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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE JURIKOM (Jurnal Riset Komputer) Simtek : Jurnal Sistem Informasi dan Teknik Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Kaputama (JIK) Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Bumigora Information Technology (BITe) Infotek : Jurnal Informatika dan Teknologi Infotech: Journal of Technology Information Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat International Journal Software Engineering and Computer Science (IJSECS) Jurnal Teknik Informatika Jurnal Nasional Teknologi Komputer Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Innovative: Journal Of Social Science Research Dedikasi Saintek Jurnal Pengabdian Masyarakat SmartComp Jurnal Ilmu Komputer dan Sistem Informasi CSRID DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer
Irawan, Ridwan Dwi
Universitas Duta Bangsa
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Comparative Analysis of SQL Injection Attack Clasification Using Naïve Bayes Method And Support Vector Machine (SVM). Pramono, Pramono; Dwi Irawan, Ridwan; Arum Sari, Aprilisa
Proceeding of the International Conference Health, Science And Technology (ICOHETECH) 2024: Proceeding of the 5th International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/icohetech.v5i1.4178

Abstract

SQL Injection is an attack that attempts to gain unauthorized access to a database by injecting code and exploiting SQL queries. SQL injection is an attack that is easy to execute but difficult to detect and classify because of the many types. The SQLI vulnerability is the result of incorrect validation of user input, enabling attackers to manipulate programmer queries by adding new SQL operators. Therefore, this study compares the use of the Naïve Bayes algorithm with the Support Vector Machine (SVM). The dataset that will be used in this study comes from a website called Kaggle. This study analyzes the comparison of methods resulting from the classification process based on the value of accuracy of confusion matrix, precision, recall. Naive Bayes, 95.594% accuracy quality while Support Vector Machine (SVM) 96.093% accuracy quality. The highest percentage of accuracy is obtained by the Support Vector Machine (SVM) while the Naïve Bayes accuracy score is slightly lower.
Analisis Prediksi Penjualan Isi Ulang Air Galon menggunakan Metode LSTM dan SARIMA Ulfarida Miftakhul Jannah; Nurmalitasari Nurmalitasari; Ridwan Dwi Irawan
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.802

Abstract

Refillable drinking water depots often face challenges in dealing with unpredictable customer demand on a daily basis. This uncertainty complicates the process of stock management, production planning, and overall operations. Without accurate sales forecasts, depots risk losing potential sales and experiencing a decline in service quality to customers. Therefore, a solution is needed that can accurately predict daily sales. The first step in this research is to collect relevant data. Once the data is available, pre-processing is conducted to prepare the data before entering the modeling process. The Long Short-Term Memory (LSTM) model has the advantage of remembering historical patterns in time series data. Meanwhile, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is an extension of ARIMA that can handle data with seasonal characteristics. In this study, the LSTM model demonstrated better performance than SARIMA. This is evidenced by the performance evaluation values: MAPE of 9.54%, RMSE of 0.17, and MAE of 0.14 for the LSTM model, which are lower than MAPE of 10.51%, RMSE of 0.19, and MAE of 0.16 for SARIMA. These values indicate that LSTM is capable of providing more accurate prediction results. Based on these results, it can be concluded that the LSTM model is more effective and recommended for use in predicting daily sales of refillable water at the Manshurin Water depot
Comparison of BARS and SKP Methods in Evaluating Lecturer Performance in Higher Education Marta Ardiyanto; Ridwan Dwi Irawan; Kresna Agung Yudhianto
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3116

Abstract

The evaluation of lecturer performance in higher education is vital for maintaining the quality of teaching, research, and community service. In Indonesia, civil servant lecturer assessments rely heavily on the Sasaran Kinerja Pegawai (SKP), which emphasizes quantitative indicators such as teaching load, publications, and community engagement. However, this approach often overlooks qualitative aspects, including teaching innovation, professional ethics, and student interaction. To address these gaps, this study proposes integrating the Behaviorally Anchored Rating Scale (BARS) with SKP to create a more comprehensive evaluation framework. The research, conducted at Universitas Duta Bangsa Surakarta in the 2024/2025 academic year, applied the Rapid Application Development (RAD) method to design a web-based prototype. Key stages included instrument design, weighting mechanisms, and reliability testing using Cronbach’s Alpha, which yielded a strong coefficient of 0.850. Evaluation involved six faculty leaders and eight rectorate leaders (bureau heads, vice rectors, and the rector), while the foundation board acted as validators to ensure objectivity. System testing demonstrated promising results: blackbox testing confirmed 100% functional accuracy, and usability testing showed an average satisfaction score of 4.2 out of 5 (84%). Findings indicate that combining SKP and BARS enhances accountability, transparency, and professional feedback. This model contributes practically to higher education human resource management by enabling data-driven decision-making and fostering lecturer development. While further validation across diverse institutions is needed, the integrated framework offers a more holistic and adaptive approach to lecturer performance evaluation.
Pelatihan Media Pembelajaran Interaktif Berbasis AI Menggunakan Gamma.App di SMKN 1 Nglipar Ridwan Dwi Irawan; Nibras Faiq Muhammad
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 6 No. 1 (2026): Januari 2026 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v6i1.979

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan kompetensi guru dalam mengembangkan media pembelajaran interaktif dengan memanfaatkan teknologi Artificial Intelligence (AI) melalui platform Gamma.App. Dalam era digital saat ini, kemampuan pendidik dalam memanfaatkan AI menjadi kunci untuk menciptakan pembelajaran yang menarik, efisien, dan adaptif terhadap kebutuhan siswa. Metode pelaksanaan kegiatan dilakukan melalui pelatihan dan pendampingan intensif kepada guru-guru SMK Negeri 1 Nglipar Gunungkidul. Materi pelatihan meliputi pengenalan konsep AI dalam media pembelajaran, penggunaan AI Presentation Generator, serta praktik langsung pembuatan presentasi interaktif berbasis AI. Hasil kegiatan menunjukkan peningkatan signifikan dalam kemampuan guru dalam mendesain PowerPoint otomatis, efisiensi waktu persiapan materi, serta kreativitas visual presentasi. Peserta juga mampu memanfaatkan fitur AI untuk penyesuaian gaya presentasi, penerjemahan otomatis, dan tata letak konten yang menarik. Kegiatan ini berkontribusi terhadap penguatan ekosistem akademik berbasis digital di lingkungan SMK dan mendorong transformasi pembelajaran vokasi menuju era AI-driven education. 
SISTEM REKOMENDASI PEMILIHAN PAKET INTERNET WIFI MENGGUNAKAN KNOWLEDGE BASED RECOMMENDATION Ardean Risqi Laksono; Nurmalitasari Nurmalitasari; Ridwan Dwi Irawan
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.437

Abstract

The internet has become a crucial need in modern society, including in rural areas such as Wonokarto. PT. Yasmin Amanah Media, as a local internet service provider, offers a variety of internet packages to meet customer needs. However, the wide range of options often leads to confusion in selecting the most suitable package. This research aims to design a WiFi internet package recommendation system based on Knowledge-Based Recommendation (KBR) that can provide personalized suggestions based on user preferences. The system development method used is the System Development Life Cycle (SDLC), which consists of planning, analysis, design, implementation, testing, and maintenance phases. This study focuses on the system design stage in the form of a web-based application. Data were collected through interviews, observation, and literature study. The designed recommendation system utilizes constraint-based techniques to match user requirement attributes with the characteristics of available internet packages. The results show that the knowledge-based recommendation approach is effective in providing internet package selection suggestions, especially for new users who have no previous interaction history. This system is expected to improve customer satisfaction, internet usage efficiency, and loyalty to the services of PT. Yasmin Amanah Media.
IMPLEMENTASI CHATBOT BERBASIS ARTIFICIAL NEURAL NETWORK UNTUK MENDUKUNG EFISIENSI LAYANAN KONSULTASI DI PT LOKAKITA KREATIF INDONESIA Muhammad Abdul Harist; Sopingi Sopingi; Ridwan Dwi Irawan
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.449

Abstract

The rapid development of information technology has driven companies to innovate in delivering faster and more efficient services. PT Lokakita Kreatif Indonesia, a company engaged in consulting and mapping services, often receives recurring questions from clients, which were previously handled manually. To address this issue, this study aims to design and develop a Chatbot system based on Artificial Neural Networks (ANN) that can understand user intent and provide automated responses. The system was developed through several stages, including data collection, text preprocessing, ANN model training using Keras, and integration into a web application built with Flask. The dataset was compiled from frequently asked questions submitted by clients and processed into a machine-readable format. Testing results show that the Chatbot can provide accurate answers with an accuracy rate of up to 87% and a relatively fast response time. These findings indicate that the system can assist the company in delivering initial consultation services in a consistent and autonomous manner. The Chatbot not only improves work efficiency but also opens opportunities for further development through integration with other communication platforms.
Forecasting Volume Penumpang Harian KRL Yogyakarta–Solo Menggunakan SARIMA, LSTM, dan SARIMA-LSTM Marta Ardiyanto; Ridwan Dwi Irawan; Esti Dwi Rahmawati
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9798

Abstract

Passenger volume forecasting in public transportation is an important aspect of supporting data-driven operational decision-making. The Yogyakarta–Solo Commuter Rail is a strategic public transportation mode that supports interregional mobility. Fluctuations in passenger volume influenced by daily patterns, weekends, national holidays, and collective leave periods require forecasting models capable of capturing seasonal patterns and possible nonlinear changes in time series data. This study aims to compare the performance of Seasonal Autoregressive Integrated Moving Average (SARIMA), Long Short-Term Memory (LSTM), and hybrid SARIMA-LSTM models in forecasting the daily passenger volume of the Yogyakarta–Solo Commuter Rail. The dataset consists of daily passenger volume data from January to December 2025. The research stages include data preprocessing, calendar-based feature engineering, chronological training and testing data splitting, SARIMA modeling, LSTM modeling, residual modeling using LSTM, and performance evaluation using MAE, RMSE, and MAPE. The results show that the SARIMA model obtained an MAE of 2644.81, an RMSE of 3299.78, and a MAPE of 10.56%. The LSTM model obtained an MAE of 1977.50, an RMSE of 2528.75, and a MAPE of 7.28%. Meanwhile, the hybrid SARIMA-LSTM model achieved an MAE of 2634.78, an RMSE of 3294.24, and a MAPE of 10.52%. Based on these results, the LSTM model achieved the best forecasting performance compared to SARIMA and hybrid SARIMA-LSTM. The hybrid SARIMA-LSTM model provided only a slight improvement over SARIMA but did not outperform the LSTM model. These findings indicate that forecasting model selection should consider dataset characteristics, residual patterns, and the adequacy of historical data. Future research is recommended to use longer historical data and incorporate relevant external variables to improve forecasting accuracy.
Klasifikasi Sentimen dan Analisis Persepsi Pengguna Aplikasi myXL pada Google Play Store Menggunakan Naïve Bayes Classifier Ardiyanto, Marta; Irawan, Ridwan Dwi; Rifa'i, Ahmad
Jurnal Informatika Kaputama (JIK) Vol 10 No 2 (2026): Volume 10, Nomor 2, Juli 2026
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v10i2.1349

Abstract

Penelitian ini dilakukan untuk menganalisis sentimen pengguna terhadap aplikasi myXL berdasarkan ulasan yang terdapat di Google Play Store. Aplikasi myXL merupakan layanan mandiri yang dirancang oleh XL Axiata, di mana ulasan penggunanya menjadi sumber data penting untuk mengetahui persepsi masyarakat terhadap kualitas layanan. Metode klasifikasi yang digunakan dalam penelitian ini adalah Naïve Bayes Classifier, dengan tahapan penelitian yang mengacu pada proses Knowledge Discovery in Database (KDD). Data yang digunakan berjumlah 1.000 ulasan yang dikumpulkan menggunakan teknik web scraping. Data ulasan tersebut dilabeli menjadi sentimen positif dan negatif secara otomatis berdasarkan rating bintang pengguna . Setelah melalui tahapan pra-pemrosesan teks, data diubah menjadi bentuk numerik menggunakan pembobotan Term Frequency-Inverse Document Frequency (TF-IDF). Pembagian dataset dilakukan dengan rasio 80% data latih dan 20% data uji. Berdasarkan hasil evaluasi pengujian, model Naïve Bayes Classifier menunjukkan performa yang baik dengan nilai accuracy sebesar 80,33%, precision 80,33%, recall 78,59%, dan F1-score 79,27%. Visualisasi Word Cloud menunjukkan bahwa kata yang dominan muncul dalam ulasan meliputi "kuota", "paket", "aplikasi", "login", dan "error". Hasil penelitian ini diharapkan dapat memberikan gambaran persepsi pengguna serta menjadi bahan evaluasi bagi pengembang untuk memperbaiki kendala teknis dan meningkatkan kualitas layanan.
Application of Deep Learning Algorithm to Detect Fraud in Online Transaction Networks Ridwan Dwi Irawan; Agus Fatkhurohman
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3890

Abstract

Online transaction fraud is a severe problem that may cost businesses and people a lot of money. This paper suggests using deep learning algorithms to detect fraud as a remedy to this issue. These algorithms were chosen based on their ability to handle large amounts of intricate data and identify patterns that are difficult to identify using traditional techniques. Important components of this research include gathering and preprocessing transaction data, creating deep learning models, and assessing model performance. This investigation examines a variety of financial transaction types that may have involved fraud. The deep learning approach uses deep neural network designs, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to maximize detection accuracy. The study's findings demonstrate that the deep learning models created are excellent at identifying questionable transactions and can lower the false positive rate, which raises the overall effectiveness of fraud detection systems. As a result, deep learning algorithms have demonstrated a high degree of efficacy in identifying fraudulent activity inside internet-based transaction networks, so they play a vital role in fraud prevention.
Implementation Of A Personalized Healthy Food Menu Recommendation System Using Content-Based Filtering Based On User Profiles Dian Dian Dwi Setyawati; Pipin Widyaningsih; Ridwan Dwi Irawan
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 9 No. 1 (2026): Jurnal Teknologi dan Open Source, June 2026
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v9i1.5794

Abstract

This study implemented a personalized healthy food menu recommendation system using content-based filtering and cosine similarity. The system translated user characteristics, dietary goals, preferences, allergies, and health-related restrictions into menu attributes. The pilot dataset contained 10 healthy menus represented by 15 binary attributes. Recommendation performance was evaluated offline using 10 controlled user-profile scenarios with realistic age, sex, weight, height, and activity values. Basal metabolic rate and total daily energy expenditure were calculated with the Mifflin-St Jeor equation. After allergen filtering, 94 user-menu candidate pairs were assessed. Reference relevance labels were determined before ranking based on meal-category compatibility, absence of allergen conflicts, and fulfillment of at least two target attributes. At a cut-off of three recommendations, the system achieved macro Precision@3 of 0.7000, Recall@3 of 0.9167, F1@3 of 0.7481, nDCG@3 of 0.8948, and Hit Rate@3 of 1.0000. All 10 functional test cases passed, and no allergen-conflicting menu appeared in the final top-three results. The findings indicated that the system produced relevant and traceable recommendations, although the small pilot dataset limited generalizability.
Co-Authors Aan Ardana Pangestu Abdul Harits, Muhammad Adam sasando Agus Fatkhurohman Akbar Pratama, Hamzah Aldio Tri Bangkit Sanjaya Alva Hendi Muhammad Amelia, Dinda Anjarwati, Pipin Antoni DM, Nikolas Aprilisa Arum Sari Aprilla Addivi Tiar Almashinta Ardean Risqi Laksono Ardiyanto, Marta Artdelia Pingkan Salsabilla Ayu Setyowati, Sintiya Brilliant Firdaus, Azkha Buwono, Ringgo Ismoyo Cahyani Adzania Rani Miftahul Jannah Choirul Ananda, Naufal Dian Dian Dwi Setyawati Dwi Hartanti Eko Purwanto Elisabet Christina Endarwati Ema Utami Esti Dwi Rahmawati Fathoni, Syahrul Agung FAULINDA ELY NASTITI Febrian Fitiraldy Fu’ad Na’im Nurfattah Gilang Pramudito, Vincensius Ihza Yuzar Vianda, Rizqi Ika Putri Pujianti, Ika Putri Pujianti Imanuel, Christian Indra Kristiawan, Yohanes Intan Rahmawati Kresna Agung Yudhianto Marta Ardiyanto Marta Ardiyanto Maulana Akbar, Reza Maulindar, Joni Muh Adha Muh Adha Muhamad Paliya Sadana Muhammad Abdul Harist Muhammad Ilham ‘Aziiz Alfarobi Muhammad Paliya Sadana Nailurrizqi, Adistya Nibras Faiq Muhammad Nibras Faiq Muhammad Nugroh, Heri Nur Aini Faza Nur Rohman, Sofyan Nurmalitasari Nurmalitasari Nurmalitasari Oktaviana Az Zahra, Erika Permatasari, Hanifah Pipin Widyaningsih Pramono Pramono Pramono Pratama, Dika Adi Rahmaning Umiadi, Amara Ridlo Fauzi Rakhmadiant Rifa'i, Ahmad Ringgo Ismoyo Buwono Riyadsyah, Deprinico Rizqy Alfiansyah, Rafif Rudi Susanto Salwa Nurussyifa Satria Yudha, Rafiq Satrio Atmojo, Fattah Setiawan, Stevania Shalikashilla Tiaramadhan, Faiza Sidik Prasetyo, Sidik Sofa Marwati Sopingi Sopingi Stevania Setiawan Suryanti, Ika Syarif , Muhammad Tri Hartanti, Ninik Ulfarida Miftakhul Jannah Vanessa Adelita Nareswari Viery, Andrean Vivi Apriya Mardani Wiji Lestari Yoga Saputra, Muchammad Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah