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Jurnal Transformatika
Published by Universitas Semarang
ISSN : 16933656     EISSN : 24606731     DOI : -
Core Subject : Science,
Computer Science & IT
Transformatika is a peer reviewed Journal in Indonesian and English published two issues per year (January and July). The aim of Transformatika is to publish high-quality articles of the latest developments in the field of Information Technology. We accept the article with the scope of Information Systems, Web Technology, Computer Networks, Artificial Intelligence, and Multimedia.
Arjuna Subject : -
Articles 344 Documents
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Analysis of the Topsis in the Recommendation System of PPA Scholarship Recipients at Universitas Islam Kebangsaan Indonesia Hasdyna, Novia; Dinata, Rozzi Kesuma; Retno, Sujacka
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.v21i2.7051

Abstract

This research implements the TOPSIS method on a recommendation system for Peningkatan Prestasi Akademik (PPA) scholarship recipients. The research data was obtained from the computer and multimedia faculty, UNIKI. The results showed that the TOPSIS method can provide the best alternative based on the highest rank. In this research, the highest rank was obtained from the results for predetermined criteria, namely GPA, achievements, parental dependents and parental income. The highest value obtained is 0.7489. The system built based on a website with the PHP programming language.
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.
Klasifikasi Jenis Buah Nanas Menggunakan Convolution Neural Network Marfianto, Jodhy Dwi; Akbar, Mutaqin
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.v21i2.6369

Abstract

Indonesia is one of the countries with gread agricultural potential. One of the products of agriculture in Indonesia is pineapple. Pineapple is a tropical plant with edible fruit and one of the maximum economically vital plants in the Bromeliaceous family. The process of selecting pineapple species is generally very dependent on human perception. The development of technology and science makes it possible to perform classification or in terms of object selection using technology based on digital image-based characteristics. Images are used as a source of information that can be used to classify objects. One of the deep learning methods used is Convolutional Neural Networks (CNN) because they have a high deep network and are widely used to image data. Deep learning in Computer Vision has good capabilities in, one of which is image classification or object classification in images, and the network in CNN has a special layer, namely the convolution layer, The image convolution process in this study uses the keras package on GoogleColab, because making a neural network model using Keras does not need to write code to express mathematical calculations individually. Testing using a sample of 120 pineapple images shows an accuracy rate of 91,66% which is considered to be able to identify 3 types of pineapple fruit.
ANDROID BASED ADVERTISING REMINDER SYSTEM Putra, Toni Wijanarko Adi; Saputro, Alif Budi; Solechan, Achmad; Hartono, Budi
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.v21i2.6067

Abstract

The aim of this research is to introduce an Android-based reminder system that automatically notifies Surya Media Advertising employees of expired leases. Android is a "Linux-based operating system used on mobile devices such as smartphones and tablet computers (PDAs)"[8]. The method used in system development is the SDLC method which is the waterfall model. B. The design step must wait for the completion of the previous step, namely the requirements step. Visual Studio Code and Android Studio are used as software for this research [12]. The results of this study are; 1) Reminder system can help capture rentals for locations faster and easier; 2) The notification function makes it easy to know when the rental period has expired.
Analisis Loyalitas Customer Perusahaan Konveksi dengan Model RFM dan Algoritma k-Means Gerian, Matthew; Nataliani, Yessica
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.v21i2.7248

Abstract

Strategi yang baik diperlukan suatu perusahaan dalam menjalankan usahanya. CV. Karunia Jaya merupakan salah satu perusahaan yang bergerak dalam bidang konveksi yanag menjual pakaian bayi. Dalam pelayanan terhadap customer CV. Karunia Jaya belum menerapkan strategi Customer Relationship Management (CRM). Untuk mengetahui loyalitas customer maka perlu dilakukan segmentasi pelanggan terhadap customer. Penelitian ini menggunakan data transaksi dari tahun 2021-2022. Algoritma k-means digunakan dalam penentuan cluster berdasarkan model Recency, Frequency, dan Moneetary (RFM), dibantu dengan tools Weka 3.8.6. Metode elbow digunakan untuk mencari jumlah cluster terbaik dari sekelompok data. Hasil dari penelitian ini yaitu terdapat 27 customer yang terbagi dalam tiga cluster, 21 customer potensi rendah, tiga customer potensi sedang, dan tiga customer potensi tinggi. Perusahaan dapat memberikan layanan yang berbeda terhadap setiap kelompok customer, sehingga hal tersebut dapat menguntungkan perusahaan.
Sentiment Analysis of YouTube Comments Toward Chat GPT Rochadiani, Theresia Herlina
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.v21i2.7033

Abstract

Sentiment analysis is used for analyzing the emotions and attitudes expressed in text data. In this study, sentiment analysis is used to understand people’s enthusiasm toward Chat GPT. The primary objective of this study is to investigate the acceptance of people of new artificial intelligence technology, Chat GPT, that may change the future. To get a deep understanding of it, a large dataset of user comments from YouTube is collected and then data pre-processing is done by removing stop words, punctuations, and irrelevant information. Using Text Blob and VADER approaches, comments are classified into positive, neutral, and negative categories. The result shows that most users have a positive sentiment to receive and use Chat GPT. The contribution of this study is to provide insights into the sentiment of people’s response to Chat GPT, which can inform user acceptance of the language model development and give guide its future applications.
Analisis Perbandingan Kecepatan Deploy Container Orchestration Menggunakan Docker Swarm dan Kubernetes Umam, Chaerul; Ervikhan, Muhammad Ibnu
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.v21i2.6792

Abstract

A good infrastructure is to be able to provide services with high capability for its clients. The infrastructure of a server is said to be good if it can handle many requests from clients and does not experience downtime. The speed of deploying on container orchestration-based infrastructure greatly affects the downtime of a system. The author uses the Docker Swarm and Kubernetes to compare the deployment speed to find good performance and is suitable. The result shows that Kubernetes has a faster deploy speed than Docker Swarm. Docker Swarm gets an average speed for deploying 3 to 30 replications in 10 consecutive tests for 1 minute 9 seconds. Meanwhile, Kubernetes gets an average deploy speed of 1 minute 2 seconds.
Rancang Bangun Sistem Pengelolaan Aset Ilmiah Digital Pada Perpustakaan Perguruan Tinggi Setiawan, Aries; Ratnawati, Juli; Prihandono, Adi; Widjajanto, Budi; Farida, Ida
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.v21i2.6938

Abstract

Perpustakaan merupakan satuan pendidikan tinggi yang mengelola perbendaharaan aset ilmiah. Aset ilmiah meliputi buku pelajaran, buku bacaan, tugas akhir mahasiswa, jurnal, CD berisi file tulisan dan video. Setiap periode selalu dianggarkan untuk pengadaan buku, setiap periode setelah pelaksanaan tugas akhir atau ujian skripsi, hardcopy tugas akhir yang akan dijadikan literatur juga bertambah. Bisa dibayangkan jika dalam satu tahun ada 3 periode ujian tugas akhir, dengan total sekitar 600 mahasiswa per periode, maka akan ada 1800 hardcopy tugas akhir mata kuliah. Hal ini berdampak pada kepenuhan ruang perpustakaan sehingga semakin sulit untuk mencari data laporan tugas akhir. Perlu dirancang sistem manajemen aset elektronik di perpustakaan yang mengumpulkan semua data buku dan literatur lainnya dalam bentuk file elektronik, pengunjung akan mudah menemukan literatur hanya dengan mencari sistem aset.
IDENTIFIKASI PENYAKIT JANTUNG MENGGUNAKAN MACHINE LEARNING: STUDI KOMPARATIF Sintiya, Endah Septa; Rizdania, Rizdania; Afrah, Ashri Shabrina; Pramudhita, Agung
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.v21i2.6274

Abstract

Heart disease is the number one cause of death globally. This condition is followed by an unhealthy lifestyle. Heart disease prediction needs to be done considering the importance of health. The presence of machine learning has made it easier for humans to make early detection of patterns that are close to heart disease. Prediction of heart disease is important given the behavior of people who are still prone to risk factors. Conditions where predictions using machine learning for heart disease have not been compared with many using machine learning methods. Predictions of heart disease are needed along with the interrelationships of the variables. This research compares 6 machine learning methods for disease classification with KNN, Naïve Bayes, Decision tree, Random forest, logistic regression, and SVM. The final classification obtained ranking accuracy with the highest value of 82% in the KNN method with the confusion matrix test, precision, accuracy, re-call, and fi-score. These results can be applied to real case studies of heart disease
Pemanfaatan Artificial Neural Network Dengan Teknik Backpropagation Untuk Prakiraan Cuaca Harian Zulfiani, Ayu; Fauzi, Chairani
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.v21i2.6937

Abstract

Berdasarkan data dari Badan Nasional Penanggulangan Bencana (BNPB), selama tahun 2022 saja, telah terjadi 3.054 bencana dengan korban meninggal sampai 392 orang, dengan jumlah kejadian cuaca ekstrim dapat mencapai 931 kejadian.  Untuk mengantisipasi dampak yang ditimbulkan oleh cuaca ekstrim, BMKG mengeluarkan prakiraan cuaca, agar masyarakat siap, ketika cuaca ekstrim itu datang. Aplikasi penggunaan teknik Artificial Neural Network (ANN) pada prakiraan cuaca yang sangat berdampak, meningkatkan kemampuan untuk menyelami luasnya big data dalam mendapatkan informasi yang diperlukan, sebagai pembantu yang tepat bagi prakiraan dan pembuatan kebijakan. Data yang digunakan pada penelitian ini adalah data unsur-unsur cuaca, seperti tekanan, suhu udara, kelembaban, arah dan kecepatan angin, serta curah hujan, yang didapatkan dari Stasiun Meteorologi Radin Inten II Lampung. Data observasi memiliki kerapatan data per 1 jam, dengan rentang waktu selama 5 tahun yaitu dari 01 Januari 2018 – 31 Desember 2022. Metode yang dipakai dalam penelitian ini adalah Backpropagation Neural Network (BPNN). Hasil penelitian menunjukkan BPNN dapat memprakirakan hujan terklasifikasi dengan baik dibandingkan metode lainnya. 

Page 28 of 35 | Total Record : 344

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