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All Journal Jurnal Infra JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sistem Informasi Triguna Dharma (JURSI TGD)
Elizabeth Nathania Witanto
Program Studi Teknik Informatika
Computer Science & IT

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Pembuatan Aplikasi Pengamanan Data dengan Metode MARS Witanto, Elizabeth Nathania; Budhi, Gregorius Satia; Purba, Kristo Radion
Jurnal Infra Vol 3, No 2 (2015)
Publisher : Jurnal Infra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (78.005 KB)

Abstract

Nowadays, technology is growing rapidly and becoming more sophisticated, especially communications technology. Information can be deployed without having to meet each other. No doubt that any confidential information sent over the Internet. Through various types of information is developing techniques for damage or maintaining the security of the dissemination of information on the internet.Securing data by using cryptography is needed for sending confidential data so that not everyone can access it. Process undertaken in this study is encrypted messages with MARS method. Input in an application that is designed in the form of plaintext messages or files to be encrypted, then the key to encrypt the message. This application is made with the Java programming language using NetBeans IDE 7.4.The test results indicate that the encryption with MARS method can be performed on any type of file (example: text file, song or audio file, video file or movie, etc.). Files can be decrypted back to normal if the password is the same as the password for the encryption.
Pembuatan Aplikasi Pengamanan Data dengan Metode MARS Elizabeth Nathania Witanto; Gregorius Satia Budhi; Kristo Radion Purba
Jurnal Infra Vol 3, No 2 (2015)
Publisher : Jurnal Infra

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.083 KB)

Abstract

Nowadays, technology is growing rapidly and becoming more sophisticated, especially communications technology. Information can be deployed without having to meet each other. No doubt that any confidential information sent over the Internet. Through various types of information is developing techniques for damage or maintaining the security of the dissemination of information on the internet.Securing data by using cryptography is needed for sending confidential data so that not everyone can access it. Process undertaken in this study is encrypted messages with MARS method. Input in an application that is designed in the form of plaintext messages or files to be encrypted, then the key to encrypt the message. This application is made with the Java programming language using NetBeans IDE 7.4.The test results indicate that the encryption with MARS method can be performed on any type of file (example: text file, song or audio file, video file or movie, etc.). Files can be decrypted back to normal if the password is the same as the password for the encryption.
Usability Evaluation on “LYFY” as an e-Marketplace Tool for the National Batik Industry Witanto, Elizabeth Nathania; Permana, Belinda Putri Adi; Wiradinata, Trianggoro; Oktian, Yustus Eko; Maryati, Indra
JATISI Vol 12 No 3 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i3.11858

Abstract

In the current digital era, e-commerce has emerged as a primary choice for many business actors, particularly Micro, Small, and Medium Enterprises (MSMEs) specializing in batik products. Live streaming commerce has gained popularity as an effective marketing strategy, enabling direct interaction between sellers and consumers. However, challenges such as audience engagement and the need for prompt responses to questions or comments during live sessions pose significant issues that need to be addressed. To assist MSMEs optimize this strategy, the application of Artificial Intelligence (AI) and Machine Learning (ML) technologies can provide innovative solutions. LYFY, an AI-driven e-marketplace platform, was developed to support batik MSMEs by facilitating product promotion, enhancing consumer interaction, and enabling efficient transactions through features such as live streaming and size prediction tools. To assess the effectiveness of LYFY's user interface and overall experience, a usability evaluation was conducted using two standardized frameworks: ISO 9241-11 and the Usability Metric for User Experience (UMUX-Lite). The finding suggests that LYFY provides a high-quality user experience and is well-positioned to support the digital transformation of Indonesia’s batik SMEs.
Peramalan Inflasi dan Harga Minyak Mentah dengan Pendekatan Hybrid Statistika-Machine Learning dan Statistika-Deep Learning Christopher Andreas; Witanto, Elizabeth Nathania; Guntur, Yohana Jocelyn; Purnomo, Felicia Joshlyn
Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Vol. 5 No. 1 (2026): EDISI JANUARI 2026
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jursi.v5i1.12342

Abstract

Inflasi dan harga minyak mentah merupakan dua indikator ekonomi strategis yang memengaruhi stabilitas ekonomi nasional dan arah kebijakan publik. Peramalan yang akurat terhadap kedua variabel ini sangat penting untuk mendukung perencanaan fiskal, moneter, serta strategi sektor industri dan perdagangan. Karakteristik keduanya berbeda, dimana inflasi cenderung memiliki pola tren dan musiman yang relatif stabil, sedangkan harga minyak mentah bersifat fluktuatif dengan pengaruh faktor eksternal global. Perbedaan ini menuntut metode peramalan yang adaptif dan mampu bekerja baik pada kondisi data yang berbeda. Penelitian ini memiliki keterkaitan dengan Sustainable Development Goals (SDG 8 dan SDG 9). Penelitian ini bertujuan mengembangkan metode time series forecasting berbasis pendekatan hybrid melalui model statistika-machine learning dan statistika-deep learning. Pendekatan statistika dengan model Autoregressive Integrated Moving Average (ARIMA) digunakan untuk menangkap pola linear, kemudian hasil prediksi atau residual dari model ARIMA diproses lebih lanjut menggunakan algoritma machine learning yaitu Support Vector Regression (SVR) dan model deep learning yaitu Long Short-Term Memory (LSTM) untuk mempelajari pola non-linear. Dalam hal ini, evaluasi akurasi model diukur dengan metrik symmetric Mean Absolute Percentage Error (sMAPE). Hasil penelitian menunjukkan bahwa model ARIMA-SVR memiliki akurasi lebih baik dalam meramalkan data inflasi dengan nilai sMAPE sebesar 0,2072. Sebaliknya, model ARIMA-LSTM lebih akurat dalam meramalkan data harga minyak dengan nilai sMAPE sebesar 0,0548. Dengan demikian, pendekatan hybrid statistika-machine learning dan statistika-deep learning memiliki akurasi yang baik dalam memprediksi data yang bersifat time series.
Co-Authors Christopher Andreas Gregorius Satia Budhi Gregorius Satia Budhi Guntur, Yohana Jocelyn Indra Maryati Kristo Radion Purba Kristo Radion Purba Permana, Belinda Putri Adi Purnomo, Felicia Joshlyn Trianggoro Wiradinata Yustus Eko Oktian