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All Journal KARMAPATI (Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika) ISSN: 2252-9063 Jurnal Informatika dan Teknik Elektro Terapan Informatics for Educators and Professional : Journal of Informatics KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Indonesian Journal of Applied Informatics Jurnal ICT : Information Communication & Technology Jurnal Teknologi Informasi dan Multimedia Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) JATI (Jurnal Mahasiswa Teknik Informatika) Media Informatika MEANS (Media Informasi Analisa dan Sistem) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Informatika Terpadu D'computare: Jurnal Ilmiah Teknologi Informasi dan Ilmu Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal) Jurnal Kecerdasan Buatan dan Teknologi Informasi Jurnal Komtika (Komputasi dan Informatika)
Raditya Danar Dana
Manajemen Informatika; STMIK IKMI CIREBON
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Other

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PEMANFAATAN ALGORITMA K-MEANS DALAM ANALISIS DATA PENJUALAN TOKO BUYUNG UPIK JS DI LAZADA Angraeni, Devita Fitri; Rahaningsih, Nining; Dana, Raditya Danar; Rohmat, Cep Lukman
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 2 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i2.6438

Abstract

Banyaknya produk yang dijual oleh Toko Buyung Upik JS di Lazada menimbulkan kesulitan dalam menentukan produk yang laku dan kurang laku, sehingga terjadi ketidakseimbangan stok, seperti kelebihan pada produk yang kurang diminati dan kekurangan pada produk yang populer. Penelitian ini bertujuan mengelompokkan produk berdasarkan pola penjualan menggunakan teknik data mining untuk membantu strategi penjualan dan pengelolaan stok yang lebih efektif. Algoritma K-Means digunakan untuk clustering data penjualan, mencakup jumlah stok, transaksi, dan harga. Proses data mining meliputi tahapan Selection, Preprocessing, Transformation, Data Mining, dan Interpretation/Evaluation. Penentuan jumlah cluster optimal dilakukan dengan Elbow Method, sedangkan kualitas clustering dievaluasi menggunakan Davies Bouldin Index (DBI). Hasil penelitian menunjukkan jumlah cluster optimal adalah empat: Cluster 0 (83 produk, penjualan stabil), Cluster 1 (121 produk, penjualan tinggi), Cluster 2 (14 produk, kurang diminati), dan Cluster 3 (38 produk, penjualan moderat). Nilai rata-rata jarak dalam cluster adalah 54.941.560,812, dengan DBI sebesar 0,386 yang menunjukkan kualitas clustering cukup baik. Hasil ini memberikan wawasan bagi toko untuk memprioritaskan pengelolaan stok dan mengoptimalkan penjualan.
Optimalisasi Layanan Kesehatan di Puskesmas Melalui Pengembangan Chatbot Berbasis Web Menggunakan Flowise AI Mulyawan Mulyawan; Raditya Danar Dana; Agus Bahtiar; Irfan Ali
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 3 (2024): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i3.617

Abstract

The development of a web-based chatbot service for Puskesmas presents a potential solution to improve the accessibility and efficiency of healthcare services. This research uses Flowise AI, a chatbot development platform that leverages machine learning technology to support dynamic information processing and provide accurate and relevant responses to users. Flowise AI is integrated with Langchain Retriever to further enhance dynamic information processing, ensuring accurate and relevant responses to users. Using the Rapid Application Development (RAD) methodology, the chatbot development follows a fast-paced cycle, enabling early prototyping and continuous user feedback. The chatbot is tested using Black Box Testing to verify functionality and System Usability Scale (SUS) to evaluate usability. The test results show that the chatbot is able to provide accurate responses to patient queries, especially on relevant health topics, with an SUS score of 75, which falls within the "good" category. This score reflects that the chatbot is easy to use and acceptable to users. This technology allows the chatbot to provide more accurate, relevant, and contextual responses to patient inquiries, while dynamically accessing information from various sources, thereby improving the efficiency and effectiveness of healthcare services.
A Comparative Analysis of Univariate and Multivariate LSTM Models for Nokia (NOK) Stock Price Prediction Saputra, Roni; Martanto, Martanto; Dana, Raditya Danar
Jurnal Komtika (Komputasi dan Informatika) Vol 9 No 2 (2025)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v9i2.15152

Abstract

Predicting stock prices is a challenging yet crucial task in financial markets. This research aims to compare the performance of two Long Short-Term Memory (LSTM) neural network models for forecasting the closing price of Nokia Corporation (NOK) stock: a univariate model using only historical closing prices and a multivariate model incorporating open, high, low, close, and volume (OHLCV) data. Utilizing historical daily data from 2015 to 2025, both models were trained to predict the next day's price based on the previous 60 days. The models' accuracy was rigorously evaluated using three key metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings revealed a decisive outcome. The univariate LSTM model consistently outperformed its multivariate counterpart across all evaluation metrics. It achieved an MAE of 0.0591, an RMSE of 0.0887, and a MAPE of 1.39%, while the multivariate model recorded higher values of 0.0623, 0.0934, and 1.45%, respectively. This study concludes that for NOK stock prediction, a simpler model with fewer features proved to be more effective. The additional data points in the multivariate model did not enhance predictive accuracy and may have introduced noise, suggesting that the historical pattern of closing prices alone is a more powerful predictor for this particular asset.
Optimasi Analisis Sentimen Ulasan Sunscreen di E-Commerce Menggunakan Algoritma SVM dan SMOTE Ayi Andini; Nining Rahaningsih; Raditya Danar Dana; Cep Lukman Rohmat
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.96221

Abstract

Abstrak : Analisis sentimen terhadap ulasan pengguna di e-commerce membantu produsen memahami kepuasan pelanggan. Penelitian ini bertujuan untuk menganalisis sentimen ulasan produk sunscreen di Facetology Official Shop menggunakan algoritma Support Vector Machine (SVM). Data ulasan dikumpulkan melalui scraping, diberi label secara manual, dan diproses menggunakan metode preprocessing seperti data cleaning, Case Folding, tokenizing, stopword removal, serta SMOTE untuk menyeimbangkan data. Ekstraksi fitur dilakukan dengan TF-IDF, dan SVM digunakan untuk mengklasifikasikan sentimen menjadi positif, negatif, dan netral. Hasil penelitian menunjukkan model SVM dengan kernel linear mencapai akurasi 93%, presisi keseluruhan 95%, recall 91%, dan F1-Score 93%. Pendekatan ini menunjukkan peningkatan performa model dengan akurasi 93% setelah penerapan SMOTE untuk penyeimbangan data. Sentimen mayoritas positif, mengindikasikan tingkat kepuasan tinggi, meskipun ada ulasan negatif terkait efek samping produk. Teknik preprocessing dan penyeimbangan data terbukti efektif dalam meningkatkan performa model. Pendekatan dapat diaplikasikan untuk analisis sentimen produk serupa guna mendukung pemahaman perusahaan terhadap konsumen==================================================Abstract :Sentiment analysis of user reviews on e-commerce platforms helps producers understand customer satisfaction. This study aims to analyze the sentiment of sunscreen product reviews in the Facetology Official Shop using the Support Vector Machine (SVM) algorithm. Review data were collected through scraping, manually labeled, and processed using preprocessing methods such as data cleaning, case folding, tokenizing, stopword removal, and SMOTE to balance the data. Feature extraction was performed using TF-IDF, and SVM was used to classify sentiments into positive, negative, and neutral categories. The results show that the SVM model with a linear kernel achieved an accuracy of 93%, an overall precision of 95%, a recall of 91%, and an F1-Score of 93%. This approach demonstrated improved model performance, with 93% accuracy achieved after applying SMOTE for data balancing. The majority of sentiments were positive, indicating a high level of customer satisfaction, although some negative reviews mentioned side effects of the product. The preprocessing techniques and data balancing proved effective in enhancing the model's performance. This approach can be applied to sentiment analysis of similar products to support companies in better understanding their consumers.
Comparative Performance Analysis of Multilayer Perceptron and Long Short-Term Memory for Daily Demand Forecasting in E-Commerce Delivery Platforms Unari, Ica; Martanto; Dana, Raditya Danar; Rifa'i, Ahmad; Hamongan, Ryan
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1846

Abstract

This study compares the performance of two deep learning architectures—Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM)—for daily demand forecasting on an e-commerce delivery platform. The dataset consists of 1,827 daily observations from 2020 to 2024 and includes operational, temporal, and behavioral features such as holiday indicators, promotion signals, active customers, and delivery time. Data preprocessing includes cleaning, feature engineering, scaling, and sequence generation using a 30-day sliding window. Both models were trained and evaluated using consistent experimental settings and performance metrics. The results show that the LSTM model achieves better accuracy than the MLP model, with an RMSE of 811.81 compared to 830.15, while the difference in MAE between the two models remains minimal. LSTM demonstrates superior capability in capturing temporal dependencies and reacting to rapid demand fluctuations, whereas both models face challenges when predicting sudden demand spikes. These findings indicate that memory-based models such as LSTM are more effective for highly volatile time-series forecasting in e-commerce operations. However, performance can be further improved with the addition of external variables such as real-time promotions, weather conditions, and multivariate features.
Predicting Student Academic Performance Based on Learning Habits Using XGBoost and SHAP Latifah, Siti; Martanto; Dana, Raditya Danar; Dikananda, Fatihanursari; Hayati, Umi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1860

Abstract

This study developed a model for predicting student academic achievement based on learning habits using the XGBoost algorithm and SHAP interpretability techniques. The secondary dataset contains 1,000 entries and 16 variables (for example, hours of study per day, mental health, frequency of exercise, social media use, hours of sleep) pre-processed including cleaning, imputation, encoding, and normalization before being divided into train–test (80:20) and validated using 5-fold CV. Three models were tested: Linear Regression, Random Forest, and XGBoost. Evaluation using RMSE, MAE, and R² showed that XGBoost achieved RMSE = 0.335, MAE = 0.266, and R² = 0.882, while Linear Regression showed the best performance according to R² in certain configurations (R² = 0.888; RMSE = 0.326). SHAP analysis revealed that the most influential features were hours of study per day, mental health scores, exercise frequency, duration of social media use, and hours spent watching Netflix. The findings confirm that students' study habits and psychological conditions are the main determinants of academic achievement variation; the use of interpretable features strengthens the readability of the model for education stakeholders. Research recommendations include testing the model on longitudinal datasets, integrating socioeconomic factors, and implementing data privacy procedures before institutional-scale implementation.
Co-Authors ., Mulyawan Abdul Ajiz, Abdul Abdul Koda Ade Irma Purnamasari Ade Rizki Rinaldi Agis Maulana Robani Ahmad Faqih Ahmad Rifai Ajiz, Abul Al Muharom, Nurul Ibnu Amalia, Dita Rizki Andi Andi Andri Yanto Angraeni, Devita Fitri Arianto , Adji Arif Rinaldi Dikananda Ayi Andini Az Zahroh, Luthfia Fahmi Azarine, Divia Azhari, Shazifa Bahrul Jawahir, Muhammad Cep Lukman Rohmat Dadang Sudrajat Danil, Supta Dikananda, Arif Rinaldi Dikananda, Fatihanursari Dimin, Egi Susanto Dita Rizki Amalia Fadhil Muhamad Basysya Fadhil Muhammad Bsysyar Fadilah, Mochammad Fauzan Fathrurrahman Fatihanursari Dikananda Garsandi, Akmal Maulana Gifthera Dwilestari Hamonangan, Ryan Hamongan, Ryan Harry Budi Santoso Hayati, Umi Herawati Hermawan, Bagus Iin Iis Mulyati Irfan Ali Kamelia Faridah Kanda Muhamad Ishak Kaslani Kencana, Junaedi Surya Kharomiyah, Kharomiyah Lingga Sabdha Auraly Martanto Martanto . Mira Miranda Moch Rifki Firdaus Mulyawan Mulyawan Mulyawan, - Nana Suarna Narasati, Riri Narasati Nining Rahaningsih Nugraha, Syahrul Nurrochmah, Dina Siti Nurul Ibnu Al Muharom Octavia Ningrum, Eka Puspita Prasetia, Deni Primandani Arsi Rahmasari, Fanny Rahmat Mauludin Ramdhan, Dadan Rifa'i, Ahmad Riyandona, Siti Aiwastopa Rizkia Amelia Rohmat, Cep Lukman Roni Saputra, Roni Saeful Anwar Saeful Karim Siti Latifah Sri Muflikah Kurniarti Suarna, Nana Syafiq, Mohammad Sayyid Tati Suprapti Tohidi, Edi Unari, Ica Wahyu Saputra Willy Prihartono