Article Per Year (5 Year)
p-Index From 2020 - 2025
1.564
P-Index
This Author published in this journals
All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Journal of System and Computer Engineering Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal Automation Computer Information System (JACIS) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Prabowo, Tito
Unknown Affiliation
Religion Aerospace Engineering Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
Penerapan Teknologi Augmented Pada Pembuatan Katalog Perumahan Sebagai Media Pemasaran Dewi, Kartika; Prabowo, Tito; Rusdian Yusron, Rizki Darma; Harliana, Harliana
Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 22 No 2 (2024): Mei 2024
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v22i2.125

Abstract

Secara definisi perumahan merupakan suatu kelompok rumah yang memiliki fasilitas lengkap dari sisi sarana dan prasarananya guna mendukung aktifitasnya. PT Bumi Mas Wahyu (BMW) Grup merupakan salah satu developer perumahan yang ada di Kabupaten Blitar Jawa Timur. Dalam mempromosikan perumahannya, saat ini PT BMW Grup masih menggunakan desain rumah secara 2D yang dicetak dalam bentuk flayer sehingga terkadang menyulitkan pengembang dalam menjelaskan detail bangunan perumahan kepada calon konsumennya. Berdasarkan hal tersebut maka penelitian ini akan menerapkan teknologi Augmented Reality untuk merepresentasikan desain perumahan yang ditawarkan oleh PT BMW secara 3D. Guna mendapatkan output yang sesuai dengan kebutuhan, maka Aplikasi AR ini telah diuji dengan menggunakan pendekatan Blackbox Testing pada 5 menu utamanya. Dan hasilnya menunjukkan bahwa semua output dan alur logic pada aplikasi telah berhasil secara penuh dan dapat berjalan dengan semestinya.
Optimasi Akurasi Model Prediksi Magnitudo Gempa Bumi dengan Integrasi Clustering DBSCAN pada Ensemble Learning (Random Forest & XGBoost) Syaifuddin, Akhmad; Prabowo, Tito
TIN: Terapan Informatika Nusantara Vol 5 No 7 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i7.6522

Abstract

Earthquake prediction is crucial for risk mitigation, particularly in taking appropriate preventive measures in the face of disasters. The magnitude of an earthquake is influenced by various factors, including location, depth, and the history of seismic activity in a region. This study aims to develop an accurate earthquake magnitude prediction model by integrating clustering and ensemble learning techniques. Earthquake catalog data from BMKG Indonesia is processed and clustered using the DBSCAN algorithm based on geographical location. The prediction model is constructed using Random Forest and XGBoost, then integrated through stacking ensemble learning techniques. Evaluation results indicate that the stacking model delivers the best performance, with the lowest Mean Squared Error (MSE) of 0.108 and the highest R-squared (R²) of 0.892, compared to individual models. This accuracy improvement is attributed to stacking’s ability to combine the predictive strengths of Random Forest and XGBoost. The study demonstrates that integrating clustering and ensemble learning can enhance earthquake magnitude prediction models. However, further research is needed to explore more comprehensive data and features and to test model generalization in other regions.
Comparison of Lexical and Semantic Approaches for Relevance Measurement in Quranic Verse Translation Retrieval Fauzan, Abd. Charis; Rouf, M. Abd.; Prabowo, Tito; Baqi, Utrodus Said Al
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 1 (2025): Article Research January 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i1.5194

Abstract

This research explores the effectiveness of lexical and semantic approaches for relevance measurement in Quranic verse translation retrieval, focusing on Indonesian translations. Quranic verses encompass complex linguistic structures and diverse contexts, making precise retrieval challenging. Two retrieval methods were evaluated: lexical similarity, which focuses on exact word matches, and semantic similarity, which captures contextual meaning using word embeddings. The study utilized a dataset of Indonesian Quranic translations, preprocessed to normalize and tokenize text, with experimental queries derived from thematic exegesis on social responsibility. Evaluation was performed using precision, recall, and F1-score on top-5, top-10, and top-15 retrieved results. The lexical approach achieved perfect precision (100%) but exhibited lower recall (46%-58%), as it failed to retrieve relevant verses lacking exact matches. Conversely, the semantic approach demonstrated higher recall (56%-59%) and F1-scores (73%-74%) by identifying verses with contextual relevance, even in the absence of lexical similarity. The results reveal that while the lexical approach ensures precise matches, it overlooks semantic richness. The semantic approach, although computationally intensive, achieves greater contextual understanding. These findings highlight the potential for hybrid retrieval systems combining both approaches to enhance accuracy and relevance in Quranic information retrieval, supporting scholarly research and user engagement with Quranic content.
Analisis Deret Waktu untuk Forecasting Populasi Ternak di Indonesia dengan Model LSTM Prabowo, Tito; Lestariningsih; Fauzan, Abd. Charis; Mafula, Veradella Yuelisa
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7566

Abstract

Livestock population in Indonesia is one of the key indicators supporting national food security, particularly in meeting the demand for animal-based protein. However, the suboptimal utilization of livestock population data for strategic planning remains a challenge in the livestock sector. This study aims to predict livestock population in Indonesia using the Long Short-Term Memory (LSTM) method, a variant of Recurrent Neural Network (RNN) designed for time series data analysis. The livestock population data used in this research was obtained from the Central Statistics Agency (BPS) for the period of 2006 to 2022. The LSTM model was trained using 80% of the data for training and 20% for testing, with evaluation conducted using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The results indicate that the LSTM model can forecast the national livestock population up to 2033 with good accuracy, particularly for livestock such as goats (MAPE 5.47%) and beef cattle (MAPE 5.64%). However, a higher error rate was observed for buffalo (MAPE 16.57%). The predictions indicate a significant growth trend in poultry populations, such as broiler chickens and laying hens. In conclusion, this model can support data-driven decision-making to ensure stable and sustainable animal protein availability, thereby strengthening national food security.
Hill Cipher-Based Visual Cryptography for Copyright Protection of Images Using Flexible Matrix Keys Mafula, Veradella Yuelisa; Fauzan, Abd. Charis; Prabowo, Tito; Ramadhan, Muhammad Rizky
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1634

Abstract

The widespread distribution of digital images on the internet has diminished the copyright protection associated with them. In some cases, copyrighted and economically valuable digital images should not be modified or distributed without permission, as altering the original image can harm its owner. This violation is common, but many internet users are unaware of it. The goal of this research is to protect intellectual property rights of digital images using visual cryptography based on the Hill Cipher algorithm with matrix key flexibility. Hill Cipher is chosen for its ability to encrypt data in blocks, making it more secure than classical cryptographic algorithms that encrypt data individually. Visual cryptography is used to secure digital images through encryption and decryption. Encryption scrambles the image, while decryption restores it. The research method involves collecting digital image datasets, preprocessing, Hill Cipher encryption, and decryption. Key flexibility includes matrix keys of 2x2, 3x3, and 4x4 to enhance security. This research has demonstrated the effectiveness of the Hill Cipher algorithm in protecting digital images through encryption and decryption processes with flexible matrix keys of size 2x2 and 3x3. The results of the experiments, including encryption and decryption using both matrix sizes, have been thoroughly analyzed with respect to various cryptographic metrics: histogram analysis, energy, entropy, and running time.
Penerapan Fuzzy – Service Quality Terhadap Tingkat Kepuasan Pelayanan BPJS Ketenagakerjaan Prabowo, Tito; Machfud, Imam; Lestari, Dewi
Journal Automation Computer Information System Vol. 2 No. 1 (2022): Mei
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v2i1.38

Abstract

Peserta BPJS Ketenagakerjaan pastinya menginginkan kualitas pelayanan yang maksimal. Oleh sebab itu, perlu suatu pengukuran tingkat kepuasan terhadap kualitas pelayanan yang diberikan oleh pihak BPJS Ketenagakerjaan. Metode pengolahan data menggunakan Fuzzy – Service Quality dengan menghitung nilai fuzzikasi dan defuzzikasi. Hasil akhir yang diperoleh nantinya akan membantu pihak BPJS Ketenagakerjaan dalam meningkatkan kualitas pelayanan. Dari hasil pengolahan data dapat terlihat bahwa gap terbesar dan perlu adanya evaluasi yaitu gap dengan nilai terbesar yaitu dimensi Assurance dengan kode D1, D2, D3, D4 dan Reability dengan kode B1, B3, B4, B6. Berdasarkan hasil perhitungan Gap secara keseluruhan menunjukkan bahwa nilai Gap positif yaitu layanan yang diterima peserta BPJS Ketenegakerjaan sesuai dengan harapan peserta
Pemanfaatan Generatif AI untuk Promosi Visual Usaha Kuliner Rumahan "Oma Rusmini" Harliana, Harliana; Alam, Yuniar; Yusron, RDR; Prabowo, Tito
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 4 (2025): Juli 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/3wtbcj65

Abstract

Penelitian ini bertujuan memberikan pelatihan kepada pelaku usaha “Oma Rusmini” dalam memanfaatkan teknologi Generatif AI untuk merancang konten promosi, deskripsi produk, serta skrip video agar lebih menarik dan disesuaikan dengan karakteristik target pasar dan membantu mengevaluasi dampak akan penggunaan Generatif AI terhadap peningkatan efektivitas pemasaran produk pada media sosial dan platform online yang dimiliki pelaku usaha “Oma Rusmini”. Upaya yang dilakukan peneliti memiliki dampak peningkatan signifikan dalam prosentase kata unik, dengan rata-rata mencapai 60% pada setiap caption. Hal ini menunjukkan kosakata yang digunakan lebih bervariatif sehingga skor pembuka memperoleh nilai 3 karena langsung menarik perhatian dengan kata khas yaitu “oma” dan “pedasnya nendang”.
Penerapan Metode Time Series Model ARIMA dalam Peramalan Jumlah Pengunjung Perpustakaan di Lembaga Pendidikan Dasar Nuha, M. Ulin; Huda, Muhmat Maariful; Prabowo, Tito
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7843

Abstract

This research is conducted to predict the number of visitors to libraries within primary education institutions by employing the Autoregressive Integrated Moving Average (ARIMA) modeling technique. The dataset comprises daily visitor records spanning from January 2023 to December 2024. The forecasting process adopts a time series framework, which includes steps such as data preprocessing, stationarity verification through the Augmented Dickey-Fuller (ADF) test, identification of parameters using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, and the selection of the optimal model based on statistical significance and performance metrics, particularly the Mean Squared Error (MSE). Out of 35 evaluated ARIMA configurations, the ARIMA(2,0,11) model demonstrated the best performance, achieving the lowest MSE score of 789.08 and exhibiting statistically meaningful parameters. Moreover, the model passed the Ljung-Box diagnostic test, confirming that the residuals behave as white noise.The forecasting results for January 2025 show a stable and realistic trend. Compared to baseline methods such as Naïve Forecast, the ARIMA model demonstrates superior performance by effectively capturing data fluctuations. Therefore, ARIMA(2,0,11) is considered effective and accurate in supporting data-driven library service planning for the future.
Perancangan Sistem Informasi Sanggar Seni Kirana Budaya Berbasis Website Menggunakan Metode Agile Development Maharani, Ayu Pramesti; Prabowo, Tito; Putra, Fatra Nonggala
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): Juli 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7874

Abstract

The Kirana Budaya Art Studio is an institution engaged in the preservation of arts and culture through various activities such as dance training and costume rental. However, the manual administration management causes various challenges, such as time efficiency, data accuracy, and limitations in conveying information to the wider community. To overcome these problems, a website-based information system was developed that was designed to support the studio's operational needs. This system was built using the Agile Development method, which allows the development process to be carried out iteratively by involving input from end users on an ongoing basis. This approach ensures that the resulting system can adapt to the dynamic needs of the studio. The main features developed include class registration, schedule management and activity classes, and costume rental. The purpose of this research is to produce a website-based information system using the Agile Development method that can disseminate information as well as become a medium for renting art products and services to the wider community in an easily accessible manner. The results of the implementation of this system show increased efficiency in data management, more effective delivery of information to users or customers, and optimization of promotions through digital media. With this website-based information system, the Kirana Budaya Art Studio can improve the quality of services and support efforts to preserve arts and culture in a more professional and modern manner.
PREDIKSI HARGA BITCOIN MENGGUNAKAN ALGORITMA LONG SHORT-TERM MEMORY Munir, M.Sirojul; Huda, Muhamat Maariful; Prabowo, Tito
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.7937

Abstract

Cryptocurrency, atau mata uang kripto, merupakan bentuk aset digital yang memanfaatkan teknologi kriptografi untuk mengamankan transaksi, mengontrol penciptaan unit-unit baru, serta memverifikasi transfer aset yang ada. Mata uang kripto yang pertama kali diperkenalkan adalah Bitcoin. Salah satu keunikan dari cryptocurrency, termasuk Bitcoin, adalah sifatnya yang terdesentralisasi, artinya tidak diatur oleh lembaga pusat seperti bank atau pemerintah, melainkan menggunakan teknologi blockchain. Perubahan harga Bitcoin dapat terjadi secara cepat dan drastis dalam waktu singkat, sehingga sulit diprediksi secara akurat. Untuk menangani permasalahan prediksi harga pada aset yang sangat volatil seperti Bitcoin, digunakan berbagai pendekatan algoritma pemodelan data, salah satunya adalah algoritma Long Short-Term Memory (LSTM). Berdasarkan hasil penelitian dapat disimpulkan bahwa time step harian (1 hari) menghasilkan nilai RMSE terkecil, yaitu 1823.24 atau 2.86% dari rata-rata nilai aktual.
Co-Authors Abd. Charis Fauzan Baqi, Utrodus Said Al Dewi Lestari FATRA NONGGALA PUTRA Harliana Harliana Harliana Harliana Huda, Muhamat Maariful Huda, Muhmat Maariful Kartika Dewi Lestariningsih Machfud, Imam Mafula, Veradella Yuelisa Maharani, Ayu Pramesti Muhammad Rizky Ramadhan Munir, M.Sirojul Nuha, M. Ulin Rouf, M. Abd. Rusdian Yusron, Rizki Darma Syaifuddin, Akhmad Yuniar Alam, Yuniar Yusron, RDR