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Iman Setiawan
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Steganografi Berbasis Citra Digital Untuk Menyembunyikan Pesan Pada Sertifikat Menggunakan Metode LSB Dengan Caesar Cipher Nabilah Ananda Putri; Abid Husein; Iman Setiawan; Ilham Maulana Cakra
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Abstrak Penelitian ini bertujuan untuk mengembangkan metode steganografi berbasis citra digital yang efisien dan aman untuk menyembunyikan pesan pada sertifikat. Fokus utama penelitian ini adalah pada penerapan teknik Least Significant Bit (LSB) dan kriptografi Caesar Cipher dalam menyisipkan dan mengamankan pesan tersembunyi. Tujuan utama penelitian ini adalah untuk meningkatkan efisiensi penyembunyian pesan tanpa mengorbankan integritas visual sertifikat dan untuk memperkuat tingkat keamanan pesan tersembunyi melalui penggunaan Caesar Cipher[1][2]. Metode yang digunakan melibatkan implementasi LSB pada piksel citra digital sebagai cara untuk menyembunyikan informasi, serta integrasi Caesar Cipher untuk meningkatkan tingkat keamanan. Desain penelitian ini mencakup serangkaian eksperimen untuk mengevaluasi efektivitas metode yang diusulkan. Pengumpulan data dilakukan melalui simulasi dan analisis statistik untuk mengukur kapasitas penyembunyian, ketahanan terhadap serangan, dan dampak visual pada sertifikat. Analisis data melibatkan evaluasi kualitatif dan kuantitatif terhadap keefektifan metode steganografi yang diusulkan. Hasil penelitian menunjukkan bahwa metode LSB dengan Caesar Cipher mampu menyembunyikan pesan secara efisien tanpa merusak tampilan visual sertifikat. Keamanan pesan tersembunyi juga terbukti meningkat dengan adopsi kriptografi Caesar Cipher. Penelitian ini memberikan kontribusi pada pengembangan teknik steganografi yang lebih canggih dan aman dalam konteks keamanan informasi, khususnya terkait dengan dokumen sertifikat dan data digital lainnya. Kata Kunci: Steganografi Berbasis Citra Digital, Pesan Tersembunyi, Metode LSB, Caesar Cipher
Analisis Blackbox Testing dan User Acceptance Testing Terhadap Sistem Kasir Point of Sale Ahmad Syukron; Abid Husein; Iman Setiawan; Delfian Ruly Havatilla
Prosiding Sains dan Teknologi Vol. 4 No. 1 (2025): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 4 - Februari 2025
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Penelitian ini bertujuan untuk menganalisis sistem kasir Point of Sale (POS) pada halaman barang dengan menggunakan metode black box testing dan User Acceptance Testing (UAT). Sistem POS diharapkan dapat meningkatkan efisiensi dan akurasi dalam pengelolaan transaksi, inventaris barang, dan pelaporan keuangan. Black box testing digunakan untuk menguji fungsionalitas pada halaman barang tanpa memperhatikan kode sumber, dengan hasil pengujian menunjukkan bahwa seluruh fungsi sistem berjalan sesuai dengan spesifikasi yang ditentukan, yang membuktikan keandalan teknis sistem. Selain itu, UAT dilakukan untuk mengukur tingkat penerimaan dan kepuasan pengguna terhadap desain, fungsionalitas, efisiensi, dan performa sistem POS. Hasil analisis UAT menunjukkan bahwa tingkat penerimaan responden pengguna sangat tinggi, dengan persentase kepuasan mencapai 80%. Secara keseluruhan, sistem POS pada halaman barang berhasil memenuhi kebutuhan fungsional dan harapan pengguna, baik dari sisi teknis maupun pengalaman pengguna. Meskipun demikian, beberapa aspek, seperti desain antarmuka dan peningkatan efisiensi, masih dapat menjadi fokus untuk pengembangan lebih lanjut.
Comparison of Nonparametric Regression Nadara - Watson Estimator Kernel Function And Local Polynomial Regression In Predicting USD Against IDR Isni Rahma; Junaidi; Iman Setiawan
Tadulako Science and Technology Journal Vol. 2 No. 2 (2022): Tadulako Science and Technology Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v2i2.17300

Abstract

Introduction: Macroeconomic problems such as inflation and exchange rates are often highlighted as benchmarks for achieving economic progress. The stability of both must be monitored by the government in order to control the inflation rate and exchange rate. This instability is a phenomenon of fluctuation, namely the phenomenon of the rise and fall of the exchange rate of a currency based on demand and supply. Given the large impact of exchange rate fluctuations on the economy, the prediction of the wage exchange rate against the US dollar is considered necessary because it is useful to anticipate and minimize bad possibilities that arise. Method: Methods that can be used to analyze fluctuating currency exchange rate data are nonparametric regression, Nadaraya-Watson estimator, Gaussian kernel function, and Local Polynomial Regression. Results and Discussion: The results of a nonparametric regression comparison between the Nadaraya-Watson estimator, Gaussian kernel function, and local polynomial regression were obtained by MAPE of 2.508% and 0.179%, respectively. This shows that the best model uses the local polynomial regression method and predicted USD exchange rate data against IDR using the best model, namely Local polynomial Regression where the MAPE value is less than 10%, which means the prediction rate is very good. Conclusion: The nonparametric regression method of the Nadaraya-Watrson estimator, Gaussian kernel function, and local polynomial regression shows that the best model uses the local polynomial regression method.
Grouping Districts / Cities in Central Sulawesi Province Based on Poverty Indicators Using the Fuzzy Geographically Weighted Clustering -Artificial Bee Colony Method Nafiul Agristya; Rais; Iman Setiawan
Tadulako Science and Technology Journal Vol. 2 No. 2 (2022): Tadulako Science and Technology Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sciencetech.v2i2.17301

Abstract

Introduction: Poverty is the main problem that is the focus of attention of the government in Indonesia. In general, poverty is a person's inability to meet basic basic needs in every aspect of life. Cluster analysis is a solution to map this problem. Method: Fuzzy Geographically Weighted Clustering-Artificial Bee Colony (FGWC-ABC) is one clustering method that is an integration of classical fuzzy clustering methods and geodemographic elements. Artificial Bee Colony is a metaheuristic algorithm that is used as a global optimization to increase cluster accuracy. Artificial Bee Colony can efficiently and effectively solve various function optimization problems in various cases. Result and Discussion: The research results obtained 3 optimum clusters with each cluster characteristic relatively different based on poverty indicators. Cluster 1 with low poverty, cluster 2 with high poverty, and cluster 3 with moderate poverty. Conclusion: By using the IFV validity index, 3 optimum clusters were obtained with different characteristics of each cluster based on its indicators. Cluster 1 consists of three regencies/cities with low poverty status, cluster 2 consists of seven regencies/cities with high poverty status, and cluster 3 consists of six regencies/cities with moderate poverty status.
APPLICATION OF THE EXTREME LEARNING MACHINE (ELM) METHOD IN PREDICTING THE COMBINED STOCK PRICE INDEX (IHSG) IN INDONESIA Nur Azizah Janad; Junaidi; Iman Setiawan
Tadulako Social Science and Humaniora Journal Vol. 2 No. 2 (2022): Tadulako Social Science and Humaniora Journal
Publisher : LPPM Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/sochum.v2i2.17382

Abstract

The stock market is one area that continues to attract the attention of investors and financial researchers. This research explores the application of the Extreme Learning Machine (ELM) method to predict the Composite Stock Price Index (IHSG) in Indonesia. ELM is known for its fast learning capabilities with minimal prerequisite network architecture. In this research, three types of activation functions, namely Sigmoid, ReLU, and Tanh, are applied to ELM to compare their performance in predicting IHSG. Monthly IHSG data is used for model training and testing. Data preprocessing steps, such as dividing the data into Training and Test sets, are applied before feeding it into the model. Model performance was evaluated using Root Mean Square Error (RMSE) and compared for each activation function. The research results show that each activation function has a different impact on the IHSG prediction performance. In this research, the ReLU activation function showed the best performance in predicting IHSG compared to other activation functions, with a Root Mean Square Error (RMSE) of 1 x 10-16. These results show that the model's predictive performance in estimating actual values is very good.
Co-Authors Abid Husein Ahmad Syukron Delfian Ruly Havatilla Ilham Maulana Cakra Isni Rahma Junaidi Junaidi Nabilah Ananda Putri Nafiul Agristya Nur Azizah Janad Rais