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All Journal Semantik Jurnal Simetris Telematika Jurnal Transformatika CESS (Journal of Computer Engineering, System and Science) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JRSI (Jurnal Rekayasa Sistem dan Industri) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Pengembangan Rekayasa dan Teknologi Jurnal ULTIMA InfoSys Voice Of Informatics Jurnal Teknologi Terpadu MULTINETICS Imajiner: Jurnal Matematika dan Pendidikan Matematika IJAIT (International Journal of Applied Information Technology) Jurnal Teknik Informatika C.I.T. Medicom JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Information Science and Library Sawala : Jurnal pengabdian Masyarakat Pembangunan Sosial, Desa dan Masyarakat Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Abdiformatika: Jurnal Pengabdian Masyarakat Informatika SmartComp Tematik Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science Jurnal DIMASTIK
Siti Asmiatun, Siti
Universitas Semarang
Religion Arts 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Jurnal Transformatika

 1 
IMPLEMENTASI KLASIFIKASI BAYESIAN UNTUK STRATEGI MENYERANG JARAK DEKAT PADA NPC (NON PLAYER CHARACTER)MENGGUNAKAN UNITY 3D Asmiatun, Siti; Hendrawan, Aria
Jurnal Tr@nsForMat!ka Vol 13, No 2 (2016)
Publisher : Jurusan Teknologi Informasi Universitas Semarang

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

Abstract

In a game against the enemy attack strategy should be applied , making it more appealing game players to finish the game up with the objectives to beachieved. This study discusses the strategy of attacking at close range to the NPC (Non Player Character ). In a game , especially for FPS ( First Person Shooter ) , we need a strategy NPC , with the aim to make the game more attractive and realistic.Strike strategy in this research is to divide some of the behavior of NPCs attack when in the position closest to the enemy. This research applies Bayesian algorithm for classifying the behavior of the NPC attack. The classification is expected to improve the strategy against an enemy attack . NPC assault classification is divided into two offensive behavior is the behavior of hitting and biting behavior. As for the variables used in Bayesian classification is health points, attack points player and a distance obtained from NPC conditions. From the test results using testing method of Bayesian classification confusion matrix , with trial games 10 times , has resulted in the presentation level of accuracy in the confusion matrix reach a percentage of 80%. This proves that the behavior classification melee attack with Bayesian classification method can be applied with good results
IMPLEMENTASI KLASIFIKASI BAYESIAN UNTUK STRATEGI MENYERANG JARAK DEKAT PADA NPC (NON PLAYER CHARACTER)MENGGUNAKAN UNITY 3D Siti Asmiatun; Aria Hendrawan
Jurnal Transformatika Vol 13, No 2 (2016): January 2016
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v13i2.139

Abstract

Abstract—Dalam sebuah game strategi penyerangan untuk melawan musuh harus diterapkan, sehingga lebih menarik pemain game untuk menyelesaikan permainan sampai dengan tujuan yang akan dicapai. Penelitian ini membahas tentang strategi menyerang jarak dekat untuk NPC (Non Player Character). Dalam sebuah game khususnya untuk game FPS (First Person Shooter), dibutuhkan suatu strategi NPC, dengan tujuan untuk membuat game menjadi lebih atraktif dan realistik. Strategi menyerang dalam penelitian ini adalah membagi beberapa perilaku penyerangan NPC ketika berada pada posisi paling dekat dengan musuh. Penelitian ini menerapkan algoritma bayesian untuk klasifikasi perilaku penyerangan NPC tersebut. Klasifikasi tersebut diharapkan dapat meningkatkan strategi menyerang melawan musuh. Klasifikasi  penyerangan NPC dibiagi menjadi dua perilaku penyerangan yaitu perilaku memukul dan perilaku menggigit. Sedangkan untuk variabel yang digunakan dalam klasifikasi bayesian adalah health point, attack point player dan jarak yang diperoleh dari kondisi NPC. Dari hasil pengujian metode klasifikasi bayesian menggunakan pengujian confusion matrix, dengan percobaan permainan sebanyak 10 kali, telah  menghasilkan presentasi tingkat akurasi pada confusion matrix mencapai nilai persentase sebesar 80 %. Hal ini membubktikan bahwa klasifikasi perilaku penyerangan jarak dekat dengan metode klasifikasi bayesian dapat diterapkan dengan hasil yang baik.
PENERAPAN ALGORITMA COLLISION DETECTION DAN BAYESIAN UNTUK STRATEGI MENYERANG JARAK DEKAT PADA NPC (NON PLAYER CHARACTER)MENGGUNAKAN UNITY 3D Siti Asmiatun
Jurnal Transformatika Vol 14, No 1 (2016): July 2016
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v14i1.382

Abstract

The game is a field of science is growing rapidly and very interesting to learn because review has potential good view of science and commercial aspect. To produce an attractive and realistic games, there are a few things to note. One of them is the attention to the attack strategy, especially for the NPC (Non Player Character). Referring to previous studies that discuss strategies melee attack for NPC (Non Player Character)[1]. This study combines two Collision Detection with Bayesian methods for attacking strategy melee. In this study is to divide some of the behavior of NPCs attack when in the position closest to the enemy. This study applies the algorithm for Bayesian classification assault NPC behavior and collision detection algorithms for decision-making behavior when NPCs hit the player. By merging algorithm aims to improve on the weaknesses of previous research. NPC assault classification is divided into two offensive behavior: attitudes and behavior kick smash. As for the variables used in Bayesian classification is health points, attack points player and the distance obtained from NPC conditions. The results of the implementation of collision detection and Bayesian methods have proved that the collision detection methods can take decisions NPC attack behavior although placed closest to the player. From the test results with the calculation methods confusion matrix gain as much as 90% accuracy.
Implementasi Data Mining Untuk Estimasi Produktivitas Kacang Hijau Dengan Menggunakan Algoritma Regresi Linier Di Kabupaten Grobogan Kusumaningrum, Yulinda; Asmiatun, Siti; Putri, Astrid Novita
Jurnal Transformatika Vol. 19 No. 1 (2021): July 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i1.9450

Abstract

The Grobogan Regency Agriculture Service is an agency that operates in the agricultural sector. One of the crop commodities in Grobogan Regency is green beans. Judging from the results obtained each year, green bean production in Grobogan Regency is inconsistent. The rise and fall of green bean productivity is influenced by several factors. Factors such as area, production, number of farmers and productivity can be estimated to determine the production of green beans in Grobogan Regency. Therefore, using the Multiple Linear Regression algorithm is expected to help to obtain results on how much green bean production is in Grogoban Regency as a reference for farmers to increase their green bean harvest each year. Based on the calculation results, it was found that the estimated productivity of green beans in Grobogan Regency reached 760.8297302 Tons/Ha, whereas previously the land was 865 Hectares (Ha) Keywords: data mining, linear regression, estimation, productivity, green beans
Co-Authors Agusta Praba Ristadi Pinem Ahmad Rifa’i Aria Hendrawan, Aria Astrid Novita Putri Astrid Novita Putri, Astrid Aziz, Toha Cholil, Saifur Rohman Edi Widodo Edi Widodo Eka Putri Rachmawati Eka Putri Rachmawati Firman Sasetyo, Danang Fitrianti, Ayang Galet Guntoro Setiaji Imawan, Thopiq Nur Kusumaningrum, Yulinda Latius Hermawan Latius Hermawan Lenny Margaretta Huizen Noviana Dini Rahmawati, Noviana Dini Nur Wakhidah paulus harsadi Prayoga, Mohamad Adrian Rastri Prathivi Saifur Rohman Cholil Saifur Rohman Cholil Setyowati, Rina Dwi Susanto Susanto Susanto Susanto Tarigan, Aditya Vikky Taufich Rizqi Fachrudin Titin Winarti Tri Daryatni Umam, Miftachul Vency Vydia Vensy Vydia Victor Gayuh Utomo Wibowo, Beautifully Margaretha Putri Wicaksana, Dinar Anggit ZAMAN, BADROE