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All Journal Jurnal Buana Informatika Techne : Jurnal Ilmiah Elektroteknika Applied Technology and Computing Science Journal Respati Jurnal Lektur Keagamaan
Putra Wanda, Putra
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Religion Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Social Sciences

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Model Pengamanan End-to-End pada M-Banking Berbasis Algoritma Kurva Hyper Elliptic Wanda, Putra
Jurnal Buana Informatika Vol 7, No 4 (2016): Jurnal Buana Informatika Volume 7 Nomor 4 Oktober 2016
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (595.357 KB) | DOI: 10.24002/jbi.v7i4.765

Abstract

Abstract. Currently, banking transactions using mobile banking has grown rapidly. The increasing the number of mobile application users becomes one of the main factors. Several approaches have been developed to improve the transaction security. Problems of message security still requires a solution to achieve computing speed and leverage security level. In this paper, we propose a security algorithms used to improve the mobile banking security with hyperelliptic curve algorithm. It will create a safe and an efficient transactions while message will be sent via public internet. Hyperelliptic curve algorithm will run a processes for authentication and encryption. it will produce fast computation and has good security level. This research produced little computing time on m-banking application while it run on Android. Hyperelliptic curve algorithm use a smaller key to achieve a good security level at m-banking application.Keywords: hyperelliptic curve algorithm, security, mobile banking.Abstrak. Saat ini, transaksi perbankan baik di dalam dan di luar menggunakan Mobile Banking semakin pesat, meningkatnya jumlah pengguna aplikasi mobile menjadi salah satu faktor utamanya. Beberapa pendekatan telah dikembangkan untuk meningkatkan keamanan transaksi pesan selama komunikasi. Masalah yang masih memerlukan solusi adalah kecepatan komputasi dan tingkat keamanan pada algoritma pengamanan yang digunakan. Penelitian ini dilakukan untuk meningkatkan keamanan pesan mobile banking dengan memanfaatkan algoritma kurva hyper elliptic. Hal ini dilakukan untuk mewujudkan transaksi yang aman dan efisien dengan penerapan metode kriptografi pada pesan. Dengan menggunakan algoritma kurva hyper elliptic maka proses autentikasi dan enkripsi pesan bisa dilakukan dengan cepat dan memiliki level keamanan yang tinggi. Penelitian ini menghasilkan waktu komputasi yang cukup cepat pada aplikasi m-banking berbasis Android. Hal ini karena, algoritma kurva hyper elliptic menggunakan panjang kunci yang lebih kecil untuk mencapai level keamanan yang baik pada aplikasi m-banking. Kata Kunci: algoritma kurva hyper elliptic, keamanan, mobile banking.
REVIEW: METODE PENGAMANAN DATA PADA PUBLIC INSTANT MESSENGER Wanda, Putra
Jurnal Teknologi Informasi RESPATI Vol 11, No 31 (2016)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/jtir.v11i31.121

Abstract

Abstrak –Saat ini, perkembangan Instant Messenger sangat pesat, salah satu aspek yang menjadi perhatian adalah tentang keamanan pesan yang dikirim melalui Mobile Instant Messenger yang harus melewati jalur komunikasi internet. Ada banyak metode yang telah dikembangkan untuk meningkatkan aspek pengamanan data meliputi penggunaan algoritma kriptografi keamanan seperti RSA-Triple DES, penggunaan algoritma AES pada protokol Off The Record (OTR), penggunaan algoritma Kurva Hyper Elliptic serta penggunaan jaringan virtual pada skema pengamanan.Pada umumnya arsitektur komunikasi Instant Messenger dapat dibagi menjadi dua arsitektur yaitu arsitektur client server dan peer to peer (P2P). Makalah ini mendeskripsikan berbagai metode pengamanan yang telah dikembangkan untuk meningkatkan keamanan komunikasi pada Public Instant Messenger.Kata Kunci : Algortima, Keamanan, Mobile Banking
TOLERANSI ANTAR UMAT BERAGAMA BERBASIS KEARIPAN LOKAL DI PULAU LOMBOK NUSA TENGGARA BARAT: Kata Kunci: Toleransi, Umat Beragama, Kearipan Lokal. Abdurrazak; Azhari, Sukron; Wanda, Putra; Ambakti, Lalu Suparman; Humamurrizqi
Jurnal Lektur Keagamaan Vol 20 No 1 (2022): Jurnal Lektur Keagamaan Vol. 20 No. 1 Tahun 2022
Publisher : Center for Research and Development of Religious Literature and Heritage, Agency for Research and Development and Training, Ministry of Religious Affairs of the Republic of Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (301.229 KB) | DOI: 10.31291/jlka.v20i1.1027

Abstract

ABSTRACT This article examines religious tolerance based on local wisdom of the Lombok people with various beliefs, cultures, traditions, ethnicities, and religions. The people of Lombok are known to highly uphold religious tolerance through the principles of existing local wisdom. This is done in order to build a harmonious society and avoid conflicts between commu­nities. By employing descriptive qualitative design, this study collects primary data in the form of observations of people's daily lives, especially in the aspect of tolerance practice based on local wisdom by the com­munity. Secondary data is obtained from articles, newspapers, and websites related to the practice of religious tolerance in Lombok. The study finds that religious tolerance in Lombok is presented in the celebration of religious holidays, where people of other religions are included in the celebration, for example: Eid al-Fitr, Vesak, and others. The Muslim community of Lombok invite their non-Muslim neighbors to join the celebration of the halal bihalal event, the Hindus also invite their neighbors to participate in the ogoh-ogoh parade which is part of the celebration of their holiday, Christians also invite their neighbors to join in the celebration of Christmas. Analysis of the data concludes that religious tolerance based on local wisdom of the Lombok people is constructed on three aspects, namely solidarity, mutual cooperation and deliberation. Keywords: Religious Diversity, Tolerance, Local Wisdom.    ABSTRAK Artikel ini mengkaji toleransi beragama berbasis kearifan lokal masyarakat Lombok dengan ragam kepercayan, budaya, tradisi, suku, dan agama. Masyarakat Lombok dikenal sangat menjujung tinggi tole­ransi beragama melalui prinsip kearifan lokal yang ada. Hal ini dilakukan dalam rangka membentuk masya­rakat yang harmonis dan menghindari konflik antar masyarakat. Dengan menggunakan metode kualitatif deskriptif, penelitian ini menggunakan data primer berupa pengamatan kehidupan masyarakat sehari-hari, terutama pada aspek praktik toleransi berbasis kearifan lokal itu dite­rap­­kan oleh masyarakat. Data sekunder diperoleh dari artikel, koran, web, dan lainya yang terkait dengan praktik toleransi beragama di Lombok. Hasil penelitian mengungkapkan bahwa toleransi ber­aga­ma di Lombok dapat disaksikan pada perayaan hari besar keagama­an, dimana umat agama lain diikutsertakan dalam perayaannya, missal: Idul Fitri, Waisak, dan lainnya. Masyarakat Muslim Lombok tidak segan mengundang tetangganya yang non-muslim untuk ikut memeriah­kan acara halal bihalalnya, umat Hindu juga mengundang tetangganya untuk ikut memeriahkan pawai ogoh-ogoh yang menjadi bagian perayaan hari besar­nya, umat Kristen juga mengun­dang tetangganya untuk ikut memeriahkan acara natalan mereka. Berda­sar­kan pada paparan tersebut, toleransi ber­aga­ma berbasis kearifan lokal ma­syarakat Lombok dibentuk berdasarkan tiga aspek, yakni solidaritas, gotong royong dan musyawarah.  Kata Kunci: Keragaman Keagamaan, Toleransi, Kearifan Lokal.
Advancing Natural Gas Price Predictions with ConcaveLSTM Diqi, Mohammad; Wanda, Putra; Hamzah; Ordiyasa, I Wayan; Fathinah, Azzah
Techné : Jurnal Ilmiah Elektroteknika Vol. 23 No. 1 (2024)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v23i1.456

Abstract

This study investigates the application of the ConcaveLSTM model, a novel machine learning approach combining the strengths of Stacked Long Short-Term Memory (LSTM) and Bidirectional LSTM, for predicting natural gas prices. Given the inherent volatility and complexity of energy markets, accurate forecasting models are crucial for effective decision-making. The research employs a comprehensive dataset from 1997 to 2020, focusing on the daily price of natural gas in US Dollars per Million British thermal units (Btu). Through rigorous testing across various model configurations, the study identifies optimal settings for the ConcaveLSTM model that significantly improve prediction accuracy. Specifically, configurations utilizing 50 input steps with neuron counts of 100 and 300 exhibit superior performance, as evidenced by lower Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), alongside higher R-squared (R2) values. These findings validate the ConcaveLSTM model's potential in financial forecasting and highlight the importance of parameter tuning in enhancing model efficacy. Despite certain limitations regarding dataset scope and market variability, the results offer promising insights into developing advanced forecasting tools. Future research directions include expanding the dataset, incorporating additional market influencers, and conducting comparative analyses with other forecasting models. This study contributes to the evolving field of machine learning applications in financial market predictions, offering a foundation for further exploration and practical implementation in the energy sector.
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
Co-Authors Abdurrazak Ambakti, Lalu Suparman Azhari, Sukron Fathinah, Azzah Hamzah Hamzah Hamzah Humamurrizqi Marselina Endah Hiswati Mohammad Diqi Ordiyasa, I Wayan Sri Hasta Mulyani Wijaya, Nurhadi