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All Journal Jurnal Simetris Telematika Jurnal Transformatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pengembangan Rekayasa dan Teknologi Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Journal of Computer and Information Systems Ampera Jurnal Pengabdian Masyarakat Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Pengabdian Mitra Masyarakat (JPMM) Tematik Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal DIMASTIK
Rastri Prathivi
Universitas Semarang
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 Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Journal : Jurnal Transformatika

 1 
GAME SCORING SUPPORTING OBJECTS MENGGUNAKAN AGEN CERDAS BERBASIS ARTIFICIAL INTELLIGENCE Novita Putri, Astrid; Prathivi, Rastri
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

Game are activity most structure, one that rdinary is done in fun and also education tool and help to develop practical skill, as training, education, simulation or psychological. On its developing current game have until 3D. In one game, include in First Person Shutter  necessary scoring  one that intent to motivate that player is more terpacu to solve game until all through,  on scoring  Super Marios game Boss, Compass does count scoring havent utilized Artifical Intelligent so so chanted, while player meet with supporting objects example ammor  ability really guns directly dead, so is so easy win. Therefore at needs a count scoring  interesting so more motivated in finishing problem Scoring accounting point for First Person Shutters game .This modelling as interesting daring in one game, since model scoring  one that effective gets to motivate that player is more terpacu in plays and keep player for back plays. Besides model scoring  can assign value that bound up with game zoom.On Research hits scoring this game will make scoring bases some criterion which is health Point, Attack point, Defence point, And  Magic  what do at have  supporting objects ,then in this research do compare two method are methodic statistic and Fuzzy. Result of this research 83,4 % on testings examination and on eventually gets to be concluded that fuzzys method in trouble finish time more long time but will player more challenging to railroad.  
FEATURE RECOGNITION BERBASIS CORNER DETECTION DENGAN METODE FAST, SURF DAN FLANN TREE UNTUK IDENTIFIKASI LOGO PADA AUGMENTED REALITY MOBILE SYSTEM Rastri Prathivi
Jurnal Transformatika Vol 11, No 2 (2014): January 2014
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v11i2.96

Abstract

Logo is a graphical symbol that is the identity of an organization, institution, or company. Logo is generally used to introduce to the public the existence of an organization, institution, or company. Through the existence of an agency logo can be seen by the public. Feature recognition is one of the processes that exist within an augmented reality system. One of uses augmented reality is able to recognize the identity of the logo through a camera.The first step to make a process of feature recognition is through the corner detection. Incorporation of several method such as FAST, SURF, and FLANN TREE for the feature detection process based corner detection feature matching up process, will have the better ability to detect the presence of a logo. Additionally when running the feature extraction process there are several issues that arise as scale invariant feature and rotation invariant feature. In this study the research object in the form of logo to the priority to make the process of feature recognition. FAST, SURF, and FLANN TREE method will detection logo with scale invariant feature and rotation invariant feature conditions. Obtained from this study will demonstration the accuracy from FAST, SURF, and FLANN TREE methods to solve the scale invariant and rotation invariant feature problems.
Klasifikasi Data Trafik Internet Menggunakan Metode Bayes Network (Studi Kasus Jaringan Internet Universitas Semarang) Rastri Prathivi
Jurnal Transformatika Vol 12, No 2 (2015): January 2015
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v12i2.81

Abstract

Pemakaian internet merupakan kebutuhan yang penting yang mendukung kinerja dan aktivitas di kampus. Bagian yang terpenting dari infrastrutur internet yang difasilitasi oleh kampus adalah tersedianya bandwidth yang cukup untuk kelancaran trafik data melalui internet. Metode klasifikasi menggunakan Bayes Network ini memanfaatkan metode klasifikasi yang dimiliki oleh data mining untuk diterapkan pada data trafik jaringan internet.Penelitian ini bertujuan untuk mengklasifikasi data pemakaian internet sehingga dari klasifikasi tersebut dapat diketahui destination network, protocol dan lebar bandwidth yang banyak diakses pada waktu tertentu.Data trafik internet diambil melalui software Wireshark. Sedangkan pengolahan data dan proses pengklasifikasian data trafik internet diolah dengan Weka
Evaluating the Popularity of Programming Languages in Indonesia using the MABAC Method Widodo, Edi; Prathivi, Rastri; Hadi, Soiful
Jurnal Transformatika Vol 21, No 1 (2023): July 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.7001

Abstract

In today's fast-paced digital era, the selection of a programming language plays a crucial role in the success of software development projects. This research aims to create an index of popularity for programming languages using the multi-attributive border approximation area comparison (MABAC) method. The study considers four data sources, including Jobstreet.Com, LinkedIn.Com, Google Trends, and Tiobe.com, to obtain the necessary information for evaluating the popularity of programming languages in Indonesia. The data range for this study is from May 1, 2020, until April 31, 2021. The results of the study indicate that the top ten programming languages in terms of popularity in Indonesia are Java, SQL, php, JavaScript, C, C++, python, C#, Visual Basic, and Assembly. The index can serve as a useful guide for strategic decision-making regarding the selection of programming languages for addressing the needs of the information technology market in Indonesia. The study's findings can be useful for software developers, IT professionals, and decision-makers in organizations who need to select a programming language for their software projects in Indonesia. The MABAC method used in this study can also be applied to other contexts for evaluating the popularity of programming languages.
Komparasi Metode SVM dan Adaboost untuk Klasifikasi Kanker Payudara Elfitrianna, Ikka Ayu; Prathivi, Rastri
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/9adm2e13

Abstract

One of the most prevalent malignancies in women and a major global cause of death is breast cancer. To determine whether a cancer is benign or malignant, early detection is essential. The usefulness of the Support Vector Machine (SVM) and Adaptive Boosting (Adaboost) algorithms for breast cancer classification using mammography data is compared in this study. 569 records make up the dataset, which was sourced from the Kaggle Repository and is split into 75% training data and 25% testing data. Preprocessing steps include feature and target variable creation, categorical-to-numerical conversion, data splitting, and normalization. SVM achieved an accuracy of 97%, with a precision of 98%, recall of 94%, and F1 score of 96%. Adaboost, on the other hand, achieved an accuracy of 96%, precision of 98%, recall of 92%, and F1 score of 95%. The results reveal that both algorithms are highly effective for breast cancer detection, with SVM marginally exceeding Adaboost in total performance. These findings emphasize the promise of machine learning techniques in facilitating early cancer diagnosis, hence boosting survival rates. It is advised that future research employ a wider range of datasets and investigate different classification techniques in order to improve accuracy and dependability even more.
Komparasi Algoritma Random Forest dan XGBoost dalam Prediksi Premi Asuransi Kesehatan karunia, windy; Windy; prathivi, rastri
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.11914

Abstract

Asuransi kesehatan saat ini menjadi salah satu hal yang banyak orang persiapkan dikarenakan adanya ketidakpastian risiko kesehatan dan biaya layanan kesehatan yang semakin naik. Perhitungan premi tiap individu dapat berbeda dikarenakan terdapat perbedaan profil kesehatan seperti usia, BMI maupun gaya hidup seperti merokok yang membuat perusahaan asuransi harus memperhitungkan premi dengan akurat agar tidak menimbulkan kerugian finansial dan sesuai dengan tingkat risiko terjadinya klaim. Adapaun tujuan dari penelitian ini adalah melakukan komparasi antara algoritma Random Forest dan XGBoost dalam memprediksi premi asuransi kesehatan berdasarkan beberapa faktor yang sulit dihitung secara manual. Evaluasi dilihat berdasarkan metrik regresi yaitu MAE, MSE, RMSE, dan R2. Pada penelitian ini, algoritma Random Forest berhasil memprediksi premi asuransi kesehatan lebih baik dari XGBoost dengan nilai MAE 2.573, MSE 24199792,43 RMSE 4919, 33 dan R2 sebesar 84.04%.
Co-Authors Alauddin Maulana Hirzan Andrianto, Diky April Firman Daru Astrid Novita Putri, Astrid Atmoko Nugroho Cholil, Saifur Rohman Ciptandini, Madyantari Ipmas Dewanti, Tita Risa Edi Widodo Edi Widodo, Edi Elfitrianna, Ikka Ayu Febrian Wahyu Christanto Galet Guntoro Setiaji Hadi, Soiful Hanif, Mohammad Burhan Hetty Catur Ellyawati, Hetty Catur karunia, windy Lenny Margaretta Huizen Liu, Meifang Mar’atuzzulfa, Salma Nurdewanti, Debi Safa Pradvenanta, Yoannes Dion Qurinta Shinta Rahmaeda, Shafara Rastri Prathivi Rosidah, Nafiati Saifur Rohman Cholil Sara Sharifzadeh Siti Asmiatun, Siti Soiful Hadi Sri Handayani Susanto Susanto Titin Winarti Titis Handayani Tria Ardianita Tunggal, Anisa Utomo, Mario Victor Gayuh Utomo Windy Yunita Kurniawati, Yunita Zalzabila, Niken