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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer International Journal of Artificial Intelligence Research BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks JCES (Journal of Character Education Society) Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Mobile and Forensics Signal and Image Processing Letters International Journal of Robotics and Control Systems SainsTech Innovation Journal
Azhari, Ahmad
Universitas Ahmad Dahlan
Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Neuroscience Social Sciences Other

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USAHA PENINGKATAN KETERAMPILAN GURU SEKOLAH MENENGAH MUHAMMADIYAH KAB GUNUNGKIDUL: PENDAYAGUNAAN LECTORA SEBAGAI MEDIA AJAR Murein Mardhia; Dwi Normawati; Ahmad Azhari
Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Vol. 2 No. 2 (2018)
Publisher : Universitas Ahmad Dahlan, Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jp.v2i2.405

Abstract

Sebuah  kegiatan  peningkatan  keterampilan  guru  telah  diadakan  di  kalangan  sekolah  menengah pertama di wilayah Kabupaten Gunungkidul  pada periode libur semester gasal tahun ajaran 2017/2018. Kegiatan  pelatihan  TIK  kali  ini  mengundang  tenaga  pendidik  dari  sekolah-sekolah  menengah  Muhammadiyah  di  bawah  pengelolaan  Majelis  Pendidikan  Dasar  dan  Menengah  (Dikdasmen) PDM Gunungkidul.  Tujuan yang ingin dicapai antara lain pengayaan keterampilan tenaga pendidik di bidang TIK sebagai media pendukung kegiatan belajar mengajar di kelas. Dengan peningkatan keterampilan guruguru  sebagai  tenaga  pendidik  melalui  TIK, diharapkan  dapat  mendukung  tercapainya  pemenuhan kompetensi guru-guru di SMP Muhammadiyah di Kabupaten Gunungkidul secara terprogram. Di bagian akhir pelaksanaan, peserta diminta untuk mengumpulkan feedback mengenai kegiatan pelatihan yang telah diikuti. Lebih dari 80% peserta yang hadir menyatakan tingkat kepuasan yang baik terhadap materi, pelaksana, dan kemanfaatan karya yang telah mereka hasilkan. Target usabilitas karya juga telah tercapai sebagai media belajar untuk diaplikasikan di semester genap tahun ajaran 2017/2018.
PENERAPAN TEKNOLOGI TOKO ONLINE UNTUK PEMASARAN PRODUK BAGI IBU-IBU AISYIYAH GUNUNG KIDUL Gita Indah Budiarti; Murein Miksa Mardhia; Ahmad Azhari
Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Vol. 3 No. 3 (2019)
Publisher : Universitas Ahmad Dahlan, Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jp.v3i3.1229

Abstract

Marketing and promotion are the most important things for product sales. Aisyiyah Gunung Kidul mothers (partners) have products, one of which is snacks from the mokaf. There needs to be innovation in marketing to increase partner sales. This activity aims to create an online shop for partners and then train partners to use it. The method used is training and evaluation. The results of this activity partners are able to use the website and application well. Partners who can upload their products by 63%. Partners are very enthusiastic about this training, and hope that there will be a follow-up to this activity.
Pengembangan Pangan Lokal Melalui Modifikasi Tepung Gita Indah Budiarti; Murein Miksa Mardhia; Ahmad Azhari
Berdikari: Jurnal Inovasi dan Penerapan Ipteks Vol 7, No 2 (2019): August
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/bdr.7261

Abstract

People with diabetes mellitus in Gunungkidul are increasing in number. This is caused by excessive consumption of flour-based foods. Communities need to be introduced to alternative food substitutes for flour such as cassava. Besides being able to reduce flour consumption in the community, training and education about non-flour snacks can also increase the income of less productive housewives. The purpose of this activity is to provide education about the dangers of consuming foods containing excessive flour and to train in making flour and food from non-wheat alternative ingredients to ladies of Aisyiyah members in ladies Gunungkidul area. The series of service activities was carry out in the form of counseling on food security and healthy food. The second activity was in the form of training in making flour substitute flour and non-flour processed foods. The evaluation phase was in the form of filling out questionnaires by participants. The results of this activisay are processed foods made from non-modified flour and improved knowledge and skills of the participants
Indonesian Waste Database: Smart Mechatronics System Haris Imam Karim Fathurrahman; Ahmad Azhari; Tole Sutikno; Li-yi Chin; Prasetya Murdaka Putra; Isro Dwian Yunandha; Gralo Yopa Rahmat Pratama; Beni Purnomo
International Journal of Robotics and Control Systems Vol 3, No 2 (2023)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v3i2.999

Abstract

Waste management is an essential component of urban management. As a waste solution, waste management is critical. The goal of this research is to develop a waste management database that is coupled with a mechatronic robot system. Compiling and gathering data on the sorts of garbage found in Indonesia is the starting point for this research. Indonesian waste is classified into six groups: cardboard, paper, metal, plastic, medical, and organic. The total images of the six groups are estimated at 1880 pictures. According to this picture database, Artificial Intelligence (AI) training was used to create the classification system. In the final AI process, the test method was performed using DenseNet121, DenseNet169, and DenseNet201. Testing using artificial intelligence DenseNet201 across 40 epochs yields the best 92,7% accuracy rate. Simultaneously with Artificial Intelligence testing, a mechatronic system is created as a direct implementation of the Artificial Intelligence output model. A four-servo arm robot with dc motor wheel mobility is included in the mechatronic system. According to these findings, the Indonesian waste database can be categorized correctly using Artificial Intelligence and the mechatronics system. This higher accuracy of the artificial intelligence model may be used to create a waste-sorting robot prototype.
Machine Learning-Based Distributed Denial of Service Attack Detection on Intrusion Detection System Regarding to Feature Selection Arif Wirawan Muhammad; Cik Feresa Mohd Foozy; Ahmad Azhari
International Journal of Artificial Intelligence Research Vol 4, No 1 (2020): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.09 KB) | DOI: 10.29099/ijair.v4i1.156

Abstract

Distributed Service Denial (DDoS) is a type of network attack, which each year increases in volume and intensity.  DDoS attacks also form part of the major types of cyber security threats so far. Early detection plays a key role in avoiding the catastrophic effects on server infrastructure from DDoS attacks. Detection techniques in the traditional Intrusion Detection System (IDS) are far from perfect compared to a number of modern techniques and tools used by attackers, because the traditional IDS only uses signature-based detection or anomaly-based detection models and causes a lot of false positive flags, since the flow of computer network data packets has complex properties in terms of both size and source. Based on the  deficiency in the ordinary IDS, this study aims to detect DDoS attacks by using machine learning techniques to enhance IDS policy development.  According to the experiment the selection of features plays an important role in the precision of the detection results and in the performance of machine learning in classification problems. The combination of seven key selected dataset features used as an input neural network classifier in this study provides the highest accuracy value at 97.76%.
Rediscover Story Of Muhammadiyah Through 3D Game By Applying Game Development Life Cycle Achmad Nur Hafizh; Ahmad Azhari
Mobile and Forensics Vol. 6 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v6i2.11388

Abstract

Though specifically in Indonesia, Muhammadiyah is already well known but there are still some who do not know their history. This makes people that do not know about Muhammadiyah and the history behind it to make unfounded assumptions about the Islamic refined organization. The purpose is to make an educational game based on Muhammadiyah Museum to further increase the wisdom of players about Muhammadiyah’s history and also to remember and know the history behind each artifact is visualized in form of a game to increase the player’s knowledge. The game development for players will be developed using the Game Development Life Cycle (GDLC) methodology and mostly the modelling technique that will be used is mesh modelling technique in Blender. Each step of this methodology is fitting for the game development and the step might be skipped or swapped according to the needs of research. There are 2 black box tests conducted, the first black box test that has 15 functions result is 47% in accordance with several bugs found which was fixed in the second black box test that resulted 87% in accordance, 13% not in accordance because of the feature was not yet implemented but listed in the main menu. The second test conducted which results and practicality of this educational game using the System Usability Scale (SUS) which consists of 10 instrument statements were scored 85.3 which means that this educational game was declared excellent and acceptable.
Machine Learning-Based Lifestyle Analysis for Health Risk Detection in Coffee Drinkers Sri Winiarti; Ahmad Azhari; Nathaniela Isya Nur Rofiah
Mobile and Forensics Vol. 8 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v8i2.16102

Abstract

This study aims to develop a machine learning-based intelligent system to detect health risks in coffee drinkers through a lifestyle analysis approach. The background of this study is based on the increasing consumption of coffee as part of a modern lifestyle, which has the potential to cause various health risks if not balanced with a healthy lifestyle. The dataset was collected through a survey covering several important variables, such as coffee consumption frequency, sugar intake, sleep duration, physical activity level, body mass index (BMI), and blood pressure. The research stages included data preprocessing, normalization, and classification using three algorithms: Random Forest and Gradient Boosting. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics to ensure the system’s reliability. Test results showed that the Random Forest algorithm performed best with an accuracy of 0.91, followed by Gradient Boosting with an accuracy of 0.90. Further analysis revealed that the variables most influential on health risks are coffee consumption frequency, sleep duration, and sugar intake. The developed system proved effective in detecting health risks early and has the potential to serve as a data-driven educational tool to raise public awareness of the importance of a healthy lifestyle.
Classification of Child Stunting Status Using the K-Nearest Neighbor (KNN) Algorithm Based on Toddler Growth Data in East Lombok Regency, West Nusa Tenggara Asno Azzawagama Firdaus; Arif Himawan; Junaedi; Anggun Sindiana; Istianah; Baiq Selviana Pertiwi; Baiq Wangi Narsih; Kartika Yundia; Ahmad Azhari
SainsTech Innovation Journal Vol. 9 No. 1 (2026): SIJ VOLUME 9 NOMOR 1 TAHUN 2026
Publisher : LPPM Universitas Qamarul Huda Badaruddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37824/sij.v9i1.2026.1351

Abstract

Stunting is a major nutritional issue in Indonesia that significantly impacts children's physical growth, cognitive development, and future quality of life. Childhood stunting can be identified using nutritional status indicators—such as weight-for-age (W/A), height-for-age (H/A), and weight-for-height (W/H)—specifically by observing a Z-score below -2 for the H/A indicator. The growing volume of anthropometric data on children under five necessitates a rapid and objective method for identifying stunting status. This study aims to classify children's stunting status using the K-Nearest Neighbor (KNN) algorithm based on growth data from children under five in East Lombok Regency, West Nusa Tenggara. The dataset comprises records for 3,416 children, including information on gender, age, weight-for-age (W/A), weight-for-height (W/H), and height-for-age (H/A). Data preprocessing involved removing duplicates, handling missing values, transforming categorical data, and applying Min-Max normalization. The data was split into 70% training data and 30% testing data, with the KNN algorithm applied using k = 5. Model evaluation was conducted using a confusion matrix. The results demonstrate that the KNN model achieved an accuracy rate of 83.15%, indicating a strong capability to classify stunting status based on the children's anthropometric characteristics. These findings confirm that the KNN algorithm can serve as a tool for healthcare professionals to identify stunting more rapidly, objectively, and efficiently, thereby supporting early prevention and management efforts.
Co-Authors Achmad Nur Hafizh Adhi Prahara Adhi Prahara Adhi Susanto Andri Pranolo Anggun Sindiana Ardiansyah, Ardiansyah Arif Himawan Arif Wirawan Muhammad Asno Azzawagama Firdaus Baiq Selviana Pertiwi Baiq Wangi Narsih Beni Purnomo Cik Feresa Mohd Foozy Dewi Pramudi Ismi DWI SURYANTO Fathia Irbati Ammatulloh Fathurrahman, Haris Imam Karim Garnis Ajeng Pamiela Gita Indah Budiarti Gralo Yopa Rahmat Pratama Isro Dwian Yunandha Istianah Junaedi Kartika Yundia Leonel Hernandez Li-yi Chin Murein Mardhia Murein Miksa Mardhia Murinto Murinto Nathaniela Isya Nur Rofiah Prahara, Adhi Prasetya Murdaka Putra Sri Winiarti Tole Sutikno Ummi Athiyah Yingchi Mao