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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 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 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 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.
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 Az Zahroh, Luthfia Fahmi Azarine, Divia Azhari, Shazifa Bahrul Jawahir, Muhammad Cep Lukman Rohmat Dadang Sudrajat Danil, Supta Dikananda, Arif Rinaldi 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 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 . 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 Riyandona, Siti Aiwastopa Rizkia Amelia Rohmat, Cep Lukman Roni Saputra, Roni Saeful Anwar Saeful Karim Sri Muflikah Kurniarti Suarna, Nana Syafiq, Mohammad Sayyid Tati Suprapti Tohidi, Edi Wahyu Saputra Willy Prihartono