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All Journal International Journal of Electrical and Computer Engineering PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Sinkron : Jurnal dan Penelitian Teknik Informatika Information System for Educators and Professionals : Journal of Information System Jurnal Sisfokom (Sistem Informasi dan Komputer) JurTI (JURNAL TEKNOLOGI INFORMASI) Krea-TIF: Jurnal Teknik Informatika E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) Madaniya Jurnal Kajian Ilmiah Journal of Applied Computer Science and Technology (JACOST) Jurnal Atma Inovasia Prosiding University Research Colloquium INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Informatika Polinema (JIP) UPGRADE: Jurnal Pendidikan Teknologi Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Muhammad Resa Arif Yudianto
Universitas Muhammadiyah Magelang
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Social Sciences Other

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Classification of Tangerines on Fruit Ripening Levels Using K-Nearest Neighbor Algorithm Rasyid, Irfan; Saputra, Imam; Suryanegara, Raden Kartika Satya; Yudianto, Muhammad Resa Arif; Maimunah, M
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Mahasiswa (Student Paper Presentation) B
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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Abstract

This journal reviews the classification of the maturity level of tangerines based on HSV using the K-Nearest Neighbor (KNN) method. This study aims to make it easier for the public to distinguish ripe and unripe when choosing citrus fruits and also to avoid fruit shops selling unripe oranges so as not to harm sellers or buyers. We take the data sources used in this study ourselves. In this study, we use the K-Nearest Neighbors (KNN) method. This method is used in the image classification process by relying on the results of feature extraction that have previously been trained. This method selects the nearest neighbor from the training dataset, then determines the closest distance value or the smallest distance value that will produce the classification output. The results of the accuracy in using this method have reached 93% with a value of k=7.
Classification of Avocado Ripeness Levels using Naïve Bayes Method Nuryani, Ira; Fadli, Aldi Muhammad Nur; Saputri, Nadila Dwi; Fadhilah, Alfira Nisa; Yudianto, Muhammad Resa Arif; Maimunah, M
Prosiding University Research Colloquium Proceeding of The 15th University Research Colloquium 2022: Mahasiswa (Student Paper Presentation) B
Publisher : Konsorsium Lembaga Penelitian dan Pengabdian kepada Masyarakat Perguruan Tinggi Muhammadiyah 'Aisyiyah (PTMA) Koordinator Wilayah Jawa Tengah - DIY

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Abstract

During this time most people in determining the ripeness of avocados for personal consumption is not difficult because they can distinguish themselves but another case if used for production, which requires a lot of labor to group ripe and raw avocados. One of the innovations in information and communication technology in agriculture and plantations is the use of classification methods with naïve bayes algorithms. The formula of the problem in this study is how to do the classification on the ripeness of avocados and see the accuracy rate of the data. The purpose of this study is to classify avocado ripeness and to acquire intelligent systems, so that it becomes the first step towards the implementation stage. Based on the results and analysis that has been done, it can be concluded that the Naive Bayes method is considered capable in classifying avocado ripeness by using RGB color features. The accuracy in testing using Naïve Bayes method reached 83.34%. The performance obtained from this intelligent system is also effective and efficient so that the classification of avocado ripeness can be implemented.
Pengenalan Deteksi Wajah Artificial Intelligence dan Achievement Motivation Training untuk Siswa SMK Kuncup Samigaluh Sukmasetya, Pristi; Primadewi, Ardhin; Yudianto, Muhammad Resa Arif; Maimunah, Maimunah; Hasani, Rofi Abul; Nugroho, Setiya
Jurnal Atma Inovasia Vol. 4 No. 3 (2024)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v4i3.9367

Abstract

— Artificial Intelligence (AI) is a field of computer science aimed at developing machines capable of performing tasks that typically require human intelligence. In recent years, the development of AI has shown significant progress, and its use has expanded across various sectors, including education. The application of AI in education offers various opportunities and challenges, such as personalized learning and enhancing students' skills, but also presents challenges in technological adaptation and ethical understanding. This paper discusses the utilization of AI-based facial recognition technology at SMK Kuncup Samigaluh, with the goal of enhancing students' competence in information technology. This community service activity involves a series of structured stages, including initial planning, activity implementation, discussion and Q&A, as well as evaluation and feedback. The results of this activity indicate a significant improvement in students' understanding of AI and facial recognition technology, as evidenced by the increase in post-test scores compared to pre-test scores. With an interactive demonstrative approach, this activity successfully provided a positive impact on students' knowledge and interest in AI, and broadened their horizons regarding career opportunities in information technology.
Peningkatan Kompetensi Digital Guru melalui Pelatihan Pembuatan Website dengan Google Sites di SMA Ma'arif 1 Yogyakarta Yudianto, Muhammad Resa Arif; Sari, Tika Novita; Nadhir Fachrul Rozam; Dzul Fadli Rahman; Masduki Zakarijah
Jurnal Atma Inovasia Vol. 5 No. 4 (2025)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v5i4.11332

Abstract

Perkembangan teknologi digital saat ini menuntut semua sektor, termasuk pendidikan, untuk beradaptasi dan memanfaatkannya dalam proses belajar mengajar. Salah satu bentuk pemanfaatan teknologi di lingkungan pendidikan adalah kemampuan guru dalam membangun identitas profesional melalui website pribadi yang juga dapat digunakan sebagai media pembelajaran. Kegiatan pengabdian masyarakat ini dilakukan di SMA Ma’arif 1 Yogyakarta dan bertujuan untuk memberikan pelatihan kepada para guru dalam membuat website pribadi menggunakan platform Google Sites. Materi pelatihan meliputi konsep pembelajaran digital, perencanaan konten, serta praktik langsung membuat dan menyusun struktur website. Selain menjadi sarana branding diri, website yang dibuat juga diharapkan mampu menjadi repositori materi pembelajaran yang dapat diakses oleh siswa kapan saja dan di mana saja. Peserta pelatihan terdiri dari guru berbagai mata pelajaran, dan sebagian besar belum memiliki pengalaman membuat website sebelumnya. Hasil kegiatan menunjukkan peningkatan pengetahuan dan keterampilan guru dalam mengelola konten digital, serta antusiasme yang tinggi dalam mengikuti pelatihan. Kegiatan ini diharapkan dapat menjadi langkah awal dalam mendorong transformasi digital di sekolah, khususnya dalam memperkuat peran guru sebagai fasilitator pembelajaran berbasis teknologi. Kata Kunci—kompetensi digital, pelatihan guru, Google Sites, media pembelajaran, website pribadi
Implementasi Metode Dempster-Shafer Untuk Deteksi Kesehatan Mental Pada Mahasiswa Berbasis Web Jalaluddin, Alif; Arumi, Endah Ratna; Sasongko, Dimas; Pinilih, Sambodo Sriadi; Yudatama, Uky; Arif Yudianto, Muhammad Resa
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4830

Abstract

Mental health is a person's soul condition to budaptasi in its environment to feel happy or get the comfort of life, so as not to experience mental disorders. Often mental health is ignored by most people because it is different from physical health that can be seen directly with the eyes and can be identified easily. Lack of awareness of mental health in the life of the people of Indonesia and the assumption that a person who goes to psychologists is a person inseasonable, often the individual who actually undergoes mental health problems reluctant to get help from experts or deny that he does not have mental health problems. Limitations of time and costs are also one of the constraints of a student reluctant to get help from experts like psychologists. Therefore, a web-based expert system is built with a dempster-shafer method to use as detection on the student and allows the user to know whether the user has a tendency of the problem on its mental health or not before the official consultation is required from the expert. Testing Accuracy Comparison System between the results of the system and experts by using 100 correspondents from students at Muhammadiyah Magelang University (UNIMMA) 89% know mental health and 65% have experienced mental disorders. The results of the SRQ29 data used and were spread among campus students, this study has used 20 sample data and produces 70% expert suit compliance. From the results of expert suitability obtained from the calculation of the system by selecting symptoms and automatically the system will calculate the accuracy of the existing Belief Valident in every symptom. Then the system will take decisions based on the results of the largest calculation value.
Optimasi Algoritma Support Vector Machine untuk Analisis Sentimen dengan Bayesian Optimization Yudianto, Muhammad Resa Arif; Zakariah, Masduki; Rozam, Nadhir Fachrul; Rahman, Dzul Fadli; Sari, Tika Novita; Mustofa, Zaenal
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 3 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i3.11524

Abstract

This study examines the effect of Bayesian Optimization in improving the performance, computational efficiency, and sustainability of Aspect-Based Sentiment Analysis models using Support Vector Machine (SVM). A dataset consisting of 988 customer reviews about Borobudur Temple, classified into six dimensions: Attractiveness, Facilities, Accessibility, Visual Image, Price, and Human Resources is used to compare two scenarios, namely Baseline SVM and SVM enhanced with Bayesian Optimization (BO). Important metrics used include accuracy, computational duration, energy usage, and carbon emissions. The results show that BO significantly improves accuracy, especially on difficult aspects such as Facilities (from 0.7294 to 0.8682) and Price (from 0.8047 to 0.9576). The most complicated aspect, namely visual image due to the very minimal number of datasets (unbalanced), achieved an increase in accuracy from 0.6729 to 0.72. In addition, BO reduces training time, especially for resource-intensive tasks such as the visual image aspect, reducing training time from 13.04 seconds to 9.4 seconds. Substantial reductions in energy consumption and CO₂ emissions are seen in line with sustainable machine learning principles. The hyperparameter adaptability of SVM, with linear kernels performing well in simpler tasks, while polynomial and sigmoid kernels improve performance for more complex parts. BO substantially alleviates the limitations of Baseline SVM, offering a robust, efficient, and environmentally friendly solution for ABSA. Future research can explore more enhancements for complex tasks to improve performance and efficiency.
Peningkatan Kompetensi Guru SMP Negeri 12 Yogyakarta dalam Membuat Bahan Ajar Digital dengan Canva Satriyo Agung Dewanto; Bekti Wulandari; Bonita Destiana; Agus Qomaruddin Munir; Yudianto, Muhammad Resa Arif; Dzul Fadli Rahman; Ramadhana Setiyawan
Jurnal Atma Inovasia Vol. 6 No. 1 (2026)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jai.v6i1.12704

Abstract

Digital transformation requires teachers to be able to develop innovative and interactive teaching materials. However, limited competence in utilizing modern design platforms remains an obstacle at SMP Negeri 12 Yogyakarta. This community service program aims to improve teachers' skills in creating interactive digital teaching materials using the Canva platform. The method utilized was a hands-on workshop with a mixed-methods evaluation approach, including pre-tests, post-tests, product assessments, and feedback questionnaires. The evaluation results showed a significant increase in competence among all participants, as evidenced by a surge in post-test scores, the quality of teaching materials that were functional and visually appealing, and a very positive perception of the benefits and implementation of the training. It was concluded that this training was effective in equipping teachers with practical skills to transform themselves into producers of digital content relevant to 21st-century learning needs.
Machine Learning-Based Network Traffic Anomaly Detection Using the CIC-IDS2017 Dataset Rozam, Nadhir Fachrul; Sari, Tika Novita; Yudianto, Muhammad Resa Arif; Rahman, Dzul Fadli
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 3 No 2 (2026): FEBRUARI
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v3i2.6174

Abstract

The increasing volume and diversity of traffic in modern networks demand more adaptive intrusion detection approaches than traditional signature-based methods. This study aims to evaluate and compare the performance of several machine learning algorithms in detecting multi-class network traffic anomalies using the  CIC-IDS2017 dataset. The research process includes data cleaning and transformation,  class imbalance handling through random undersampling, and the implementation of five classification models: Logistic Regression, Gaussian NaïveBayes, Random Forest, K-Nearest Neighbors, and Support Vector Machine. Model performance is assessed using accuracy, precision, recall, and F1-score, supported by confusion matrix analysis and feature contribution evaluation. The results indicate that Random Forest achieves the best performance with an accuracy of 99.44% and consistently high evaluation metrics, while Gaussian Naïve Bayes shows the lowest performance. Furthermore, flow-based features are found to play a dominant role in improving classification accuracy, while misclassifications mainly occur among classes with similar traffic patterns. The findings highlight that selecting appropriate algorithms and applying effective preprocessing strategies are critical for developing more accurate and adaptive intrusion detection systems capable of addressing evolving cyber threats.
Co-Authors Acihmah Sidauruk Agus Qomaruddin Munir Aldi Muhammad Nur Fadli Alfira Nisa Fadhilah Arazka Firdaus Anavyanto Ardhin Primadewi Arham Rahim Arif Dwi Laksito Arya Prayoga Bayu Agustian Bekti Wulandari Bonita Destiana Burhanudin, Nazil Ilham Darmawan, Briga Devi Oktaviani Dimas Sasongko Dzul Fadli Rahman Ema Utami Endah Ratna Arumi, Endah Ratna Fadhilah, Alfira Nisa Fadli, Aldi Muhammad Nur Febriyanto, Yusril Hanif Al Fatta Hanif Al Fatta Hasani, Rofi Abul Imam Saputra Imam Saputra Ira Nuryani Irfan Rasyid Irfan Rasyid Jalaluddin, Alif Kusrini Kusrini Kusrini, Kusrini Lusi Nurlatifah Maimunah Maimunah Maimunah, M Maimunah, Maimunah Masduki Zakarijah Meidar Hadi Avizenna Nadhir Fachrul Rozam Nadila Dwi Saputri Nuryani, Ira R Arri Widyanto Raden Kartika Satya Suryanegara Rahman, Dzul Fadli Rahmi, Alfie Nur Ramadhana Setiyawan Rasyid, Irfan Rofi Abul Hasani Sambodo Sriadi Pinilih Saputri, Nadila Dwi Satriyo Agung Dewanto Setiya Nugroho Sukmasetya, Pristi Suryanegara, Raden Kartika Satya Tika Novita Sari Tuessi Ari Purnomo Tuessi Ari Purnomo Uky Yudatama, Uky Yusril Febriyanto Zaenal Mustofa Zakariah, Masduki