta Zeniarja Khuddus, Lutfhi Abdil Lin, wei Jhe Liya Umaroh, Liya Marjuni, Aris Mohammad Reza Maulana, Mohammad Reza Muchamad Arif Al Ardha Muljono Muljono Mulyanto, Edy Nining Widyah Kusnanik Nurhasan Nurhasan, Nurhasan Octaviani, Dhita Aulia Oman Somantri Paramita, Cinantya Pitaloka, Tia Amika Prabowo, Suryanto Agung Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Rahadian, Arief Ramadhan Rakhmat Sani Rizka Safriyani Rizki, Ainun Zulfikar Romi Satria Wahono Rusdiawan, Afif Rustam, Suhardi Safar, Noor Zuraidin Mohd Sindhu Rakasiwi Sudibyo, Usman Sulistyana, Caturia Sasti Swanny Trikajanti Widyaatmadja Syamsiar, Syamsiar T. Sutojo Wahyu Adi Nugroho Wakhidah, Elfa Wahyu Wildanil Ghozi Yang, Chung Bing Yuhantini, Eva Ferdita YUSUF FUAD