cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Priyo Wibowo
Contact Email
garuda@apji.org
Phone
+6285885852706
Journal Mail Official
sarisuswati@aptii.or.id
Editorial Address
Perum Cluster G11 Nomor 17 Jl. Plamongan Indah, Kadungwringin, Pedurungan, Semarang, Provinsi Jawa Tengah, 50195
Location
Kota semarang,
Jawa tengah
INDONESIA
Repeater: Publikasi Teknik Informatika dan Jaringan
Published by Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia
ISSN : 30467284     EISSN : 30467276     DOI : 10.62951
Core Subject : Science,
Computer Science & IT
Repeater : Publikasi Teknik Informatika dan Jaringan berisikan naskah hasil penelitian di bidang Teknik Informatika dan Jaringan
Arjuna Subject : Ilmu Komputer (semua kategori) - Jaringan Komputer dan Komunikasi
Articles 94 Documents
 6   7   8   9   10 
Implementasi Pipeline ETL dan Pemodelan Prediktif ARIMA dalam Memetakan Pola Pembelian Konsumen pada Dataset Marketplace I Wayan Manik Mas Sri Dantya; I Wayan Sudiarsa; I Putu Kabinawa Raesa Putra; Brian Adi Sapurta; I Komang Hari Sastrawan
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.799

Abstract

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.
Penentuan Penerima Beasiswa Menggunakan Metode Fuzzy Tsukamoto Sri Bintan; Adhistya Aulia Dh; Khairul Shaleh
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.800

Abstract

The determination of scholarship recipients is a very important process in supporting students’ educational success, particularly in providing fair opportunities for high-achieving students who require financial assistance. However, in practice, this process often faces various challenges, such as assessor subjectivity and uncertainty in evaluating the applied criteria. Therefore, a decision support system is needed to assist decision-making in an objective and measurable manner. This study aims to implement the Fuzzy Tsukamoto method as a decision support system for determining scholarship eligibility. The criteria used in this study include Grade Point Average (GPA) as an indicator of academic achievement and parents’ income as an indicator of students’ economic conditions. The Fuzzy Tsukamoto method was selected because it is capable of producing crisp output values based on predefined fuzzy rules. Student data were processed through several stages, namely fuzzification to transform input data into fuzzy values, inference using the minimum operator, and defuzzification using the weighted average method. The results of the study indicate that the application of the Fuzzy Tsukamoto method is able to generate more objective, consistent, and measurable decisions. Based on the calculation results, a scholarship eligibility score of 63.9 was obtained, which falls into the eligible category. Thus, the Fuzzy Tsukamoto method can be considered an effective alternative to support fair, systematic, and transparent decision-making in determining scholarship recipients.
Analisis Konsep Logika Fuzzy sebagai Pendukung Penilaian Prestasi Akademik Mahasiswa Universitas Asahan Muhammad Agil Zuhairi; Syahrul Syahrul; Khairul Shaleh
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.817

Abstract

The assessment of students’ academic performance in higher education is generally still dominated by conventional numerical approaches, which are less capable of representing qualitative and subjective variables such as classroom activeness and student participation. These approaches often result in evaluations that are not holistic and do not fully reflect students’ overall academic achievements. Therefore, this study aims to analyze the concept of fuzzy logic as a support tool for assessing students’ academic performance in higher education, with a case study of students at Universitas Asahan. The research method employs a descriptive qualitative and quantitative approach by applying Mamdani fuzzy logic. The input variables consist of exam scores, assignment scores, and classroom activeness, while the system output is the category of academic performance, namely sufficient, good, and very good. The sample data consist of ten active undergraduate students from Universitas Asahan. The data processing stages include fuzzification, the construction of fuzzy rules (rule base), fuzzy inference, and defuzzification using the centroid method. The results indicate that fuzzy logic is able to integrate quantitative and qualitative variables and accommodate uncertainty in academic assessment. The resulting evaluation is more proportional and realistic compared to conventional assessment methods based solely on average scores. Therefore, fuzzy logic can be considered an effective and flexible alternative approach to support student academic performance assessment systems in higher education.
Penerapan Deep Learning untuk Pengenalan Aktivitas Manusia Secara Non-Intrusif Menggunakan Wi-Fi Channel State Information Reza Pahlevi; Ervin Yohannes
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v4i1.818

Abstract

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.

Page 10 of 10 | Total Record : 94

 6   7   8   9   10 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2026): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 4 (2025): Oktober: Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 3 (2025): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 2 (2025): April: Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 1 (2025): Januari: Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 4 (2024): Oktober: Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 3 (2024): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 2 (2024): April : Repeater : Publikasi Teknik Informatika dan Jaringan