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Journal of Data Mining and Information Systems
Published by Yayasan Pendidikan Penelitian Pengabdian Algero
ISSN : 29865271     EISSN : 29863473     DOI : https://doi.org/10.54259/jdmis
Core Subject : Science,
Computer Science & IT
Journal of Data Mining and Information Systems (JDMIS) is intended as a medium for scientific studies of research results, thoughts, and critical-analytic studies regarding research in the field of computer science and technology, including Information Technology, Informatics Management, Data Mining, and Information Systems. It is part of the spirit of disseminating knowledge resulting from research and thoughts for the service of the wider community. In addition, it serves as a reference source for academics in Computer Science and Information Technology. JDMIS publishes papers regularly two times a year, namely in February and August. All publications in JDMIS are open, allowing articles to be freely available online without a subscription.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 53 Documents
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Developing Business Intelligence Dashboard for Sales KPI Monitoring in Advertising Agency: A Human-Centered Design Approach Ince Ahmad Zarqan; Dimas Yudistira Nugraha; Ganda Tua Sitompul; Adli Abdillah Nababan
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.6596

Abstract

Digital advertising agencies in South Jakarta face significant challenges in monitoring sales performance due to data fragmentation across multiple platforms such as CRM, spreadsheets, and digital advertising tools. Conventional manual reporting processes lead to data latency, high error rates, and delayed strategic decision-making. This study aims to develop a Business Intelligence (BI) dashboard to monitor Sales Key Performance Indicators (KPIs) in real-time, utilizing a Human-Centered Design (HCD) approach to ensure high usability and adoption. The research methodology follows the ISO 9241-210 standard for HCD, encompassing four iterative phases: understanding the context of use, specifying user requirements, producing design solutions, and evaluating designs. The system was developed using Google Looker Studio with a data warehouse architecture integrating Google BigQuery. Testing was conducted involving 15 internal stakeholders using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). The results demonstrated a SUS score of 82.5 (Excellent) and positive benchmarks in efficiency and perspicuity metrics. The implementation of the dashboard reduced reporting time by 60% and improved data accessibility for executive decision-making. This study contributes to the literature by demonstrating how HCD principles can bridge the gap between technical BI capabilities and end-user cognitive needs in the creative industry context.
Implementasi Algoritma K-Means Dengan Normalisasi Min-Max Pada Analisis Data Ketidakbersekolahan Anak Elsahday Tambunan; Yuni Br Limbeng; Sardo Sipayung
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.7064

Abstract

Anak-anak yang tidak bersekolah merupakan suatu masalah dalam dunia pendidikan yang masih menjadi tantangan, terutama di kalangan masyarakat dengan ekonomi rendah. Tingginya jumlah anak yang tidak mengenyam pendidikan dapat mengurangi kualitas sumber daya manusia dan memperbesar kesenjangan sosial. Penelitian ini bertujuan untuk mengkaji ketidakbersekolahan pada anak berdasarkan level pendidikan dan kelompok pengeluaran, dengan menggunakan pendekatan data mining. Metode yang diterapkan mencakup normalisasi Min-Max sebagai langkah awal dalam memproses data serta algoritma K-means Clustering untuk proses pengelompokan. Normalisasi Min-Max digunakan untuk menyamakan skala data dalam rentang 0 hingga 1, sehingga setiap variabel memiliki peran yang seimbang dalam perhitungan jarak. Data yang digunakan adalah data angka anak tidak sekolah Tahun 2023, yang mencakup tingkat pendidikan SD, SMP, dan SMA rentang kelompok pengeluaran dari kuantil 1 hingga 5. Temuan penelitian ini menunjukkan bahwa algoritma K-Means dengan k = 3 dapat mengelompokkan data menjadi tiga kluster utama, yakni tingkat ketidakbersekolahan yang tinggi, sedang, rendah. Ini mengindikasikan adanya hubungan antara level pengeluaran dan partisipasi anak dalam pendidikan.
Penerapan Normalisasi Data pada Angkatan Kerja Indonesia Bulan Februari 2025 Berdasarkan Kelompok Umur Anastasya Jesica Sidauruk; Juan Sebastian Sirait; Sardo Sipayung
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.7023

Abstract

Data normalization is a crucial initial step in the data mining process, aiming to reduce scale differences in numerical attributes, allowing for more objective and accurate analysis. This study aims to implement and evaluate data normalization techniques on the Indonesian workforce in February 2025 based on age category. The data used is secondary data obtained from the Central Bureau of Statistics (BPS) thru the National Labor Force Survey (SAKERNAS), which includes numerical attributes such as the number of employed people, the number of unemployed, the size of the labor force, and the percentage of the working population. The normalization methods used in this study consist of Min-Max Normalization, Z-Score Normalization, and Decimal Scaling Normalization. The research process includes data collection, selection of data from the period February 2025, data cleaning, application of normalization techniques, and analysis of the normalization results. The research findings indicate that all three normalization methods successfully leveled the value scales across attributes that previously showed significant differences in their value ranges. Min-Max normalization is effective in converting data to a specific range, Z-Score can identify deviations from the mean value, while Decimal Scaling facilitates proportional comparisons between age categories. Empirically, this study confirms that the 25-44 age group will be the most dominant in the structure of the Indonesian workforce in February 2025. Implementing data normalization has proven to improve data quality and support more accurate labor analysis.
Analisis Kualitas Layanan Terhadap Loyalty Behavior ada Aplikasi SRIBU Menggunakan Metode E-Servqual Faiz Rizki Saputra; Bayu Waspodo; Evy Nurmiati
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.7085

Abstract

The phenomenon of low app ratings and technical complaints on the Google Play Store became the background of this main research to expand which service quality dimensions are able to maintain the user base. The method used is E-SERVQUAL which includes seven dimensions: Efficiency, System Availability, Fulfillment, Privacy, Responsiveness, Contact, and Compensation. Data were collected through questionnaires from 405 respondents who use the SRIBU Mobile application and analyzed using the Partial Least Square-Structural Equation Modeling approach through SmartPLS software. The results showed that the aggregate service quality was assessed as good with an average value of 1.0379. The results of hypothesis testing confirmed that the dimensions of Efficiency, System Availability, Fulfillment, Privacy, Responsiveness, and Contact have a significant effect on user satisfaction. Furthermore, user satisfaction was proven to have a very strong positive and significant influence on loyalty behavior with a path coefficient value of 0.875. However, the Compensation dimension was found to have no significant influence on satisfaction. In addition, the Contact and Compensation dimensions showed poor values ​​indicating that there are aspects of the service that have not met user expectations. This study recommends that PT SRIBU Digital Kreatif prioritize improvements to customer support channels and compensation policies to minimize service failures and strengthen user loyalty amidst intense digital economic competition.
Prediksi Diabetes Berbasis Decision Tree Dengan Menggunakan Dataset Pima Indians Diabetes Yustri Insani; Marcel Filemon Naibaho; Sardo Pardingotan Sipayung
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.7107

Abstract

Diabetes mellitus is a chronic disease characterized by increased blood glucose levels and can lead to various serious complications if not treated early. This research aims to predict diabetes using the Decision Tree algorithm with the Pima Indians Diabetes dataset. The research stages include data processing, forming a Decision Tree model using the entropy criterion, and evaluating model performance. The results show that the model achieved an accuracy of 76.62%. Testing through a confusion matrix produced 83 True Negative samples, 35 True Positive samples, 16 False Positive samples, and 20 False Negative samples. The Glucose attribute was found to be the most dominant factor in the diagnosis, followed by BMI and Age. The resulting model is able to form clear and easy-to-understand decision rules so that it can be used as a decision support system in the early diagnosis of diabetes.
Klasifikasi Gempa Bumi Menggunakan Algoritma Decision Tree Berbasis Data BMKG Dessianna Natalia Sembiring; Beata Berlina Halawa; Sardo P Sipayung
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 1 (2026): February 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i1.7118

Abstract

This study was conducted to classify earthquakes using the Decision Tree algorithm based on data from the Meteorology, Climatology, and Geophysics Agency. Indonesia is a region with high seismic activity, which requires a systematic classification method to group earthquakes according to their characteristics. The data used in this study consisted of earthquake magnitude and depth parameters, which were classified into light, moderate, and strong earthquake classes. The research stages included data collection, data preprocessing, determination of earthquake classes, construction of a classification model using the Decision Tree algorithm, and evaluation of the classification results. The results showed that the Decision Tree algorithm was able to classify earthquakes effectively based on the combination of magnitude and depth values. The resulting model generated clear and easily interpretable decision rules to distinguish between light, moderate, and strong earthquake classes. The conclusion of this study indicated that the Decision Tree algorithm could be used as an effective and interpretable method for earthquake classification based on data from the Meteorology, Climatology, and Geophysics Agency.
Analisis Pola Asosiasi Transaksi dengan Algoritma Frequent Pattern Growth pada Dataset Takeaway Food Orders Elya Septi Mulyani; Natasya Apriesti Putri; Elvi Rariska; Etis Sunandi
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.7390

Abstract

The rapid expansion of the takeaway food industry through digital platforms has generated a vast volume of transaction data. A significant challenge lies in efficiently extracting valuable insights from large databases without overtaxing computational memory. This research proposed the implementation of the Frequent Pattern Growth (FP-Growth) algorithm to map association patterns between food items without the need for iterative candidate itemset generation. The experiment was conducted on the Takeaway Food Orders dataset from an Indian restaurant in London, comprising 13,397 transactions and 74,818 data rows. The methodology involved data pre-processing, FP-Tree construction, and the determination of strong association rules. Results demonstrated that the algorithm effectively identified customer ordering trends. By applying a 3% support threshold and 75% confidence, seven significant association rules were identified, with Plain Papadum serving as the consequent in all rules. These rules indicate that Plain Papadum has a high tendency to be ordered together with Mint Sauce, Onion Chutney, Mango Chutney, Pilau Rice, and Red Sauce. This study concluded that FP-Growth-based data mining provides accurate strategic insights for business owners to optimize promotional bundling and enhance customer loyalty.
Analisis Perbandingan Metode K-Means dan DBSCAN dalam Pengelompokan Provinsi Di Indonesia Berdasarkan Kasus Penyakit Wahyu Aditya; Razib Ramadhan; Masna Wati; Joan Angelina Widians
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.7676

Abstract

This study compares the performance of K-Means and DBSCAN algorithms in clustering 38 Indonesian provinces based on six infectious disease indicators in 2025, namely Tuberculosis (TB), HIV/AIDS, leprosy, malaria, and Dengue Hemorrhagic Fever (DHF). The uneven distribution of diseases across regions requires accurate mapping for targeted public health interventions. The research stages included handling missing values with median imputation, Z-Score normalization, clustering modeling, and evaluation using Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) metrics. Evaluation results indicate that the DBSCAN algorithm (eps=1.2, MinPts=2) consistently outperformed K-Means (K=6) across all three parameters. DBSCAN achieved a Silhouette Score of 0.6282, DBI of 0.4059, and CHI of 21.8583, whereas K-Means only reached a Silhouette Score of 0.4395, DBI of 0.6349, and CHI of 15.8653. DBSCAN's superiority lies in its ability to isolate extreme outliers, such as malaria cases in Papua and DHF in Bali, as noise. In conclusion, DBSCAN is proven to be more robust and representative for clustering national-scale epidemiological data with right-skewed distributions. The resulting cluster map can be utilized by the government to prioritize specific and targeted health program allocations in each region.
Perancangan Aplikasi SIAKAD Kampus STMIK Time Berbasis Android Felix Felix; Octara Pribadi; Robby Wijaya
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.7785

Abstract

The Academic Information System (SIAKAD) is the main pillar in administrative services in higher education. Currently, STMIK TIME already has a web-based SIAKAD portal, but accessibility via mobile devices is still limited. This research aims to design and develop the frontend of the STMIK TIME Campus SIAKAD application based on Android to facilitate students in accessing academic data in real-time. The application development method uses the Flutter framework with the Dart programming language, implementing Clean Architecture patterns and BLoC State Management to maintain scalability and ease of code maintenance. Data integration is carried out through REST API to ensure synchronization between the mobile application and the web portal server. The result of this research is a mobile application prototype that includes features for KRS registration, KHS checking, academic transcripts, and student profile management. Internal testing shows that the implementation of a modular code structure and efficient state management is able to provide stable and responsive application performance for users.
Penerapan Algoritma K-Means dan DBSCAN Untuk Segmentasi Pelanggan pada Data Transaksi E-Commerce Howard Chen; Joni Joni; Devi Devi
JDMIS: Journal of Data Mining and Information Systems Vol. 4 No. 2 (2026): August 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v4i2.7813

Abstract

This study aimed to perform customer segmentation on e-commerce transaction data using K-Means and Density-Based Spatial clustering of Applications with noise (DBSCAN) algorithms. Transaction data were transformed using the Recency, Frequency, and Monetary (RFM) model and normalized using the Min-max scaling method. The performance of both algorithms was evaluated based on the silhouette score, the number of generated clusters, and computational time efficiency. The experimental results indicated that the K-Means algorithm was superior and more functional for customer segmentation on this dataset, successfully forming two stable clusters with an instant processing time of 0.004 seconds. Conversely, the DBSCAN algorithm failed to perform proper segmentation by generating only a single cluster, despite producing a mathematically higher silhouette score of 0.427. Additionally, this study implemented a web-based simulation system using the Flask framework to facilitate the visualization of segmentation results. This study concluded that the K-Means algorithm was the most adaptive and recommended method to support business decision-making.

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