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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi IJoICT (International Journal on Information and Communication Technology) Faktor Exacta Jurnal Riset Informatika JSAI (Journal Scientific and Applied Informatics) ILKOMNIKA: Journal of Computer Science and Applied Informatics Walisongo Journal of Information Technology Brilliance: Research of Artificial Intelligence DIGIMAS: Jurnal Transformasi Digital Masyarakat
Muhammad Andryan Wahyu Saputra
Universitas Jember
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Other

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Perancangan Game First Person Shooter 3D “Saving Islamic Kingdom” dengan Menggunakan Finite State Machine (FSM) Wahyu Saputra, Muhammad Andryan; Fadila, Juniardi Nur; Nugroho, Fresy
Walisongo Journal of Information Technology Vol 2, No 2 (2020): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2020.2.2.6981

Abstract

Video Game merupakan sebuah permainan dengan manifestasi sebuah visual atau gambar yang dapat memberikan reaksi balik kepada pengguna jika diberikan perintah - perintah tertentu yang terdapat pada alat kontrol sistem elektronik. Banyak inovasi teknologi yang dapat diterapkan dalam sebuah game salah satunya penerapan kecerdasan buatan atau artificial intelligence. Variasi game yang akan diterapkan salah satunya adalah game First Person Shooter (FPS). Game First Person Shooter (FPS) merupakan salah satu jenis game yang sering dimainkan menggunakan pangamatan orang pertama di mana player sebagai karakter utama dalam game. Kecerdasan buatan yang dikembangkan dengan menerapkan Finite State Machine (FSM) berfungsi untuk melihat respon perilaku Non Player Character (NPC) atau musuh untuk menyerang pemain utama. Hasil dari penelitian ini adalah game Saving Islamic Kingdom memberikan hasil kecerdasan buatan pada perilaku Non Player Character dengan mengimplementasikan algoritma Finite State Machine (FSM) sehingga musuh atau NPC dapat berperilaku sesuai dengan intruksi yang dilakukan oleh player.
Survei Teknik-Teknik Pengukuran Kualitas Perangkat Lunak Muhammad Andryan Wahyu Saputra; Wildan Alif Rioditama; Hanis Setyowati; Muhammad Ainul Yaqin
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 3 No 1 (2021): Volume 3, Nomor 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v3i1.38

Abstract

Tujuan dari penelitian ini adalah menganalisis teknik teknik pengukuran untuk kualitas dari perangkat lunak. Dengan menggunakan data yang berasal dari perpustakaan digital dan pola survey System Literature Review (SLR) sehingga paper ini menjabarkan berbagai jenis software quality model yang ada beserta komponen-komponen yang digunakan dalam melakukan penilaian dari masing-masing model. Hasil penelitian ini berupa perbandingan dari beberapa teknik yang digunakan dalam mengukur kualitas perangkat lunak. Berdasarkan survei yang telah dilakukan maka dapat disimpulkan bahwa pengukuran kualitas perangkat lunak dapat dilakukan dengan menggunakan salah satu dari berbagai model yang ada. Pada penggunaan model Boehm dan ServQual, kekurangan yang ada sama yaitu kurangnya kriteria yang digunakan. Akan tetapi model Boehm dapat mempertimbangkan kualitas dari sudut pandang yang berbeda, dan model ServQual menitik beratkan pada faktor kualitas.
Perancangan Game First Person Shooter 3D “Saving Islamic Kingdom” dengan Menggunakan Finite State Machine (FSM) Muhammad Andryan Wahyu Saputra; Juniardi Nur Fadila; Fresy Nugroho
Walisongo Journal of Information Technology Vol 2, No 2 (2020): Walisongo Journal of Information Technology
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2020.2.2.6981

Abstract

Video Game merupakan sebuah permainan dengan manifestasi sebuah visual atau gambar yang dapat memberikan reaksi balik kepada pengguna jika diberikan perintah - perintah tertentu yang terdapat pada alat kontrol sistem elektronik. Banyak inovasi teknologi yang dapat diterapkan dalam sebuah game salah satunya penerapan kecerdasan buatan atau artificial intelligence. Variasi game yang akan diterapkan salah satunya adalah game First Person Shooter (FPS). Game First Person Shooter (FPS) merupakan salah satu jenis game yang sering dimainkan menggunakan pangamatan orang pertama di mana player sebagai karakter utama dalam game. Kecerdasan buatan yang dikembangkan dengan menerapkan Finite State Machine (FSM) berfungsi untuk melihat respon perilaku Non Player Character (NPC) atau musuh untuk menyerang pemain utama. Hasil dari penelitian ini adalah game Saving Islamic Kingdom memberikan hasil kecerdasan buatan pada perilaku Non Player Character dengan mengimplementasikan algoritma Finite State Machine (FSM) sehingga musuh atau NPC dapat berperilaku sesuai dengan intruksi yang dilakukan oleh player.
Survey Perangkat Lunak Untuk Manajemen Big Data Menggunakan Metode SLR (Systematic Literature Review) Muhammad Andryan Wahyu Saputra; Hamim Tohari; U'un Setiawati
Faktor Exacta Vol 15, No 1 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i1.11735

Abstract

Big data technology is a whole technology that can handle processing related to data analysis to explore the potential in it. Some uses of big data are based on the largest sources of data traffic, such as social media, financial transactions, public data, sensor data, and enterprise data. In the research conducted there is a problem, namely how to determine and find out the best software to manage big data. The following problems can be solved by conducting a software comparison survey to find out the best software that can be used and is suitable for big data management problems. The software survey research for big data management uses secondary data from national and international journals that have been published on Scopus, Semantic Scholar, and Google Scholar in the period 2017-2021 as data reference. The software survey in this study used the Systematic Literature Review (SLR) method. The results of research using SLR software state that the software that can be used to determine big data management is 7 software, Apache Hadoop software is software that is often used to perform big data analysis because Apache Hadoop software can have many features and managed to obtain the minimum results to maximize the results of the analysis so that this is different from the application of other software and also the application of software.
Java Island Health Profile Clustering using K-Means Data Mining Muhammad Andryan Wahyu Saputra; Sri Harini
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.606

Abstract

Health is the best gift in life, because with health humans can carry out daily activities. Administratively, Java Island consists of 85 administrative regions and 34 cities. Therefore, it is very important to understand the health level of each area. The main objective of this research is to divide each region (district and city) into several groups and use the K-means method to determine health status based on 8 data parameters into certain groups. Algorithm in groups, will place the data based on the similarity of characteristics between groups. The results showed that there were 4 clusters of health profiles in Java, with 1 high health quality cluster in Central Jakarta, 55 regencies/municipalities with low health quality, 52 regencies/cities with low health quality. and the quality of health is quite low there are 13 districts/cities, it can be concluded that the health indicators in Java
Penerapan Tata Kelola Teknologi Informasi Menggunakan COBIT Framework 4.1 pada Pondok Pesantren Al Islam Muhammad Andryan Wahyu Saputra
Walisongo Journal of Information Technology Vol 4, No 2 (2022)
Publisher : Universitas Islam Negeri Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjit.2022.4.2.9765

Abstract

Saat ini, diyakini bahwa teknologi informasi (TI) berperan penting dalam meningkatkan keunggulan kompetitif. Fakta telah membuktikan bahwa teknologi informasi menciptakan nilai bagi organisasi. Organisasi semakin bergantung pada teknologi informasi agar tetap kompetitif, termasuk di Pondok Pesantren Al Islam, Kidul Pasar, Kota Malang. Dalam penelitian ini disajikan rancangan model tata kelola TI Pondok Pesantren Al Islam dengan menggunakan framework Control Objective for Information Technologies (COBIT) versi 4.1 di domain Deliver and Support (DS) dan Monitor and Evaluate (ME). Desain model disesuaikan dengan karakteristik bisnis, strategi dan tujuan Pondok Pesantren Al Islam. Penelitian ini diharapkan dapat menjadi acuan dalam pengelolaan TI di Pondok Pesantren Al Islam dan pondok pesantren lainnya. Metode penelitian dimulai dengan identifikasi visi, misi dan tujuan organisasi pondok pesantren. Langkah selanjutnya adalah identifikasi kesadaran manajemen terhadap fungsi-fungsi aset TI dalam mendukung pencapaian visi dan misi pondok pesantren. Dari data tersebut dapat ditentukan target maturitas yang sesuai untuk Pondok Pesantren Al Islam. Setelah itu, dilanjutkan dengan menilai tingkat kematangan saat ini melalui kuisioner dan wawancara kepada responden terkait dengan manajemen TI. Tindak lanjut yang dilakukan adalah dengan membuat rekomendasi pada 16 kontrol tujuan pada domain DS dan 4 kontrol tujuan pada domain ME.
K-Means Binary Search Centroid with Dynamic Cluster for Java Island Health Clustering Muhammad Andryan Wahyu Saputra; Muhammad Faisal; Ririen Kusumawati
Jurnal Riset Informatika Vol. 5 No. 3 (2023): June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (932.207 KB) | DOI: 10.34288/jri.v5i3.218

Abstract

This study is focused on determining the health status of each district/city in Java using the K-means Binary Search Centroid and Dynamic Kmeans algorithms. The research data uses data on the health profile of Java Island in 2020. Comparative algorithms were tested using the Davies Bound Index and Calinski-Harabasz Index methods on the traditional k-means algorithm and dynamic binary search centroid k-means. Based on the test, 5 clusters were found in the distribution area, including 11 regions with very high health quality cluster 1, 24 regions with high health quality, 28 regions with moderate health quality, and 28 clusters 4 with low health quality, 45 regions, and cluster 5 with poor health quality is 11 regions, with the best validation value of DBI 1.8175 and CHI 67.7868. Overall optimization of the dynamic k-means algorithm based on binary search centroid results in a better average cluster quality and a smaller number of iterations than the traditional k-means algorithm. The test results can be used as one of the best methods in evaluating the level of health in the Java Island area and a reference for decision-making in determining policies for related agencies.
Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture Saputra, Muhammad Andryan Wahyu; Novtahaning, Damar; Narandha Arya Ranggianto; Dwi Wijonarko
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5054

Abstract

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.
Perbandingan Performa Algoritma Random Tree, K-NN, dan A-NN untuk Deteksi Serangan DDoS pada Software Defined Network (SDN) Akbar Pandu Segara; Muhammad Andryan Wahyu Saputra; Narandha Arya Ranggianto
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8387

Abstract

Software-Defined Networks (SDNs) with a centralized architecture are vulnerable to Distributed Denial of Service (DDoS) attacks, which can cause widespread network service failures. This study aims to compare the performance of three Machine Learning algorithms—K-Nearest Neighbor (K-NN), Artificial Neural Network (ANN), and Random Tree—in detecting DDoS attacks in an SDN environment. The DDoS-SDN dataset, consisting of 104,345 rows and 23 columns, was used with a data split of 70% for training and 30% for testing. Evaluation was conducted using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results showed that ANN achieved the best performance with an accuracy of 96.85%, precision of 94.35%, recall of 97.79%, F1-score of 96.04%, and AUC of 0.994, followed by K-NN with an accuracy of 88.89% and Random Tree with the lowest accuracy of 86.49%. The superiority of ANN is attributed to its ability to capture complex non-linear patterns, perform automatic feature extraction, and adapt to the heterogeneity of data from the 22 features used. These findings indicate that ANN is the optimal choice for implementing a real-time DDoS attack detection system in an SDN environment, providing a strong foundation for the development of intelligent and adaptive Machine Learning-based network security systems
Implementation of YOLO in Cabbage Plant Disease Detection for Smart and Sustainable Agriculture Saputra, Muhammad Andryan Wahyu; Novtahaning, Damar; Narandha Arya Ranggianto; Dwi Wijonarko
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.5054

Abstract

Cabbage plants are a commodity needed by the community and an export commodity that must have good quality and be worth selling. There are approaches to create detection systems, namely rule-based and image-based. The use of images allows the system to be reorganized by training data, resulting in a flexible system. The image will be detected by the model and then predict the cabbage plant disease. The data used is image data, namely Alternaria Spots, Healthy, Black Root, and White Rust. Implementation This research tests the YOLO model in making a detection system with the highest precision-confidence result for all labels is 78,5%. While in confusion-matrix testing, the highest result is 0.67 in White Rust disease. This indicates that the YOLO model can identify diseases in cabbage plants based on data that has been trained with great results.
Co-Authors Akbar Pandu Segara Damar Novtahaning Damar Novtahaning Dwi Wijonarko Erik Yohan Kartiko Fresy Nugroho Fresy Nugroho Hamim Tohari Hanis Setyowati Juniardi Nur Fadila M. Habibullah Arief Mokhamad Amin Hariyadi Muhammad Faisal Narandha Arya Ranggianto Ririen Kusumawati Rizky Alfanio Atmoko Sri Harini Sri Harini Tamaulina Br Sembiring U'un Setiawati Wildan Alif Rioditama