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Andri Wijaya
Sistem Informasi, Sekolah Tinggi Teknik Musi, Palembang 30133
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Pengembangan Model Prediksi Penjualan dan Persediaan dengan Klasifikasi Produk untuk Efisiensi Manajemen Stok Toko Berkat Plastik Agustin, Evelyn; Setiawan, Ferdy; Wijaya, Andri
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5191

Abstract

Penelitian ini bertujuan untuk mengembangkan model prediksi penjualan dan persediaan pada Toko Berkat Plastik guna meningkatkan efisiensi manajemen stok dan mengurangi risiko overstock maupun stockout. Metode yang digunakan adalah analisis berbasis data mining dengan pendekatan Knowledge Discovery in Database (KDD), meliputi tahap seleksi data, pre-processing, transformasi, pemodelan, dan evaluasi. Algoritma Naïve Bayes digunakan untuk mengklasifikasikan tingkat penjualan dan kondisi stok ke dalam kategori Rendah, Sedang, Tinggi serta Aman, Menipis, dan Kritis. Hasil penelitian menunjukkan bahwa model prediksi stok mencapai akurasi 75%, sementara model penjualan memperoleh akurasi 68,42%. Model menunjukkan performa terbaik pada kategori stok Aman dan penjualan Tinggi, sedangkan kategori lain memiliki akurasi lebih rendah akibat karakteristik data yang fluktuatif. Kesimpulan penelitian ini menyatakan bahwa model Naïve Bayes efektif digunakan dalam memprediksi permintaan dan kondisi persediaan, serta dapat dimanfaatkan sebagai alat pendukung keputusan untuk mengoptimalkan perencanaan pembelian dan pengendalian inventori pada usaha retail skala menengah.
A Klasifikasi Mahasiswa Berprestasi Berdasarkan Nilai Mahasiswa Universitas X Dengan Algoritma C4.5 Marcello, Daniel; Ardika, Petra Putri; Wijaya, Andri
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5213

Abstract

Penelitian ini bertujuan mengklasifikasikan mahasiswa berprestasi berdasarkan nilai akademik dengan memanfaatkan algoritma C4.5 sebagai salah satu teknik klasifikasi yang populer dalam data mining. Data yang digunakan mencakup IPK semester 1–3, jumlah SKS yang telah ditempuh, status keaktifan dalam kegiatan, serta status prestasi mahasiswa. Tahapan penelitian meliputi proses data cleaning, seleksi atribut untuk menentukan variabel paling relevan, pembagian data menjadi training dan testing, hingga pembangunan model decision tree menggunakan aplikasi RapidMiner. Hasil penelitian menunjukkan bahwa atribut jumlah SKS dan IPK per semester merupakan faktor paling berpengaruh dalam menentukan kategori prestasi mahasiswa. Model C4.5 yang dihasilkan memperoleh tingkat akurasi sebesar 97%, dengan nilai precision untuk kelas “Ya” mencapai 100%, menunjukkan performa model yang sangat baik. Struktur pohon keputusan yang terbentuk mengindikasikan bahwa mahasiswa dengan jumlah SKS tinggi dan IPK yang stabil memiliki peluang lebih besar untuk diklasifikasikan sebagai mahasiswa berprestasi. Temuan ini membuktikan bahwa algoritma C4.5 efektif sebagai alat bantu dalam proses seleksi dan dapat mendukung pengambilan keputusan akademik secara objektif dan terukur.
Penerapan Metode K-Means Clustering dalam Pengelompokan Produk Kayu sebagai Sistem Pendukung Keputusan Produksi dan Penjualan (Studi kasus: CV Sutan Piko Mandiri) Seli; Nababan, Clara; Wijaya, Andri
Journal of Technology and System Information Vol. 3 No. 1 (2026): January
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jtsi.v3i1.5215

Abstract

Pengelolaan persediaan merupakan salah satu aspek krusial dalam industri pengolahan kayu karena berpengaruh langsung terhadap kelancaran produksi, efisiensi biaya, dan kemampuan perusahaan dalam memenuhi permintaan pasar. Ketidaktepatan dalam menentukan jumlah stok sering menimbulkan permasalahan berupa kelebihan atau kekurangan persediaan, yang pada akhirnya dapat berdampak pada kerugian operasional. Oleh karena itu, diperlukan pendekatan berbasis data untuk mendukung pengambilan keputusan yang lebih akurat. Penelitian ini bertujuan untuk menerapkan metode K-Means Clustering dalam mengelompokkan produk kayu pada CV Sutan Piko Mandiri berdasarkan pola penjualan sebagai dasar pendukung keputusan produksi dan pengelolaan persediaan. Data yang digunakan terdiri dari 1.000 transaksi penjualan tahun 2025 dengan atribut harga per unit, volume unit, dan jumlah terjual. Tahapan penelitian meliputi pengumpulan data, preprocessing untuk menangani data hilang dan pemilihan atribut, normalisasi data, serta penentuan jumlah cluster optimal menggunakan elbow method melalui analisis nilai Sum of Squared Error (SSE). Hasil analisis menunjukkan bahwa nilai K optimal adalah 4, sehingga produk kayu dapat dikelompokkan ke dalam empat cluster dengan karakteristik penjualan yang berbeda. Analisis hasil clustering menunjukkan bahwa jenis kayu Mahoni, Kamper, dan Durian termasuk dalam kategori penjualan tinggi sehingga direkomendasikan sebagai prioritas produksi dan perlu dijaga ketersediaan stoknya. Kayu Balam berada pada kategori penjualan menengah, sedangkan kayu Jati tergolong dalam kategori penjualan rendah sehingga memerlukan pengendalian produksi yang lebih ketat untuk menghindari penumpukan stok. Temuan ini membuktikan bahwa penerapan metode K-Means Clustering mampu memberikan wawasan yang bermanfaat bagi perusahaan dalam menyusun strategi produksi dan pengelolaan persediaan secara lebih efektif, efisien, dan berbasis data.
Perancangan Scema Data Warehouse Studi Kasus Penyakit Diabetes Melitus Ardi Riyadi; Johan Abisay Tambunan; Andri Wijaya
Ar-Rasyid: Jurnal Publikasi Penelitian Ilmiah Vol. 1 No. 6 (2025): Ar-Rasyid: Jurnal Publikasi Penelitian Ilmiah (Bulan Desember 2025)
Publisher : PT. Saha Kreasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64788/ar-rasyid.v1i6.230

Abstract

Diabetes Mellitus is a chronic disease with an increasing global prevalence, requiring integrated and data-driven health data management. However, healthcare institutions often face challenges in managing patient data that are distributed across multiple formats and fragmented storage systems (data silos), which hinders strategic analysis and effective decision-making. This study aims to design and implement a Data Warehouse to integrate diabetes patient data as a foundation for clinical and managerial decision support. The research methodology applies a multidimensional schema design using the Star Schema approach, consisting of one fact table and three main dimension tables: demographics, lifestyle, and medical conditions. The Extract, Transform, and Load (ETL) process was implemented using SQL Server Integration Services (SSIS) to cleanse and centralize data from operational sources. The dataset used in this study consists of 10,000 anonymized patient records that have undergone data profiling and data cleansing processes. The results indicate that the developed Data Warehouse is capable of integrating data consistently and supporting multidimensional analysis. Data visualization using Tableau Public reveals a correlation between Body Mass Index (BMI) and diabetes status, where patients diagnosed with diabetes exhibit a higher average BMI compared to non-diabetic patients. This implementation improves data access efficiency and facilitates the identification of health risk patterns. Therefore, the proposed Data Warehouse can serve as a foundation for a healthcare analytics system that supports data-driven strategies for diabetes prevention and management.
Klasifikasi Penentuan Stunting Menggunakan Metode Naïve Bayes Turnip, Hans Rafael Gabriel; Muhamad Raka Nur Habibi; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Stunting merupakan masalah gizi kronis yang mempengaruhi pertumbuhan dan perkembangan anak. Deteksi dini stunting sangat penting untuk intervensi tepat waktu. Penelitian ini bertujuan mengembangkan model klasifikasi untuk menentukan status stunting menggunakan metode Naïve Bayes. Dataset yang digunakan terdiri dari 1000 sampel dengan atribut umur, jenis kelamin, dan tinggi badan. Data diproses menggunakan RapidMiner dengan pembagian 70% data training dan 30% data testing. Hasil penelitian menunjukkan akurasi model mencapai 87,3% dengan precision 0,86 dan recall 0,87. Model ini dapat menjadi alat pendukung keputusan bagi tenaga kesehatan dalam identifikasi dini stunting.
Penerapan Data Mining untuk Memprediksi Kelulusan Mahasiswa Menggunakan Decision Tree Shevchenko, Angelus Galang; Maharani, Wianti; Wijaya, Andri
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

This study aims to apply data mining techniques to predict student graduation using the C4.5 Decision Tree algorithm in the Information Systems Study Program, Faculty of Science and Technology, Musi Charitas Catholic University. The data used in this research consist of academic records of students from 2018 to 2020, including Grade Point Average per semester (GPA) and Cumulative Grade Point Average (CGPA). The research method follows the Knowledge Discovery in Database (KDD) stages, namely data selection, preprocessing, transformation, data mining, and interpretation. Model development and evaluation were conducted using RapidMiner software with the 10-Fold Cross Validation method. The results indicate that Semester 8 GPA is the most influential attribute in determining student graduation status, followed by Semester 4 GPA as a supporting indicator. The generated decision tree model achieved an accuracy rate of 75.68%, indicating a good predictive performance. These findings demonstrate that the C4.5 Decision Tree algorithm can serve as an effective decision-support tool for early detection of students at risk of delayed graduation, thereby assisting academic institutions in improving on-time graduation rates and academic management quality.
Perancangan Data Warehouse Penjualan Minimarket XYZ Menggunakan Kimball Modeling dan Analisis OLAP Wikananda, Ketut Agus; Muhammad Raka Nur Habibi; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Minimarkets generate large volumes of transaction data every day that have not been fully utilized for business analysis. This study aims to design a minimarket sales data warehouse using the Kimball method and to conduct OLAP analysis using RapidMiner. The steps carried out include identifying requirements, designing a dimensional model based on a star schema, performing ETL processes, and conducting OLAP analysis. Sales data are processed into a fact table and several dimension tables. The analysis results indicate that the data warehouse can display sales information multidimensionally based on time, product, and region, which is useful for managerial decision-making.
Analisis Segmentasi Pelanggan Menggunakan K-Means Untuk Personalisasi Penawaran Produk Dalam Konteks E-Commerce Welmi Simanjuntak; Nova Magdalena Br Hombing; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

The rapid growth of e-commerce requires companies to gain deeper insights into customer behavior in order to enhance marketing effectiveness. This study aims to segment e-commerce customers using the Recency, Frequency, and Monetary (RFM) model combined with the K-Means algorithm. The research utilizes an Online Retail Dataset, with stages including preprocessing of 10,001 initial records into 2,311 valid data points, RFM calculation, and Min-Max normalization. The results indicate that the K-Means algorithm successfully groups customers into five clusters with distinct transaction behavior characteristics. Each cluster represents different levels of customer activity, ranging from high-value customers (Cluster 3) to at-risk customers (Clusters 0, 2, and 4). These segments serve as a basis for designing personalized product offerings and improving customer retention strategies in the e-commerce context.
Perancangan Data Warehouse Untuk Penentuan Rencana Strategis Penjualan Motor Hans Rafael Gabriel Turnip; Juni Lapita Hasugian; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Motorcycle sales generate large volumes of transactional data that require systematic management and analysis to support business decision making. This study aims to design and implement a motorcycle sales data warehouse using the Kimball Nine Steps methodology and to perform data analysis and visualization using RapidMiner. The dataset consists of motorcycle sales data including production year, price, model, type, and technical specifications. The results show that the implementation of a star schema successfully integrates data in a structured manner and supports multidimensional analysis. Three-dimensional scatter plot visualizations reveal sales patterns concentrated on mid-aged motorcycles within the medium price range, as well as the dominance of specific models. This study concludes that the integration of data warehousing and data visualization effectively improves information quality and supports data-driven decision making.
Penerapan Data Mining Menggunakan Algoritma K-Means Untuk Menentukan Stok Smartphone Berdasarkan Pola Harga Penjualanan Juni Lapita Hasugian; Andri Wijaya
Jurnal Sains Dan Teknologi | E-ISSN : 3063-9980 Vol. 2 No. 3 (2026): Januari - Maret
Publisher : GLOBAL SCIENTS PUBLISHER

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Abstract

Intensifying competition in the smartphone retail sector encourages businesses to utilize sales data more effectively to support strategic decision-making, especially in inventory planning. In many cases,sales records are primarily used for routine administrative purposes and are not thoroughly analayzed to uncover sales trends that can guide stock prioritization.This research focuses on the application of data mining techniques to determine smartphone stock priorities by analyzing sales patterns. The dataset used in this study consists of smartphone sales records obtained from a retail store, incorporating attributes such as product pricing and sales volume. The research process includes data preprocessing stages, namely data cleaning and normalization, followed by the implementation of the K-Means clustering algorithm. Through the clustering process, smartphone products are categorized into several groups that reflect high, moderate, and low sales performance. The findings indicate that the K-Means-based data mining approach is effective in identifying sales patterns and classifying products according to their sales levels. The resulting clusters serve as a valuable reference for establishing stock priorities, improving inventory management efficiency, and supporting strategic decision-making in smartphone retail operations. Consequently, the application of data mining techniques offers an effective approach to enhancing inventory control in smartphone retail businesses.
Co-Authors Aditiya Hermawan Aditya, Putra Adtiya, Setenilaus Afifah Azzahra Agustin, Evelyn Agustio Dwitama Aguswan, Michael Junius Alessandro, Andreas Alexander Chandra Alexius Hendra Gunawan Amat Basri Andreas Alessandro Andreas Alessandro Fernando Putra Andri Wijaya Andronikus G Anggoro, Deo Ardi Riyadi Ardika, Petra Putri Arif Aliyanto Arif Aliyanto Arif Aliyanto Arron Mosses Jhon Hadi Arvin Lawistra Asek, Ambo Ayu Elisya Natama Sianturi Azahra, Khalida Zia Fitrah Azzahra, Afifah Azzahra, Violina Baiturrahman, Ridwan Benny Daniawan Bima Aprianto S Br Hombing, Nova Magdalena Branchris Buchori Asyik Chintia Cantika cia, crecia Crecia Crecia Crecia, Crecia Daely, Septia Angelika Gettin Damayanti, Lily Daniawan, Benny Deo anggoro Dwitama, Agustio Effendy, Ellena Endri Yuliati Enjeli, Margareta Erwin Erwin Filikano, Thomas Gunawan, Andronikus Halim, Ardie Hambali, M Syahbani Hans Rafael Gabriel Turnip IFAH KHADIJAH, IFAH Iskandar Mirza Iskandar Syah Jacqueline Henny P Jessen Laorenza Suwandi Johan Abisay Tambunan Julian Masidin, Nevin JUNAEDI Juni Lapita Hasugian Ketut Agus Wiikananda Kevin Alexander Yech Kevin kevin Kurniawan Maranto, Ardiane Rossi Kusneti, Leni Latius Hermawan Leni Kusneti Lily Damayanti Lusia Komala Widiastuti Maharani, Wianti Marcello, Daniel Maria Bellaniar Ismiati Masidin, Nevin Julian Mayer Dani Sitompul Meilinda Meilinda Meilinda Michael Imanuel Michael Junius Aguswan Muhamad Raka Nur Habibi Muhammad Basri Muhammad Firdaus Muhammad Raka Nur Habibi Mujiyanto Mujiyanto Mutia Maharani Nababan, Clara Nova Magdalena Br Hombing Novaldi, Alexander Nurhadi, M Wiran Jaya Oktarina, Theresia Pamungkas, Martinus Ponco Pratama, Paskalis Arindra Putra, Steven Adi Raditya Rimbawan Raditya Rimbawan O Ratu, Anggitta Rika Solihah, Rika Riski Surya Saputra Rosana Rosana Samuel Dimas Sutikno Sanjaya, Aloisius Egi Seli Septi Putri Septi Putri Azzahra Septia Angelika Gettin daely Setiawan, Ferdy Shevchenko, Angelus Galang Silaban, Bintang Jelita Nasrani Simanjuntak, Welmi Simbolon, Defrianti Sri Andayani Sri Andayani Stefanus Charles Selvianto Stenilaus A Sugiarti, Sabar Sumual, Imanuel Marcell Supriadi, Jonathan Suwitno Suwitno, Suwitno Thomas Filikano Turnip, Hans Rafael Gabriel Verri Kuswanto Welmi Simanjuntak Wikananda, Ketut Agus Wiyono Yakub, Handoyo Yo Ceng Giap Yoel, Yoel Marcelino Pribadi Yohanes Agung Apriyanto Yusuf Kurnia Zalukhu, Indri Feni Asih