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All Journal Jurnal Nalar Pendidikan CommIT (Communication & Information Technology) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Knowledge Engineering and Data Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Mantik Progresif: Jurnal Ilmiah Komputer Infotekmesin Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Madani : Indonesian Journal of Civil Society Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Tadris : Jurnal Penelitian dan Pemikiran Pendidikan Islam Journal of Dinda : Data Science, Information Technology, and Data Analytics Prosiding Seminar Nasional Pengabdian Kepada Masyarakat IJCOSIN : Indonesian Journal of Community Service and Innovation Proceeding of International Conference Health, Science And Technology (ICOHETECH) eProceedings of Engineering Madani : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal MediaTIK Journal of Mechatronics and Artificial Intelligence Transaction on Informatics and Data Science
Ummi Athiyah
Institut Teknologi Telkom Purwokerto
Religion 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 Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Pengenalan Aksara Jawa Dengan Algoritma Convolutional Neural Network (CNN) Dwi Setiawan, Brandon; Athiyah, Ummi
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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

Abstract

Penelitian ini mengkaji pengenalan aksara Jawamenggunakan algoritma Convolutional Neural Network (CNN)dan arsitektur VGG16. Penelitian ini bertujuan untukmengevaluasi dan membandingkan performa kedua modeldalam klasifikasi citra aksara Jawa yang kompleks. Datasetyang digunakan terdiri dari 5.000 citra aksara Jawa dengan 100jenis motif, yang diperoleh melalui pengambilan gambarmanual. Proses preprocessing meliputi filtering, augmentasi,dan pembagian dataset untuk pelatihan dan pengujian. ModelCNN dirancang menggunakan empat lapisan konvolusi denganjumlah neuron bertingkat dan pooling, sementara VGG16memanfaatkan arsitektur bertingkat dengan 16 lapisankonvolusi. Hasil menunjukkan bahwa VGG16 memiliki akurasipelatihan dan validasi tertinggi masing-masing sebesar 99,83%dan 99,50%, mengungguli CNN yang mencapai akurasipelatihan 87,70% dan validasi 97,10%. Namun, CNNmenunjukkan potensi keandalan lebih tinggi dengan nilai lossvalidasi lebih rendah. Penelitian ini menegaskan pentingnyapemilihan arsitektur model dalam klasifikasi citra aksara Jawayang kompleks.Kata kunci— Aksara Jawa, Augmentasi Data, ConvolutionalNeural Network (CNN), Klasifikasi Citra; VGG-16
The Application of Modified K-Nearest Neighbor Algorithm for Classification of Groundwater Quality Based on Image Processing and pH, TDS, and Temperature Sensors Amalia, Hasna Shafa; Athiyah, Ummi; Muhammad, Arif Wirawan
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.2827

Abstract

The limited availability of water in remote areas makes rural communities pay less attention to the water quality they use. Water quality analysis is needed to determine the level of groundwater quality used using the Modified K-Nearest Neighbor Algorithm to minimize exposure to a disease. The data used in this study was images combined with sensor data obtained from pH (Potential of Hydrogen), TDS (Total Dissolved Solids) sensors and Temperature Sensors. The test used the Weight voting value as the highest class majority determination and was evaluated using the K-Fold Cross Validation and Multi Class Confusion Matrix algorithms, obtaining the highest accuracy value of 78% at K-Fold = 2, K-Fold = 9, and K- Fold = 10. Meanwhile, the results of testing the effect of the K value obtained the highest accuracy value at K = 5 of 67.90% with a precision value of 0.32, 0.37 recall, and 0.33 F1-Score. From the results of the tests carried out, it can be concluded that most of the water conditions are suitable for use.
Pengembangan Perangkat Lunak Untuk Deteksi DDoS Berbasis Neural Network Arif Wirawan Muhammad; Muhammad Nur Faiz; Ummi Athiyah
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1544

Abstract

System security issues are a vital factor that needs to be considered in the operation of systems and networks, which will later be used for disaster mitigation and preventing attacks on the network. Distributed Denial of Services (DDoS) is a form of attack carried out by individuals or groups to damage data through servers or malware in the form of flooding packets, therefore it can paralyze the network system used. Network security is a factor that must be maintained and considered in an information system. DDoS can take the form of Ping of Death, flood, Remote control attack, User Data Protocol (UDP) flood, and Smurf Attack. This study aims to develop software to detect DDoS attacks based on network traffic logs. The software has been tested and run according to the neural network algorithm. This software was developed with an interface that makes it easier for users to detect the source IP whether the IP is carrying out a DDoS attack or normal.
Komparasi Model Analisis Sentimen Pada Twitter Terhadap Kemahalan Minyak Goreng dengan Metode Naive Bayes dan Support Vector Machine Al Fachri, Moh. Aminullah; Athiyah, Ummi
Infotekmesin Vol 14 No 2 (2023): Infotekmesin: Juli, 2023
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v14i2.1759

Abstract

At the end of 2021, people are shocked by the drastically reduced supply of cooking oil and high prices. This makes people talk about it a lot through social media like Twitter. Freedom on Twitter raises many responses from the public. The number of negative and positive responses on Twitter makes comparisons between the two responses difficult to observe. This study aims to determine the comparison of positive responses and negative responses. Machine learning with the naïve Bayes method and support vector machine is able to overcome this problem. The research conducted examines how the comparison between positive responses and negative responses and which method has higher accuracy. The data used is 10,000 Indonesian language tweets. Model testing was carried out with 1839 test data. the Naive Bayes method gets an accuracy of 74.06% with the results of predicting two positive tweets and 1837 negative tweets. The SVM method was tested on linear, polynomial, RBF, and sigmoid kernels. The kernel with the highest accuracy value is the sigmoid kernel with an accuracy of 81.8% with the predicted results of 266 positive tweets and 1573 negative tweets.
Classification of Cavendish Banana Quality using Convolutional Neural Network Suryani, Ajeng Ayu; Athiyah, Ummi; Nur, Yohani Setiya Rafika; Warto
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12191

Abstract

Indonesia's agricultural production is divided into two main categories: vegetables and fruits. The vegetable category includes shallots, garlic, chilies, mushrooms, spinach, cabbage, and potatoes. One of the fruit commodities from the fruit horticulture subsector is bananas, which are divided into several types, including ambon, plantains, Cavendish, pipit, and horn bananas. One of the bananas that has a good selling value in Indonesia is the Cavendish banana, but the selling value of the Cavendish banana is determined by the quality of the banana fruit. A classification process is necessary to find out the quality of bananas. We perform classification using one of the deep learning algorithms, namely Convolutional Neural Network. The experiment uses 1047 images, divided into 65% training data, 15% validation data, and 20% testing data by using epochs 20 times with 16 batch sizes, the accurate results obtained are 99%. The results indicate the effectiveness of the confusion matrix in identifying training data and detecting images. It can be concluded that using more training data leads to higher accuracy, as fewer image reading errors occur when fewer images are processed. This classification is expected to be able to classify bananas with good quality like the real condition.
Peningkatan Kapasitas Penjualan Pada Kader Pemberdayaan Masyarakat Desa Melalui Pelatihan Pemasaran Digital Athiyah, Ummi; Alika, Shintia Dwi; Dewi, Atika Ratna; Habiburrahman, Muhammad Quthb; Sa’adah, Oktavia Jazilatus; Arif Wirawan Muhammad
Madani : Indonesian Journal of Civil Society Vol. 6 No. 2 (2024): Madani : Agustus 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/madani.v6i2.2193

Abstract

Empowering rural communities is essential for sustainable development, especially in the economic sector. This community service program aims to increase the sales capacity of the Sunyalangu Village Community Empowerment Cadres (KPMD) through digital marketing training. The main problems include simple packaging, conventional marketing methods, and poor business management practices. This program uses a community service method, Service Learning (SL), which involves practical steps such as product packaging training and digital marketing strategy workshops. This project significantly improved participants' skills in using sealer machines and promoting products online, especially on platforms like Shopee. The method of implementing strategic digital marketing communication training was carried out with a structured and interactive approach over two meetings. The results showed the importance of digital literacy in rural areas to achieve maximum business potential and improve economic sustainability. This training has successfully introduced participants to the world of online trading and provided them with practical skills in utilizing digital platforms to market processed products from the community.
PENGEMBANGAN PROFESIONAL PKG PAUD KECAMATAN BALAPULANG DENGAN INOVASI PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI Athiyah, Ummi; Dewi, Atika Ratna; Alika, Shintia Dwi; Yuniati, Trihastuti
MADANI: Jurnal Pengabdian Kepada Masyarakat Vol 11 No 1 (2025): MADANI: Jurnal Pengabdian Kepada Masyarakat (In Press)
Publisher : LPPM UPN Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53834/mdn.v11i1.10680

Abstract

Pendidikan anak usia dini (PAUD) merupakan salah satu program prioritas pemerintah dalam membangun fondasi pendidikan yang kuat bagi anak-anak prasekolah. Salah satu faktor utama dalam peningkatan kualitas PAUD adalah pengembangan profesionalisme guru, khususnya dalam literasi digital. Namun, masih banyak guru PAUD yang menghadapi kendala dalam pemanfaatan teknologi informasi dalam pembelajaran. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan literasi digital guru PAUD di Kecamatan Balapulang melalui pelatihan pemanfaatan microsite dan wordwall sebagai media pembelajaran interaktif. Pelatihan ini dirancang untuk membekali guru dengan keterampilan membuat bahan ajar yang interaktif menggunakan wordwall dan mengelola microsite sebagai pusat distribusi materi ajar. Evaluasi kegiatan dilakukan melalui kuesioner dengan skala Likert (1–5), yang menunjukkan tingkat kepuasan tinggi di antara peserta, dengan skor rata-rata 4,3–4,7 pada berbagai aspek kepuasan dan manfaat pelatihan. Hasil kegiatan menunjukkan adanya peningkatan keterampilan guru dalam memanfaatkan teknologi informasi serta peningkatan interaktivitas dalam proses pembelajaran. Dengan adanya pelatihan ini, guru-guru PAUD menjadi lebih percaya diri dalam mengintegrasikan teknologi dalam pembelajaran, sehingga dapat meningkatkan motivasi dan keterlibatan siswa. Program ini diharapkan dapat berkontribusi dalam menciptakan ekosistem pembelajaran yang lebih modern, efektif, dan sesuai dengan tuntutan pendidikan abad ke-21.
Classification of Instagram and TikTok Addiction Levels among University Students Using the Naive Bayes Classifier Silalahi, Indri Monica Cristiani; Athiyah, Ummi; Fransisca, Diandra Chika
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15583

Abstract

The widespread use of gadgets and internet connectivity has become an essential aspect of daily life, especially through intensive interaction with social media platforms. Excessive usage can lead to addictive behaviors that disrupt students’ academic productivity and concentration. Although research on social media addiction continues to grow, few studies specifically examine platform-level addiction (Instagram vs. TikTok) using multi-class classification approaches. Therefore, this study aims to assess the level of social media addiction among university students, focusing on users of Instagram and TikTok at Telkom University Purwokerto. The analysis employs the Naive Bayes Classifier algorithm using data collected from 100 respondents. Model performance is evaluated through a multi-class confusion matrix to compute accuracy, precision, recall, and F1-score. Separate datasets for Instagram and TikTok are used to enable platform-specific behavioral assessment. The results show that the Naive Bayes Classifier achieves strong performance, with 93% accuracy for the Instagram dataset and 90% for the TikTok dataset. Precision scores reach 95% and 91%, recall values 93% and 90%, and F1-scores 93% and 90%, respectively. These findings confirm that Naive Bayes is effective for classifying students’ levels of social media addiction. Overall, this research contributes a reliable machine-learning–based approach for evaluating digital behavior and provides insights for early detection, enabling universities to design targeted interventions for students at risk of problematic usage. The methodology may also be extended to analyze engagement patterns on emerging social media platforms in future studies.
Implementation of Forward Chaining And Certainty Factor Methods for Android-Based Red Onion Diagnosis Ghozali, Imam; Athiyah, Ummi; Nur, Yohani Setiya Rafika
Journal of INISTA Vol 8 No 1 (2025): November 2025
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v7i2.1779

Abstract

Shallots are one of the crucial horticultural commodities in Indonesia, used in various social layers. Brebes is one of the main shallot producing regions with a significant increase in production. However, farmers often experience reduced yields due to disease attacks and lack of guidance from experts. This researcher aims to develop an Android-based expert system that applies the Certainty Factor and Forward Chaining methods to identify diseases in shallot plants. This system uses rules to identify onion disease symptoms and calculates the confidence level for each possible diagnosis. The Forward Chaining method helps identify symptoms sequentially, while the Certainty Factor calculates confidence in the possibility of disease. The research results show that this method is effective in providing an accurate diagnosis of onion diseases from the 5 diseases tested by the recommended system with a percentage value of 100%. In conclusion, the expert system created for diagnosing shallot plants using the Android-based forward chaining and certainty factor method was successfully built. Then, for Functionality Testing based on black box testing carried out by experts, the results were obtained with 100% accuracy, which means the system is in accordance with its functional requirements.
Co-Authors Adam Ikbal Perdana Adela Putri Handayani Aditya Dwi Putro Aditya Dwi Putro Wicaksono Adytia Abi Restianto Agus Priyanto Agustyawan, Arif Ahmad Muslih Syafi'i Al Fachri, Moh. Aminullah Alam Patria Utama Alameka, Faza Alifta Salma Shafira Alika, Shintia Dwi Amalia, Hasna Shafa Andreas Rony Wijaya Arif Wirawan Muhammad Arif Wirawan Muhammad Arnelka Hananta Atika Ratna Dewi Azhari, Ahmad Diandra Chika Fransisca Dwi Setiawan, Brandon Elisabeth Angeline Wilhelmina Bakowatun Erlina Marfianti, Erlina Faisal Dharma Adhinata Faiz Rizky Fahlevi Felia Citra Dwiyani Putri Rosyadi Firda Millennianita Firda Millennianita Habiburrahman, Muhammad Quthb Hafidz Daffa Hekmatyar Hasan Nizar Hikmah Quddustiani Hulqi, Filfimo Yulfiz Ahsanul Imam Ghozali Irmayatul Hikmah Ismail , Moh Izzati Muhimmah Jannah , Uzlifatul Juvandio Aufaresa Kholidiyah Masykuroh Luthfi Rakan Nabila Made Riza Kartika Maya Nurachmawati Adiningtias Moh. Aminullah Al Fachri Muhammad Alvi Awliya Muhammad Nur Faiz Muhammad Nur Faiz Muhammad Quthb Habiburrahman Muhammad Yusril Aldean Naden, Yoga Nikmatul Khayati Novanda Alim Setya Nugraha Novantri Prasetya Putra Novian Adi Prasetyo Oktavia Jazilatus Sa’adah Pangestu, Happy Gery Puguh Ika Listyorini Rafian Ramadhani Rara Nur Salsabila Rayhan Hidayat Regina Putri Wanda Zahirah Reno Agil Saputra Rheni Aprilia Ningrum Ridha Berlianny Sulistiaputri Sa’adah, Oktavia Jazilatus Saputro, Satria Nur Sausan Silalahi, Indri Monica Cristiani Sinaga, Rifaldo Yohannes Siti Khomsah, Siti Sudianto Suryani, Ajeng Ayu Taufik Maulidi Theo Felix Harianto Purba Tri Ginanjar Laksana Trihastuti Yuniati Tufail Akhmad Satrio Ulya, Fadilla Zundina Vico Meylana Eka Putra Warto Yehezekiel Ramasyah Putra Haloho Yohani Setiya Rafika Nur Yunita Wisda Tumarta Arif