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All Journal Jurnal sistem informasi, Teknologi informasi dan komputer Infotech Journal Paradigma JITK (Jurnal Ilmu Pengetahuan dan Komputer) JATI (Jurnal Mahasiswa Teknik Informatika) Sains, Aplikasi, Komputasi dan Teknologi Informasi Jurnal Teknologi Informatika dan Komputer Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal AbdiMas Nusa Mandiri Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Dirgamaya: Jurnal Manajemen dan Sistem Informasi Jurnal Teknologi Sistem Informasi Jurnal Locus Penelitian dan Pengabdian Jurnal Ilmu Komputer Digital Transformation Technology (Digitech) Jurnal Pengabdian Kreatif Cemerlang Indonesia SATIN - Sains dan Teknologi Informasi AMMA : Jurnal Pengabdian Masyarakat Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Informatika Polinema (JIP) Journal of Innovative and Creativity Adpebi International Journal of Multidisciplinary Sciences Journal of Intelligent Systems and Information Technology Fusion : Journal of Research in Engineering, Technology and Applied Sciences Akademika
Albert Riyandi
Universitas Bina Sarana Informatika
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Social Sciences Other

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PERANCANGAN SISTEM INFORMASI INVENTORY BERBASIS WEB DENGAN METODE PROTOTYPE (STUDI KASUS KALAM STORE) Hermansyah, Hermansyah; Riyandi, Albert
Jurnal Ilmu Komputer (JUIK) Vol 5, No 1 (2025): February 2025
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/juik.v5i1.3994

Abstract

This study aims to design and implement a web-based inventory information system to enhance efficiency in stock management at Kalam Store. The prototyping method was employed as the system development approach due to its capability to iteratively adapt to user needs. System testing was conducted using the User Acceptance Testing (UAT) method to evaluate system performance based on ease of use, recording accuracy, and effectiveness in supporting operations. The test results indicate that the developed system improves stock recording accuracy, reduces the risk of data loss, and accelerates the inventory reporting process. Therefore, the implementation of this web-based inventory information system can serve as an effective solution for enhancing efficiency and accuracy in stock management at Kalam Store.
SENTIMEN ANALISIS PENGGUNA TWITTER TERHADAP SEA GAMES 2023 DENGAN METODE NAIVE BAYES Gumilang, Wahyu; Riyandi, Albert
JURNAL AKADEMIKA Vol 16 No 1 (2023): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v16i1.1125

Abstract

The development of computers and telecommunications has made people often express their opinions through social media Twitter. As recently, after the 2023 SEA Games were held in Cambodia, various kinds of tweets appeared from various sports. From this opinion, it can be seen that the country's success in holding the Sea Games by conducting a sentiment analysis. So this research was conducted to find out how the public opinion regarding the holding of the 2023 SEA Games using the Naive Bayes Classifier algorithm. The data used was 1888 data obtained by crawling Twitter data using the keywords SEA Games and RapidMiner tools. The division of training data and testing data is carried out into two comparisons, namely 50:50 and 60:40. The best accuracy results are obtained with a data comparison of 50:50, which is 70.15%, while a data comparison of 60:40 has a smaller result, namely 70.07%.
Analisis Pengalaman Pengguna Terhadap Desain Antarmuka Pengguna Website Ezpz Top Up dengan Pendekatan User Experience Questionnaire Dermawan, Raihan Ali Putra; Anwar, Rian Septian; Riyandi, Albert
Digital Transformation Technology Vol. 5 No. 1 (2025): Periode Maret 2025
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v5i1.6360

Abstract

Website merupakan salah satu layanan digital yang membutuhkan desain antarmuka agar dapat memberikan kenyamanan dan pengalaman pengguna yang optimal. Tujuan dari penelitian ini ialah untuk melakukan analisis pengalaman pengguna terkait desian antarmuka pengguna pada website Ezpz Top Up dengan menggunakan pendekatan User Experience Questionnaire. Data yang terdapat dalam penelitian ini merupakan hasil dari metode kuantitatif dari pre-test dan post-test yang meliputi 6 (Enam) dimensi utamanya yaitu Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, serta Novelty. Jumlah responden yang terdapat pada penelitian ini sebanyak 294 orang yang didapat dengan menggunakan rumus Slovin, responden ini akan diberikan 2 kuesioner yaitu pre-test dan post-test. Hasil dari pre-test memperlihatkan bahwa seluruh nilai pada setiap dimensi berada di kategori positif namun rendah (<0,8) yang dapat diambil kesimpulan bahwa perlu adanya perbaikan dalam aspek desain antarmuka. Kemudian setelah dilakukannya perbaikan dengan menggunakan tools Figma, penulis mendapatkan hasil dari post-test yang dapat membuktikan bahwa terdapat sedikit kenaikan nilai di setiap dimensi utamanya, meskipun nilainya masih berada pada kategori yang sama yaitu positif rendah. Peningkatan ini dapat menunjukkan bahwa dengan dilakukannya perbaikan desain antarmuka pengguna dapat menaikan kualitas dari kenyamanan dan pengalaman pengguna. Oleh karena itu, penelitian ini dapat memberikan sebuah usulan perbaikan kepada pengembang agar kualitas pengalaman pengguna terhadap desain antarmuka menjadi lebih baik dan optimal, penelitian ini juga dapat dijadikan sebagai landasan dalam melakukan pengukuran atau analisis pengalaman pengguna terhadap suatu layanan digital
Penerapan Metode Moora dalam Sistem Pendukung Keputusan Pemilihan Aggregator Food Delivery Service Adiwiharja, Atep; Riyandi, Albert
Jurnal Locus Penelitian dan Pengabdian Vol. 1 No. 11 (2022): Jurnal Locus Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v1i8.229

Abstract

Pandemi COVID-19, mendorong perubahan prilaku konsumen untuk konsumsi makanan, untuk bisa bertahan di era new normal ini pelaku industri, harus beradaptasi dengan trend. Penggunaan Aggregator delivery service merupakan salah salah satu cara buat pelaku industry makanan untuk bertahan dan mengembangkan bisnisnya. Dalam pemilihan aggregator, selain profit margin,ada banyak kriteria yang harus di perhatikan diantaranya overall satisfaction, Application convenience, delivery time dan offer. Dalam penelitian yang diusulkan ini, data yang di gunakan di ambil dari Total Customer satisfaction di Food central chowking orient, dan di olah dengn menggunakan metode MOORA untuk pemilihan Aggregator. Hasil penerapan metode MOORA dengan perhitungan bobot pada setiap kriteria, hasil akhir menunjukkan peringkat alternatif yang bisa di jadikan acuan dalam pemilihan aggregator.
Prediksi Harga Penutupan Indeks Harga Gabungan (IHSG) Menggunakan Algoritma Long Short-Term Memory Berbasis Data Historis Muhammad Farhan Fadhila; Rian Septian Anwar; Albert Riyandi
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.4318

Abstract

The dynamic fluctuation of stock prices makes market movement prediction a significant challenge in financial analysis. This study aims to develop and evaluate a Long Short-Term Memory (LSTM) model to predict the daily closing price of the Indonesia Composite Index (IDX Composite) using historical data from 2005 to 2025. The methodology employed includes systematic data pre-processing, such as normalization and the creation of sequential input, as well as the implementation of a two-layer LSTM architecture. Model performance was evaluated through two approaches, namely experimental testing on test datasets and practical validation in short-term prediction scenarios. Experimental results demonstrate very high accuracy, with a Mean Absolute Percentage Error (MAPE) value of 0.69% on the test data. This performance consistency is reinforced by the practical validation, which yielded an overall MAPE of 0.34%, proving the model's capability in predicting previously unseen data. Thus, the hypothesis that the LSTM model can achieve significant prediction accuracy (MAPE<10%) is accepted. Overall, the developed LSTM model is proven to be highly effective and valid, making it a suitable tool to support investment decisions in the stock market.
Pemanfaatan AI Menggunakan ChatGPT Untuk Meningkatkan Kinerja Anggota Fatayat NU PC Tangerang Frisma Handayanna; Maryanah Safitri; Tri Santoso; Albert Riyandi
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 9 : Oktober (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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

Abstract

AI developers are currently working to ensure that the technology is used responsibly and benefits all parties. Collaboration between different fields and stakeholders is essential to create a just and sustainable AI future. The transformation of AI from a theoretical concept to a practical technology that impacts everyday life is an amazing journey. Like Fatayat NU. This organization faces several problems in implementing their activities. The problems that organizations often face can be very diverse, and usually involve various operational, strategic, and human aspects. Here are some of the problems faced: 1) Ineffective communication where information does not reach team members clearly, causing confusion or errors. 2) Lack of coordination between teams where different teams do not collaborate well, causing delays and low efficiency. 3) Difficulty in decision making where decisions are slow or less accurate due to lack of information or analysis. 4) Challenges in recruitment where the recruitment process is long and complicated, or difficulty finding the right candidate. This community service activity aims to improve the ability of female members of Fatayat NU PC Kota Tangerang in using the ChatGPT application and develop their knowledge and skills. The same goal is also to obtain publication in editorial media of journals that are not yet accredited or that have been accredited.
Detection of Rupiah Nominal Values Based on Computer Vision and OCR for Low Vision Accessibility Doucoure Mohammed Hakeem; Trisna Almuti; Syahbil Afriza Baharaji; Muhammad Iqbal; Albert Riyandi
Fusion : Journal of Research in Engineering, Technology and Applied Sciences Vol. 3 No. 1 (2026): Fusion - April
Publisher : PT. Faaslib Serambi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66341/fusion.v3i1.338

Abstract

The ability to recognize banknotes' nominal value is a fundamental skill in daily economic transactions. However, for low-vision individuals, this simple task poses a major challenge, risking transaction errors and fraud. This study aims to build a web-based application capable of detecting Rupiah currency nominals in real-time by integrating computer vision and Optical Character Recognition (OCR) as an independent accessibility feature. The method combines a custom object detection model based on the YOLO architecture via the Roboflow platform and Tesseract OCR for nominal text verification, which is then integrated with the Web Speech API for voice-based output (Text-to-Speech). The system test results indicate that the combined "Roboflow + OCR" approach significantly improves detection reliability compared to using the object model alone. The system achieved a classification accuracy rate of 94.5% under optimal lighting conditions, with an average Text-to-Speech response latency of 1.8 seconds. This implementation proves that the synergy of image processing and OCR can provide an effective and inclusive assistive technology solution for visually impaired groups in Indonesia.
Comparative Analysis of Machine Learning Algorithms in Detecting DDoS Attacks on CICIDS2017 Dataset Dika Kurnia Putra; Chandra Ari Pradana; Muhammad Hilal Gilardin; Albert Riyandi
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.182

Abstract

The rapid escalation of Distributed Denial of Service (DDoS) attacks has posed significant threats to global cybersecurity. This research presents a comparative analysis of three supervised machine learning algorithms—K-Nearest Neighbor (KNN), Decision Tree (DT), and Random Forest (RF)—in detecting DDoS attacks using the CICIDS2017 dataset. While many studies focus on broader intrusion detection, this study concentrates specifically on binary classification between benign and DDoS traffic. The CICIDS2017 dataset was chosen for its comprehensive and realistic representation of modern network traffic. The methodology involved preprocessing, training, and evaluating the models in Orange Data Mining using 10-fold cross-validation. Evaluation metrics included Accuracy, Precision, Recall, F1-Score, AUC, and Matthews Correlation Coefficient (MCC). Empirical results show that the Random Forest algorithm outperformed both KNN and Decision Tree, achieving perfect scores across all metrics (1.000). These findings highlight the robustness of ensemble learning in intrusion detection. The results have practical implications for the development of more reliable, efficient, and automated Intrusion Detection Systems (IDS), especially in real-world scenarios prone to volumetric DDoS attacks. Future work should explore multiclass classification and real-time implementation.
Analisis Komparatif Model ARIMA, LSTM, dan GRU dalam Forecasting Harga Bitcoin Berbasis Streamlit Nurardian, Ridwana Septian; Riyandi, Albert
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.12126

Abstract

Bitcoin is a cryptocurrency asset characterized by high volatility and non-linear movement patterns that trigger sudden market risks. Considering that the majority of previous studies have solely focused on mathematical metric evaluations without providing practical solutions, this study aims to compare the performance of the conventional statistical model Autoregressive Integrated Moving Average (ARIMA) with Deep Learning architectures, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), and to implement the best-performing model into an interactive dashboard using Streamlit. The dataset used encompasses daily Bitcoin closing prices for the 2020–2026 period. The methodology includes data preprocessing, MinMaxScaler normalization, sequential data generation using a 30-day sliding window technique, chronological data splitting (80:20), and evaluation using MAE, RMSE, MAPE, Accuracy, and R² Score. Experimental results prove that ARIMA fails to adapt to non-stationary data, whereas GRU outperforms LSTM in terms of architectural efficiency and precision. The GRU model achieved the best performance with an MAE of IDR 34,773,098.88, an RMSE of IDR 46,684,832.94, a MAPE of 2.26%, an Accuracy of 97.74%, and an R² Score of 0.9661. The GRU model was then successfully implemented into a Streamlit web dashboard that facilitates the real-time visualization of historical and predicted prices. In conclusion, the GRU architecture is the most effective and efficient approach for Bitcoin price forecasting, successfully bridging the gap between theoretical analysis and the availability of practical applications.
Co-Authors Adiwiharja, Atep Adjat Sudradjat Agus Wiyatno Alfiandi, Sendi Anggraeni, Arfina Ardiansyah, Ivan Nur Aripin Bagas Tri Hadiansyah Bambang Wijonarko Chaerul Bachri Chandra Ari Pradana Dany, Rahmad Dermawan, Raihan Ali Putra Desy Amalia Dika Kurnia Putra Dony Oscar Doucoure Mohammed Hakeem fahleyi, muhammad fahreza Frisma Handayanna Gumilang, Wahyu Haidir Ramadhani Hermansyah Hermansyah Imron Imron Ishak Komarudin Maryanah Safitri Meizi Fachrizal Achmad Monalisa Monalisa Muhammad Farhan Fadhila Muhammad Hilal Gilardin Muhammad Iqbal Muhammad Rinaldi Nia Wahyuningsih Norman Ardian Numan Musyaffa Nurajijah Nurardian, Ridwana Septian Rachman Komarudin Rendy Alfianto Retno Wati Rian Septian Anwar Rizki Aulianita RR. Ella Evrita Hestiandari Santi Rahmawati Santi Sopian, Abu Sudibyo, Aji Syahbil Afriza Baharaji Tri Santoso trimarni gulo Trisna Almuti Tsamarah Tsabitah Tyas Setiyorini Vito Triantori Wahyudi Wahyudi