cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
+6282161108110
Journal Mail Official
tin.journal@fkpt.org
Editorial Address
Jalan Sisingamangaraja No. 338, Medan, Sumatera Utara
Location
Kota medan,
Sumatera utara
INDONESIA
TIN: TERAPAN INFORMATIKA NUSANTARA
Published by Forum Kerjasama Pendidikan TInggi
ISSN : -     EISSN : 27227987     DOI : -
Core Subject : Economy, Health, Science, Education, Art, Social, Engineering,
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other
Jurnal TIN: TERAPAN INFORMATIKA NUSANTARA memuat tentang Kajian Bunga Rampai dari berbagai ide dan hasil penelitian para peneliti, mahasiswa, dan dosen yang berkompeten di bidangnya dari berbagai disiplin ilmu seperti: Komputer, Informatika, Industri, Elektro, Telekomunikasi, Kesehatan, Agama, Pertanian, Pembelajaran, Pendidikan, Teknologi Pendidikan, Ekonomi dan Bisnis, Manajemen, Akuntansi, dan Hukum
Arjuna Subject : Umum - Umum
Articles 549 Documents
 51   52   53   54   55 
Penerapan Metode Asosiasi Menggunakan Algoritma Apriori pada Penjualan Produk Tenun Abthol, Muhammad Rijalul; Wibowo, Gentur Wahyu Nyipto; Maori, Nadia Annisa
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9044

Abstract

The development of information technology has led to a significant increase in the volume of sales transaction data stored in business information systems. Such data possess substantial potential to generate strategic insights when properly analyzed. However, in many small and medium-sized enterprises (SMEs), transaction data have not yet been optimally utilized. This study aims to apply association analysis using the Apriori algorithm to sales transaction data of woven products at Sientong Tenun in order to identify consumer purchasing patterns based on support and confidence values. The research adopts a quantitative approach employing data mining methods on sales transaction data that have undergone a data preprocessing stage. The final dataset used in this study consists of 120 sales transactions. The parameters applied in the analysis include a minimum support threshold of 20% and a minimum confidence threshold of 60%. The results indicate that all main products meet the criteria for frequent 1-itemsets, with Woven Fabric and Shawl exhibiting the highest support values, at 65.00% and 58.33%, respectively. The strongest association rule identified is Woven Fabric → Shawl with a confidence value of 70.51%, followed by Woven Fabric and Shawl → Woven Sarong with a confidence value of 63.64%. These findings demonstrate a significant purchasing relationship among woven products. The results of this study can be utilized by business practitioners to support marketing strategies, sales bundle development, product arrangement, and data-driven inventory management. Furthermore, this research contributes academically to the application of the Apriori algorithm within the culturally based creative industry sector.
Klasifikasi Fraud Pada Transaksi Finansial Melalui Integrasi TabTransformer dan Oversampling Generatif CTGAN Prana Welas Sukma, Tangguh; Hartati, Ery
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9056

Abstract

Extreme class imbalance in the BankSim dataset (1.2% fraud) is a major hurdle to building reliable detection systems. This study proposes the integration of the TabTransformer architecture with the Conditional Tabular GAN (CTGAN) oversampling technique to address majority class bias. Data quality evaluations indicate that CTGAN produces synthetic data with an overall quality score of 90.05% and a column pair correlation trend of 91.63%. Experimental findings prove the proposed model delivers superior performance, achieving an F1-Score of 85.34%, a Recall of 81.39%, and a Balanced Accuracy of 90.64%. These results significantly outperform the SMOTE technique, which recorded an F1-Score of 83.99% but suffered from probability calibration failure with an extreme optimal threshold of 0.98. In contrast, the CTGAN scenario demonstrates efficient decision threshold stability at 0.46. Validation through SHAP analysis confirms that engineered variables such as merchantRisk, custStepDiff, and amtZScoreByCat provide dominant contributions to model predictions. This research concludes that the synergy of the Data-Centric AI paradigm facilitates the creation of robust, precise, and highly accountable classification models for digital banking protection within financial transaction systems.
Peramalan Deret Waktu Penjualan Bulanan Menggunakan Pendekatan Deep Learning Metode Long Short-Term Memory Permatasari, Intan; Puspitodjati, Sulistyo
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9058

Abstract

Companies often perform sales forecasting to determine inventory levels in order to control stock and ensure product availability prior to sales activities. This study aims to compare the performance of the Autoregressive Integrated Moving Average (ARIMA) method and the Long Short-Term Memory (LSTM) method in forecasting monthly sales quantities. This research employs a quantitative experimental approach, where the LSTM model as part of a deep learning framework is used to learn structured time series patterns. The dataset consists of historical monthly demand data from 2012 to 2018 using a one time step prediction approach. Model performance is evaluated using Root Mean Square Error (RMSE) as the accuracy metric. The results show that the LSTM model achieves an average RMSE of 5.39 across ten experimental runs, which is lower than that of the ARIMA method reported in previous studies using the same dataset. These findings indicate that the LSTM method provides better forecasting performance and can serve as a reliable basis for monthly inventory planning decisions.
Efektivitas Google Class Room Terhadap Kinerja Guru di Sekolah Menengah Kejuruan Mahdhuroh, Umi; Hindarto, Hindarto; Jasno, Jasno; Widiantoro, Rulli; Murtiyasa, Budi
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9071

Abstract

In the field of education, speed has become a vital factor in achieving objectives, including the improvement of teacher performance. Digital transformation in education has driven the adoption of various technologies, one of which is Google Classroom. In Vocational High Schools (SMK), which are oriented toward competency and job readiness, the integration of technology in learning has become an absolute necessity.This research aims to:1) Analyze the effectiveness of implementing a Learning Management System (LMS) in improving the performance of SMK teachers.2) Identify the obstacles faced by administrators and teachers in implementing Google Classroom. This study employs a Mixed Methods approach (quantitative and qualitative) with a descriptive design. Data collection was conducted using three methods: interviews, observations, and documentation (documents or texts). The results of this study found that the application of Google Classroom, when implemented optimally, can significantly improve administrative efficiency, the quality of lesson planning and execution, collaboration, and the teacher's ability to conduct assessments and evaluations. The primary challenges lie in infrastructure readiness, training, and a shift in mindset. The research concludes that Google Classroom is not merely a supporting tool but a catalyst for enhancing teacher professionalism in the digital era. It reveals that the comprehensive and optimal implementation of Google Classroom contributes significantly to improved teacher performance. Concrete benefits identified include: Increased administrative efficiency and material management. Enhanced quality of instructional planning and interaction. Easier collaboration among teachers and with students.Strengthened capabilities in diverse and data-driven assessments and evaluations. However, behind this potential, the study also identified several key challenges. These challenges are not only technical, such as infrastructure and network limitations, but also encompass human resource aspects, such as the need for continuous training and, most crucially, a change in mindset shifting from conventional methods toward a digital learning paradigm that is more collaborative and flexible.
Analisis Pengembangan E-Learning Platform Pembayaran Pemerintah untuk Meningkatkan Kualitas Proses Pembayaran Tagihan Belanja Negara Ashari, Hasan; Saksono, RN. Afsdy; Hidayat, Achmad Rinaldi
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9086

Abstract

The utilization of the Government Payment Platform (PPP) is a platform for work‑unit financial officers to ensure the accuracy and timeliness of state expenditure payments. This study analyzes the implementation of e‑learning of PPP on the Kemenkeu Learning Center (KLC) within the Budget and Treasury Education and Training Center an agile learning model grounded in the Learning Experience Platform (LXP) approach. The research employs a qualitative case study method with twelve (12) purposively selected informants representing policy, technical, pedagogical, and end‑user roles. Data were collected through in‑depth interviews and document analysis, and examined interactively across stages of data collection, condensation, presentation, and conclusion drawing, supplemented by method and source triangulation.Findings indicate key constraints: non‑specific participant segmentation by role, absence of dedicated learning time, limited interoperability between KLC and external platforms, insufficient per‑module discussion forums, and suboptimal use of AI (chatbots and recommendations). The study’s novelty lies in an agile LXP‑based model comprising: role‑scaled interactive curricula; a learning journey to personalize PPP competency pathways; AI‑driven chatbots serving as digital tutors and content recommendation engines; and cross‑platform collaboration compliant with information security standards. The conclusion underscores that agile PPP e‑learning has the potential to enhance learning effectiveness, efficiency, and relevance, while strengthening the competencies of financial managers within a continually transforming government payment ecosystem.
Desain dan Evaluasi Usabilitas Game Edukasi: Integrasi Laboratorium Virtual dan Petualangan untuk Pembelajaran Berpikir Komputasional Perdana, Yuda Okta Setia; Sukirman, Sukirman
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9088

Abstract

Computational Thinking is an important competency that junior high school students must master, but the learning process is often hampered by the abstract nature of the material and the lack of engaging interactive media. As a result, students' learning motivation decreases, and they have difficulty understanding key stages such as decomposition, abstraction, and algorithm design. This study aims to develop and evaluate the usability of the educational game LEGEND AETHERIA, which is specifically designed as an interactive learning tool to support computational thinking skills for junior high school students. The method used is Research and Development (R&D) with the ADDIE model, which includes analysis, design, development, implementation, and evaluation. This game was built using Construct 3, integrating adventure mechanisms and virtual laboratory simulations. The adventure and virtual laboratory mechanisms are designed to help students practice stages of computational thinking, such as decomposition, abstraction, and algorithm design, through structured and contextual game-based activities. The evaluation was conducted through a limited trial involving 30 junior high school students, using the System Usability Scale (SUS) instrument to measure usability. The results showed an average SUS score of 85.25, which is classified as Very Good. These findings indicate that the LEGEND AETHERIA game has a very high level of ease of use and is well received by users. Thus, it can be concluded that the game developed can be used as an interactive learning medium for junior high school students.
A System Dynamics Model for Environmental Quality and Urban Quality of Life through Smart City Implementation Imani Cahya, Rifky Roudana; Suryani, Erma
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9094

Abstract

Smart city programs are increasingly adopted to enhance urban environmental performance through digital innovation and sustainable governance; however, solid waste accumulation and carbon dioxide (CO₂) emissions remain persistent challenges in rapidly expanding cities. This study evaluates the environmental impacts of smart city implementation in Surabaya by quantitatively examining waste stock dynamics and CO₂ emission behavior using a system dynamics approach. The analysis focuses on the environmental subsystem, particularly smart waste management and air emission control, which are modeled through a Causal Loop Diagram (CLD) and a Stock and Flow Diagram (SFD) to capture feedback mechanisms between waste generation, waste processing, stock accumulation, and emission growth. Simulation results for the 2020–2024 period show that the implementation scenario performs significantly better than the base model, achieving an average reduction of approximately 6–7% in waste stock and about 5% lower CO₂ emission levels by the end of the simulation horizon. These improvements are primarily driven by increased waste processing efficiency, which directly suppresses waste accumulation and indirectly slows emission growth. Nevertheless, the magnitude of environmental benefits is strongly influenced by technological readiness and the operational capacity of the environmental management system. Overall, the findings provide quantitative evidence that integrating waste management and emission control within smart city frameworks is essential for achieving measurable and sustainable improvements in urban environmental quality.
Perancangan Skema Evaluasi untuk Sistem Rekomendasi Berita Menggunakan Metrik Precision, Recall, dan F1‑Score Phan, Irwan Kurnia; Yuricha, Yuricha
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9106

Abstract

Standardization of evaluation for news recommendation systems remains minimal, despite the importance of these systems in addressing information overload in the digital era. This research was designed to develop a comprehensive evaluation scheme for content-based news recommendation systems using five primary evaluation metrics: Precision, Recall, F1-Score, Hit Rate, and Mean Reciprocal Rank (MRR). The study utilized the News Category Dataset from HuffPost, which contains 209,527 news articles across 41 categories. Evaluation was conducted by simulating user feedback through three approaches: random baseline as a comparison reference, content-based filtering with TF-IDF, and Approximate Nearest Neighbor (ANN) based on Faiss. For the final evaluation, 10,000 randomly selected articles were used. Results demonstrate that TF-IDF achieved Precision@10 of 20.20%, Recall@10 of 0.57%, F1-Score@10 of 1.10%, and Hit Rate@10 of 69%, while ANN yielded Precision@10 of 11.50%, Recall@10 of 0.33%, F1-Score@10 of 0.63%, and Hit Rate@10 of 43%. The Hit Rate@10 metric shows that TF-IDF successfully provides at least one relevant article in 69% of queries, compared to ANN which achieves 43% and Random Baseline which only achieves 27%. TF-IDF surpasses ANN by 1.76 times in terms of Precision@10 (20.20% vs 11.50%) and 1.73 times in terms of Recall@10 (0.57% vs 0.33%). In terms of computational efficiency, TF-IDF achieves a runtime of 0.0100 seconds per recommendation, only 1.04 times faster than ANN which achieves 0.0104 seconds, showing a very minimal difference. The primary contribution of this research is a structured evaluation scheme using five complementary metrics that can be applied to various news recommendation systems and provides a framework for objective comparison among different methods.
Perancangan Sistem Informasi Inventory Barang Berbasis Web Menggunakan Metode Waterfall Yanti, Riska; Kurniawan, Wawan
TIN: Terapan Informatika Nusantara Vol 6 No 8 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i8.9167

Abstract

This study addresses inventory management issues at PT. BPI Jakarta, where manual recording processes have led to data inconsistencies, slow information retrieval, and limited stock monitoring. The purpose of this research is to design and develop a web-based inventory information system that improves data accuracy and accelerates administrative processes. The Waterfall method is applied as a structured system development approach, consisting of requirements analysis, system design, implementation, and testing stages. Data collection was conducted through direct observation and interviews with relevant stakeholders. The system was developed using the CodeIgniter 4 framework with PHP as the programming language and MySQL as the database management system. The resulting system provides features for managing inventory data, recording incoming and outgoing goods, handling item borrowing, and automatically presenting stock information. Black-box testing results indicate that all system functionalities operate as expected, demonstrating that the system enhances operational efficiency and improves the accuracy of inventory management.

Page 55 of 55 | Total Record : 549

 51   52   53   54   55 

Filter by Year

2020 2026


Reset
Filter By Issues
All Issue Vol 6 No 8 (2026): January 2026 Vol 5 No 12 (2025): May 2025 Vol 5 No 11 (2025): April 2025 Vol 5 No 10 (2025): March 2025 Vol 6 No 7 (2025): December 2025 Vol 6 No 6 (2025): November 2025 Vol 6 No 5 (2025): October 2025 Vol 6 No 4 (2025): September 2025 Vol 6 No 3 (2025): August 2025 Vol 6 No 2 (2025): July 2025 Vol 6 No 1 (2025): June 2025 Vol 5 No 9 (2025): February 2025 Vol 5 No 8 (2025): January 2025 Vol 4 No 12 (2024): May 2024 Vol 4 No 11 (2024): April 2024 Vol 4 No 10 (2024): March 2024 Vol 5 No 7 (2024): December 2024 Vol 5 No 6 (2024): November 2024 Vol 5 No 5 (2024): October 2024 Vol 5 No 4 (2024): September 2024 Vol 5 No 3 (2024): August 2024 Vol 5 No 2 (2024): July 2024 Vol 5 No 1 (2024): June 2024 Vol 4 No 9 (2024): February 2024 Vol 4 No 8 (2024): January 2023 Vol 3 No 12 (2023): May 2023 Vol 3 No 11 (2023): April 2023 Vol 3 No 10 (2023): March 2023 Vol 4 No 7 (2023): December 2023 Vol 4 No 6 (2023): November 2023 Vol 4 No 5 (2023): October 2023 Vol 4 No 4 (2023): September 2023 Vol 4 No 3 (2023): August 2023 Vol 4 No 2 (2023): July 2023 Vol 4 No 1 (2023): June 2023 Vol 3 No 9 (2023): February 2023 Vol 3 No 8 (2023): January 2023 Vol 2 No 10 (2022): Maret 2022 Vol 3 No 7 (2022): December 2022 Vol 3 No 6 (2022): November 2022 Vol 3 No 5 (2022): October 2022 Vol 3 No 4 (2022): September 2022 Vol 3 No 3 (2022): August 2022 Vol 3 No 2 (2022): July 2022 Vol 3 No 1 (2022): June 2022 Vol 2 No 9 (2022): Februari 2022 Vol 2 No 8 (2022): Januari 2022 Vol 1 No 12 (2021): Mei 2021 Vol 1 No 11 (2021): April 2021 Vol 1 No 10 (2021): Maret 2021 Vol 2 No 7 (2021): Desember 2021 Vol 2 No 6 (2021): November 2021 Vol 2 No 5 (2021): Oktober 2021 Vol 2 No 4 (2021): September 2021 Vol 2 No 3 (2021): Agustus 2021 Vol 2 No 3 (2021): Agustus 2021 (in press) Vol 2 No 2 (2021): Juli 2021 Vol 2 No 1 (2021): Juni 2021 Vol 1 No 9 (2021): Februari 2021 Vol 1 No 8 (2020): Januari 2021 Vol 1 No 7 (2020): Desember 2020 Vol 1 No 6 (2020): November 2020 Vol 1 No 5 (2020): Oktober 2020 Vol 1 No 4 (2020): TIN: September 2020 Vol 1 No 3 (2020): TIN: Agustus 2020 Vol 1 No 2 (2020): TIN: Juli 2020 Vol 1 No 1 (2020): Juni 2020 More Issue