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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) Journal of Innovation Information Technology and Application (JINITA) 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 MATHunesa: Jurnal Ilmiah Matematika 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 Transaction on Informatics and Data Science Journal of Mechatronics and Artificial Intelligence
Ummi Athiyah
Telkom University
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 Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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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.
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.
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.
Classification of Cavendish Banana Quality using Convolutional Neural Network Ajeng Ayu Suryani; Ummi Athiyah; Yohani Setiya Rafika Nur; Warto
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

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.
Machine Learning-Based Diabetes Mellitus Classification Using Multi-Dataset Evaluation and Class Imbalance Resampling Wijiyanto; Agustinus Eko Setiawan; Ferly Ardhy; Ritzkal; Ummi Athiyah
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3315

Abstract

Diabetes mellitus (DM) remains a major global health challenge due to its increasing prevalence and long-term complications, emphasizing the need for accurate early prediction systems. This study proposes a machine learning-based framework for DM classification using a multi-dataset setting while addressing class imbalance issues. Two independent datasets from Iraq and Germany were employed to evaluate model robustness across different population characteristics. The experimental workflow consisted of data preprocessing, stratified train-test splitting, imbalance handling using Synthetic Minority Over-sampling Technique (SMOTE) and SMOTE-Tomek, 10-fold cross-validation, and hyperparameter optimization via GridSearchCV. Four classification algorithms were compared, namely Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). Experimental results demonstrate that data distribution significantly affects classification performance. Under imbalanced conditions, RF achieved the best performance on the Iraqi dataset with an accuracy of 0.98 and an AUC of 1.00, while KNN and RF reached perfect accuracy (1.00) on the German dataset. After applying SMOTE, all models showed more stable performance, particularly in recall, which reached 1.00, indicating effective minority-class detection. In contrast, SMOTE-Tomek produced only marginal additional improvements. The findings suggest that no single classifier is universally optimal for DM prediction. Instead, model effectiveness depends on dataset characteristics and preprocessing strategies. From a practical perspective, the combination of RF and SMOTE shows strong potential for early diabetes screening and clinical decision-support systems. Further validation using larger and more heterogeneous external datasets is recommended.
Human Intestinal Condition Identification Based-on Blended Spatial and Morphological Feature using Artificial Neural Network Classifier Athiyah, Ummi; Muhammad, Arif Wirawan; Azhari, Ahmad
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Colon cancer is a type of disease that attacks the intestinal walls cell of humans. Colorectal endoscopic screening technique is a common step carried out by the health expert/gynecologist to determine the condition of the human intestine. Manual interpretation requires quite a long time to reach a result. Along with the development of increasingly advanced digital computing techniques, then some of the weaknesses of the manually endoscopic image interpretation analysis model can be corrected by automating the detection process of the presence or absence of cancerous cells in the gut. Identification of human intestinal conditions using an artificial neural network method with the blended input feature produces a higher accuracy value compared to the artificial neural network with the non-blended input feature. The difference in classifier performance produced between the two is quite significant, that is equal to 0.065 (6.5%) for accuracy; 0.074 (7.4%) for recall; 0.05 (5.0%) for precision; and 0.063 (6.3%) for f-measure.
PENGEMBANGAN PROFESIONAL PKG PAUD KECAMATAN BALAPULANG DENGAN INOVASI PEMBELAJARAN BERBASIS TEKNOLOGI INFORMASI Ummi Athiyah; Atika Ratna Dewi; Shintia Dwi Alika; Trihastuti Yuniati
MADANI: Jurnal Pengabdian Kepada Masyarakat Vol 11 No 1 (2025): MADANI: Jurnal Pengabdian Kepada Masyarakat
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.
PEMODELAN REGRESI DATA PANEL UNTUK MENGKAJI KEMISKINAN DI PULAU JAWA Atika Ratna Dewi Dewi; Ummi Athiyah
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 02 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n02.p1-9

Abstract

Poverty, a widespread socio-economic phenomenon, delineates the struggle of individuals and families to meet basic needs due to limited resources. Indonesia grapples with notably high poverty rates, especially within the ASEAN region. Java Island, home to over half of Indonesia's population, serves as a microcosm reflecting the nation's economic landscape. Analyzing Java's dynamics provides insights into Indonesia's overall poverty profile. A meticulous panel data regression analysis, drawing from authoritative sources like the Badan Pusat Statistik (BPS) and provincial government websites, elucidated poverty determinants on Java Island. Results underscored key factors: unemployment rate, average schooling length, and labor force participation, explaining variations with an R-squared of 0.901217. Such findings equip policymakers to craft targeted interventions. Leveraging empirical insights, policymakers can devise evidence-based strategies addressing socio-economic challenges fueling poverty. Through education reform, employment initiatives, and labor market interventions, policymakers aim to alleviate poverty, fostering inclusive growth and social development across Java Island and beyond.
Co-Authors Adam Ikbal Perdana Adela Putri Handayani Aditya Dwi Putro Aditya Dwi Putro Wicaksono Adytia Abi Restianto Agus Priyanto Agustyawan, Arif Ahmad Azhari Ahmad Muslih Syafi'i Ajeng Ayu Suryani Al Fachri, Moh. Aminullah Alam Patria Utama Alameka, Faza Alifta Salma Shafira Amalia, Hasna Shafa Andreas Rony Wijaya Arif Wirawan Muhammad Arif Wirawan Muhammad Arif Wirawan Muhammad Arnelka Hananta Atika Ratna Dewi Azhari, Ahmad Diandra Chika Fransisca Dwi Setiawan, Brandon Eko Setiawan, Agustinus Elisabeth Angeline Wilhelmina Bakowatun Erlina Marfianti, Erlina Faisal Dharma Adhinata Faiz Rizky Fahlevi Felia Citra Dwiyani Putri Rosyadi Ferly Ardhy Firda Millennianita 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 Ritzkal, Ritzkal Sa’adah, Oktavia Jazilatus Saputro, Satria Nur Sausan Shintia Dwi Alika Silalahi, Indri Monica Cristiani Sinaga, Rifaldo Yohannes Siti Khomsah, Siti Sudianto Taufik Maulidi Theo Felix Harianto Purba Tri Ginanjar Laksana Trihastuti Yuniati Tufail Akhmad Satrio Ulya, Fadilla Zundina Vico Meylana Eka Putra Warto Wijiyanto Yehezekiel Ramasyah Putra Haloho Yohani Setiya Rafika Nur Yunita Wisda Tumarta Arif