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All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH) Internet of Things and Artificial Intelligence Journal AnoaTIK: Jurnal Teknologi Informasi dan Komputer ANOA: Jurnal Pengabdian Masyarakat Fakultas Teknik Ilmu Manajemen Sosial Humaniora (JIMSH) Intechno Journal : Information Technology Journal
Rauf, Budi Wijaya
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Religion Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Kombinasi Steganografi Bit Matching dan Kriptografi Playfair Cipher, Hill Cipher dan Blowfish Budi Wijaya Rauf
(JurTI) Jurnal Teknologi Informasi Vol 4, No 2 (2020): DESEMBER 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i2.1346

Abstract

Abstract - To secure data, cryptographic techniques are needed, but many cryptographic methods are vulnerable that require attacks. But not with the Blowfish method which includes a symmetric key algorithm that has the same key to encrypt and decrypt data. The blowfish algorithm is a block cipher and until now. Blowfish is still superior in the field of strength endurance will receive attacks from outside by people who are not responsible. In this study, a test will be carried out to see the time used to make a suitable steganographic combination and to combine several cryptographic methods such as Playfair, hill cipher, and blowfish. The average embedding average 28.275 seconds embedding extraction 27.843 seconds and for imaging the average embedding 13.0208 seconds and extraction 12.7986 seconds.Keywords  -   Steganography, Playfair, Hill Cipher, Blowfish. Bit Matching Abstrak - Untuk melakukan pengamanan data dibutuhkan tekhnik kriptografi akan tetapi sudah banyak metode-metode kriptografi yang rentan terkena serangan. Namun tidak dengan metode Blowfish yang termasuk algoritma kunci simetris yang memiliki kunci yang sama untuk mengenkripsi dan mendekripsi suatu data. Algoritma Blowfish merupakan cipher blok dan sampai saat ini algoritma Blowfish masih unggul dibidang ketahanannya yang kuat akan menerima serangan dari luar oleh orang-orang yang tidak bertanggung jawab. Pada penelitian kali ini akan dilakukan pengujian untuk melihat waktu yang dipakai untuk melakukan kombinasi steganografi bit matching serta mengkombinasikan beberapa metode kriptografi seperti playfair, hill cipher dan blowfish. Hasilnya ialah untuk citra hitam putih diperoleh waktu rata-rata embedding 28.275 detik ekstraksi 27.843 detik dan untuk citra berwarna waktu rata-rata embedding 13.0208 detik dan ekstraksi 12,7986 detik.Kata Kunci - Steganografi, Playfair, Hill Cipher, Blowfish, Bit Matching.
Deteksi Clickbait dengan Sentence Scoring Based On Frequency di Detik.Com Budi Wijaya Rauf; Suwanto Raharjo; Heri Sismoro
(JurTI) Jurnal Teknologi Informasi Vol 4, No 2 (2020): DESEMBER 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i2.1381

Abstract

Abstract - The clickbait phenomenon has become one of the powerful ways to increase the number of readers for a website. With the increasing number of visitors to the site, the higher the income on the website. However, this clickbait technique is like a double-edged knife. Many people who do not like this technique because of incompatibility of the title and content of the article being read. This study aims to detect clickbait articles on the Indonesian news site detik.com by using python and sentence scoring based on frequency algorithms to find a match between the title and content of the article. Appropriate titles and contents will be given a value of 1 (one) and those that are not appropriate will be given a value of 0 (zero), the results of the test system are compared with existing datasets and produce an accuracy rate of 75%, 57% precision and 80% recall.Keywords  - Clickbait Detection, Sentence Scoring Based on Frequency, Python. Abstrak – Fenomena clickbait sudah menjadi salah satu cara ampuh untuk meningkatkan jumlah pembaca untuk sebuah website. Dengan meningkatnya jumlah pengunjung pada situs maka semakin tinggi pula pendapatan pada website tersebut. Namun, Teknik clickbait ini seperti pisau bermata dua. Banyak masyarakat yang tidak suka dengan Teknik ini dikarenakan ketidaksesuaian judul dan isi artikel yang dibaca. Penelitian ini bertujuan untuk mendeteksi artikel clickbait pada situs berita Indonesia detik.com dengan menggunakan python dan algoritma sentence scoring based on frequency guna mencari kecocokan antara judul dan isi dari artikel tersebut. Judul dan isi yang sesuai akan diberikan nilai 1 (satu) dan yang tidak sesuai akan diberikan nilai 0 (nol), hasil uji coba system tersebut dibandingkan dengan dataset yang telah ada dan menghasilkan tingkat akurasi sebesar 75%, presisi 57% dan recall 80%.Kata Kunci - Deteksi Clickbait, Sentence Scoring Based on Frequency, Python.
Prediksi Penduduk Miskin di Daerah Tertinggal Indonesia dengan Algoritma Prophet Budi Wijaya Rauf
Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH) Vol. 5 No. 2 (2023): Agustus 2023, Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH)
Publisher : LP3M Universitas Muhammadiyah Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/jimsh.v5i2.1024

Abstract

Kemiskinan menjadi salah satu masalah umum yang dihadapi oleh setiap negara di dunia, termasuk Indonesia. Pemerintah dari berbagai daerah yang ada di Indonesia sedang berjuang untuk memberantas kemiskinan, begitu pun dengan daerah-daerah tertinggal. Tercatat di Badan Pusat Statistik (BPS) bahwa terjadi kenaikan angka kemiskinan dari 18,87% (2015) kini menjadi 24,56% (2022) di daerah-daerah tertinggal yang terdiri dari 62 daerah. Dengan memprediksi jumlah penduduk miskin di daerah tertinggal, diharapkan pemerintah bisa mengambil langkah untuk memberantas kemiskinan. Penelitian ini menggunakan algoritma Prophet yang selanjutnya dibangun ke dalam pemodelan machine learning dalam memprediksikan penduduk miskin di daerah tertinggal. Data yang digunakan adalah data dari tahun 2015-2022, tujuan dari penelitian ini adalah untuk melihat tingkat akurasi dan error dengan menggunakan parameter Mean Squared Error (MSE) dan Mean Absolute Percentage Error (MAPE). Hasil dari prediksi menunjukkan kenaikan masyarakat miskin menjadi 35% pada tahun 2024 dan diprediksikan akan terus naik hingga 35% pada tahun 2027 dengan parameter MSE sebesar 2% dan MAPE sebesar 1%.
Pengenalan dan Instalasi Kali Linux untuk Langkah Awal Pengetahuan Tentang Keamanan Sistem Informasi Muhammad Irwan Syahib; Muhammad Akbar Yasin; Budi Wijaya Rauf
ANOA: JURNAL PENGABDIAN MASYARAKAT FAKULTAS TEKNIK Vol 1 No 02 (2023): Edisi Juni Tahun 2023 ANOA: Jurnal Pengabdian Masyarakat Fakultas Teknik
Publisher : FAKULTAS TEKNIK UMKENDARI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/anoa.v1i02.275

Abstract

Di era digital saat ini banyak sekali kejahatan cyber yang dilakukan oleh orang-orang yang tidak bertanggung jawab untuk memperoleh keuntungan dengan cara meretas sebuah sistem pemerintahan ataupun bisnis. Seperti yang dikutip oleh Tempo.co pada tanggal 19 mei 2023, dengan berita yang berjudul ”BSI Kena Serangan Ransomware, Nasabah Mengaku Rugi Ratusan Juta” yang menuliskan bahwa serangan tersebuat dikarenakan kelemahan sistem dan juga brainwere yang masih sangat rendah dalam hal sadar kejahatan cyber. Pelatihan tentang pengenalan dan instalasi Kali Linux sangat relevan dengan peran mahasiswa dan kondisi kejahatan cyber di Indonesia. Sebagai generasi muda, mahasiswa memiliki potensi besar untuk menjadi agen perubahan dalam memperkuat keamanan sistem informasi. Dengan pelatihan ini, mahasiswa dapat meningkatkan keterampilan dan pengetahuan mereka dalam menghadapi tantangan keamanan cyber yang semakin kompleks. Mereka dapat berkontribusi dalam meningkatkan keamanan sistem informasi, melakukan penetration testing, menyebarkan kesadaran tentang ancaman cyber, maupun menjadi tenaga ahli keamanan yang dibutuhkan di Indonesia. Pelatihan ini mempersiapkan mahasiswa untuk menghadapi tantangan tersebut dengan pengetahuan dan keterampilan yang relevan, serta membantu menciptakan budaya keamanan cyber yang kuat di kalangan mahasiswa dan masyarakat umum.
Prediksi Penduduk Miskin di Daerah Tertinggal Indonesia dengan Algoritma Prophet Budi Wijaya Rauf
Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH) Vol. 5 No. 2 (2023): August, Jurnal Ilmu Manajemen Sosial Humaniora (JIMSH)
Publisher : LP3M, Universitas Muhammadiyah Kendari

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

Abstract

Poverty is one of the common problems faced by every country in the world, including Indonesia. Governments from various regions in Indonesia are striving to eradicate poverty, particularly in underdeveloped areas. According to the Central Bureau of Statistics (BPS), the poverty rate has increased from 18.87% in 2015 to 24.56% in 2022 in these underdeveloped areas, which consist of 62 regions. By predicting the number of poor people in these regions, the government hopes to take steps to combat poverty. This study employs the Prophet algorithm, which is then integrated into a machine learning model to predict the number of poor people in underdeveloped areas. The data used covers the years 2015 to 2022, and the aim of this research is to assess the accuracy and error rate using the Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) parameters. The prediction results indicate an increase in the number of impoverished individuals to 35% in the year 2024, and it is projected to continue rising to 35% in 2027, with an MSE parameter of 2% and an MAPE parameter of 1%.
Pengenalan Visualisasi Programming untuk Membuka Pintu Kecerdasan Teknologi Anak SD Pesisir Sitti Najmia Rifai; Budi Wijaya Rauf; Muhammad Akbar Yasin; Muhammad Irwan Syahib; Ilcham Ilcham
ANOA: JURNAL PENGABDIAN MASYARAKAT FAKULTAS TEKNIK Vol 2 No 01 (2023): Edisi Desember Tahun 2023 ANOA: Jurnal Pengabdian Masyarakat Fakultas Teknik
Publisher : FAKULTAS TEKNIK UMKENDARI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/anoa.v2i01.370

Abstract

Increasing technological intelligence in elementary school children is a necessity in this digital era. Therefore, this research explores the application of programming visualization using the Scratch application as an innovative method in supporting programming learning for elementary school children. Through a visual programming block approach, Scratch provides a fun and intuitive learning experience, helping children to develop an understanding of basic programming concepts. This study involved a group of elementary school children as participants, and tracked the development of their understanding of programming logic and creativity through interactive activities in Scratch. The research results show that the application of Scratch significantly increases children's grasp of programming concepts, while stimulating their creativity.
Performance Comparison Analysis on Weather Prediction using LSTM and TKAN Wardhana, Ajie Kusuma; Riwanto, Yudha; Rauf, Budi Wijaya
Internet of Things and Artificial Intelligence Journal Vol. 4 No. 3 (2024): Volume 4 Issue 3, 2024 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v4i3.808

Abstract

The development of machine learning methods in the last few decades has shown great potential in various predictive applications, including in domains such as financial prediction, medical diagnosis, and big data analysis. One of the most widely used methods in prediction tasks is Long Short-Term Memory (LSTM). LSTM has become popular because of its ability to handle time series data by retaining relevant information in the long term and the ability to forget irrelevant information through the forget-gate mechanism. However, along with the development of technology and the need to improve accuracy and efficiency, new methods such as the Kolmogorov Arnold Network (KAN)  have emerged. KAN was then developed into the Temporal Kolmogorov Arnold Network (TKAN), which was designed to match or even surpass the performance of LSTM. The TKAN architecture has produced significant improvements in the management of both new and historical information. Because of this capability, TKAN can excel in multi-step predictions, demonstrating a clear advantage over conventional models such as LSTM and GRU, particularly in the context of long-term forecasting. This research goal is to give insight into the comparison of both the TKAN and LSTM models for weather prediction using model loss and mean absolute error evaluation (MAE). The model for both LSTM and TKAN achieved 0.09 and 0.11 for model loss and 0.08 and 0.96 for MAE.
Forecasting A Major Banking Corporation Stock Prices Using LSTM Neural Networks Rauf, Budi Wijaya
Intechno Journal : Information Technology Journal Vol. 6 No. 2 (2024): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2024v6i2.1888

Abstract

The increasing complexity of stock market predictions necessitates advanced computational techniques to address the unique challenges posed by financial data's non-linear and volatile nature. This study aims to leverage Long Short-Term Memory (LSTM) neural networks to accurately forecast stock prices, using historical data collected from a major banking corporation as a primary source. The LSTM model excels at processing sequential time-series data, allowing it to predict monthly stock closing prices over a one-year horizon with a high degree of precision. Our findings indicate a Root Mean Squared Error (RMSE) of 3.2, underscoring the model's efficiency and reliability in financial forecasting tasks. The novelty of this research lies in the systematic incorporation of preprocessing techniques and fine-tuned hyperparameters to optimize model performance. Furthermore, this study explores the practical implications of implementing LSTM models in real-world trading scenarios, analyzing their adaptability to dynamic market conditions and their potential integration into automated trading systems. These findings contribute to the growing body of knowledge in financial analytics and demonstrate the viability of machine learning-based solutions for accurate and robust market predictions.
ANALISIS SENTIMEN APLIKASI PEMINJAMAN ONLINE BERDASARKAN ULASAN PADA PLAY STORE MENGGUNAKAN METODE NAÏVE BAYES DAN SUPPORT VECTOR MACHINE (STUDI KASUS : ADAKAMI DAN EASYCASH) Abdillah Sam Mongkito, La Ode Muhammad Hafidz; Ransi, Natalis; Surimi, La; Tenriawaru, Andi; Gunawan, Gunawan; Wijaya Rauf, Budi
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 2 No 2 (2024): Desember 2024
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v2i2.71

Abstract

This research aims to analyze the sentiment of online lending applications based on reviews on the Google Play Store using the Naïve Bayes and Support Vector Machine methods and determine which online lending applications are more trustworthy. AdaKami is an online lending application under the auspices of PT Pemfinaan Digital Indonesia. EasyCash is an online lending application which is a financial technology company owned by PT. Indonesia Fintopia Technology which provides a digital financial service portal, especially online lending. However, to determine whether this online lending application is reliable or trustworthy, it requires a collection of information that comes from previous user experience. The Naïve Bayes and Support Vector Machine methods are used to analyze loan application sentiment based on relevant review data which is processed using the Python programming language with Google Colabs as a tool for carrying out the research stage. The research results show that the Naïve Bayes and Support Vector Machine methods can be applied in analyzing the sentiment of online lending applications and based on the results of application analysis using the Naïve Bayes Adakami method, it is more trusted by previous users because it produces 95% positive review data and the Easycash application produces positive review data of 95%. 93% and the results using the Adakami Support Vector Machine method produced positive review data of 91% and the Easycash application produced positive review data of 83%.review data while the Easycash application produces 93% positive review data.
IMPLEMENTASI ALGORITMA LONG SHORT-TERM MEMORY PADA SISTEM KLASIFIKASI MAHASISWA BERPOTENSI DROP OUT Mutiara, Niken; Saidi, La Ode; Wijaya Rauf, Budi
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 3 No 1 (2025): Juni 2025
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v3i1.109

Abstract

This research aims to produce a classification system for students who have the potential to drop out. This classification system is expected to help identify students who have the potential to drop out early on in prevention efforts. This research uses academic data in the form of Semester Grade Point Average (IPS) 1-7, Cumulative Grade Point Average Semester 7 (IPKS7), and Cumulative SKS 7, as well as non-academic data including Study Program and Entry Path as classification parameters. The method used is the Long Short-Term Memory (LSTM) algorithm with system development using the CRISP-DM approach. System testing is done using black box testing method and performance evaluation using confusion matrix. The results showed that the classification system developed achieved an accuracy rate of 93% based on confusion matrix evaluation, and all system functionality runs as expected based on the results of black box testing.
Co-Authors Abdillah Sam Mongkito, La Ode Muhammad Hafidz Andi Tenriawaru Gunawan Gunawan Heri Sismoro Ilcham Ilcham La Ode Saidi La Surimi, La Muhammad Akbar Yasin Muhammad Irwan Syahib Mutiara, Niken Natalis Ransi, Natalis Riwanto, Yudha Sitti Najmia Rifai Suwanto Raharjo Wardhana, Ajie Kusuma